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β-secretase inhibitors and their preparation and use for the treatment of diseases by: Trabanco-Suarez, Andres Avelino; Tresadern, Gary John; Delgado-Jimenez, Francisca^?World Intellectual Property Organization, WO2012038438 A1 2012-03-29 | Language: English, Database: CAplus + +> (16) +lig_06 + +> (16) +-0.1235415122161309 -0.087366916860143348 -0.11141990932325523 -0.23489524486164251 -0.0918347096691529 -0.10341146650413673 -0.10341146650413673 -0.11812717529634635 0.3299799555291732 +-0.032306057090560593 -0.014534120137492815 -0.12638892295459905 0.042418509100874267 0.042418509100874267 -0.11559166076282661 0.42550355133910972 -0.035416510577003159 0.2093922191609939 +-0.11064486267666022 -0.46351593794922036 0.072893440102537468 -0.34898954691986245 -0.47939556899170083 -0.082309865082303688 -0.082309865082303688 0.15575215158363184 0.14894379613300165 +0.1408604079236587 0.17387884793182215 0.14980167088409266 0.15859524843593439 0.15859524843593439 0.16922251818080744 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17.5781 H 0 0 0 0 0 0 0 0 0 0 0 0 + -33.2326 -44.0649 15.7136 H 0 0 0 0 0 0 0 0 0 0 0 0 + -39.0846 -44.8594 15.8793 H 0 0 0 0 0 0 0 0 0 0 0 0 + -41.3151 -45.1774 16.8279 H 0 0 0 0 0 0 0 0 0 0 0 0 + -35.6446 -46.1977 18.5425 H 0 0 0 0 0 0 0 0 0 0 0 0 + -37.3767 -45.1829 19.8192 H 0 0 0 0 0 0 0 0 0 0 0 0 + -35.3216 -42.9701 14.8835 H 0 0 0 0 0 0 0 0 0 0 0 0 + -41.2830 -45.8704 22.6252 H 0 0 0 0 0 0 0 0 0 0 0 0 + -41.4515 -44.3711 21.7241 H 0 0 0 0 0 0 0 0 0 0 0 0 + -44.2483 -46.1902 20.2729 H 0 0 0 0 0 0 0 0 0 0 0 0 + -43.7158 -44.6876 21.0018 H 0 0 0 0 0 0 0 0 0 0 0 0 + -43.7926 -46.1628 21.9919 H 0 0 0 0 0 0 0 0 0 0 0 0 + -42.6909 -48.0552 19.8797 H 0 0 0 0 0 0 0 0 0 0 0 0 + -42.0619 -48.0718 21.5404 H 0 0 0 0 0 0 0 0 0 0 0 0 + -40.9725 -47.9242 20.1444 H 0 0 0 0 0 0 0 0 0 0 0 0 + -37.3730 -43.6990 24.2373 H 0 0 0 0 0 0 0 0 0 0 0 0 + -36.1567 -44.9674 23.9434 H 0 0 0 0 0 0 0 0 0 0 0 0 + -36.3255 -43.6246 22.7896 H 0 0 0 0 0 0 0 0 0 0 0 0 + -39.2564 -47.6658 21.4559 H 0 0 0 0 0 0 0 0 0 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0.70055627413903887 0.48263132163201983 -0.11058447173918071 0.067200224482170917 0.18152233549272237 -0.11062539032781901 -0.11062539032781901 +0.053826991104237408 -0.65043092182958906 -0.42453009299124067 -0.40579355172003095 -0.47590816429937666 -0.45984420589292829 -0.32098704985464399 0.14327594228898702 0.15019799359952626 +0.14514892884408651 0.16679718681489644 0.15555485674058614 0.13223166354810414 0.027830237158290718 0.089686285339943744 0.089686285339943744 0.058868813895336956 0.058868813895336956 +0.058868813895336956 0.058868813895336956 0.058868813895336956 0.058868813895336956 0.082591738187424518 0.082591738187424518 0.082591738187424518 0.33651199527894671 0.32671993561898882 +0.32671993561898882 + +> (18) +{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} + +$$$$ diff --git a/mixed_networks/inputs/protein.pdb b/mixed_networks/inputs/protein.pdb new file mode 100644 index 0000000..9a9ff3b --- /dev/null +++ b/mixed_networks/inputs/protein.pdb @@ -0,0 +1,6064 @@ +REMARK 1 CREATED WITH OPENMM 8.2, 2026-10-27 +HETATM 1 C ACE A 1 -25.006 -74.654 28.933 1.00 0.00 C +HETATM 2 O ACE A 1 -26.174 -74.741 29.237 1.00 0.00 O +HETATM 3 CH3 ACE A 1 -24.203 -75.902 28.655 1.00 0.00 C +HETATM 4 H1 ACE A 1 -24.845 -76.787 28.778 1.00 0.00 H +HETATM 5 H2 ACE A 1 -23.819 -75.869 27.625 1.00 0.00 H +HETATM 6 H3 ACE A 1 -23.360 -75.961 29.359 1.00 0.00 H +ATOM 7 N THR A 2 -24.429 -73.459 28.843 1.00 0.00 N +ATOM 8 CA THR A 2 -25.094 -72.181 29.086 1.00 0.00 C +ATOM 9 C THR A 2 -24.875 -71.228 27.913 1.00 0.00 C +ATOM 10 O THR A 2 -25.648 -70.293 27.711 1.00 0.00 O +ATOM 11 CB THR A 2 -24.597 -71.525 30.391 1.00 0.00 C +ATOM 12 OG1 THR A 2 -23.171 -71.382 30.346 1.00 0.00 O +ATOM 13 CG2 THR A 2 -24.977 -72.388 31.590 1.00 0.00 C +ATOM 14 H THR A 2 -23.462 -73.433 28.589 1.00 0.00 H +ATOM 15 HA THR A 2 -26.175 -72.355 29.186 1.00 0.00 H +ATOM 16 HB THR A 2 -25.074 -70.539 30.495 1.00 0.00 H +ATOM 17 HG1 THR A 2 -22.935 -70.488 30.566 1.00 0.00 H +ATOM 18 HG21 THR A 2 -24.618 -71.911 32.514 1.00 0.00 H +ATOM 19 HG22 THR A 2 -26.071 -72.494 31.633 1.00 0.00 H +ATOM 20 HG23 THR A 2 -24.516 -73.382 31.487 1.00 0.00 H +ATOM 21 N THR A 3 -23.817 -71.467 27.141 1.00 0.00 N +ATOM 22 CA THR A 3 -23.558 -70.739 25.906 1.00 0.00 C +ATOM 23 C THR A 3 -23.330 -71.747 24.787 1.00 0.00 C +ATOM 24 O THR A 3 -23.306 -72.961 25.007 1.00 0.00 O +ATOM 25 CB THR A 3 -22.353 -69.798 26.043 1.00 0.00 C +ATOM 26 OG1 THR A 3 -21.157 -70.573 26.197 1.00 0.00 O +ATOM 27 CG2 THR A 3 -22.515 -68.880 27.249 1.00 0.00 C +ATOM 28 H THR A 3 -23.172 -72.178 27.421 1.00 0.00 H +ATOM 29 HA THR A 3 -24.440 -70.133 25.652 1.00 0.00 H +ATOM 30 HB THR A 3 -22.286 -69.180 25.135 1.00 0.00 H +ATOM 31 HG1 THR A 3 -21.385 -71.467 26.422 1.00 0.00 H +ATOM 32 HG21 THR A 3 -21.641 -68.217 27.326 1.00 0.00 H +ATOM 33 HG22 THR A 3 -23.425 -68.274 27.129 1.00 0.00 H +ATOM 34 HG23 THR A 3 -22.596 -69.487 28.163 1.00 0.00 H +ATOM 35 N GLY A 4 -23.166 -71.240 23.572 1.00 0.00 N +ATOM 36 CA GLY A 4 -22.945 -72.134 22.447 1.00 0.00 C +ATOM 37 C GLY A 4 -22.972 -71.402 21.124 1.00 0.00 C +ATOM 38 O GLY A 4 -22.606 -70.224 21.036 1.00 0.00 O +ATOM 39 H GLY A 4 -23.194 -70.250 23.431 1.00 0.00 H +ATOM 40 HA2 GLY A 4 -21.964 -72.617 22.565 1.00 0.00 H +ATOM 41 HA3 GLY A 4 -23.733 -72.901 22.444 1.00 0.00 H +ATOM 42 N SER A 5 -23.406 -72.124 20.093 1.00 0.00 N +ATOM 43 CA SER A 5 -23.430 -71.623 18.726 1.00 0.00 C +ATOM 44 C SER A 5 -24.873 -71.472 18.272 1.00 0.00 C +ATOM 45 O SER A 5 -25.628 -72.450 18.257 1.00 0.00 O +ATOM 46 CB SER A 5 -22.675 -72.560 17.785 1.00 0.00 C +ATOM 47 OG SER A 5 -22.613 -72.012 16.484 1.00 0.00 O +ATOM 48 H SER A 5 -23.731 -73.054 20.265 1.00 0.00 H +ATOM 49 HA SER A 5 -22.948 -70.635 18.692 1.00 0.00 H +ATOM 50 HB2 SER A 5 -21.653 -72.707 18.164 1.00 0.00 H +ATOM 51 HB3 SER A 5 -23.195 -73.529 17.745 1.00 0.00 H +ATOM 52 HG SER A 5 -23.490 -71.953 16.123 1.00 0.00 H +ATOM 53 N PHE A 6 -25.250 -70.252 17.902 1.00 0.00 N +ATOM 54 CA PHE A 6 -26.577 -69.994 17.361 1.00 0.00 C +ATOM 55 C PHE A 6 -26.422 -69.411 15.968 1.00 0.00 C +ATOM 56 O PHE A 6 -26.699 -68.232 15.732 1.00 0.00 O +ATOM 57 CB PHE A 6 -27.363 -69.067 18.288 1.00 0.00 C +ATOM 58 CG PHE A 6 -27.640 -69.667 19.635 1.00 0.00 C +ATOM 59 CD1 PHE A 6 -26.680 -69.636 20.636 1.00 0.00 C +ATOM 60 CD2 PHE A 6 -28.856 -70.277 19.897 1.00 0.00 C +ATOM 61 CE1 PHE A 6 -26.933 -70.198 21.876 1.00 0.00 C +ATOM 62 CE2 PHE A 6 -29.113 -70.836 21.135 1.00 0.00 C +ATOM 63 CZ PHE A 6 -28.150 -70.797 22.123 1.00 0.00 C +ATOM 64 H PHE A 6 -24.606 -69.493 17.998 1.00 0.00 H +ATOM 65 HA PHE A 6 -27.125 -70.944 17.280 1.00 0.00 H +ATOM 66 HB2 PHE A 6 -26.784 -68.143 18.430 1.00 0.00 H +ATOM 67 HB3 PHE A 6 -28.324 -68.827 17.809 1.00 0.00 H +ATOM 68 HD1 PHE A 6 -25.725 -69.169 20.446 1.00 0.00 H +ATOM 69 HD2 PHE A 6 -29.611 -70.316 19.126 1.00 0.00 H +ATOM 70 HE1 PHE A 6 -26.178 -70.167 22.648 1.00 0.00 H +ATOM 71 HE2 PHE A 6 -30.067 -71.303 21.329 1.00 0.00 H +ATOM 72 HZ PHE A 6 -28.350 -71.236 23.090 1.00 0.00 H +ATOM 73 N VAL A 7 -25.995 -70.265 15.038 1.00 0.00 N +ATOM 74 CA VAL A 7 -25.383 -69.808 13.798 1.00 0.00 C +ATOM 75 C VAL A 7 -26.389 -69.142 12.869 1.00 0.00 C +ATOM 76 O VAL A 7 -26.014 -68.281 12.065 1.00 0.00 O +ATOM 77 CB VAL A 7 -24.663 -70.995 13.123 1.00 0.00 C +ATOM 78 CG1 VAL A 7 -25.667 -71.911 12.419 1.00 0.00 C +ATOM 79 CG2 VAL A 7 -23.588 -70.509 12.172 1.00 0.00 C +ATOM 80 H VAL A 7 -26.098 -71.247 15.196 1.00 0.00 H +ATOM 81 HA VAL A 7 -24.616 -69.060 14.046 1.00 0.00 H +ATOM 82 HB VAL A 7 -24.174 -71.581 13.915 1.00 0.00 H +ATOM 83 HG11 VAL A 7 -25.132 -72.748 11.946 1.00 0.00 H +ATOM 84 HG12 VAL A 7 -26.384 -72.303 13.155 1.00 0.00 H +ATOM 85 HG13 VAL A 7 -26.207 -71.340 11.649 1.00 0.00 H +ATOM 86 HG21 VAL A 7 -23.092 -71.373 11.706 1.00 0.00 H +ATOM 87 HG22 VAL A 7 -24.045 -69.883 11.391 1.00 0.00 H +ATOM 88 HG23 VAL A 7 -22.847 -69.917 12.729 1.00 0.00 H +ATOM 89 N GLU A 8 -27.669 -69.509 12.958 1.00 0.00 N +ATOM 90 CA GLU A 8 -28.667 -68.879 12.097 1.00 0.00 C +ATOM 91 C GLU A 8 -29.004 -67.461 12.522 1.00 0.00 C +ATOM 92 O GLU A 8 -29.515 -66.688 11.704 1.00 0.00 O +ATOM 93 CB GLU A 8 -29.962 -69.690 12.059 1.00 0.00 C +ATOM 94 CG GLU A 8 -29.991 -70.723 10.972 1.00 0.00 C +ATOM 95 CD GLU A 8 -29.171 -71.926 11.343 1.00 0.00 C +ATOM 96 OE1 GLU A 8 -28.378 -72.407 10.507 1.00 0.00 O +ATOM 97 OE2 GLU A 8 -29.324 -72.379 12.493 1.00 0.00 O1- +ATOM 98 H GLU A 8 -27.944 -70.213 13.612 1.00 0.00 H +ATOM 99 HA GLU A 8 -28.269 -68.837 11.073 1.00 0.00 H +ATOM 100 HB2 GLU A 8 -30.082 -70.200 13.026 1.00 0.00 H +ATOM 101 HB3 GLU A 8 -30.802 -68.997 11.903 1.00 0.00 H +ATOM 102 HG2 GLU A 8 -31.032 -71.037 10.803 1.00 0.00 H +ATOM 103 HG3 GLU A 8 -29.586 -70.284 10.049 1.00 0.00 H +ATOM 104 N MET A 9 -28.750 -67.105 13.776 1.00 0.00 N +ATOM 105 CA MET A 9 -29.115 -65.786 14.264 1.00 0.00 C +ATOM 106 C MET A 9 -27.947 -64.818 14.282 1.00 0.00 C +ATOM 107 O MET A 9 -28.156 -63.620 14.493 1.00 0.00 O +ATOM 108 CB MET A 9 -29.738 -65.909 15.658 1.00 0.00 C +ATOM 109 CG MET A 9 -31.090 -66.608 15.624 1.00 0.00 C +ATOM 110 SD MET A 9 -31.937 -66.770 17.202 1.00 0.00 S +ATOM 111 CE MET A 9 -31.293 -68.329 17.799 1.00 0.00 C +ATOM 112 H MET A 9 -28.302 -67.753 14.392 1.00 0.00 H +ATOM 113 HA MET A 9 -29.883 -65.369 13.596 1.00 0.00 H +ATOM 114 HB2 MET A 9 -29.057 -66.486 16.301 1.00 0.00 H +ATOM 115 HB3 MET A 9 -29.871 -64.901 16.077 1.00 0.00 H +ATOM 116 HG2 MET A 9 -31.745 -66.039 14.948 1.00 0.00 H +ATOM 117 HG3 MET A 9 -30.935 -67.619 15.220 1.00 0.00 H +ATOM 118 HE1 MET A 9 -32.093 -68.886 18.309 1.00 0.00 H +ATOM 119 HE2 MET A 9 -30.919 -68.920 16.950 1.00 0.00 H +ATOM 120 HE3 MET A 9 -30.471 -68.140 18.505 1.00 0.00 H +ATOM 121 N VAL A 10 -26.731 -65.314 14.063 1.00 0.00 N +ATOM 122 CA VAL A 10 -25.581 -64.439 13.892 1.00 0.00 C +ATOM 123 C VAL A 10 -25.802 -63.545 12.682 1.00 0.00 C +ATOM 124 O VAL A 10 -26.155 -64.017 11.593 1.00 0.00 O +ATOM 125 CB VAL A 10 -24.295 -65.269 13.753 1.00 0.00 C +ATOM 126 CG1 VAL A 10 -23.200 -64.454 13.077 1.00 0.00 C +ATOM 127 CG2 VAL A 10 -23.835 -65.764 15.122 1.00 0.00 C +ATOM 128 H VAL A 10 -26.606 -66.305 14.015 1.00 0.00 H +ATOM 129 HA VAL A 10 -25.480 -63.799 14.781 1.00 0.00 H +ATOM 130 HB VAL A 10 -24.516 -66.144 13.124 1.00 0.00 H +ATOM 131 HG11 VAL A 10 -22.290 -65.065 12.987 1.00 0.00 H +ATOM 132 HG12 VAL A 10 -23.536 -64.148 12.075 1.00 0.00 H +ATOM 133 HG13 VAL A 10 -22.983 -63.560 13.680 1.00 0.00 H +ATOM 134 HG21 VAL A 10 -22.915 -66.356 15.008 1.00 0.00 H +ATOM 135 HG22 VAL A 10 -23.638 -64.902 15.776 1.00 0.00 H +ATOM 136 HG23 VAL A 10 -24.621 -66.391 15.568 1.00 0.00 H +ATOM 137 N ASP A 11 -25.629 -62.235 12.886 1.00 0.00 N +ATOM 138 CA ASP A 11 -25.671 -61.233 11.822 1.00 0.00 C +ATOM 139 C ASP A 11 -27.075 -61.053 11.245 1.00 0.00 C +ATOM 140 O ASP A 11 -27.229 -60.766 10.058 1.00 0.00 O +ATOM 141 CB ASP A 11 -24.666 -61.565 10.710 1.00 0.00 C +ATOM 142 CG ASP A 11 -24.356 -60.372 9.835 1.00 0.00 C +ATOM 143 OD1 ASP A 11 -24.051 -59.305 10.401 1.00 0.00 O +ATOM 144 OD2 ASP A 11 -24.426 -60.493 8.594 1.00 0.00 O1- +ATOM 145 H ASP A 11 -25.461 -61.922 13.821 1.00 0.00 H +ATOM 146 HA ASP A 11 -25.370 -60.267 12.253 1.00 0.00 H +ATOM 147 HB2 ASP A 11 -23.732 -61.916 11.172 1.00 0.00 H +ATOM 148 HB3 ASP A 11 -25.087 -62.364 10.081 1.00 0.00 H +ATOM 149 N ASN A 12 -28.112 -61.194 12.072 1.00 0.00 N +ATOM 150 CA ASN A 12 -29.479 -61.028 11.590 1.00 0.00 C +ATOM 151 C ASN A 12 -30.011 -59.606 11.736 1.00 0.00 C +ATOM 152 O ASN A 12 -31.161 -59.355 11.367 1.00 0.00 O +ATOM 153 CB ASN A 12 -30.431 -62.011 12.298 1.00 0.00 C +ATOM 154 CG ASN A 12 -30.514 -61.789 13.801 1.00 0.00 C +ATOM 155 OD1 ASN A 12 -29.875 -60.893 14.348 1.00 0.00 O +ATOM 156 ND2 ASN A 12 -31.316 -62.609 14.474 1.00 0.00 N +ATOM 157 H ASN A 12 -27.951 -61.416 13.034 1.00 0.00 H +ATOM 158 HA ASN A 12 -29.496 -61.275 10.518 1.00 0.00 H +ATOM 159 HB2 ASN A 12 -31.438 -61.891 11.871 1.00 0.00 H +ATOM 160 HB3 ASN A 12 -30.075 -63.035 12.115 1.00 0.00 H +ATOM 161 HD21 ASN A 12 -31.417 -62.509 15.464 1.00 0.00 H +ATOM 162 HD22 ASN A 12 -31.818 -63.326 13.990 1.00 0.00 H +ATOM 163 N LEU A 13 -29.222 -58.669 12.250 1.00 0.00 N +ATOM 164 CA LEU A 13 -29.687 -57.298 12.414 1.00 0.00 C +ATOM 165 C LEU A 13 -29.063 -56.380 11.369 1.00 0.00 C +ATOM 166 O LEU A 13 -27.891 -56.517 11.013 1.00 0.00 O +ATOM 167 CB LEU A 13 -29.375 -56.758 13.814 1.00 0.00 C +ATOM 168 CG LEU A 13 -29.989 -57.456 15.023 1.00 0.00 C +ATOM 169 CD1 LEU A 13 -29.711 -56.665 16.297 1.00 0.00 C +ATOM 170 CD2 LEU A 13 -31.482 -57.690 14.857 1.00 0.00 C +ATOM 171 H LEU A 13 -28.292 -58.909 12.529 1.00 0.00 H +ATOM 172 HA LEU A 13 -30.778 -57.274 12.278 1.00 0.00 H +ATOM 173 HB2 LEU A 13 -28.283 -56.795 13.939 1.00 0.00 H +ATOM 174 HB3 LEU A 13 -29.712 -55.711 13.839 1.00 0.00 H +ATOM 175 HG LEU A 13 -29.504 -58.438 15.125 1.00 0.00 H +ATOM 176 HD11 LEU A 13 -30.161 -57.183 17.157 1.00 0.00 H +ATOM 177 HD12 LEU A 13 -28.625 -56.582 16.447 1.00 0.00 H +ATOM 178 HD13 LEU A 13 -30.147 -55.659 16.206 1.00 0.00 H +ATOM 179 HD21 LEU A 13 -31.876 -58.194 15.752 1.00 0.00 H +ATOM 180 HD22 LEU A 13 -31.992 -56.724 14.726 1.00 0.00 H +ATOM 181 HD23 LEU A 13 -31.658 -58.321 13.973 1.00 0.00 H +ATOM 182 N ARG A 14 -29.864 -55.433 10.882 1.00 0.00 N +ATOM 183 CA ARG A 14 -29.381 -54.404 9.970 1.00 0.00 C +ATOM 184 C ARG A 14 -29.895 -53.051 10.430 1.00 0.00 C +ATOM 185 O ARG A 14 -30.754 -52.956 11.309 1.00 0.00 O +ATOM 186 CB ARG A 14 -29.818 -54.672 8.522 1.00 0.00 C +ATOM 187 CG ARG A 14 -29.081 -55.816 7.847 1.00 0.00 C +ATOM 188 CD ARG A 14 -27.655 -55.418 7.506 1.00 0.00 C +ATOM 189 NE ARG A 14 -26.883 -56.546 6.989 1.00 0.00 N +ATOM 190 CZ ARG A 14 -26.286 -57.457 7.754 1.00 0.00 C +ATOM 191 NH1 ARG A 14 -25.601 -58.450 7.201 1.00 0.00 N +ATOM 192 NH2 ARG A 14 -26.373 -57.379 9.076 1.00 0.00 N1+ +ATOM 193 H ARG A 14 -30.827 -55.428 11.151 1.00 0.00 H +ATOM 194 HA ARG A 14 -28.281 -54.384 9.998 1.00 0.00 H +ATOM 195 HB2 ARG A 14 -30.892 -54.909 8.525 1.00 0.00 H +ATOM 196 HB3 ARG A 14 -29.645 -53.758 7.936 1.00 0.00 H +ATOM 197 HG2 ARG A 14 -29.061 -56.681 8.526 1.00 0.00 H +ATOM 198 HG3 ARG A 14 -29.610 -56.088 6.922 1.00 0.00 H +ATOM 199 HD2 ARG A 14 -27.680 -54.625 6.744 1.00 0.00 H +ATOM 200 HD3 ARG A 14 -27.165 -55.038 8.415 1.00 0.00 H +ATOM 201 HE ARG A 14 -26.798 -56.639 5.997 1.00 0.00 H +ATOM 202 HH11 ARG A 14 -25.155 -59.133 7.780 1.00 0.00 H +ATOM 203 HH12 ARG A 14 -25.531 -58.515 6.206 1.00 0.00 H +ATOM 204 HH21 ARG A 14 -25.924 -58.066 9.648 1.00 0.00 H +ATOM 205 HH22 ARG A 14 -26.887 -56.634 9.500 1.00 0.00 H +ATOM 206 N GLY A 15 -29.363 -51.995 9.826 1.00 0.00 N +ATOM 207 CA GLY A 15 -29.873 -50.666 10.090 1.00 0.00 C +ATOM 208 C GLY A 15 -29.128 -49.621 9.296 1.00 0.00 C +ATOM 209 O GLY A 15 -28.031 -49.863 8.785 1.00 0.00 O +ATOM 210 H GLY A 15 -28.605 -52.118 9.185 1.00 0.00 H +ATOM 211 HA2 GLY A 15 -30.938 -50.631 9.817 1.00 0.00 H +ATOM 212 HA3 GLY A 15 -29.762 -50.447 11.162 1.00 0.00 H +ATOM 213 N LYS A 16 -29.757 -48.454 9.189 1.00 0.00 N +ATOM 214 CA LYS A 16 -29.100 -47.266 8.674 1.00 0.00 C +ATOM 215 C LYS A 16 -28.494 -46.504 9.843 1.00 0.00 C +ATOM 216 O LYS A 16 -29.074 -46.456 10.933 1.00 0.00 O +ATOM 217 CB LYS A 16 -30.087 -46.377 7.919 1.00 0.00 C +ATOM 218 CG LYS A 16 -30.929 -47.118 6.897 1.00 0.00 C +ATOM 219 CD LYS A 16 -30.115 -47.509 5.675 1.00 0.00 C +ATOM 220 CE LYS A 16 -30.983 -48.224 4.645 1.00 0.00 C +ATOM 221 NZ LYS A 16 -31.496 -49.528 5.154 1.00 0.00 N1+ +ATOM 222 H LYS A 16 -30.714 -48.393 9.473 1.00 0.00 H +ATOM 223 HA LYS A 16 -28.293 -47.558 7.986 1.00 0.00 H +ATOM 224 HB2 LYS A 16 -30.763 -45.911 8.651 1.00 0.00 H +ATOM 225 HB3 LYS A 16 -29.517 -45.595 7.396 1.00 0.00 H +ATOM 226 HG2 LYS A 16 -31.334 -48.029 7.362 1.00 0.00 H +ATOM 227 HG3 LYS A 16 -31.758 -46.468 6.580 1.00 0.00 H +ATOM 228 HD2 LYS A 16 -29.691 -46.602 5.220 1.00 0.00 H +ATOM 229 HD3 LYS A 16 -29.300 -48.179 5.985 1.00 0.00 H +ATOM 230 HE2 LYS A 16 -31.838 -47.580 4.393 1.00 0.00 H +ATOM 231 HE3 LYS A 16 -30.383 -48.407 3.741 1.00 0.00 H +ATOM 232 HZ1 LYS A 16 -31.955 -50.018 4.413 1.00 0.00 H +ATOM 233 HZ2 LYS A 16 -30.732 -50.077 5.494 1.00 0.00 H +ATOM 234 HZ3 LYS A 16 -32.145 -49.364 5.897 1.00 0.00 H +ATOM 235 N SER A 17 -27.315 -45.926 9.615 1.00 0.00 N +ATOM 236 CA SER A 17 -26.605 -45.217 10.672 1.00 0.00 C +ATOM 237 C SER A 17 -27.482 -44.140 11.297 1.00 0.00 C +ATOM 238 O SER A 17 -27.815 -43.143 10.648 1.00 0.00 O +ATOM 239 CB SER A 17 -25.315 -44.601 10.131 1.00 0.00 C +ATOM 240 OG SER A 17 -24.566 -43.992 11.169 1.00 0.00 O +ATOM 241 H SER A 17 -26.910 -45.980 8.702 1.00 0.00 H +ATOM 242 HA SER A 17 -26.333 -45.934 11.460 1.00 0.00 H +ATOM 243 HB2 SER A 17 -24.707 -45.391 9.665 1.00 0.00 H +ATOM 244 HB3 SER A 17 -25.568 -43.841 9.377 1.00 0.00 H +ATOM 245 HG SER A 17 -24.726 -43.056 11.168 1.00 0.00 H +ATOM 246 N GLY A 18 -27.886 -44.355 12.546 1.00 0.00 N +ATOM 247 CA GLY A 18 -28.618 -43.359 13.301 1.00 0.00 C +ATOM 248 C GLY A 18 -30.128 -43.475 13.269 1.00 0.00 C +ATOM 249 O GLY A 18 -30.810 -42.558 13.743 1.00 0.00 O +ATOM 250 H GLY A 18 -27.678 -45.233 12.978 1.00 0.00 H +ATOM 251 HA2 GLY A 18 -28.298 -43.431 14.351 1.00 0.00 H +ATOM 252 HA3 GLY A 18 -28.348 -42.370 12.902 1.00 0.00 H +ATOM 253 N GLN A 19 -30.681 -44.571 12.744 1.00 0.00 N +ATOM 254 CA GLN A 19 -32.121 -44.676 12.565 1.00 0.00 C +ATOM 255 C GLN A 19 -32.744 -45.940 13.141 1.00 0.00 C +ATOM 256 O GLN A 19 -33.975 -46.042 13.152 1.00 0.00 O +ATOM 257 CB GLN A 19 -32.475 -44.562 11.074 1.00 0.00 C +ATOM 258 CG GLN A 19 -32.361 -43.131 10.545 1.00 0.00 C +ATOM 259 CD GLN A 19 -32.628 -43.022 9.061 1.00 0.00 C +ATOM 260 OE1 GLN A 19 -32.649 -44.024 8.350 1.00 0.00 O +ATOM 261 NE2 GLN A 19 -32.837 -41.798 8.582 1.00 0.00 N +ATOM 262 H GLN A 19 -30.095 -45.333 12.469 1.00 0.00 H +ATOM 263 HA GLN A 19 -32.590 -43.821 13.074 1.00 0.00 H +ATOM 264 HB2 GLN A 19 -31.791 -45.204 10.500 1.00 0.00 H +ATOM 265 HB3 GLN A 19 -33.509 -44.908 10.931 1.00 0.00 H +ATOM 266 HG2 GLN A 19 -33.089 -42.502 11.078 1.00 0.00 H +ATOM 267 HG3 GLN A 19 -31.343 -42.764 10.745 1.00 0.00 H +ATOM 268 HE21 GLN A 19 -33.020 -41.667 7.608 1.00 0.00 H +ATOM 269 HE22 GLN A 19 -32.811 -41.009 9.196 1.00 0.00 H +ATOM 270 N GLY A 20 -31.952 -46.889 13.632 1.00 0.00 N +ATOM 271 CA GLY A 20 -32.508 -48.035 14.324 1.00 0.00 C +ATOM 272 C GLY A 20 -32.056 -49.391 13.821 1.00 0.00 C +ATOM 273 O GLY A 20 -31.856 -49.584 12.619 1.00 0.00 O +ATOM 274 H GLY A 20 -30.961 -46.811 13.523 1.00 0.00 H +ATOM 275 HA2 GLY A 20 -32.228 -47.958 15.385 1.00 0.00 H +ATOM 276 HA3 GLY A 20 -33.603 -47.988 14.230 1.00 0.00 H +ATOM 277 N TYR A 21 -31.887 -50.339 14.742 1.00 0.00 N +ATOM 278 CA TYR A 21 -31.529 -51.710 14.404 1.00 0.00 C +ATOM 279 C TYR A 21 -32.797 -52.537 14.239 1.00 0.00 C +ATOM 280 O TYR A 21 -33.649 -52.558 15.130 1.00 0.00 O +ATOM 281 CB TYR A 21 -30.648 -52.326 15.491 1.00 0.00 C +ATOM 282 CG TYR A 21 -29.245 -51.758 15.588 1.00 0.00 C +ATOM 283 CD1 TYR A 21 -28.971 -50.664 16.398 1.00 0.00 C +ATOM 284 CD2 TYR A 21 -28.189 -52.335 14.890 1.00 0.00 C +ATOM 285 CE1 TYR A 21 -27.691 -50.153 16.500 1.00 0.00 C +ATOM 286 CE2 TYR A 21 -26.902 -51.826 14.984 1.00 0.00 C +ATOM 287 CZ TYR A 21 -26.661 -50.734 15.792 1.00 0.00 C +ATOM 288 OH TYR A 21 -25.390 -50.219 15.897 1.00 0.00 O +ATOM 289 H TYR A 21 -32.011 -50.099 15.705 1.00 0.00 H +ATOM 290 HA TYR A 21 -30.974 -51.722 13.454 1.00 0.00 H +ATOM 291 HB2 TYR A 21 -31.145 -52.171 16.460 1.00 0.00 H +ATOM 292 HB3 TYR A 21 -30.565 -53.404 15.290 1.00 0.00 H +ATOM 293 HD1 TYR A 21 -29.772 -50.204 16.958 1.00 0.00 H +ATOM 294 HD2 TYR A 21 -28.375 -53.195 14.264 1.00 0.00 H +ATOM 295 HE1 TYR A 21 -27.498 -49.300 17.133 1.00 0.00 H +ATOM 296 HE2 TYR A 21 -26.095 -52.281 14.429 1.00 0.00 H +ATOM 297 HH TYR A 21 -25.397 -49.475 16.488 1.00 0.00 H +ATOM 298 N TYR A 22 -32.918 -53.228 13.111 1.00 0.00 N +ATOM 299 CA TYR A 22 -34.131 -53.975 12.814 1.00 0.00 C +ATOM 300 C TYR A 22 -33.830 -55.415 12.428 1.00 0.00 C +ATOM 301 O TYR A 22 -32.769 -55.729 11.880 1.00 0.00 O +ATOM 302 CB TYR A 22 -34.956 -53.301 11.705 1.00 0.00 C +ATOM 303 CG TYR A 22 -34.247 -53.062 10.390 1.00 0.00 C +ATOM 304 CD1 TYR A 22 -34.170 -54.060 9.426 1.00 0.00 C +ATOM 305 CD2 TYR A 22 -33.697 -51.822 10.090 1.00 0.00 C +ATOM 306 CE1 TYR A 22 -33.542 -53.835 8.207 1.00 0.00 C +ATOM 307 CE2 TYR A 22 -33.071 -51.590 8.879 1.00 0.00 C +ATOM 308 CZ TYR A 22 -32.996 -52.596 7.942 1.00 0.00 C +ATOM 309 OH TYR A 22 -32.371 -52.362 6.739 1.00 0.00 O +ATOM 310 H TYR A 22 -32.163 -53.235 12.455 1.00 0.00 H +ATOM 311 HA TYR A 22 -34.755 -53.998 13.720 1.00 0.00 H +ATOM 312 HB2 TYR A 22 -35.829 -53.938 11.503 1.00 0.00 H +ATOM 313 HB3 TYR A 22 -35.294 -52.326 12.085 1.00 0.00 H +ATOM 314 HD1 TYR A 22 -34.606 -55.027 9.628 1.00 0.00 H +ATOM 315 HD2 TYR A 22 -33.759 -51.025 10.816 1.00 0.00 H +ATOM 316 HE1 TYR A 22 -33.482 -54.624 7.472 1.00 0.00 H +ATOM 317 HE2 TYR A 22 -32.642 -50.622 8.669 1.00 0.00 H +ATOM 318 HH TYR A 22 -32.869 -52.767 6.039 1.00 0.00 H +ATOM 319 N VAL A 23 -34.796 -56.279 12.720 1.00 0.00 N +ATOM 320 CA VAL A 23 -34.743 -57.694 12.391 1.00 0.00 C +ATOM 321 C VAL A 23 -35.870 -57.994 11.414 1.00 0.00 C +ATOM 322 O VAL A 23 -36.937 -57.373 11.470 1.00 0.00 O +ATOM 323 CB VAL A 23 -34.857 -58.572 13.655 1.00 0.00 C +ATOM 324 CG1 VAL A 23 -36.231 -58.415 14.301 1.00 0.00 C +ATOM 325 CG2 VAL A 23 -34.572 -60.027 13.329 1.00 0.00 C +ATOM 326 H VAL A 23 -35.607 -55.936 13.194 1.00 0.00 H +ATOM 327 HA VAL A 23 -33.785 -57.920 11.900 1.00 0.00 H +ATOM 328 HB VAL A 23 -34.100 -58.227 14.375 1.00 0.00 H +ATOM 329 HG11 VAL A 23 -36.289 -59.049 15.198 1.00 0.00 H +ATOM 330 HG12 VAL A 23 -36.385 -57.363 14.584 1.00 0.00 H +ATOM 331 HG13 VAL A 23 -37.009 -58.720 13.586 1.00 0.00 H +ATOM 332 HG21 VAL A 23 -34.659 -60.632 14.243 1.00 0.00 H +ATOM 333 HG22 VAL A 23 -35.297 -60.384 12.583 1.00 0.00 H +ATOM 334 HG23 VAL A 23 -33.553 -60.119 12.924 1.00 0.00 H +ATOM 335 N GLU A 24 -35.624 -58.929 10.506 1.00 0.00 N +ATOM 336 CA GLU A 24 -36.665 -59.346 9.582 1.00 0.00 C +ATOM 337 C GLU A 24 -37.617 -60.310 10.275 1.00 0.00 C +ATOM 338 O GLU A 24 -37.184 -61.226 10.981 1.00 0.00 O +ATOM 339 CB GLU A 24 -36.052 -59.992 8.339 1.00 0.00 C +ATOM 340 CG GLU A 24 -37.089 -60.483 7.335 1.00 0.00 C +ATOM 341 CD GLU A 24 -36.475 -60.980 6.040 1.00 0.00 C +ATOM 342 OE1 GLU A 24 -36.038 -60.142 5.220 1.00 0.00 O +ATOM 343 OE2 GLU A 24 -36.422 -62.213 5.845 1.00 0.00 O1- +ATOM 344 H GLU A 24 -34.718 -59.349 10.457 1.00 0.00 H +ATOM 345 HA GLU A 24 -37.239 -58.464 9.263 1.00 0.00 H +ATOM 346 HB2 GLU A 24 -35.410 -59.250 7.842 1.00 0.00 H +ATOM 347 HB3 GLU A 24 -35.442 -60.850 8.658 1.00 0.00 H +ATOM 348 HG2 GLU A 24 -37.656 -61.307 7.792 1.00 0.00 H +ATOM 349 HG3 GLU A 24 -37.772 -59.653 7.103 1.00 0.00 H +ATOM 350 N MET A 25 -38.915 -60.090 10.086 1.00 0.00 N +ATOM 351 CA MET A 25 -39.944 -60.963 10.628 1.00 0.00 C +ATOM 352 C MET A 25 -41.027 -61.156 9.574 1.00 0.00 C +ATOM 353 O MET A 25 -41.151 -60.371 8.632 1.00 0.00 O +ATOM 354 CB MET A 25 -40.567 -60.396 11.922 1.00 0.00 C +ATOM 355 CG MET A 25 -39.574 -59.931 12.988 1.00 0.00 C +ATOM 356 SD MET A 25 -40.371 -59.212 14.441 1.00 0.00 S +ATOM 357 CE MET A 25 -41.076 -60.663 15.226 1.00 0.00 C +ATOM 358 H MET A 25 -39.194 -59.293 9.550 1.00 0.00 H +ATOM 359 HA MET A 25 -39.505 -61.944 10.860 1.00 0.00 H +ATOM 360 HB2 MET A 25 -41.194 -59.535 11.646 1.00 0.00 H +ATOM 361 HB3 MET A 25 -41.196 -61.181 12.366 1.00 0.00 H +ATOM 362 HG2 MET A 25 -38.976 -60.797 13.310 1.00 0.00 H +ATOM 363 HG3 MET A 25 -38.912 -59.174 12.542 1.00 0.00 H +ATOM 364 HE1 MET A 25 -42.060 -60.410 15.649 1.00 0.00 H +ATOM 365 HE2 MET A 25 -41.192 -61.463 14.480 1.00 0.00 H +ATOM 366 HE3 MET A 25 -40.408 -61.006 16.030 1.00 0.00 H +ATOM 367 N THR A 26 -41.798 -62.227 9.727 1.00 0.00 N +ATOM 368 CA THR A 26 -42.965 -62.478 8.893 1.00 0.00 C +ATOM 369 C THR A 26 -44.206 -62.555 9.768 1.00 0.00 C +ATOM 370 O THR A 26 -44.158 -63.084 10.883 1.00 0.00 O +ATOM 371 CB THR A 26 -42.840 -63.778 8.086 1.00 0.00 C +ATOM 372 OG1 THR A 26 -42.938 -64.901 8.969 1.00 0.00 O +ATOM 373 CG2 THR A 26 -41.518 -63.831 7.344 1.00 0.00 C +ATOM 374 H THR A 26 -41.568 -62.886 10.443 1.00 0.00 H +ATOM 375 HA THR A 26 -43.090 -61.645 8.186 1.00 0.00 H +ATOM 376 HB THR A 26 -43.657 -63.810 7.351 1.00 0.00 H +ATOM 377 HG1 THR A 26 -43.763 -64.864 9.439 1.00 0.00 H +ATOM 378 HG21 THR A 26 -41.454 -64.770 6.775 1.00 0.00 H +ATOM 379 HG22 THR A 26 -41.451 -62.978 6.653 1.00 0.00 H +ATOM 380 HG23 THR A 26 -40.690 -63.783 8.066 1.00 0.00 H +ATOM 381 N VAL A 27 -45.309 -62.014 9.257 1.00 0.00 N +ATOM 382 CA VAL A 27 -46.601 -62.061 9.926 1.00 0.00 C +ATOM 383 C VAL A 27 -47.627 -62.617 8.947 1.00 0.00 C +ATOM 384 O VAL A 27 -47.606 -62.290 7.755 1.00 0.00 O +ATOM 385 CB VAL A 27 -47.029 -60.670 10.447 1.00 0.00 C +ATOM 386 CG1 VAL A 27 -45.992 -60.113 11.408 1.00 0.00 C +ATOM 387 CG2 VAL A 27 -47.241 -59.703 9.293 1.00 0.00 C +ATOM 388 H VAL A 27 -45.248 -61.553 8.372 1.00 0.00 H +ATOM 389 HA VAL A 27 -46.539 -62.743 10.787 1.00 0.00 H +ATOM 390 HB VAL A 27 -47.981 -60.783 10.987 1.00 0.00 H +ATOM 391 HG11 VAL A 27 -46.316 -59.125 11.766 1.00 0.00 H +ATOM 392 HG12 VAL A 27 -45.882 -60.795 12.264 1.00 0.00 H +ATOM 393 HG13 VAL A 27 -45.027 -60.017 10.890 1.00 0.00 H +ATOM 394 HG21 VAL A 27 -47.544 -58.722 9.687 1.00 0.00 H +ATOM 395 HG22 VAL A 27 -46.304 -59.597 8.727 1.00 0.00 H +ATOM 396 HG23 VAL A 27 -48.028 -60.091 8.630 1.00 0.00 H +ATOM 397 N GLY A 28 -48.506 -63.482 9.446 1.00 0.00 N +ATOM 398 CA GLY A 28 -49.646 -63.919 8.667 1.00 0.00 C +ATOM 399 C GLY A 28 -49.400 -65.179 7.856 1.00 0.00 C +ATOM 400 O GLY A 28 -48.311 -65.754 7.824 1.00 0.00 O +ATOM 401 H GLY A 28 -48.378 -63.836 10.373 1.00 0.00 H +ATOM 402 HA2 GLY A 28 -50.482 -64.110 9.356 1.00 0.00 H +ATOM 403 HA3 GLY A 28 -49.920 -63.110 7.973 1.00 0.00 H +ATOM 404 N SER A 29 -50.463 -65.602 7.173 1.00 0.00 N +ATOM 405 CA SER A 29 -50.455 -66.803 6.340 1.00 0.00 C +ATOM 406 C SER A 29 -51.149 -66.478 5.028 1.00 0.00 C +ATOM 407 O SER A 29 -52.359 -66.165 5.030 1.00 0.00 O +ATOM 408 CB SER A 29 -51.151 -67.967 7.049 1.00 0.00 C +ATOM 409 OG SER A 29 -50.579 -68.211 8.319 1.00 0.00 O +ATOM 410 H SER A 29 -51.308 -65.071 7.233 1.00 0.00 H +ATOM 411 HA SER A 29 -49.416 -67.094 6.127 1.00 0.00 H +ATOM 412 HB2 SER A 29 -52.216 -67.724 7.176 1.00 0.00 H +ATOM 413 HB3 SER A 29 -51.053 -68.872 6.432 1.00 0.00 H +ATOM 414 HG SER A 29 -51.067 -67.738 8.983 1.00 0.00 H +ATOM 415 N PRO A 30 -50.448 -66.520 3.880 1.00 0.00 N +ATOM 416 CA PRO A 30 -49.014 -66.806 3.737 1.00 0.00 C +ATOM 417 C PRO A 30 -48.153 -65.677 4.330 1.00 0.00 C +ATOM 418 O PRO A 30 -48.698 -64.599 4.577 1.00 0.00 O +ATOM 419 CB PRO A 30 -48.839 -66.932 2.219 1.00 0.00 C +ATOM 420 CG PRO A 30 -49.945 -66.121 1.641 1.00 0.00 C +ATOM 421 CD PRO A 30 -51.100 -66.305 2.577 1.00 0.00 C +ATOM 422 HA PRO A 30 -48.758 -67.760 4.221 1.00 0.00 H +ATOM 423 HB2 PRO A 30 -47.862 -66.534 1.907 1.00 0.00 H +ATOM 424 HB3 PRO A 30 -48.924 -67.983 1.905 1.00 0.00 H +ATOM 425 HG2 PRO A 30 -49.658 -65.061 1.587 1.00 0.00 H +ATOM 426 HG3 PRO A 30 -50.203 -66.483 0.635 1.00 0.00 H +ATOM 427 HD2 PRO A 30 -51.705 -67.178 2.290 1.00 0.00 H +ATOM 428 HD3 PRO A 30 -51.737 -65.409 2.598 1.00 0.00 H +ATOM 429 N PRO A 31 -46.859 -65.913 4.568 1.00 0.00 N +ATOM 430 CA PRO A 31 -46.092 -64.976 5.404 1.00 0.00 C +ATOM 431 C PRO A 31 -45.835 -63.661 4.683 1.00 0.00 C +ATOM 432 O PRO A 31 -45.469 -63.641 3.505 1.00 0.00 O +ATOM 433 CB PRO A 31 -44.781 -65.726 5.682 1.00 0.00 C +ATOM 434 CG PRO A 31 -44.914 -67.078 5.030 1.00 0.00 C +ATOM 435 CD PRO A 31 -45.991 -66.958 4.003 1.00 0.00 C +ATOM 436 HA PRO A 31 -46.617 -64.786 6.352 1.00 0.00 H +ATOM 437 HB2 PRO A 31 -43.931 -65.178 5.250 1.00 0.00 H +ATOM 438 HB3 PRO A 31 -44.631 -65.840 6.766 1.00 0.00 H +ATOM 439 HG2 PRO A 31 -43.965 -67.363 4.552 1.00 0.00 H +ATOM 440 HG3 PRO A 31 -45.188 -67.835 5.780 1.00 0.00 H +ATOM 441 HD2 PRO A 31 -45.580 -66.649 3.031 1.00 0.00 H +ATOM 442 HD3 PRO A 31 -46.536 -67.907 3.886 1.00 0.00 H +ATOM 443 N GLN A 32 -46.034 -62.561 5.403 1.00 0.00 N +ATOM 444 CA GLN A 32 -45.751 -61.219 4.910 1.00 0.00 C +ATOM 445 C GLN A 32 -44.488 -60.709 5.594 1.00 0.00 C +ATOM 446 O GLN A 32 -44.453 -60.577 6.822 1.00 0.00 O +ATOM 447 CB GLN A 32 -46.929 -60.280 5.171 1.00 0.00 C +ATOM 448 CG GLN A 32 -48.212 -60.695 4.466 1.00 0.00 C +ATOM 449 CD GLN A 32 -49.377 -59.777 4.787 1.00 0.00 C +ATOM 450 OE1 GLN A 32 -49.205 -58.569 4.953 1.00 0.00 O +ATOM 451 NE2 GLN A 32 -50.571 -60.349 4.882 1.00 0.00 N +ATOM 452 H GLN A 32 -46.396 -62.660 6.330 1.00 0.00 H +ATOM 453 HA GLN A 32 -45.570 -61.258 3.826 1.00 0.00 H +ATOM 454 HB2 GLN A 32 -47.120 -60.256 6.254 1.00 0.00 H +ATOM 455 HB3 GLN A 32 -46.653 -59.273 4.825 1.00 0.00 H +ATOM 456 HG2 GLN A 32 -48.039 -60.678 3.380 1.00 0.00 H +ATOM 457 HG3 GLN A 32 -48.472 -61.717 4.780 1.00 0.00 H +ATOM 458 HE21 GLN A 32 -51.375 -59.795 5.096 1.00 0.00 H +ATOM 459 HE22 GLN A 32 -50.666 -61.334 4.740 1.00 0.00 H +ATOM 460 N THR A 33 -43.463 -60.416 4.798 1.00 0.00 N +ATOM 461 CA THR A 33 -42.169 -60.004 5.329 1.00 0.00 C +ATOM 462 C THR A 33 -42.179 -58.533 5.724 1.00 0.00 C +ATOM 463 O THR A 33 -42.702 -57.682 4.998 1.00 0.00 O +ATOM 464 CB THR A 33 -41.071 -60.253 4.295 1.00 0.00 C +ATOM 465 OG1 THR A 33 -41.081 -61.633 3.913 1.00 0.00 O +ATOM 466 CG2 THR A 33 -39.702 -59.905 4.862 1.00 0.00 C +ATOM 467 H THR A 33 -43.583 -60.480 3.807 1.00 0.00 H +ATOM 468 HA THR A 33 -41.940 -60.600 6.224 1.00 0.00 H +ATOM 469 HB THR A 33 -41.263 -59.618 3.417 1.00 0.00 H +ATOM 470 HG1 THR A 33 -40.428 -62.106 4.415 1.00 0.00 H +ATOM 471 HG21 THR A 33 -38.931 -60.092 4.100 1.00 0.00 H +ATOM 472 HG22 THR A 33 -39.683 -58.843 5.149 1.00 0.00 H +ATOM 473 HG23 THR A 33 -39.503 -60.528 5.747 1.00 0.00 H +ATOM 474 N LEU A 34 -41.594 -58.238 6.885 1.00 0.00 N +ATOM 475 CA LEU A 34 -41.463 -56.870 7.368 1.00 0.00 C +ATOM 476 C LEU A 34 -40.158 -56.726 8.142 1.00 0.00 C +ATOM 477 O LEU A 34 -39.686 -57.679 8.764 1.00 0.00 O +ATOM 478 CB LEU A 34 -42.636 -56.473 8.272 1.00 0.00 C +ATOM 479 CG LEU A 34 -44.049 -56.434 7.687 1.00 0.00 C +ATOM 480 CD1 LEU A 34 -45.073 -56.271 8.798 1.00 0.00 C +ATOM 481 CD2 LEU A 34 -44.159 -55.308 6.677 1.00 0.00 C +ATOM 482 H LEU A 34 -41.232 -58.983 7.445 1.00 0.00 H +ATOM 483 HA LEU A 34 -41.438 -56.181 6.511 1.00 0.00 H +ATOM 484 HB2 LEU A 34 -42.654 -57.188 9.108 1.00 0.00 H +ATOM 485 HB3 LEU A 34 -42.419 -55.465 8.656 1.00 0.00 H +ATOM 486 HG LEU A 34 -44.239 -57.387 7.172 1.00 0.00 H +ATOM 487 HD11 LEU A 34 -46.084 -56.244 8.365 1.00 0.00 H +ATOM 488 HD12 LEU A 34 -44.995 -57.118 9.495 1.00 0.00 H +ATOM 489 HD13 LEU A 34 -44.881 -55.332 9.339 1.00 0.00 H +ATOM 490 HD21 LEU A 34 -45.177 -55.286 6.261 1.00 0.00 H +ATOM 491 HD22 LEU A 34 -43.945 -54.349 7.172 1.00 0.00 H +ATOM 492 HD23 LEU A 34 -43.435 -55.472 5.865 1.00 0.00 H +ATOM 493 N ASN A 35 -39.584 -55.524 8.103 1.00 0.00 N +ATOM 494 CA ASN A 35 -38.448 -55.157 8.943 1.00 0.00 C +ATOM 495 C ASN A 35 -38.974 -54.498 10.212 1.00 0.00 C +ATOM 496 O ASN A 35 -39.796 -53.576 10.139 1.00 0.00 O +ATOM 497 CB ASN A 35 -37.507 -54.205 8.209 1.00 0.00 C +ATOM 498 CG ASN A 35 -36.673 -54.901 7.155 1.00 0.00 C +ATOM 499 OD1 ASN A 35 -36.405 -56.099 7.245 1.00 0.00 O +ATOM 500 ND2 ASN A 35 -36.245 -54.148 6.155 1.00 0.00 N +ATOM 501 H ASN A 35 -39.948 -54.842 7.469 1.00 0.00 H +ATOM 502 HA ASN A 35 -37.888 -56.062 9.221 1.00 0.00 H +ATOM 503 HB2 ASN A 35 -38.108 -53.423 7.722 1.00 0.00 H +ATOM 504 HB3 ASN A 35 -36.831 -53.743 8.944 1.00 0.00 H +ATOM 505 HD21 ASN A 35 -35.680 -54.549 5.434 1.00 0.00 H +ATOM 506 HD22 ASN A 35 -36.486 -53.178 6.121 1.00 0.00 H +ATOM 507 N ILE A 36 -38.502 -54.963 11.369 1.00 0.00 N +ATOM 508 CA ILE A 36 -39.090 -54.608 12.660 1.00 0.00 C +ATOM 509 C ILE A 36 -37.993 -54.120 13.600 1.00 0.00 C +ATOM 510 O ILE A 36 -37.008 -54.831 13.827 1.00 0.00 O +ATOM 511 CB ILE A 36 -39.836 -55.796 13.288 1.00 0.00 C +ATOM 512 CG1 ILE A 36 -40.949 -56.280 12.361 1.00 0.00 C +ATOM 513 CG2 ILE A 36 -40.448 -55.406 14.598 1.00 0.00 C +ATOM 514 CD1 ILE A 36 -42.011 -55.235 12.129 1.00 0.00 C +ATOM 515 H ILE A 36 -37.715 -55.579 11.353 1.00 0.00 H +ATOM 516 HA ILE A 36 -39.810 -53.789 12.515 1.00 0.00 H +ATOM 517 HB ILE A 36 -39.123 -56.618 13.449 1.00 0.00 H +ATOM 518 HG12 ILE A 36 -40.506 -56.552 11.392 1.00 0.00 H +ATOM 519 HG13 ILE A 36 -41.420 -57.167 12.811 1.00 0.00 H +ATOM 520 HG21 ILE A 36 -40.976 -56.270 15.028 1.00 0.00 H +ATOM 521 HG22 ILE A 36 -39.657 -55.078 15.289 1.00 0.00 H +ATOM 522 HG23 ILE A 36 -41.161 -54.583 14.439 1.00 0.00 H +ATOM 523 HD11 ILE A 36 -42.783 -55.639 11.457 1.00 0.00 H +ATOM 524 HD12 ILE A 36 -42.470 -54.959 13.090 1.00 0.00 H +ATOM 525 HD13 ILE A 36 -41.555 -54.345 11.671 1.00 0.00 H +ATOM 526 N LEU A 37 -38.186 -52.933 14.176 1.00 0.00 N +ATOM 527 CA LEU A 37 -37.185 -52.340 15.056 1.00 0.00 C +ATOM 528 C LEU A 37 -37.052 -53.135 16.352 1.00 0.00 C +ATOM 529 O LEU A 37 -38.050 -53.460 17.002 1.00 0.00 O +ATOM 530 CB LEU A 37 -37.563 -50.890 15.359 1.00 0.00 C +ATOM 531 CG LEU A 37 -36.706 -50.099 16.352 1.00 0.00 C +ATOM 532 CD1 LEU A 37 -35.364 -49.732 15.749 1.00 0.00 C +ATOM 533 CD2 LEU A 37 -37.430 -48.851 16.838 1.00 0.00 C +ATOM 534 H LEU A 37 -39.037 -52.438 14.001 1.00 0.00 H +ATOM 535 HA LEU A 37 -36.210 -52.343 14.547 1.00 0.00 H +ATOM 536 HB2 LEU A 37 -37.541 -50.343 14.405 1.00 0.00 H +ATOM 537 HB3 LEU A 37 -38.590 -50.899 15.753 1.00 0.00 H +ATOM 538 HG LEU A 37 -36.521 -50.744 17.224 1.00 0.00 H +ATOM 539 HD11 LEU A 37 -34.773 -49.166 16.485 1.00 0.00 H +ATOM 540 HD12 LEU A 37 -34.823 -50.649 15.472 1.00 0.00 H +ATOM 541 HD13 LEU A 37 -35.522 -49.114 14.852 1.00 0.00 H +ATOM 542 HD21 LEU A 37 -36.789 -48.307 17.548 1.00 0.00 H +ATOM 543 HD22 LEU A 37 -37.659 -48.202 15.980 1.00 0.00 H +ATOM 544 HD23 LEU A 37 -38.366 -49.141 17.338 1.00 0.00 H +ATOM 545 N VAL A 38 -35.812 -53.438 16.728 1.00 0.00 N +ATOM 546 CA VAL A 38 -35.537 -54.157 17.968 1.00 0.00 C +ATOM 547 C VAL A 38 -35.432 -53.140 19.099 1.00 0.00 C +ATOM 548 O VAL A 38 -34.558 -52.267 19.087 1.00 0.00 O +ATOM 549 CB VAL A 38 -34.258 -54.997 17.852 1.00 0.00 C +ATOM 550 CG1 VAL A 38 -33.956 -55.691 19.178 1.00 0.00 C +ATOM 551 CG2 VAL A 38 -34.399 -56.022 16.732 1.00 0.00 C +ATOM 552 H VAL A 38 -35.047 -53.165 16.145 1.00 0.00 H +ATOM 553 HA VAL A 38 -36.375 -54.834 18.188 1.00 0.00 H +ATOM 554 HB VAL A 38 -33.421 -54.326 17.610 1.00 0.00 H +ATOM 555 HG11 VAL A 38 -33.038 -56.288 19.079 1.00 0.00 H +ATOM 556 HG12 VAL A 38 -33.818 -54.935 19.965 1.00 0.00 H +ATOM 557 HG13 VAL A 38 -34.795 -56.350 19.446 1.00 0.00 H +ATOM 558 HG21 VAL A 38 -33.476 -56.617 16.660 1.00 0.00 H +ATOM 559 HG22 VAL A 38 -35.248 -56.687 16.949 1.00 0.00 H +ATOM 560 HG23 VAL A 38 -34.575 -55.502 15.779 1.00 0.00 H +ATOM 561 N ASP A 39 -36.312 -53.265 20.090 1.00 0.00 N +ATOM 562 CA ASP A 39 -36.560 -52.177 21.033 1.00 0.00 C +ATOM 563 C ASP A 39 -36.770 -52.745 22.434 1.00 0.00 C +ATOM 564 O ASP A 39 -37.857 -53.236 22.752 1.00 0.00 O +ATOM 565 CB ASP A 39 -37.766 -51.366 20.576 1.00 0.00 C +ATOM 566 CG ASP A 39 -38.013 -50.148 21.437 1.00 0.00 C +ATOM 567 OD1 ASP A 39 -39.138 -49.600 21.385 1.00 0.00 O +ATOM 568 OD2 ASP A 39 -37.086 -49.736 22.161 1.00 0.00 O1- +ATOM 569 H ASP A 39 -36.815 -54.123 20.191 1.00 0.00 H +ATOM 570 HA ASP A 39 -35.684 -51.512 21.057 1.00 0.00 H +ATOM 571 HB2 ASP A 39 -37.596 -51.036 19.541 1.00 0.00 H +ATOM 572 HB3 ASP A 39 -38.657 -52.010 20.614 1.00 0.00 H +ATOM 573 N THR A 40 -35.738 -52.652 23.276 1.00 0.00 N +ATOM 574 CA THR A 40 -35.851 -53.101 24.657 1.00 0.00 C +ATOM 575 C THR A 40 -36.516 -52.076 25.560 1.00 0.00 C +ATOM 576 O THR A 40 -36.688 -52.348 26.752 1.00 0.00 O +ATOM 577 CB THR A 40 -34.475 -53.449 25.226 1.00 0.00 C +ATOM 578 OG1 THR A 40 -33.651 -52.278 25.256 1.00 0.00 O +ATOM 579 CG2 THR A 40 -33.808 -54.521 24.379 1.00 0.00 C +ATOM 580 H THR A 40 -34.873 -52.268 22.953 1.00 0.00 H +ATOM 581 HA THR A 40 -36.462 -54.015 24.679 1.00 0.00 H +ATOM 582 HB THR A 40 -34.607 -53.839 26.246 1.00 0.00 H +ATOM 583 HG1 THR A 40 -33.506 -52.015 26.157 1.00 0.00 H +ATOM 584 HG21 THR A 40 -32.820 -54.760 24.800 1.00 0.00 H +ATOM 585 HG22 THR A 40 -34.433 -55.426 24.374 1.00 0.00 H +ATOM 586 HG23 THR A 40 -33.688 -54.153 23.350 1.00 0.00 H +ATOM 587 N GLY A 41 -36.896 -50.918 25.030 1.00 0.00 N +ATOM 588 CA GLY A 41 -37.516 -49.891 25.841 1.00 0.00 C +ATOM 589 C GLY A 41 -39.019 -49.783 25.682 1.00 0.00 C +ATOM 590 O GLY A 41 -39.620 -48.792 26.103 1.00 0.00 O +ATOM 591 H GLY A 41 -36.751 -50.753 24.054 1.00 0.00 H +ATOM 592 HA2 GLY A 41 -37.298 -50.109 26.897 1.00 0.00 H +ATOM 593 HA3 GLY A 41 -37.072 -48.923 25.567 1.00 0.00 H +ATOM 594 N SER A 42 -39.641 -50.788 25.073 1.00 0.00 N +ATOM 595 CA SER A 42 -41.090 -50.805 24.924 1.00 0.00 C +ATOM 596 C SER A 42 -41.556 -52.250 24.924 1.00 0.00 C +ATOM 597 O SER A 42 -40.748 -53.177 24.971 1.00 0.00 O +ATOM 598 CB SER A 42 -41.531 -50.074 23.652 1.00 0.00 C +ATOM 599 OG SER A 42 -41.161 -50.794 22.490 1.00 0.00 O +ATOM 600 H SER A 42 -39.103 -51.548 24.709 1.00 0.00 H +ATOM 601 HA SER A 42 -41.546 -50.296 25.786 1.00 0.00 H +ATOM 602 HB2 SER A 42 -42.625 -49.956 23.667 1.00 0.00 H +ATOM 603 HB3 SER A 42 -41.056 -49.082 23.626 1.00 0.00 H +ATOM 604 HG SER A 42 -40.467 -50.330 22.037 1.00 0.00 H +ATOM 605 N SER A 43 -42.878 -52.440 24.848 1.00 0.00 N +ATOM 606 CA SER A 43 -43.429 -53.774 25.049 1.00 0.00 C +ATOM 607 C SER A 43 -44.540 -54.156 24.076 1.00 0.00 C +ATOM 608 O SER A 43 -45.210 -55.169 24.302 1.00 0.00 O +ATOM 609 CB SER A 43 -43.953 -53.905 26.478 1.00 0.00 C +ATOM 610 OG SER A 43 -42.914 -53.684 27.413 1.00 0.00 O +ATOM 611 H SER A 43 -43.486 -51.670 24.654 1.00 0.00 H +ATOM 612 HA SER A 43 -42.617 -54.506 24.927 1.00 0.00 H +ATOM 613 HB2 SER A 43 -44.749 -53.163 26.640 1.00 0.00 H +ATOM 614 HB3 SER A 43 -44.360 -54.917 26.622 1.00 0.00 H +ATOM 615 HG SER A 43 -43.159 -54.054 28.253 1.00 0.00 H +ATOM 616 N ASN A 44 -44.768 -53.393 23.016 1.00 0.00 N +ATOM 617 CA ASN A 44 -45.759 -53.752 22.014 1.00 0.00 C +ATOM 618 C ASN A 44 -45.085 -54.243 20.741 1.00 0.00 C +ATOM 619 O ASN A 44 -44.004 -53.779 20.369 1.00 0.00 O +ATOM 620 CB ASN A 44 -46.676 -52.569 21.683 1.00 0.00 C +ATOM 621 CG ASN A 44 -47.680 -52.281 22.787 1.00 0.00 C +ATOM 622 OD1 ASN A 44 -47.477 -51.391 23.610 1.00 0.00 O +ATOM 623 ND2 ASN A 44 -48.777 -53.034 22.803 1.00 0.00 N +ATOM 624 H ASN A 44 -44.245 -52.548 22.903 1.00 0.00 H +ATOM 625 HA ASN A 44 -46.385 -54.567 22.406 1.00 0.00 H +ATOM 626 HB2 ASN A 44 -46.055 -51.674 21.529 1.00 0.00 H +ATOM 627 HB3 ASN A 44 -47.225 -52.797 20.758 1.00 0.00 H +ATOM 628 HD21 ASN A 44 -49.475 -52.886 23.503 1.00 0.00 H +ATOM 629 HD22 ASN A 44 -48.903 -53.748 22.115 1.00 0.00 H +ATOM 630 N PHE A 45 -45.727 -55.212 20.093 1.00 0.00 N +ATOM 631 CA PHE A 45 -45.399 -55.597 18.724 1.00 0.00 C +ATOM 632 C PHE A 45 -46.354 -54.805 17.842 1.00 0.00 C +ATOM 633 O PHE A 45 -47.532 -55.145 17.716 1.00 0.00 O +ATOM 634 CB PHE A 45 -45.529 -57.102 18.511 1.00 0.00 C +ATOM 635 CG PHE A 45 -45.125 -57.568 17.133 1.00 0.00 C +ATOM 636 CD1 PHE A 45 -44.059 -56.984 16.464 1.00 0.00 C +ATOM 637 CD2 PHE A 45 -45.799 -58.615 16.522 1.00 0.00 C +ATOM 638 CE1 PHE A 45 -43.687 -57.422 15.202 1.00 0.00 C +ATOM 639 CE2 PHE A 45 -45.429 -59.057 15.263 1.00 0.00 C +ATOM 640 CZ PHE A 45 -44.375 -58.461 14.603 1.00 0.00 C +ATOM 641 H PHE A 45 -46.465 -55.696 20.563 1.00 0.00 H +ATOM 642 HA PHE A 45 -44.367 -55.294 18.491 1.00 0.00 H +ATOM 643 HB2 PHE A 45 -44.891 -57.611 19.248 1.00 0.00 H +ATOM 644 HB3 PHE A 45 -46.579 -57.385 18.677 1.00 0.00 H +ATOM 645 HD1 PHE A 45 -43.512 -56.179 16.932 1.00 0.00 H +ATOM 646 HD2 PHE A 45 -46.622 -59.091 17.034 1.00 0.00 H +ATOM 647 HE1 PHE A 45 -42.861 -56.952 14.688 1.00 0.00 H +ATOM 648 HE2 PHE A 45 -45.967 -59.870 14.797 1.00 0.00 H +ATOM 649 HZ PHE A 45 -44.088 -58.805 13.620 1.00 0.00 H +ATOM 650 N ALA A 46 -45.856 -53.714 17.276 1.00 0.00 N +ATOM 651 CA ALA A 46 -46.662 -52.804 16.478 1.00 0.00 C +ATOM 652 C ALA A 46 -46.094 -52.758 15.070 1.00 0.00 C +ATOM 653 O ALA A 46 -44.885 -52.577 14.892 1.00 0.00 O +ATOM 654 CB ALA A 46 -46.692 -51.404 17.094 1.00 0.00 C +ATOM 655 H ALA A 46 -44.886 -53.508 17.403 1.00 0.00 H +ATOM 656 HA ALA A 46 -47.694 -53.181 16.426 1.00 0.00 H +ATOM 657 HB1 ALA A 46 -47.308 -50.741 16.469 1.00 0.00 H +ATOM 658 HB2 ALA A 46 -47.121 -51.458 18.105 1.00 0.00 H +ATOM 659 HB3 ALA A 46 -45.668 -51.007 17.151 1.00 0.00 H +ATOM 660 N VAL A 47 -46.960 -52.945 14.078 1.00 0.00 N +ATOM 661 CA VAL A 47 -46.550 -53.001 12.682 1.00 0.00 C +ATOM 662 C VAL A 47 -47.444 -52.074 11.870 1.00 0.00 C +ATOM 663 O VAL A 47 -48.653 -51.990 12.120 1.00 0.00 O +ATOM 664 CB VAL A 47 -46.601 -54.442 12.127 1.00 0.00 C +ATOM 665 CG1 VAL A 47 -45.778 -55.385 12.996 1.00 0.00 C +ATOM 666 CG2 VAL A 47 -48.034 -54.945 12.011 1.00 0.00 C +ATOM 667 H VAL A 47 -47.929 -53.051 14.299 1.00 0.00 H +ATOM 668 HA VAL A 47 -45.514 -52.641 12.597 1.00 0.00 H +ATOM 669 HB VAL A 47 -46.159 -54.432 11.120 1.00 0.00 H +ATOM 670 HG11 VAL A 47 -45.829 -56.403 12.583 1.00 0.00 H +ATOM 671 HG12 VAL A 47 -44.731 -55.048 13.013 1.00 0.00 H +ATOM 672 HG13 VAL A 47 -46.180 -55.384 14.020 1.00 0.00 H +ATOM 673 HG21 VAL A 47 -48.032 -55.971 11.614 1.00 0.00 H +ATOM 674 HG22 VAL A 47 -48.507 -54.937 13.004 1.00 0.00 H +ATOM 675 HG23 VAL A 47 -48.599 -54.290 11.331 1.00 0.00 H +ATOM 676 N GLY A 48 -46.847 -51.367 10.912 1.00 0.00 N +ATOM 677 CA GLY A 48 -47.617 -50.570 9.981 1.00 0.00 C +ATOM 678 C GLY A 48 -48.602 -51.445 9.234 1.00 0.00 C +ATOM 679 O GLY A 48 -48.236 -52.518 8.743 1.00 0.00 O +ATOM 680 H GLY A 48 -45.850 -51.387 10.835 1.00 0.00 H +ATOM 681 HA2 GLY A 48 -48.167 -49.795 10.535 1.00 0.00 H +ATOM 682 HA3 GLY A 48 -46.936 -50.093 9.261 1.00 0.00 H +ATOM 683 N ALA A 49 -49.864 -51.015 9.158 1.00 0.00 N +ATOM 684 CA ALA A 49 -50.880 -51.849 8.528 1.00 0.00 C +ATOM 685 C ALA A 49 -51.815 -51.032 7.643 1.00 0.00 C +ATOM 686 O ALA A 49 -52.943 -51.464 7.373 1.00 0.00 O +ATOM 687 CB ALA A 49 -51.676 -52.618 9.581 1.00 0.00 C +ATOM 688 H ALA A 49 -50.112 -50.122 9.533 1.00 0.00 H +ATOM 689 HA ALA A 49 -50.376 -52.588 7.887 1.00 0.00 H +ATOM 690 HB1 ALA A 49 -52.436 -53.240 9.085 1.00 0.00 H +ATOM 691 HB2 ALA A 49 -50.995 -53.261 10.158 1.00 0.00 H +ATOM 692 HB3 ALA A 49 -52.170 -51.906 10.259 1.00 0.00 H +ATOM 693 N ALA A 50 -51.371 -49.864 7.203 1.00 0.00 N +ATOM 694 CA ALA A 50 -52.081 -49.028 6.247 1.00 0.00 C +ATOM 695 C ALA A 50 -51.038 -48.187 5.528 1.00 0.00 C +ATOM 696 O ALA A 50 -49.929 -48.009 6.037 1.00 0.00 O +ATOM 697 CB ALA A 50 -53.119 -48.139 6.941 1.00 0.00 C +ATOM 698 H ALA A 50 -50.492 -49.536 7.550 1.00 0.00 H +ATOM 699 HA ALA A 50 -52.596 -49.662 5.510 1.00 0.00 H +ATOM 700 HB1 ALA A 50 -53.635 -47.522 6.190 1.00 0.00 H +ATOM 701 HB2 ALA A 50 -53.852 -48.771 7.463 1.00 0.00 H +ATOM 702 HB3 ALA A 50 -52.614 -47.486 7.668 1.00 0.00 H +ATOM 703 N PRO A 51 -51.350 -47.690 4.330 1.00 0.00 N +ATOM 704 CA PRO A 51 -50.343 -46.928 3.580 1.00 0.00 C +ATOM 705 C PRO A 51 -49.851 -45.709 4.347 1.00 0.00 C +ATOM 706 O PRO A 51 -50.550 -45.146 5.192 1.00 0.00 O +ATOM 707 CB PRO A 51 -51.086 -46.526 2.301 1.00 0.00 C +ATOM 708 CG PRO A 51 -52.127 -47.584 2.133 1.00 0.00 C +ATOM 709 CD PRO A 51 -52.553 -47.953 3.524 1.00 0.00 C +ATOM 710 HA PRO A 51 -49.489 -47.572 3.322 1.00 0.00 H +ATOM 711 HB2 PRO A 51 -51.551 -45.536 2.415 1.00 0.00 H +ATOM 712 HB3 PRO A 51 -50.401 -46.512 1.440 1.00 0.00 H +ATOM 713 HG2 PRO A 51 -52.981 -47.195 1.560 1.00 0.00 H +ATOM 714 HG3 PRO A 51 -51.706 -48.458 1.615 1.00 0.00 H +ATOM 715 HD2 PRO A 51 -53.394 -47.327 3.857 1.00 0.00 H +ATOM 716 HD3 PRO A 51 -52.840 -49.014 3.579 1.00 0.00 H +ATOM 717 N HIS A 52 -48.611 -45.316 4.042 1.00 0.00 N +ATOM 718 CA HIS A 52 -47.939 -44.185 4.662 1.00 0.00 C +ATOM 719 C HIS A 52 -46.787 -43.824 3.742 1.00 0.00 C +ATOM 720 O HIS A 52 -46.141 -44.734 3.205 1.00 0.00 O +ATOM 721 CB HIS A 52 -47.423 -44.522 6.068 1.00 0.00 C +ATOM 722 CG HIS A 52 -46.993 -43.327 6.863 1.00 0.00 C +ATOM 723 ND1 HIS A 52 -45.739 -42.765 6.747 1.00 0.00 N +ATOM 724 CD2 HIS A 52 -47.648 -42.594 7.794 1.00 0.00 C +ATOM 725 CE1 HIS A 52 -45.644 -41.734 7.568 1.00 0.00 C +ATOM 726 NE2 HIS A 52 -46.788 -41.610 8.216 1.00 0.00 N +ATOM 727 H HIS A 52 -48.114 -45.832 3.344 1.00 0.00 H +ATOM 728 HA HIS A 52 -48.627 -43.330 4.730 1.00 0.00 H +ATOM 729 HB2 HIS A 52 -48.227 -45.032 6.618 1.00 0.00 H +ATOM 730 HB3 HIS A 52 -46.561 -45.198 5.967 1.00 0.00 H +ATOM 731 HD2 HIS A 52 -48.659 -42.753 8.140 1.00 0.00 H +ATOM 732 HE1 HIS A 52 -44.778 -41.100 7.689 1.00 0.00 H +ATOM 733 HE2 HIS A 52 -46.994 -40.913 8.903 1.00 0.00 H +ATOM 734 N PRO A 53 -46.511 -42.538 3.525 1.00 0.00 N +ATOM 735 CA PRO A 53 -45.452 -42.169 2.567 1.00 0.00 C +ATOM 736 C PRO A 53 -44.086 -42.747 2.896 1.00 0.00 C +ATOM 737 O PRO A 53 -43.277 -42.944 1.981 1.00 0.00 O +ATOM 738 CB PRO A 53 -45.444 -40.633 2.627 1.00 0.00 C +ATOM 739 CG PRO A 53 -46.203 -40.272 3.877 1.00 0.00 C +ATOM 740 CD PRO A 53 -47.204 -41.361 4.069 1.00 0.00 C +ATOM 741 HA PRO A 53 -45.742 -42.484 1.554 1.00 0.00 H +ATOM 742 HB2 PRO A 53 -44.412 -40.256 2.682 1.00 0.00 H +ATOM 743 HB3 PRO A 53 -45.941 -40.212 1.741 1.00 0.00 H +ATOM 744 HG2 PRO A 53 -45.521 -40.221 4.739 1.00 0.00 H +ATOM 745 HG3 PRO A 53 -46.709 -39.303 3.752 1.00 0.00 H +ATOM 746 HD2 PRO A 53 -47.442 -41.498 5.134 1.00 0.00 H +ATOM 747 HD3 PRO A 53 -48.129 -41.152 3.511 1.00 0.00 H +ATOM 748 N PHE A 54 -43.800 -43.035 4.162 1.00 0.00 N +ATOM 749 CA PHE A 54 -42.495 -43.574 4.525 1.00 0.00 C +ATOM 750 C PHE A 54 -42.455 -45.097 4.542 1.00 0.00 C +ATOM 751 O PHE A 54 -41.368 -45.670 4.682 1.00 0.00 O +ATOM 752 CB PHE A 54 -42.061 -43.046 5.894 1.00 0.00 C +ATOM 753 CG PHE A 54 -42.007 -41.546 5.983 1.00 0.00 C +ATOM 754 CD1 PHE A 54 -41.821 -40.763 4.851 1.00 0.00 C +ATOM 755 CD2 PHE A 54 -42.145 -40.918 7.209 1.00 0.00 C +ATOM 756 CE1 PHE A 54 -41.776 -39.382 4.945 1.00 0.00 C +ATOM 757 CE2 PHE A 54 -42.100 -39.539 7.307 1.00 0.00 C +ATOM 758 CZ PHE A 54 -41.914 -38.772 6.177 1.00 0.00 C +ATOM 759 H PHE A 54 -44.487 -42.880 4.872 1.00 0.00 H +ATOM 760 HA PHE A 54 -41.757 -43.229 3.786 1.00 0.00 H +ATOM 761 HB2 PHE A 54 -42.774 -43.412 6.648 1.00 0.00 H +ATOM 762 HB3 PHE A 54 -41.059 -43.442 6.115 1.00 0.00 H +ATOM 763 HD1 PHE A 54 -41.710 -41.236 3.887 1.00 0.00 H +ATOM 764 HD2 PHE A 54 -42.290 -41.512 8.099 1.00 0.00 H +ATOM 765 HE1 PHE A 54 -41.633 -38.783 4.057 1.00 0.00 H +ATOM 766 HE2 PHE A 54 -42.211 -39.063 8.270 1.00 0.00 H +ATOM 767 HZ PHE A 54 -41.876 -37.695 6.254 1.00 0.00 H +ATOM 768 N LEU A 55 -43.601 -45.762 4.410 1.00 0.00 N +ATOM 769 CA LEU A 55 -43.674 -47.219 4.401 1.00 0.00 C +ATOM 770 C LEU A 55 -43.800 -47.707 2.963 1.00 0.00 C +ATOM 771 O LEU A 55 -44.709 -47.286 2.239 1.00 0.00 O +ATOM 772 CB LEU A 55 -44.863 -47.721 5.221 1.00 0.00 C +ATOM 773 CG LEU A 55 -44.996 -47.413 6.711 1.00 0.00 C +ATOM 774 CD1 LEU A 55 -46.285 -48.024 7.241 1.00 0.00 C +ATOM 775 CD2 LEU A 55 -43.799 -47.933 7.490 1.00 0.00 C +ATOM 776 H LEU A 55 -44.448 -45.240 4.313 1.00 0.00 H +ATOM 777 HA LEU A 55 -42.751 -47.634 4.833 1.00 0.00 H +ATOM 778 HB2 LEU A 55 -45.765 -47.315 4.739 1.00 0.00 H +ATOM 779 HB3 LEU A 55 -44.861 -48.817 5.130 1.00 0.00 H +ATOM 780 HG LEU A 55 -45.049 -46.322 6.838 1.00 0.00 H +ATOM 781 HD11 LEU A 55 -46.382 -47.803 8.314 1.00 0.00 H +ATOM 782 HD12 LEU A 55 -47.142 -47.597 6.700 1.00 0.00 H +ATOM 783 HD13 LEU A 55 -46.263 -49.114 7.092 1.00 0.00 H +ATOM 784 HD21 LEU A 55 -43.924 -47.696 8.557 1.00 0.00 H +ATOM 785 HD22 LEU A 55 -43.724 -49.023 7.363 1.00 0.00 H +ATOM 786 HD23 LEU A 55 -42.882 -47.456 7.114 1.00 0.00 H +ATOM 787 N HIS A 56 -42.899 -48.600 2.553 1.00 0.00 N +ATOM 788 CA HIS A 56 -42.996 -49.219 1.238 1.00 0.00 C +ATOM 789 C HIS A 56 -43.656 -50.592 1.276 1.00 0.00 C +ATOM 790 O HIS A 56 -43.866 -51.193 0.216 1.00 0.00 O +ATOM 791 CB HIS A 56 -41.610 -49.316 0.594 1.00 0.00 C +ATOM 792 CG HIS A 56 -41.079 -47.998 0.121 1.00 0.00 C +ATOM 793 ND1 HIS A 56 -39.844 -47.514 0.495 1.00 0.00 N +ATOM 794 CD2 HIS A 56 -41.620 -47.060 -0.693 1.00 0.00 C +ATOM 795 CE1 HIS A 56 -39.647 -46.335 -0.068 1.00 0.00 C +ATOM 796 NE2 HIS A 56 -40.709 -46.037 -0.794 1.00 0.00 N +ATOM 797 H HIS A 56 -42.143 -48.849 3.159 1.00 0.00 H +ATOM 798 HA HIS A 56 -43.613 -48.573 0.596 1.00 0.00 H +ATOM 799 HB2 HIS A 56 -40.910 -49.728 1.335 1.00 0.00 H +ATOM 800 HB3 HIS A 56 -41.673 -49.996 -0.268 1.00 0.00 H +ATOM 801 HD1 HIS A 56 -39.198 -47.982 1.098 1.00 0.00 H +ATOM 802 HD2 HIS A 56 -42.586 -47.108 -1.173 1.00 0.00 H +ATOM 803 HE1 HIS A 56 -38.767 -45.720 0.046 1.00 0.00 H +ATOM 804 N ARG A 57 -43.984 -51.092 2.465 1.00 0.00 N +ATOM 805 CA ARG A 57 -44.830 -52.264 2.624 1.00 0.00 C +ATOM 806 C ARG A 57 -45.474 -52.190 3.999 1.00 0.00 C +ATOM 807 O ARG A 57 -45.005 -51.470 4.884 1.00 0.00 O +ATOM 808 CB ARG A 57 -44.042 -53.569 2.442 1.00 0.00 C +ATOM 809 CG ARG A 57 -42.815 -53.690 3.326 1.00 0.00 C +ATOM 810 CD ARG A 57 -41.992 -54.919 2.967 1.00 0.00 C +ATOM 811 NE ARG A 57 -40.767 -54.999 3.759 1.00 0.00 N +ATOM 812 CZ ARG A 57 -39.822 -55.920 3.590 1.00 0.00 C +ATOM 813 NH1 ARG A 57 -39.958 -56.848 2.651 1.00 0.00 N +ATOM 814 NH2 ARG A 57 -38.740 -55.911 4.359 1.00 0.00 N1+ +ATOM 815 H ARG A 57 -43.633 -50.641 3.286 1.00 0.00 H +ATOM 816 HA ARG A 57 -45.628 -52.238 1.867 1.00 0.00 H +ATOM 817 HB2 ARG A 57 -44.713 -54.410 2.669 1.00 0.00 H +ATOM 818 HB3 ARG A 57 -43.717 -53.631 1.393 1.00 0.00 H +ATOM 819 HG2 ARG A 57 -42.192 -52.792 3.199 1.00 0.00 H +ATOM 820 HG3 ARG A 57 -43.136 -53.768 4.375 1.00 0.00 H +ATOM 821 HD2 ARG A 57 -42.596 -55.819 3.153 1.00 0.00 H +ATOM 822 HD3 ARG A 57 -41.725 -54.870 1.901 1.00 0.00 H +ATOM 823 HE ARG A 57 -40.630 -54.316 4.476 1.00 0.00 H +ATOM 824 HH11 ARG A 57 -39.246 -57.539 2.524 1.00 0.00 H +ATOM 825 HH12 ARG A 57 -40.772 -56.856 2.071 1.00 0.00 H +ATOM 826 HH21 ARG A 57 -38.029 -56.603 4.230 1.00 0.00 H +ATOM 827 HH22 ARG A 57 -38.636 -55.213 5.067 1.00 0.00 H +ATOM 828 N TYR A 58 -46.567 -52.928 4.168 1.00 0.00 N +ATOM 829 CA TYR A 58 -47.274 -52.913 5.441 1.00 0.00 C +ATOM 830 C TYR A 58 -48.071 -54.200 5.600 1.00 0.00 C +ATOM 831 O TYR A 58 -48.228 -54.981 4.658 1.00 0.00 O +ATOM 832 CB TYR A 58 -48.178 -51.677 5.572 1.00 0.00 C +ATOM 833 CG TYR A 58 -49.232 -51.535 4.496 1.00 0.00 C +ATOM 834 CD1 TYR A 58 -50.497 -52.079 4.663 1.00 0.00 C +ATOM 835 CD2 TYR A 58 -48.970 -50.841 3.322 1.00 0.00 C +ATOM 836 CE1 TYR A 58 -51.467 -51.949 3.688 1.00 0.00 C +ATOM 837 CE2 TYR A 58 -49.938 -50.704 2.339 1.00 0.00 C +ATOM 838 CZ TYR A 58 -51.184 -51.262 2.529 1.00 0.00 C +ATOM 839 OH TYR A 58 -52.151 -51.131 1.557 1.00 0.00 O +ATOM 840 H TYR A 58 -46.905 -53.496 3.418 1.00 0.00 H +ATOM 841 HA TYR A 58 -46.534 -52.873 6.254 1.00 0.00 H +ATOM 842 HB2 TYR A 58 -48.689 -51.730 6.545 1.00 0.00 H +ATOM 843 HB3 TYR A 58 -47.538 -50.783 5.543 1.00 0.00 H +ATOM 844 HD1 TYR A 58 -50.728 -52.614 5.572 1.00 0.00 H +ATOM 845 HD2 TYR A 58 -47.995 -50.400 3.172 1.00 0.00 H +ATOM 846 HE1 TYR A 58 -52.444 -52.385 3.835 1.00 0.00 H +ATOM 847 HE2 TYR A 58 -49.717 -50.164 1.430 1.00 0.00 H +ATOM 848 HH TYR A 58 -51.738 -51.085 0.703 1.00 0.00 H +ATOM 849 N TYR A 59 -48.563 -54.406 6.818 1.00 0.00 N +ATOM 850 CA TYR A 59 -49.293 -55.615 7.177 1.00 0.00 C +ATOM 851 C TYR A 59 -50.707 -55.545 6.615 1.00 0.00 C +ATOM 852 O TYR A 59 -51.491 -54.666 6.992 1.00 0.00 O +ATOM 853 CB TYR A 59 -49.305 -55.752 8.698 1.00 0.00 C +ATOM 854 CG TYR A 59 -50.098 -56.899 9.291 1.00 0.00 C +ATOM 855 CD1 TYR A 59 -50.202 -58.122 8.648 1.00 0.00 C +ATOM 856 CD2 TYR A 59 -50.723 -56.754 10.522 1.00 0.00 C +ATOM 857 CE1 TYR A 59 -50.920 -59.167 9.214 1.00 0.00 C +ATOM 858 CE2 TYR A 59 -51.437 -57.787 11.091 1.00 0.00 C +ATOM 859 CZ TYR A 59 -51.537 -58.991 10.434 1.00 0.00 C +ATOM 860 OH TYR A 59 -52.253 -60.017 11.004 1.00 0.00 O +ATOM 861 H TYR A 59 -48.426 -53.703 7.516 1.00 0.00 H +ATOM 862 HA TYR A 59 -48.786 -56.492 6.749 1.00 0.00 H +ATOM 863 HB2 TYR A 59 -48.262 -55.868 9.027 1.00 0.00 H +ATOM 864 HB3 TYR A 59 -49.717 -54.819 9.110 1.00 0.00 H +ATOM 865 HD1 TYR A 59 -49.718 -58.264 7.693 1.00 0.00 H +ATOM 866 HD2 TYR A 59 -50.648 -55.812 11.045 1.00 0.00 H +ATOM 867 HE1 TYR A 59 -50.995 -60.114 8.700 1.00 0.00 H +ATOM 868 HE2 TYR A 59 -51.916 -57.651 12.049 1.00 0.00 H +ATOM 869 HH TYR A 59 -52.229 -60.776 10.433 1.00 0.00 H +ATOM 870 N GLN A 60 -51.035 -56.458 5.709 1.00 0.00 N +ATOM 871 CA GLN A 60 -52.357 -56.496 5.083 1.00 0.00 C +ATOM 872 C GLN A 60 -53.148 -57.634 5.718 1.00 0.00 C +ATOM 873 O GLN A 60 -52.996 -58.799 5.342 1.00 0.00 O +ATOM 874 CB GLN A 60 -52.241 -56.661 3.573 1.00 0.00 C +ATOM 875 CG GLN A 60 -51.624 -55.467 2.860 1.00 0.00 C +ATOM 876 CD GLN A 60 -51.635 -55.635 1.357 1.00 0.00 C +ATOM 877 OE1 GLN A 60 -52.494 -55.089 0.666 1.00 0.00 O +ATOM 878 NE2 GLN A 60 -50.686 -56.405 0.842 1.00 0.00 N +ATOM 879 H GLN A 60 -50.356 -57.143 5.445 1.00 0.00 H +ATOM 880 HA GLN A 60 -52.882 -55.551 5.290 1.00 0.00 H +ATOM 881 HB2 GLN A 60 -51.618 -57.544 3.369 1.00 0.00 H +ATOM 882 HB3 GLN A 60 -53.250 -56.823 3.165 1.00 0.00 H +ATOM 883 HG2 GLN A 60 -52.196 -54.564 3.120 1.00 0.00 H +ATOM 884 HG3 GLN A 60 -50.583 -55.352 3.197 1.00 0.00 H +ATOM 885 HE21 GLN A 60 -50.649 -56.561 -0.145 1.00 0.00 H +ATOM 886 HE22 GLN A 60 -50.007 -56.830 1.441 1.00 0.00 H +ATOM 887 N ARG A 61 -53.997 -57.289 6.688 1.00 0.00 N +ATOM 888 CA ARG A 61 -54.701 -58.310 7.455 1.00 0.00 C +ATOM 889 C ARG A 61 -55.684 -59.096 6.600 1.00 0.00 C +ATOM 890 O ARG A 61 -55.939 -60.272 6.879 1.00 0.00 O +ATOM 891 CB ARG A 61 -55.422 -57.665 8.635 1.00 0.00 C +ATOM 892 CG ARG A 61 -54.485 -57.095 9.685 1.00 0.00 C +ATOM 893 CD ARG A 61 -55.194 -56.096 10.582 1.00 0.00 C +ATOM 894 NE ARG A 61 -55.442 -54.828 9.903 1.00 0.00 N +ATOM 895 CZ ARG A 61 -56.206 -53.856 10.391 1.00 0.00 C +ATOM 896 NH1 ARG A 61 -56.805 -54.006 11.564 1.00 0.00 N +ATOM 897 NH2 ARG A 61 -56.376 -52.735 9.703 1.00 0.00 N1+ +ATOM 898 H ARG A 61 -54.152 -56.322 6.891 1.00 0.00 H +ATOM 899 HA ARG A 61 -53.963 -59.019 7.858 1.00 0.00 H +ATOM 900 HB2 ARG A 61 -56.053 -56.849 8.253 1.00 0.00 H +ATOM 901 HB3 ARG A 61 -56.057 -58.426 9.112 1.00 0.00 H +ATOM 902 HG2 ARG A 61 -54.098 -57.918 10.303 1.00 0.00 H +ATOM 903 HG3 ARG A 61 -53.648 -56.591 9.181 1.00 0.00 H +ATOM 904 HD2 ARG A 61 -56.157 -56.524 10.898 1.00 0.00 H +ATOM 905 HD3 ARG A 61 -54.569 -55.908 11.467 1.00 0.00 H +ATOM 906 HE ARG A 61 -55.009 -54.681 9.014 1.00 0.00 H +ATOM 907 HH11 ARG A 61 -57.383 -53.275 11.927 1.00 0.00 H +ATOM 908 HH12 ARG A 61 -56.679 -54.850 12.085 1.00 0.00 H +ATOM 909 HH21 ARG A 61 -56.954 -52.006 10.069 1.00 0.00 H +ATOM 910 HH22 ARG A 61 -55.926 -52.618 8.818 1.00 0.00 H +ATOM 911 N GLN A 62 -56.234 -58.483 5.551 1.00 0.00 N +ATOM 912 CA GLN A 62 -57.235 -59.185 4.757 1.00 0.00 C +ATOM 913 C GLN A 62 -56.630 -60.294 3.904 1.00 0.00 C +ATOM 914 O GLN A 62 -57.374 -61.137 3.393 1.00 0.00 O +ATOM 915 CB GLN A 62 -58.001 -58.201 3.869 1.00 0.00 C +ATOM 916 CG GLN A 62 -57.247 -57.750 2.633 1.00 0.00 C +ATOM 917 CD GLN A 62 -56.247 -56.649 2.918 1.00 0.00 C +ATOM 918 OE1 GLN A 62 -55.920 -56.365 4.073 1.00 0.00 O +ATOM 919 NE2 GLN A 62 -55.755 -56.019 1.861 1.00 0.00 N +ATOM 920 H GLN A 62 -55.962 -57.551 5.311 1.00 0.00 H +ATOM 921 HA GLN A 62 -57.960 -59.649 5.441 1.00 0.00 H +ATOM 922 HB2 GLN A 62 -58.934 -58.686 3.545 1.00 0.00 H +ATOM 923 HB3 GLN A 62 -58.240 -57.311 4.469 1.00 0.00 H +ATOM 924 HG2 GLN A 62 -56.708 -58.614 2.217 1.00 0.00 H +ATOM 925 HG3 GLN A 62 -57.974 -57.381 1.895 1.00 0.00 H +ATOM 926 HE21 GLN A 62 -55.091 -55.281 1.983 1.00 0.00 H +ATOM 927 HE22 GLN A 62 -56.048 -56.281 0.942 1.00 0.00 H +ATOM 928 N LEU A 63 -55.310 -60.316 3.740 1.00 0.00 N +ATOM 929 CA LEU A 63 -54.642 -61.370 2.988 1.00 0.00 C +ATOM 930 C LEU A 63 -54.132 -62.503 3.868 1.00 0.00 C +ATOM 931 O LEU A 63 -53.494 -63.426 3.353 1.00 0.00 O +ATOM 932 CB LEU A 63 -53.468 -60.799 2.185 1.00 0.00 C +ATOM 933 CG LEU A 63 -53.724 -59.713 1.141 1.00 0.00 C +ATOM 934 CD1 LEU A 63 -52.466 -59.480 0.317 1.00 0.00 C +ATOM 935 CD2 LEU A 63 -54.896 -60.084 0.248 1.00 0.00 C +ATOM 936 H LEU A 63 -54.759 -59.586 4.146 1.00 0.00 H +ATOM 937 HA LEU A 63 -55.359 -61.798 2.272 1.00 0.00 H +ATOM 938 HB2 LEU A 63 -52.757 -60.381 2.913 1.00 0.00 H +ATOM 939 HB3 LEU A 63 -53.000 -61.644 1.659 1.00 0.00 H +ATOM 940 HG LEU A 63 -53.971 -58.779 1.668 1.00 0.00 H +ATOM 941 HD11 LEU A 63 -52.657 -58.697 -0.432 1.00 0.00 H +ATOM 942 HD12 LEU A 63 -51.648 -59.161 0.979 1.00 0.00 H +ATOM 943 HD13 LEU A 63 -52.183 -60.413 -0.192 1.00 0.00 H +ATOM 944 HD21 LEU A 63 -55.059 -59.288 -0.493 1.00 0.00 H +ATOM 945 HD22 LEU A 63 -54.677 -61.029 -0.271 1.00 0.00 H +ATOM 946 HD23 LEU A 63 -55.801 -60.204 0.862 1.00 0.00 H +ATOM 947 N SER A 64 -54.391 -62.464 5.169 1.00 0.00 N +ATOM 948 CA SER A 64 -53.883 -63.471 6.091 1.00 0.00 C +ATOM 949 C SER A 64 -55.026 -64.351 6.574 1.00 0.00 C +ATOM 950 O SER A 64 -55.944 -63.870 7.245 1.00 0.00 O +ATOM 951 CB SER A 64 -53.175 -62.826 7.280 1.00 0.00 C +ATOM 952 OG SER A 64 -52.932 -63.797 8.279 1.00 0.00 O +ATOM 953 H SER A 64 -54.953 -61.719 5.528 1.00 0.00 H +ATOM 954 HA SER A 64 -53.157 -64.108 5.564 1.00 0.00 H +ATOM 955 HB2 SER A 64 -52.218 -62.398 6.947 1.00 0.00 H +ATOM 956 HB3 SER A 64 -53.809 -62.028 7.693 1.00 0.00 H +ATOM 957 HG SER A 64 -52.833 -63.368 9.121 1.00 0.00 H +ATOM 958 N SER A 65 -54.950 -65.642 6.251 1.00 0.00 N +ATOM 959 CA SER A 65 -55.974 -66.585 6.677 1.00 0.00 C +ATOM 960 C SER A 65 -55.940 -66.852 8.176 1.00 0.00 C +ATOM 961 O SER A 65 -56.924 -67.366 8.718 1.00 0.00 O +ATOM 962 CB SER A 65 -55.816 -67.899 5.914 1.00 0.00 C +ATOM 963 OG SER A 65 -54.546 -68.480 6.168 1.00 0.00 O +ATOM 964 H SER A 65 -54.177 -65.968 5.707 1.00 0.00 H +ATOM 965 HA SER A 65 -56.962 -66.169 6.430 1.00 0.00 H +ATOM 966 HB2 SER A 65 -56.603 -68.598 6.233 1.00 0.00 H +ATOM 967 HB3 SER A 65 -55.914 -67.704 4.836 1.00 0.00 H +ATOM 968 HG SER A 65 -54.539 -68.858 7.040 1.00 0.00 H +ATOM 969 N THR A 66 -54.841 -66.525 8.855 1.00 0.00 N +ATOM 970 CA THR A 66 -54.704 -66.775 10.284 1.00 0.00 C +ATOM 971 C THR A 66 -54.945 -65.538 11.140 1.00 0.00 C +ATOM 972 O THR A 66 -54.773 -65.602 12.361 1.00 0.00 O +ATOM 973 CB THR A 66 -53.315 -67.354 10.589 1.00 0.00 C +ATOM 974 OG1 THR A 66 -52.325 -66.652 9.827 1.00 0.00 O +ATOM 975 CG2 THR A 66 -53.273 -68.834 10.237 1.00 0.00 C +ATOM 976 H THR A 66 -54.083 -66.093 8.366 1.00 0.00 H +ATOM 977 HA THR A 66 -55.447 -67.531 10.577 1.00 0.00 H +ATOM 978 HB THR A 66 -53.114 -67.239 11.664 1.00 0.00 H +ATOM 979 HG1 THR A 66 -52.075 -65.859 10.286 1.00 0.00 H +ATOM 980 HG21 THR A 66 -52.274 -69.236 10.460 1.00 0.00 H +ATOM 981 HG22 THR A 66 -54.026 -69.373 10.830 1.00 0.00 H +ATOM 982 HG23 THR A 66 -53.489 -68.962 9.166 1.00 0.00 H +ATOM 983 N TYR A 67 -55.345 -64.425 10.536 1.00 0.00 N +ATOM 984 CA TYR A 67 -55.605 -63.210 11.295 1.00 0.00 C +ATOM 985 C TYR A 67 -56.870 -63.355 12.131 1.00 0.00 C +ATOM 986 O TYR A 67 -57.869 -63.924 11.682 1.00 0.00 O +ATOM 987 CB TYR A 67 -55.733 -62.016 10.347 1.00 0.00 C +ATOM 988 CG TYR A 67 -56.384 -60.794 10.958 1.00 0.00 C +ATOM 989 CD1 TYR A 67 -55.680 -59.970 11.825 1.00 0.00 C +ATOM 990 CD2 TYR A 67 -57.696 -60.451 10.649 1.00 0.00 C +ATOM 991 CE1 TYR A 67 -56.266 -58.851 12.381 1.00 0.00 C +ATOM 992 CE2 TYR A 67 -58.292 -59.330 11.201 1.00 0.00 C +ATOM 993 CZ TYR A 67 -57.570 -58.532 12.065 1.00 0.00 C +ATOM 994 OH TYR A 67 -58.141 -57.411 12.624 1.00 0.00 O +ATOM 995 H TYR A 67 -55.470 -64.421 9.544 1.00 0.00 H +ATOM 996 HA TYR A 67 -54.761 -63.021 11.975 1.00 0.00 H +ATOM 997 HB2 TYR A 67 -54.724 -61.735 10.010 1.00 0.00 H +ATOM 998 HB3 TYR A 67 -56.335 -62.328 9.481 1.00 0.00 H +ATOM 999 HD1 TYR A 67 -54.655 -60.209 12.069 1.00 0.00 H +ATOM 1000 HD2 TYR A 67 -58.260 -61.070 9.967 1.00 0.00 H +ATOM 1001 HE1 TYR A 67 -55.705 -58.227 13.061 1.00 0.00 H +ATOM 1002 HE2 TYR A 67 -59.314 -59.082 10.957 1.00 0.00 H +ATOM 1003 HH TYR A 67 -59.009 -57.282 12.259 1.00 0.00 H +ATOM 1004 N ARG A 68 -56.824 -62.837 13.358 1.00 0.00 N +ATOM 1005 CA ARG A 68 -57.989 -62.798 14.235 1.00 0.00 C +ATOM 1006 C ARG A 68 -58.112 -61.405 14.832 1.00 0.00 C +ATOM 1007 O ARG A 68 -57.197 -60.933 15.515 1.00 0.00 O +ATOM 1008 CB ARG A 68 -57.900 -63.857 15.336 1.00 0.00 C +ATOM 1009 CG ARG A 68 -57.932 -65.286 14.804 1.00 0.00 C +ATOM 1010 CD ARG A 68 -58.039 -66.307 15.924 1.00 0.00 C +ATOM 1011 NE ARG A 68 -57.634 -67.640 15.484 1.00 0.00 N +ATOM 1012 CZ ARG A 68 -58.410 -68.717 15.523 1.00 0.00 C +ATOM 1013 NH1 ARG A 68 -59.646 -68.633 15.991 1.00 0.00 N +ATOM 1014 NH2 ARG A 68 -57.942 -69.884 15.103 1.00 0.00 N1+ +ATOM 1015 H ARG A 68 -55.959 -62.461 13.690 1.00 0.00 H +ATOM 1016 HA ARG A 68 -58.892 -62.998 13.640 1.00 0.00 H +ATOM 1017 HB2 ARG A 68 -56.959 -63.710 15.886 1.00 0.00 H +ATOM 1018 HB3 ARG A 68 -58.750 -63.721 16.021 1.00 0.00 H +ATOM 1019 HG2 ARG A 68 -58.800 -65.398 14.137 1.00 0.00 H +ATOM 1020 HG3 ARG A 68 -57.008 -65.475 14.238 1.00 0.00 H +ATOM 1021 HD2 ARG A 68 -57.389 -65.994 16.755 1.00 0.00 H +ATOM 1022 HD3 ARG A 68 -59.082 -66.348 16.270 1.00 0.00 H +ATOM 1023 HE ARG A 68 -56.706 -67.749 15.128 1.00 0.00 H +ATOM 1024 HH11 ARG A 68 -60.224 -69.449 16.027 1.00 0.00 H +ATOM 1025 HH12 ARG A 68 -60.002 -67.754 16.309 1.00 0.00 H +ATOM 1026 HH21 ARG A 68 -58.522 -70.698 15.140 1.00 0.00 H +ATOM 1027 HH22 ARG A 68 -57.009 -69.951 14.749 1.00 0.00 H +ATOM 1028 N ASP A 69 -59.241 -60.753 14.562 1.00 0.00 N +ATOM 1029 CA ASP A 69 -59.491 -59.396 15.031 1.00 0.00 C +ATOM 1030 C ASP A 69 -59.889 -59.424 16.501 1.00 0.00 C +ATOM 1031 O ASP A 69 -60.880 -60.064 16.865 1.00 0.00 O +ATOM 1032 CB ASP A 69 -60.590 -58.763 14.176 1.00 0.00 C +ATOM 1033 CG ASP A 69 -60.807 -57.287 14.471 1.00 0.00 C +ATOM 1034 OD1 ASP A 69 -60.238 -56.756 15.450 1.00 0.00 O +ATOM 1035 OD2 ASP A 69 -61.556 -56.648 13.703 1.00 0.00 O1- +ATOM 1036 H ASP A 69 -59.943 -61.211 14.017 1.00 0.00 H +ATOM 1037 HA ASP A 69 -58.575 -58.796 14.926 1.00 0.00 H +ATOM 1038 HB2 ASP A 69 -60.313 -58.870 13.117 1.00 0.00 H +ATOM 1039 HB3 ASP A 69 -61.532 -59.299 14.365 1.00 0.00 H +ATOM 1040 N LEU A 70 -59.120 -58.732 17.349 1.00 0.00 N +ATOM 1041 CA LEU A 70 -59.461 -58.639 18.764 1.00 0.00 C +ATOM 1042 C LEU A 70 -60.504 -57.566 19.052 1.00 0.00 C +ATOM 1043 O LEU A 70 -60.984 -57.482 20.189 1.00 0.00 O +ATOM 1044 CB LEU A 70 -58.198 -58.388 19.596 1.00 0.00 C +ATOM 1045 CG LEU A 70 -57.362 -59.618 19.961 1.00 0.00 C +ATOM 1046 CD1 LEU A 70 -55.950 -59.214 20.383 1.00 0.00 C +ATOM 1047 CD2 LEU A 70 -58.045 -60.407 21.066 1.00 0.00 C +ATOM 1048 H LEU A 70 -58.300 -58.272 17.008 1.00 0.00 H +ATOM 1049 HA LEU A 70 -59.881 -59.604 19.084 1.00 0.00 H +ATOM 1050 HB2 LEU A 70 -57.553 -57.703 19.025 1.00 0.00 H +ATOM 1051 HB3 LEU A 70 -58.507 -57.904 20.534 1.00 0.00 H +ATOM 1052 HG LEU A 70 -57.286 -60.260 19.071 1.00 0.00 H +ATOM 1053 HD11 LEU A 70 -55.372 -60.114 20.639 1.00 0.00 H +ATOM 1054 HD12 LEU A 70 -55.457 -58.685 19.554 1.00 0.00 H +ATOM 1055 HD13 LEU A 70 -56.005 -58.552 21.260 1.00 0.00 H +ATOM 1056 HD21 LEU A 70 -57.436 -61.287 21.319 1.00 0.00 H +ATOM 1057 HD22 LEU A 70 -58.157 -59.770 21.956 1.00 0.00 H +ATOM 1058 HD23 LEU A 70 -59.037 -60.735 20.723 1.00 0.00 H +ATOM 1059 N ARG A 71 -60.859 -56.752 18.053 1.00 0.00 N +ATOM 1060 CA ARG A 71 -61.970 -55.801 18.137 1.00 0.00 C +ATOM 1061 C ARG A 71 -61.781 -54.816 19.293 1.00 0.00 C +ATOM 1062 O ARG A 71 -62.630 -54.674 20.176 1.00 0.00 O +ATOM 1063 CB ARG A 71 -63.312 -56.537 18.247 1.00 0.00 C +ATOM 1064 CG ARG A 71 -63.645 -57.342 17.008 1.00 0.00 C +ATOM 1065 CD ARG A 71 -64.951 -58.104 17.141 1.00 0.00 C +ATOM 1066 NE ARG A 71 -65.015 -58.929 18.344 1.00 0.00 N +ATOM 1067 CZ ARG A 71 -65.992 -58.853 19.242 1.00 0.00 C +ATOM 1068 NH1 ARG A 71 -65.975 -59.637 20.311 1.00 0.00 N +ATOM 1069 NH2 ARG A 71 -66.995 -58.003 19.061 1.00 0.00 N1+ +ATOM 1070 H ARG A 71 -60.337 -56.795 17.201 1.00 0.00 H +ATOM 1071 HA ARG A 71 -61.992 -55.215 17.206 1.00 0.00 H +ATOM 1072 HB2 ARG A 71 -63.268 -57.220 19.108 1.00 0.00 H +ATOM 1073 HB3 ARG A 71 -64.108 -55.794 18.408 1.00 0.00 H +ATOM 1074 HG2 ARG A 71 -63.723 -56.655 16.152 1.00 0.00 H +ATOM 1075 HG3 ARG A 71 -62.834 -58.062 16.827 1.00 0.00 H +ATOM 1076 HD2 ARG A 71 -65.778 -57.379 17.170 1.00 0.00 H +ATOM 1077 HD3 ARG A 71 -65.066 -58.757 16.263 1.00 0.00 H +ATOM 1078 HE ARG A 71 -64.281 -59.590 18.501 1.00 0.00 H +ATOM 1079 HH11 ARG A 71 -66.717 -59.587 20.979 1.00 0.00 H +ATOM 1080 HH12 ARG A 71 -65.220 -60.278 20.449 1.00 0.00 H +ATOM 1081 HH21 ARG A 71 -67.736 -57.955 19.730 1.00 0.00 H +ATOM 1082 HH22 ARG A 71 -67.009 -57.411 18.255 1.00 0.00 H +ATOM 1083 N LYS A 72 -60.642 -54.126 19.264 1.00 0.00 N +ATOM 1084 CA LYS A 72 -60.322 -53.133 20.281 1.00 0.00 C +ATOM 1085 C LYS A 72 -59.197 -52.254 19.761 1.00 0.00 C +ATOM 1086 O LYS A 72 -58.177 -52.766 19.292 1.00 0.00 O +ATOM 1087 CB LYS A 72 -59.920 -53.803 21.600 1.00 0.00 C +ATOM 1088 CG LYS A 72 -59.433 -52.841 22.679 1.00 0.00 C +ATOM 1089 CD LYS A 72 -59.489 -53.491 24.051 1.00 0.00 C +ATOM 1090 CE LYS A 72 -60.903 -53.472 24.612 1.00 0.00 C +ATOM 1091 NZ LYS A 72 -61.330 -52.083 24.941 1.00 0.00 N1+ +ATOM 1092 H LYS A 72 -59.990 -54.294 18.525 1.00 0.00 H +ATOM 1093 HA LYS A 72 -61.204 -52.502 20.466 1.00 0.00 H +ATOM 1094 HB2 LYS A 72 -60.795 -54.343 21.991 1.00 0.00 H +ATOM 1095 HB3 LYS A 72 -59.112 -54.519 21.389 1.00 0.00 H +ATOM 1096 HG2 LYS A 72 -58.395 -52.550 22.460 1.00 0.00 H +ATOM 1097 HG3 LYS A 72 -60.072 -51.946 22.678 1.00 0.00 H +ATOM 1098 HD2 LYS A 72 -59.150 -54.534 23.968 1.00 0.00 H +ATOM 1099 HD3 LYS A 72 -58.824 -52.943 24.735 1.00 0.00 H +ATOM 1100 HE2 LYS A 72 -61.592 -53.892 23.865 1.00 0.00 H +ATOM 1101 HE3 LYS A 72 -60.936 -54.084 25.525 1.00 0.00 H +ATOM 1102 HZ1 LYS A 72 -61.976 -52.104 25.704 1.00 0.00 H +ATOM 1103 HZ2 LYS A 72 -60.530 -51.540 25.197 1.00 0.00 H +ATOM 1104 HZ3 LYS A 72 -61.770 -51.672 24.143 1.00 0.00 H +ATOM 1105 N GLY A 73 -59.390 -50.939 19.836 1.00 0.00 N +ATOM 1106 CA GLY A 73 -58.365 -50.017 19.407 1.00 0.00 C +ATOM 1107 C GLY A 73 -57.312 -49.789 20.473 1.00 0.00 C +ATOM 1108 O GLY A 73 -57.447 -50.194 21.627 1.00 0.00 O +ATOM 1109 H GLY A 73 -60.255 -50.586 20.194 1.00 0.00 H +ATOM 1110 HA2 GLY A 73 -57.877 -50.424 18.509 1.00 0.00 H +ATOM 1111 HA3 GLY A 73 -58.836 -49.053 19.163 1.00 0.00 H +ATOM 1112 N VAL A 74 -56.229 -49.137 20.066 1.00 0.00 N +ATOM 1113 CA VAL A 74 -55.183 -48.737 21.000 1.00 0.00 C +ATOM 1114 C VAL A 74 -54.548 -47.455 20.482 1.00 0.00 C +ATOM 1115 O VAL A 74 -54.338 -47.288 19.277 1.00 0.00 O +ATOM 1116 CB VAL A 74 -54.142 -49.860 21.211 1.00 0.00 C +ATOM 1117 CG1 VAL A 74 -53.456 -50.226 19.904 1.00 0.00 C +ATOM 1118 CG2 VAL A 74 -53.120 -49.455 22.274 1.00 0.00 C +ATOM 1119 H VAL A 74 -56.128 -48.915 19.096 1.00 0.00 H +ATOM 1120 HA VAL A 74 -55.643 -48.519 21.975 1.00 0.00 H +ATOM 1121 HB VAL A 74 -54.677 -50.749 21.576 1.00 0.00 H +ATOM 1122 HG11 VAL A 74 -52.723 -51.026 20.086 1.00 0.00 H +ATOM 1123 HG12 VAL A 74 -54.208 -50.575 19.181 1.00 0.00 H +ATOM 1124 HG13 VAL A 74 -52.942 -49.342 19.500 1.00 0.00 H +ATOM 1125 HG21 VAL A 74 -52.389 -50.266 22.409 1.00 0.00 H +ATOM 1126 HG22 VAL A 74 -52.598 -48.542 21.952 1.00 0.00 H +ATOM 1127 HG23 VAL A 74 -53.637 -49.265 23.226 1.00 0.00 H +ATOM 1128 N TYR A 75 -54.273 -46.537 21.403 1.00 0.00 N +ATOM 1129 CA TYR A 75 -53.693 -45.240 21.085 1.00 0.00 C +ATOM 1130 C TYR A 75 -52.448 -45.060 21.939 1.00 0.00 C +ATOM 1131 O TYR A 75 -52.528 -45.138 23.170 1.00 0.00 O +ATOM 1132 CB TYR A 75 -54.709 -44.119 21.331 1.00 0.00 C +ATOM 1133 CG TYR A 75 -54.131 -42.723 21.336 1.00 0.00 C +ATOM 1134 CD1 TYR A 75 -53.418 -42.239 20.249 1.00 0.00 C +ATOM 1135 CD2 TYR A 75 -54.321 -41.879 22.422 1.00 0.00 C +ATOM 1136 CE1 TYR A 75 -52.895 -40.958 20.250 1.00 0.00 C +ATOM 1137 CE2 TYR A 75 -53.805 -40.597 22.430 1.00 0.00 C +ATOM 1138 CZ TYR A 75 -53.091 -40.141 21.341 1.00 0.00 C +ATOM 1139 OH TYR A 75 -52.571 -38.864 21.344 1.00 0.00 O +ATOM 1140 H TYR A 75 -54.474 -46.747 22.360 1.00 0.00 H +ATOM 1141 HA TYR A 75 -53.399 -45.219 20.025 1.00 0.00 H +ATOM 1142 HB2 TYR A 75 -55.471 -44.170 20.540 1.00 0.00 H +ATOM 1143 HB3 TYR A 75 -55.182 -44.296 22.308 1.00 0.00 H +ATOM 1144 HD1 TYR A 75 -53.269 -42.873 19.387 1.00 0.00 H +ATOM 1145 HD2 TYR A 75 -54.882 -42.230 23.275 1.00 0.00 H +ATOM 1146 HE1 TYR A 75 -52.335 -40.600 19.399 1.00 0.00 H +ATOM 1147 HE2 TYR A 75 -53.960 -39.955 23.285 1.00 0.00 H +ATOM 1148 HH TYR A 75 -52.792 -38.434 22.162 1.00 0.00 H +ATOM 1149 N VAL A 76 -51.312 -44.841 21.296 1.00 0.00 N +ATOM 1150 CA VAL A 76 -50.017 -44.784 21.960 1.00 0.00 C +ATOM 1151 C VAL A 76 -49.352 -43.458 21.616 1.00 0.00 C +ATOM 1152 O VAL A 76 -48.801 -43.292 20.526 1.00 0.00 O +ATOM 1153 CB VAL A 76 -49.125 -45.970 21.562 1.00 0.00 C +ATOM 1154 CG1 VAL A 76 -47.764 -45.855 22.238 1.00 0.00 C +ATOM 1155 CG2 VAL A 76 -49.793 -47.284 21.923 1.00 0.00 C +ATOM 1156 H VAL A 76 -51.345 -44.708 20.305 1.00 0.00 H +ATOM 1157 HA VAL A 76 -50.169 -44.821 23.049 1.00 0.00 H +ATOM 1158 HB VAL A 76 -48.977 -45.941 20.472 1.00 0.00 H +ATOM 1159 HG11 VAL A 76 -47.135 -46.709 21.945 1.00 0.00 H +ATOM 1160 HG12 VAL A 76 -47.278 -44.918 21.927 1.00 0.00 H +ATOM 1161 HG13 VAL A 76 -47.896 -45.854 23.330 1.00 0.00 H +ATOM 1162 HG21 VAL A 76 -49.140 -48.120 21.631 1.00 0.00 H +ATOM 1163 HG22 VAL A 76 -49.970 -47.319 23.008 1.00 0.00 H +ATOM 1164 HG23 VAL A 76 -50.753 -47.365 21.392 1.00 0.00 H +ATOM 1165 N PRO A 77 -49.388 -42.484 22.521 1.00 0.00 N +ATOM 1166 CA PRO A 77 -48.608 -41.260 22.327 1.00 0.00 C +ATOM 1167 C PRO A 77 -47.233 -41.359 22.968 1.00 0.00 C +ATOM 1168 O PRO A 77 -47.047 -41.956 24.031 1.00 0.00 O +ATOM 1169 CB PRO A 77 -49.463 -40.197 23.029 1.00 0.00 C +ATOM 1170 CG PRO A 77 -50.101 -40.949 24.148 1.00 0.00 C +ATOM 1171 CD PRO A 77 -50.335 -42.357 23.641 1.00 0.00 C +ATOM 1172 HA PRO A 77 -48.512 -41.019 21.258 1.00 0.00 H +ATOM 1173 HB2 PRO A 77 -48.836 -39.378 23.412 1.00 0.00 H +ATOM 1174 HB3 PRO A 77 -50.222 -39.789 22.346 1.00 0.00 H +ATOM 1175 HG2 PRO A 77 -49.435 -40.966 25.023 1.00 0.00 H +ATOM 1176 HG3 PRO A 77 -51.057 -40.481 24.424 1.00 0.00 H +ATOM 1177 HD2 PRO A 77 -50.118 -43.098 24.425 1.00 0.00 H +ATOM 1178 HD3 PRO A 77 -51.372 -42.482 23.295 1.00 0.00 H +ATOM 1179 N TYR A 78 -46.249 -40.785 22.290 1.00 0.00 N +ATOM 1180 CA TYR A 78 -44.916 -40.611 22.842 1.00 0.00 C +ATOM 1181 C TYR A 78 -44.649 -39.119 23.002 1.00 0.00 C +ATOM 1182 O TYR A 78 -45.493 -38.276 22.681 1.00 0.00 O +ATOM 1183 CB TYR A 78 -43.855 -41.266 21.954 1.00 0.00 C +ATOM 1184 CG TYR A 78 -44.101 -42.724 21.634 1.00 0.00 C +ATOM 1185 CD1 TYR A 78 -43.969 -43.706 22.606 1.00 0.00 C +ATOM 1186 CD2 TYR A 78 -44.438 -43.120 20.348 1.00 0.00 C +ATOM 1187 CE1 TYR A 78 -44.184 -45.045 22.306 1.00 0.00 C +ATOM 1188 CE2 TYR A 78 -44.654 -44.451 20.039 1.00 0.00 C +ATOM 1189 CZ TYR A 78 -44.526 -45.410 21.020 1.00 0.00 C +ATOM 1190 OH TYR A 78 -44.741 -46.735 20.710 1.00 0.00 O +ATOM 1191 H TYR A 78 -46.431 -40.459 21.362 1.00 0.00 H +ATOM 1192 HA TYR A 78 -44.871 -41.079 23.836 1.00 0.00 H +ATOM 1193 HB2 TYR A 78 -43.814 -40.711 21.005 1.00 0.00 H +ATOM 1194 HB3 TYR A 78 -42.885 -41.188 22.467 1.00 0.00 H +ATOM 1195 HD1 TYR A 78 -43.695 -43.424 23.612 1.00 0.00 H +ATOM 1196 HD2 TYR A 78 -44.534 -42.374 19.573 1.00 0.00 H +ATOM 1197 HE1 TYR A 78 -44.084 -45.797 23.075 1.00 0.00 H +ATOM 1198 HE2 TYR A 78 -44.922 -44.737 19.033 1.00 0.00 H +ATOM 1199 HH TYR A 78 -44.615 -47.267 21.487 1.00 0.00 H +ATOM 1200 N THR A 79 -43.456 -38.792 23.501 1.00 0.00 N +ATOM 1201 CA THR A 79 -43.103 -37.387 23.669 1.00 0.00 C +ATOM 1202 C THR A 79 -43.055 -36.679 22.321 1.00 0.00 C +ATOM 1203 O THR A 79 -43.607 -35.585 22.159 1.00 0.00 O +ATOM 1204 CB THR A 79 -41.765 -37.267 24.398 1.00 0.00 C +ATOM 1205 OG1 THR A 79 -41.807 -38.033 25.608 1.00 0.00 O +ATOM 1206 CG2 THR A 79 -41.490 -35.815 24.748 1.00 0.00 C +ATOM 1207 H THR A 79 -42.806 -39.507 23.759 1.00 0.00 H +ATOM 1208 HA THR A 79 -43.871 -36.897 24.285 1.00 0.00 H +ATOM 1209 HB THR A 79 -40.965 -37.637 23.740 1.00 0.00 H +ATOM 1210 HG1 THR A 79 -42.711 -38.171 25.865 1.00 0.00 H +ATOM 1211 HG21 THR A 79 -40.526 -35.740 25.272 1.00 0.00 H +ATOM 1212 HG22 THR A 79 -41.454 -35.216 23.826 1.00 0.00 H +ATOM 1213 HG23 THR A 79 -42.292 -35.437 25.400 1.00 0.00 H +ATOM 1214 N GLN A 80 -42.406 -37.297 21.338 1.00 0.00 N +ATOM 1215 CA GLN A 80 -42.353 -36.784 19.970 1.00 0.00 C +ATOM 1216 C GLN A 80 -42.828 -37.908 19.052 1.00 0.00 C +ATOM 1217 O GLN A 80 -42.047 -38.792 18.686 1.00 0.00 O +ATOM 1218 CB GLN A 80 -40.948 -36.311 19.610 1.00 0.00 C +ATOM 1219 CG GLN A 80 -40.838 -35.663 18.241 1.00 0.00 C +ATOM 1220 CD GLN A 80 -39.479 -35.035 17.996 1.00 0.00 C +ATOM 1221 OE1 GLN A 80 -39.345 -34.090 17.215 1.00 0.00 O +ATOM 1222 NE2 GLN A 80 -38.460 -35.563 18.662 1.00 0.00 N +ATOM 1223 H GLN A 80 -41.932 -38.153 21.545 1.00 0.00 H +ATOM 1224 HA GLN A 80 -43.045 -35.935 19.871 1.00 0.00 H +ATOM 1225 HB2 GLN A 80 -40.627 -35.578 20.365 1.00 0.00 H +ATOM 1226 HB3 GLN A 80 -40.275 -37.181 19.635 1.00 0.00 H +ATOM 1227 HG2 GLN A 80 -41.014 -36.431 17.473 1.00 0.00 H +ATOM 1228 HG3 GLN A 80 -41.607 -34.880 18.160 1.00 0.00 H +ATOM 1229 HE21 GLN A 80 -37.538 -35.195 18.539 1.00 0.00 H +ATOM 1230 HE22 GLN A 80 -38.614 -36.330 19.285 1.00 0.00 H +ATOM 1231 N GLY A 81 -44.111 -37.884 18.700 1.00 0.00 N +ATOM 1232 CA GLY A 81 -44.687 -38.923 17.869 1.00 0.00 C +ATOM 1233 C GLY A 81 -45.830 -39.663 18.536 1.00 0.00 C +ATOM 1234 O GLY A 81 -45.951 -39.659 19.765 1.00 0.00 O +ATOM 1235 H GLY A 81 -44.691 -37.133 19.016 1.00 0.00 H +ATOM 1236 HA2 GLY A 81 -45.063 -38.461 16.944 1.00 0.00 H +ATOM 1237 HA3 GLY A 81 -43.898 -39.649 17.622 1.00 0.00 H +ATOM 1238 N LYS A 82 -46.675 -40.305 17.733 1.00 0.00 N +ATOM 1239 CA LYS A 82 -47.818 -41.045 18.252 1.00 0.00 C +ATOM 1240 C LYS A 82 -48.345 -41.958 17.157 1.00 0.00 C +ATOM 1241 O LYS A 82 -48.112 -41.728 15.969 1.00 0.00 O +ATOM 1242 CB LYS A 82 -48.924 -40.103 18.745 1.00 0.00 C +ATOM 1243 CG LYS A 82 -49.549 -39.241 17.657 1.00 0.00 C +ATOM 1244 CD LYS A 82 -50.616 -38.315 18.224 1.00 0.00 C +ATOM 1245 CE LYS A 82 -51.231 -37.440 17.142 1.00 0.00 C +ATOM 1246 NZ LYS A 82 -50.238 -36.514 16.528 1.00 0.00 N1+ +ATOM 1247 H LYS A 82 -46.521 -40.279 16.745 1.00 0.00 H +ATOM 1248 HA LYS A 82 -47.493 -41.668 19.098 1.00 0.00 H +ATOM 1249 HB2 LYS A 82 -49.719 -40.714 19.199 1.00 0.00 H +ATOM 1250 HB3 LYS A 82 -48.493 -39.436 19.507 1.00 0.00 H +ATOM 1251 HG2 LYS A 82 -48.762 -38.634 17.186 1.00 0.00 H +ATOM 1252 HG3 LYS A 82 -50.008 -39.896 16.902 1.00 0.00 H +ATOM 1253 HD2 LYS A 82 -51.409 -38.923 18.684 1.00 0.00 H +ATOM 1254 HD3 LYS A 82 -50.159 -37.669 18.988 1.00 0.00 H +ATOM 1255 HE2 LYS A 82 -51.643 -38.089 16.355 1.00 0.00 H +ATOM 1256 HE3 LYS A 82 -52.041 -36.844 17.588 1.00 0.00 H +ATOM 1257 HZ1 LYS A 82 -50.532 -35.569 16.668 1.00 0.00 H +ATOM 1258 HZ2 LYS A 82 -49.345 -36.652 16.956 1.00 0.00 H +ATOM 1259 HZ3 LYS A 82 -50.171 -36.700 15.548 1.00 0.00 H +ATOM 1260 N TRP A 83 -49.048 -43.009 17.573 1.00 0.00 N +ATOM 1261 CA TRP A 83 -49.737 -43.858 16.611 1.00 0.00 C +ATOM 1262 C TRP A 83 -50.996 -44.438 17.235 1.00 0.00 C +ATOM 1263 O TRP A 83 -51.157 -44.475 18.458 1.00 0.00 O +ATOM 1264 CB TRP A 83 -48.838 -44.983 16.068 1.00 0.00 C +ATOM 1265 CG TRP A 83 -48.243 -45.902 17.097 1.00 0.00 C +ATOM 1266 CD1 TRP A 83 -47.017 -45.788 17.677 1.00 0.00 C +ATOM 1267 CD2 TRP A 83 -48.830 -47.097 17.636 1.00 0.00 C +ATOM 1268 NE1 TRP A 83 -46.807 -46.822 18.554 1.00 0.00 N +ATOM 1269 CE2 TRP A 83 -47.905 -47.640 18.548 1.00 0.00 C +ATOM 1270 CE3 TRP A 83 -50.052 -47.754 17.443 1.00 0.00 C +ATOM 1271 CZ2 TRP A 83 -48.159 -48.810 19.265 1.00 0.00 C +ATOM 1272 CZ3 TRP A 83 -50.304 -48.915 18.159 1.00 0.00 C +ATOM 1273 CH2 TRP A 83 -49.361 -49.431 19.057 1.00 0.00 C +ATOM 1274 H TRP A 83 -49.103 -43.216 18.550 1.00 0.00 H +ATOM 1275 HA TRP A 83 -50.043 -43.238 15.755 1.00 0.00 H +ATOM 1276 HB2 TRP A 83 -49.441 -45.593 15.379 1.00 0.00 H +ATOM 1277 HB3 TRP A 83 -48.010 -44.515 15.515 1.00 0.00 H +ATOM 1278 HD1 TRP A 83 -46.310 -44.996 17.476 1.00 0.00 H +ATOM 1279 HE1 TRP A 83 -45.985 -46.957 19.108 1.00 0.00 H +ATOM 1280 HE3 TRP A 83 -50.783 -47.364 16.750 1.00 0.00 H +ATOM 1281 HZ2 TRP A 83 -47.434 -49.211 19.958 1.00 0.00 H +ATOM 1282 HZ3 TRP A 83 -51.243 -49.430 18.022 1.00 0.00 H +ATOM 1283 HH2 TRP A 83 -49.587 -50.339 19.597 1.00 0.00 H +ATOM 1284 N GLU A 84 -51.887 -44.891 16.360 1.00 0.00 N +ATOM 1285 CA GLU A 84 -53.164 -45.471 16.732 1.00 0.00 C +ATOM 1286 C GLU A 84 -53.363 -46.728 15.901 1.00 0.00 C +ATOM 1287 O GLU A 84 -52.928 -46.797 14.748 1.00 0.00 O +ATOM 1288 CB GLU A 84 -54.310 -44.470 16.499 1.00 0.00 C +ATOM 1289 CG GLU A 84 -55.611 -44.775 17.229 1.00 0.00 C +ATOM 1290 CD GLU A 84 -56.536 -43.572 17.248 1.00 0.00 C +ATOM 1291 OE1 GLU A 84 -56.386 -42.693 16.373 1.00 0.00 O +ATOM 1292 OE2 GLU A 84 -57.403 -43.494 18.143 1.00 0.00 O1- +ATOM 1293 H GLU A 84 -51.664 -44.829 15.387 1.00 0.00 H +ATOM 1294 HA GLU A 84 -53.151 -45.748 17.797 1.00 0.00 H +ATOM 1295 HB2 GLU A 84 -53.964 -43.478 16.825 1.00 0.00 H +ATOM 1296 HB3 GLU A 84 -54.525 -44.447 15.420 1.00 0.00 H +ATOM 1297 HG2 GLU A 84 -56.119 -45.608 16.721 1.00 0.00 H +ATOM 1298 HG3 GLU A 84 -55.380 -45.064 18.265 1.00 0.00 H +ATOM 1299 N GLY A 85 -54.001 -47.733 16.490 1.00 0.00 N +ATOM 1300 CA GLY A 85 -54.098 -48.990 15.778 1.00 0.00 C +ATOM 1301 C GLY A 85 -55.207 -49.875 16.301 1.00 0.00 C +ATOM 1302 O GLY A 85 -55.943 -49.519 17.222 1.00 0.00 O +ATOM 1303 H GLY A 85 -54.403 -47.625 17.399 1.00 0.00 H +ATOM 1304 HA2 GLY A 85 -54.289 -48.778 14.715 1.00 0.00 H +ATOM 1305 HA3 GLY A 85 -53.143 -49.526 15.880 1.00 0.00 H +ATOM 1306 N GLU A 86 -55.301 -51.053 15.691 1.00 0.00 N +ATOM 1307 CA GLU A 86 -56.344 -52.026 15.983 1.00 0.00 C +ATOM 1308 C GLU A 86 -55.702 -53.330 16.435 1.00 0.00 C +ATOM 1309 O GLU A 86 -54.820 -53.865 15.751 1.00 0.00 O +ATOM 1310 CB GLU A 86 -57.226 -52.252 14.752 1.00 0.00 C +ATOM 1311 CG GLU A 86 -57.609 -50.956 14.045 1.00 0.00 C +ATOM 1312 CD GLU A 86 -58.586 -51.154 12.899 1.00 0.00 C +ATOM 1313 OE1 GLU A 86 -58.751 -52.303 12.435 1.00 0.00 O +ATOM 1314 OE2 GLU A 86 -59.195 -50.152 12.470 1.00 0.00 O1- +ATOM 1315 H GLU A 86 -54.620 -51.282 14.996 1.00 0.00 H +ATOM 1316 HA GLU A 86 -56.978 -51.648 16.799 1.00 0.00 H +ATOM 1317 HB2 GLU A 86 -56.679 -52.890 14.042 1.00 0.00 H +ATOM 1318 HB3 GLU A 86 -58.147 -52.763 15.070 1.00 0.00 H +ATOM 1319 HG2 GLU A 86 -58.069 -50.281 14.782 1.00 0.00 H +ATOM 1320 HG3 GLU A 86 -56.694 -50.494 13.646 1.00 0.00 H +ATOM 1321 N LEU A 87 -56.149 -53.835 17.582 1.00 0.00 N +ATOM 1322 CA LEU A 87 -55.545 -55.012 18.193 1.00 0.00 C +ATOM 1323 C LEU A 87 -56.069 -56.298 17.565 1.00 0.00 C +ATOM 1324 O LEU A 87 -57.247 -56.408 17.220 1.00 0.00 O +ATOM 1325 CB LEU A 87 -55.813 -55.019 19.698 1.00 0.00 C +ATOM 1326 CG LEU A 87 -55.111 -53.925 20.503 1.00 0.00 C +ATOM 1327 CD1 LEU A 87 -55.540 -53.980 21.959 1.00 0.00 C +ATOM 1328 CD2 LEU A 87 -53.603 -54.071 20.379 1.00 0.00 C +ATOM 1329 H LEU A 87 -56.922 -53.394 18.038 1.00 0.00 H +ATOM 1330 HA LEU A 87 -54.456 -54.976 18.041 1.00 0.00 H +ATOM 1331 HB2 LEU A 87 -56.897 -54.906 19.848 1.00 0.00 H +ATOM 1332 HB3 LEU A 87 -55.487 -55.992 20.094 1.00 0.00 H +ATOM 1333 HG LEU A 87 -55.403 -52.948 20.089 1.00 0.00 H +ATOM 1334 HD11 LEU A 87 -55.027 -53.188 22.523 1.00 0.00 H +ATOM 1335 HD12 LEU A 87 -56.628 -53.832 22.026 1.00 0.00 H +ATOM 1336 HD13 LEU A 87 -55.277 -54.961 22.382 1.00 0.00 H +ATOM 1337 HD21 LEU A 87 -53.108 -53.280 20.961 1.00 0.00 H +ATOM 1338 HD22 LEU A 87 -53.297 -55.055 20.764 1.00 0.00 H +ATOM 1339 HD23 LEU A 87 -53.312 -53.984 19.322 1.00 0.00 H +ATOM 1340 N GLY A 88 -55.180 -57.273 17.428 1.00 0.00 N +ATOM 1341 CA GLY A 88 -55.502 -58.556 16.851 1.00 0.00 C +ATOM 1342 C GLY A 88 -54.357 -59.510 17.102 1.00 0.00 C +ATOM 1343 O GLY A 88 -53.417 -59.196 17.833 1.00 0.00 O +ATOM 1344 H GLY A 88 -54.243 -57.111 17.739 1.00 0.00 H +ATOM 1345 HA2 GLY A 88 -56.418 -58.950 17.315 1.00 0.00 H +ATOM 1346 HA3 GLY A 88 -55.658 -58.444 15.768 1.00 0.00 H +ATOM 1347 N THR A 89 -54.444 -60.694 16.498 1.00 0.00 N +ATOM 1348 CA THR A 89 -53.372 -61.677 16.590 1.00 0.00 C +ATOM 1349 C THR A 89 -53.083 -62.239 15.200 1.00 0.00 C +ATOM 1350 O THR A 89 -53.929 -62.200 14.303 1.00 0.00 O +ATOM 1351 CB THR A 89 -53.722 -62.811 17.581 1.00 0.00 C +ATOM 1352 OG1 THR A 89 -54.783 -63.606 17.038 1.00 0.00 O +ATOM 1353 CG2 THR A 89 -54.146 -62.246 18.944 1.00 0.00 C +ATOM 1354 H THR A 89 -55.264 -60.914 15.969 1.00 0.00 H +ATOM 1355 HA THR A 89 -52.461 -61.180 16.954 1.00 0.00 H +ATOM 1356 HB THR A 89 -52.826 -63.433 17.725 1.00 0.00 H +ATOM 1357 HG1 THR A 89 -55.118 -63.193 16.251 1.00 0.00 H +ATOM 1358 HG21 THR A 89 -54.388 -63.075 19.626 1.00 0.00 H +ATOM 1359 HG22 THR A 89 -53.322 -61.652 19.367 1.00 0.00 H +ATOM 1360 HG23 THR A 89 -55.031 -61.606 18.816 1.00 0.00 H +ATOM 1361 N ASP A 90 -51.851 -62.708 15.011 1.00 0.00 N +ATOM 1362 CA ASP A 90 -51.503 -63.502 13.837 1.00 0.00 C +ATOM 1363 C ASP A 90 -50.328 -64.406 14.185 1.00 0.00 C +ATOM 1364 O ASP A 90 -49.692 -64.256 15.230 1.00 0.00 O +ATOM 1365 CB ASP A 90 -51.165 -62.630 12.622 1.00 0.00 C +ATOM 1366 CG ASP A 90 -51.703 -63.221 11.325 1.00 0.00 C +ATOM 1367 OD1 ASP A 90 -51.713 -64.466 11.208 1.00 0.00 O +ATOM 1368 OD2 ASP A 90 -52.123 -62.457 10.429 1.00 0.00 O1- +ATOM 1369 H ASP A 90 -51.146 -62.511 15.692 1.00 0.00 H +ATOM 1370 HA ASP A 90 -52.359 -64.140 13.571 1.00 0.00 H +ATOM 1371 HB2 ASP A 90 -51.606 -61.633 12.767 1.00 0.00 H +ATOM 1372 HB3 ASP A 90 -50.071 -62.539 12.544 1.00 0.00 H +ATOM 1373 N LEU A 91 -50.046 -65.349 13.287 1.00 0.00 N +ATOM 1374 CA LEU A 91 -48.837 -66.154 13.412 1.00 0.00 C +ATOM 1375 C LEU A 91 -47.622 -65.322 13.018 1.00 0.00 C +ATOM 1376 O LEU A 91 -47.670 -64.538 12.068 1.00 0.00 O +ATOM 1377 CB LEU A 91 -48.917 -67.410 12.542 1.00 0.00 C +ATOM 1378 CG LEU A 91 -50.007 -68.432 12.880 1.00 0.00 C +ATOM 1379 CD1 LEU A 91 -49.907 -69.663 11.987 1.00 0.00 C +ATOM 1380 CD2 LEU A 91 -49.946 -68.821 14.346 1.00 0.00 C +ATOM 1381 H LEU A 91 -50.669 -65.506 12.521 1.00 0.00 H +ATOM 1382 HA LEU A 91 -48.717 -66.467 14.460 1.00 0.00 H +ATOM 1383 HB2 LEU A 91 -49.081 -67.083 11.505 1.00 0.00 H +ATOM 1384 HB3 LEU A 91 -47.948 -67.925 12.615 1.00 0.00 H +ATOM 1385 HG LEU A 91 -50.982 -67.958 12.695 1.00 0.00 H +ATOM 1386 HD11 LEU A 91 -50.701 -70.376 12.254 1.00 0.00 H +ATOM 1387 HD12 LEU A 91 -50.022 -69.363 10.935 1.00 0.00 H +ATOM 1388 HD13 LEU A 91 -48.925 -70.139 12.127 1.00 0.00 H +ATOM 1389 HD21 LEU A 91 -50.736 -69.554 14.565 1.00 0.00 H +ATOM 1390 HD22 LEU A 91 -48.964 -69.264 14.567 1.00 0.00 H +ATOM 1391 HD23 LEU A 91 -50.093 -67.926 14.969 1.00 0.00 H +ATOM 1392 N VAL A 92 -46.531 -65.479 13.763 1.00 0.00 N +ATOM 1393 CA VAL A 92 -45.359 -64.622 13.610 1.00 0.00 C +ATOM 1394 C VAL A 92 -44.106 -65.486 13.606 1.00 0.00 C +ATOM 1395 O VAL A 92 -43.981 -66.408 14.419 1.00 0.00 O +ATOM 1396 CB VAL A 92 -45.270 -63.566 14.731 1.00 0.00 C +ATOM 1397 CG1 VAL A 92 -44.043 -62.687 14.534 1.00 0.00 C +ATOM 1398 CG2 VAL A 92 -46.528 -62.719 14.786 1.00 0.00 C +ATOM 1399 H VAL A 92 -46.513 -66.206 14.449 1.00 0.00 H +ATOM 1400 HA VAL A 92 -45.420 -64.094 12.647 1.00 0.00 H +ATOM 1401 HB VAL A 92 -45.166 -64.095 15.690 1.00 0.00 H +ATOM 1402 HG11 VAL A 92 -43.993 -61.940 15.340 1.00 0.00 H +ATOM 1403 HG12 VAL A 92 -43.138 -63.311 14.556 1.00 0.00 H +ATOM 1404 HG13 VAL A 92 -44.111 -62.175 13.563 1.00 0.00 H +ATOM 1405 HG21 VAL A 92 -46.436 -61.977 15.593 1.00 0.00 H +ATOM 1406 HG22 VAL A 92 -46.664 -62.201 13.825 1.00 0.00 H +ATOM 1407 HG23 VAL A 92 -47.397 -63.365 14.980 1.00 0.00 H +ATOM 1408 N SER A 93 -43.175 -65.183 12.703 1.00 0.00 N +ATOM 1409 CA SER A 93 -41.903 -65.881 12.631 1.00 0.00 C +ATOM 1410 C SER A 93 -40.760 -64.884 12.503 1.00 0.00 C +ATOM 1411 O SER A 93 -40.956 -63.713 12.166 1.00 0.00 O +ATOM 1412 CB SER A 93 -41.862 -66.861 11.451 1.00 0.00 C +ATOM 1413 OG SER A 93 -42.825 -67.885 11.613 1.00 0.00 O +ATOM 1414 H SER A 93 -43.358 -64.448 12.050 1.00 0.00 H +ATOM 1415 HA SER A 93 -41.755 -66.456 13.557 1.00 0.00 H +ATOM 1416 HB2 SER A 93 -42.071 -66.313 10.520 1.00 0.00 H +ATOM 1417 HB3 SER A 93 -40.861 -67.313 11.393 1.00 0.00 H +ATOM 1418 HG SER A 93 -43.684 -67.497 11.732 1.00 0.00 H +ATOM 1419 N ILE A 94 -39.564 -65.373 12.788 1.00 0.00 N +ATOM 1420 CA ILE A 94 -38.324 -64.657 12.523 1.00 0.00 C +ATOM 1421 C ILE A 94 -37.461 -65.535 11.626 1.00 0.00 C +ATOM 1422 O ILE A 94 -36.825 -66.480 12.092 1.00 0.00 O +ATOM 1423 CB ILE A 94 -37.589 -64.295 13.825 1.00 0.00 C +ATOM 1424 CG1 ILE A 94 -38.537 -63.594 14.794 1.00 0.00 C +ATOM 1425 CG2 ILE A 94 -36.392 -63.408 13.523 1.00 0.00 C +ATOM 1426 CD1 ILE A 94 -37.915 -63.306 16.136 1.00 0.00 C +ATOM 1427 H ILE A 94 -39.509 -66.280 13.206 1.00 0.00 H +ATOM 1428 HA ILE A 94 -38.550 -63.725 11.984 1.00 0.00 H +ATOM 1429 HB ILE A 94 -37.231 -65.224 14.293 1.00 0.00 H +ATOM 1430 HG12 ILE A 94 -38.853 -62.641 14.344 1.00 0.00 H +ATOM 1431 HG13 ILE A 94 -39.416 -64.237 14.948 1.00 0.00 H +ATOM 1432 HG21 ILE A 94 -35.875 -63.156 14.461 1.00 0.00 H +ATOM 1433 HG22 ILE A 94 -35.700 -63.942 12.855 1.00 0.00 H +ATOM 1434 HG23 ILE A 94 -36.735 -62.484 13.034 1.00 0.00 H +ATOM 1435 HD11 ILE A 94 -38.650 -62.803 16.781 1.00 0.00 H +ATOM 1436 HD12 ILE A 94 -37.602 -64.250 16.605 1.00 0.00 H +ATOM 1437 HD13 ILE A 94 -37.039 -62.655 16.002 1.00 0.00 H +ATOM 1438 N PRO A 95 -37.424 -65.252 10.322 1.00 0.00 N +ATOM 1439 CA PRO A 95 -36.715 -66.146 9.387 1.00 0.00 C +ATOM 1440 C PRO A 95 -35.282 -66.454 9.788 1.00 0.00 C +ATOM 1441 O PRO A 95 -34.867 -67.617 9.712 1.00 0.00 O +ATOM 1442 CB PRO A 95 -36.788 -65.375 8.064 1.00 0.00 C +ATOM 1443 CG PRO A 95 -38.032 -64.542 8.189 1.00 0.00 C +ATOM 1444 CD PRO A 95 -38.102 -64.141 9.634 1.00 0.00 C +ATOM 1445 HA PRO A 95 -37.271 -67.090 9.282 1.00 0.00 H +ATOM 1446 HB2 PRO A 95 -35.902 -64.736 7.936 1.00 0.00 H +ATOM 1447 HB3 PRO A 95 -36.867 -66.068 7.213 1.00 0.00 H +ATOM 1448 HG2 PRO A 95 -37.965 -63.652 7.546 1.00 0.00 H +ATOM 1449 HG3 PRO A 95 -38.918 -65.130 7.910 1.00 0.00 H +ATOM 1450 HD2 PRO A 95 -37.576 -63.190 9.806 1.00 0.00 H +ATOM 1451 HD3 PRO A 95 -39.146 -64.048 9.968 1.00 0.00 H +ATOM 1452 N HIS A 96 -34.514 -65.458 10.219 1.00 0.00 N +ATOM 1453 CA HIS A 96 -33.174 -65.702 10.747 1.00 0.00 C +ATOM 1454 C HIS A 96 -33.242 -65.721 12.274 1.00 0.00 C +ATOM 1455 O HIS A 96 -32.675 -64.880 12.973 1.00 0.00 O +ATOM 1456 CB HIS A 96 -32.200 -64.650 10.223 1.00 0.00 C +ATOM 1457 CG HIS A 96 -32.201 -64.518 8.730 1.00 0.00 C +ATOM 1458 ND1 HIS A 96 -31.466 -65.347 7.909 1.00 0.00 N1+ +ATOM 1459 CD2 HIS A 96 -32.845 -63.655 7.908 1.00 0.00 C +ATOM 1460 CE1 HIS A 96 -31.659 -65.003 6.649 1.00 0.00 C +ATOM 1461 NE2 HIS A 96 -32.491 -63.977 6.620 1.00 0.00 N +ATOM 1462 H HIS A 96 -34.861 -64.521 10.181 1.00 0.00 H +ATOM 1463 HA HIS A 96 -32.829 -66.689 10.407 1.00 0.00 H +ATOM 1464 HB2 HIS A 96 -32.473 -63.677 10.658 1.00 0.00 H +ATOM 1465 HB3 HIS A 96 -31.185 -64.925 10.547 1.00 0.00 H +ATOM 1466 HD1 HIS A 96 -30.878 -66.093 8.220 1.00 0.00 H +ATOM 1467 HD2 HIS A 96 -33.513 -62.861 8.209 1.00 0.00 H +ATOM 1468 HE1 HIS A 96 -31.213 -65.479 5.788 1.00 0.00 H +ATOM 1469 HE2 HIS A 96 -32.811 -63.511 5.795 1.00 0.00 H +ATOM 1470 N GLY A 97 -33.968 -66.715 12.789 1.00 0.00 N +ATOM 1471 CA GLY A 97 -34.288 -66.768 14.196 1.00 0.00 C +ATOM 1472 C GLY A 97 -34.302 -68.164 14.787 1.00 0.00 C +ATOM 1473 O GLY A 97 -33.548 -69.053 14.379 1.00 0.00 O +ATOM 1474 H GLY A 97 -34.299 -67.441 12.186 1.00 0.00 H +ATOM 1475 HA2 GLY A 97 -33.541 -66.172 14.741 1.00 0.00 H +ATOM 1476 HA3 GLY A 97 -35.285 -66.325 14.338 1.00 0.00 H +ATOM 1477 N PRO A 98 -35.174 -68.372 15.775 1.00 0.00 N +ATOM 1478 CA PRO A 98 -35.184 -69.637 16.525 1.00 0.00 C +ATOM 1479 C PRO A 98 -35.983 -70.758 15.889 1.00 0.00 C +ATOM 1480 O PRO A 98 -36.198 -71.781 16.552 1.00 0.00 O +ATOM 1481 CB PRO A 98 -35.813 -69.215 17.861 1.00 0.00 C +ATOM 1482 CG PRO A 98 -36.754 -68.124 17.483 1.00 0.00 C +ATOM 1483 CD PRO A 98 -36.124 -67.392 16.327 1.00 0.00 C +ATOM 1484 HA PRO A 98 -34.153 -69.978 16.703 1.00 0.00 H +ATOM 1485 HB2 PRO A 98 -36.353 -70.055 18.323 1.00 0.00 H +ATOM 1486 HB3 PRO A 98 -35.045 -68.846 18.556 1.00 0.00 H +ATOM 1487 HG2 PRO A 98 -37.723 -68.547 17.181 1.00 0.00 H +ATOM 1488 HG3 PRO A 98 -36.901 -67.439 18.331 1.00 0.00 H +ATOM 1489 HD2 PRO A 98 -36.881 -67.114 15.579 1.00 0.00 H +ATOM 1490 HD3 PRO A 98 -35.600 -66.489 16.673 1.00 0.00 H +ATOM 1491 N ASN A 99 -36.445 -70.594 14.648 1.00 0.00 N +ATOM 1492 CA ASN A 99 -37.055 -71.679 13.876 1.00 0.00 C +ATOM 1493 C ASN A 99 -38.385 -72.135 14.479 1.00 0.00 C +ATOM 1494 O ASN A 99 -38.723 -73.321 14.463 1.00 0.00 O +ATOM 1495 CB ASN A 99 -36.084 -72.857 13.735 1.00 0.00 C +ATOM 1496 CG ASN A 99 -36.504 -73.838 12.659 1.00 0.00 C +ATOM 1497 OD1 ASN A 99 -37.183 -73.473 11.701 1.00 0.00 O +ATOM 1498 ND2 ASN A 99 -36.107 -75.095 12.819 1.00 0.00 N +ATOM 1499 H ASN A 99 -36.371 -69.690 14.226 1.00 0.00 H +ATOM 1500 HA ASN A 99 -37.265 -71.304 12.863 1.00 0.00 H +ATOM 1501 HB2 ASN A 99 -35.088 -72.464 13.483 1.00 0.00 H +ATOM 1502 HB3 ASN A 99 -36.035 -73.389 14.696 1.00 0.00 H +ATOM 1503 HD21 ASN A 99 -36.360 -75.789 12.145 1.00 0.00 H +ATOM 1504 HD22 ASN A 99 -35.556 -75.348 13.614 1.00 0.00 H +ATOM 1505 N VAL A 100 -39.155 -71.191 15.015 1.00 0.00 N +ATOM 1506 CA VAL A 100 -40.483 -71.489 15.536 1.00 0.00 C +ATOM 1507 C VAL A 100 -41.448 -70.394 15.107 1.00 0.00 C +ATOM 1508 O VAL A 100 -41.059 -69.244 14.883 1.00 0.00 O +ATOM 1509 CB VAL A 100 -40.494 -71.647 17.074 1.00 0.00 C +ATOM 1510 CG1 VAL A 100 -39.731 -72.899 17.494 1.00 0.00 C +ATOM 1511 CG2 VAL A 100 -39.924 -70.410 17.741 1.00 0.00 C +ATOM 1512 H VAL A 100 -38.813 -70.252 15.061 1.00 0.00 H +ATOM 1513 HA VAL A 100 -40.830 -72.437 15.100 1.00 0.00 H +ATOM 1514 HB VAL A 100 -41.540 -71.760 17.395 1.00 0.00 H +ATOM 1515 HG11 VAL A 100 -39.752 -72.991 18.590 1.00 0.00 H +ATOM 1516 HG12 VAL A 100 -40.203 -73.784 17.043 1.00 0.00 H +ATOM 1517 HG13 VAL A 100 -38.688 -72.825 17.152 1.00 0.00 H +ATOM 1518 HG21 VAL A 100 -39.940 -70.541 18.833 1.00 0.00 H +ATOM 1519 HG22 VAL A 100 -38.888 -70.257 17.406 1.00 0.00 H +ATOM 1520 HG23 VAL A 100 -40.531 -69.534 17.468 1.00 0.00 H +ATOM 1521 N THR A 101 -42.718 -70.767 14.980 1.00 0.00 N +ATOM 1522 CA THR A 101 -43.790 -69.844 14.640 1.00 0.00 C +ATOM 1523 C THR A 101 -44.773 -69.797 15.798 1.00 0.00 C +ATOM 1524 O THR A 101 -45.239 -70.844 16.260 1.00 0.00 O +ATOM 1525 CB THR A 101 -44.503 -70.271 13.355 1.00 0.00 C +ATOM 1526 OG1 THR A 101 -43.564 -70.299 12.272 1.00 0.00 O +ATOM 1527 CG2 THR A 101 -45.632 -69.307 13.025 1.00 0.00 C +ATOM 1528 H THR A 101 -42.948 -71.729 15.125 1.00 0.00 H +ATOM 1529 HA THR A 101 -43.374 -68.837 14.491 1.00 0.00 H +ATOM 1530 HB THR A 101 -44.929 -71.274 13.507 1.00 0.00 H +ATOM 1531 HG1 THR A 101 -43.175 -69.439 12.167 1.00 0.00 H +ATOM 1532 HG21 THR A 101 -46.132 -69.630 12.100 1.00 0.00 H +ATOM 1533 HG22 THR A 101 -46.359 -69.298 13.850 1.00 0.00 H +ATOM 1534 HG23 THR A 101 -45.222 -68.296 12.886 1.00 0.00 H +ATOM 1535 N VAL A 102 -45.086 -68.589 16.264 1.00 0.00 N +ATOM 1536 CA VAL A 102 -45.950 -68.400 17.420 1.00 0.00 C +ATOM 1537 C VAL A 102 -47.070 -67.432 17.081 1.00 0.00 C +ATOM 1538 O VAL A 102 -46.917 -66.530 16.251 1.00 0.00 O +ATOM 1539 CB VAL A 102 -45.165 -67.885 18.648 1.00 0.00 C +ATOM 1540 CG1 VAL A 102 -44.073 -68.871 19.037 1.00 0.00 C +ATOM 1541 CG2 VAL A 102 -44.577 -66.513 18.370 1.00 0.00 C +ATOM 1542 H VAL A 102 -44.714 -67.783 15.803 1.00 0.00 H +ATOM 1543 HA VAL A 102 -46.402 -69.366 17.690 1.00 0.00 H +ATOM 1544 HB VAL A 102 -45.868 -67.797 19.490 1.00 0.00 H +ATOM 1545 HG11 VAL A 102 -43.527 -68.488 19.911 1.00 0.00 H +ATOM 1546 HG12 VAL A 102 -44.527 -69.842 19.285 1.00 0.00 H +ATOM 1547 HG13 VAL A 102 -43.376 -68.997 18.195 1.00 0.00 H +ATOM 1548 HG21 VAL A 102 -44.023 -66.165 19.254 1.00 0.00 H +ATOM 1549 HG22 VAL A 102 -43.894 -66.575 17.510 1.00 0.00 H +ATOM 1550 HG23 VAL A 102 -45.388 -65.806 18.143 1.00 0.00 H +ATOM 1551 N ARG A 103 -48.217 -67.633 17.725 1.00 0.00 N +ATOM 1552 CA ARG A 103 -49.302 -66.669 17.643 1.00 0.00 C +ATOM 1553 C ARG A 103 -49.073 -65.576 18.679 1.00 0.00 C +ATOM 1554 O ARG A 103 -48.817 -65.867 19.851 1.00 0.00 O +ATOM 1555 CB ARG A 103 -50.649 -67.349 17.865 1.00 0.00 C +ATOM 1556 CG ARG A 103 -51.819 -66.397 17.737 1.00 0.00 C +ATOM 1557 CD ARG A 103 -53.128 -67.187 17.567 1.00 0.00 C +ATOM 1558 NE ARG A 103 -53.158 -67.953 16.299 1.00 0.00 N +ATOM 1559 CZ ARG A 103 -54.039 -67.781 15.305 1.00 0.00 C +ATOM 1560 NH1 ARG A 103 -54.932 -66.831 15.448 1.00 0.00 N +ATOM 1561 NH2 ARG A 103 -54.003 -68.479 14.170 1.00 0.00 N1+ +ATOM 1562 H ARG A 103 -48.334 -68.460 18.274 1.00 0.00 H +ATOM 1563 HA ARG A 103 -49.306 -66.207 16.645 1.00 0.00 H +ATOM 1564 HB2 ARG A 103 -50.765 -68.149 17.119 1.00 0.00 H +ATOM 1565 HB3 ARG A 103 -50.660 -67.784 18.875 1.00 0.00 H +ATOM 1566 HG2 ARG A 103 -51.884 -65.776 18.643 1.00 0.00 H +ATOM 1567 HG3 ARG A 103 -51.668 -65.751 16.860 1.00 0.00 H +ATOM 1568 HD2 ARG A 103 -53.230 -67.889 18.408 1.00 0.00 H +ATOM 1569 HD3 ARG A 103 -53.972 -66.481 17.575 1.00 0.00 H +ATOM 1570 HE ARG A 103 -52.461 -68.659 16.175 1.00 0.00 H +ATOM 1571 HH11 ARG A 103 -55.577 -66.636 14.710 1.00 0.00 H +ATOM 1572 HH12 ARG A 103 -54.967 -66.301 16.296 1.00 0.00 H +ATOM 1573 HH21 ARG A 103 -54.653 -68.276 13.438 1.00 0.00 H +ATOM 1574 HH22 ARG A 103 -53.326 -69.205 14.051 1.00 0.00 H +ATOM 1575 N ALA A 104 -49.123 -64.320 18.238 1.00 0.00 N +ATOM 1576 CA ALA A 104 -48.777 -63.191 19.089 1.00 0.00 C +ATOM 1577 C ALA A 104 -49.760 -62.052 18.869 1.00 0.00 C +ATOM 1578 O ALA A 104 -50.397 -61.946 17.819 1.00 0.00 O +ATOM 1579 CB ALA A 104 -47.344 -62.706 18.824 1.00 0.00 C +ATOM 1580 H ALA A 104 -49.407 -64.148 17.295 1.00 0.00 H +ATOM 1581 HA ALA A 104 -48.842 -63.502 20.142 1.00 0.00 H +ATOM 1582 HB1 ALA A 104 -47.117 -61.855 19.482 1.00 0.00 H +ATOM 1583 HB2 ALA A 104 -46.637 -63.524 19.026 1.00 0.00 H +ATOM 1584 HB3 ALA A 104 -47.252 -62.392 17.774 1.00 0.00 H +ATOM 1585 N ASN A 105 -49.879 -61.203 19.887 1.00 0.00 N +ATOM 1586 CA ASN A 105 -50.652 -59.979 19.753 1.00 0.00 C +ATOM 1587 C ASN A 105 -49.956 -59.030 18.787 1.00 0.00 C +ATOM 1588 O ASN A 105 -48.726 -58.929 18.769 1.00 0.00 O +ATOM 1589 CB ASN A 105 -50.831 -59.312 21.117 1.00 0.00 C +ATOM 1590 CG ASN A 105 -51.750 -60.092 22.029 1.00 0.00 C +ATOM 1591 OD1 ASN A 105 -52.812 -60.555 21.611 1.00 0.00 O +ATOM 1592 ND2 ASN A 105 -51.344 -60.249 23.283 1.00 0.00 N +ATOM 1593 H ASN A 105 -49.430 -61.410 20.756 1.00 0.00 H +ATOM 1594 HA ASN A 105 -51.647 -60.218 19.350 1.00 0.00 H +ATOM 1595 HB2 ASN A 105 -49.846 -59.226 21.599 1.00 0.00 H +ATOM 1596 HB3 ASN A 105 -51.254 -58.308 20.965 1.00 0.00 H +ATOM 1597 HD21 ASN A 105 -51.908 -60.761 23.931 1.00 0.00 H +ATOM 1598 HD22 ASN A 105 -50.474 -59.856 23.580 1.00 0.00 H +ATOM 1599 N ILE A 106 -50.747 -58.342 17.968 1.00 0.00 N +ATOM 1600 CA ILE A 106 -50.219 -57.417 16.970 1.00 0.00 C +ATOM 1601 C ILE A 106 -51.068 -56.153 16.979 1.00 0.00 C +ATOM 1602 O ILE A 106 -52.292 -56.222 16.825 1.00 0.00 O +ATOM 1603 CB ILE A 106 -50.196 -58.037 15.556 1.00 0.00 C +ATOM 1604 CG1 ILE A 106 -49.307 -59.279 15.508 1.00 0.00 C +ATOM 1605 CG2 ILE A 106 -49.708 -57.031 14.539 1.00 0.00 C +ATOM 1606 CD1 ILE A 106 -49.230 -59.916 14.142 1.00 0.00 C +ATOM 1607 H ILE A 106 -51.737 -58.463 18.038 1.00 0.00 H +ATOM 1608 HA ILE A 106 -49.188 -57.144 17.240 1.00 0.00 H +ATOM 1609 HB ILE A 106 -51.222 -58.333 15.293 1.00 0.00 H +ATOM 1610 HG12 ILE A 106 -48.291 -58.991 15.815 1.00 0.00 H +ATOM 1611 HG13 ILE A 106 -49.707 -60.020 16.215 1.00 0.00 H +ATOM 1612 HG21 ILE A 106 -49.700 -57.493 13.541 1.00 0.00 H +ATOM 1613 HG22 ILE A 106 -50.379 -56.160 14.534 1.00 0.00 H +ATOM 1614 HG23 ILE A 106 -48.690 -56.708 14.802 1.00 0.00 H +ATOM 1615 HD11 ILE A 106 -48.576 -60.799 14.185 1.00 0.00 H +ATOM 1616 HD12 ILE A 106 -50.237 -60.221 13.823 1.00 0.00 H +ATOM 1617 HD13 ILE A 106 -48.821 -59.192 13.422 1.00 0.00 H +ATOM 1618 N ALA A 107 -50.427 -55.000 17.170 1.00 0.00 N +ATOM 1619 CA ALA A 107 -51.094 -53.708 17.018 1.00 0.00 C +ATOM 1620 C ALA A 107 -50.899 -53.262 15.575 1.00 0.00 C +ATOM 1621 O ALA A 107 -49.813 -52.823 15.188 1.00 0.00 O +ATOM 1622 CB ALA A 107 -50.550 -52.686 18.010 1.00 0.00 C +ATOM 1623 H ALA A 107 -49.460 -55.019 17.425 1.00 0.00 H +ATOM 1624 HA ALA A 107 -52.171 -53.832 17.202 1.00 0.00 H +ATOM 1625 HB1 ALA A 107 -51.070 -51.727 17.871 1.00 0.00 H +ATOM 1626 HB2 ALA A 107 -50.715 -53.047 19.036 1.00 0.00 H +ATOM 1627 HB3 ALA A 107 -49.472 -52.547 17.839 1.00 0.00 H +ATOM 1628 N ALA A 108 -51.958 -53.390 14.777 1.00 0.00 N +ATOM 1629 CA ALA A 108 -51.941 -52.999 13.373 1.00 0.00 C +ATOM 1630 C ALA A 108 -52.112 -51.489 13.281 1.00 0.00 C +ATOM 1631 O ALA A 108 -53.210 -50.971 13.505 1.00 0.00 O +ATOM 1632 CB ALA A 108 -53.045 -53.729 12.613 1.00 0.00 C +ATOM 1633 H ALA A 108 -52.801 -53.771 15.158 1.00 0.00 H +ATOM 1634 HA ALA A 108 -50.972 -53.270 12.929 1.00 0.00 H +ATOM 1635 HB1 ALA A 108 -53.024 -53.427 11.555 1.00 0.00 H +ATOM 1636 HB2 ALA A 108 -52.885 -54.815 12.688 1.00 0.00 H +ATOM 1637 HB3 ALA A 108 -54.022 -53.471 13.048 1.00 0.00 H +ATOM 1638 N ILE A 109 -51.037 -50.783 12.942 1.00 0.00 N +ATOM 1639 CA ILE A 109 -51.032 -49.324 12.983 1.00 0.00 C +ATOM 1640 C ILE A 109 -51.760 -48.785 11.761 1.00 0.00 C +ATOM 1641 O ILE A 109 -51.323 -48.986 10.623 1.00 0.00 O +ATOM 1642 CB ILE A 109 -49.602 -48.774 13.054 1.00 0.00 C +ATOM 1643 CG1 ILE A 109 -48.941 -49.214 14.357 1.00 0.00 C +ATOM 1644 CG2 ILE A 109 -49.612 -47.261 12.924 1.00 0.00 C +ATOM 1645 CD1 ILE A 109 -47.443 -49.068 14.346 1.00 0.00 C +ATOM 1646 H ILE A 109 -50.210 -51.265 12.651 1.00 0.00 H +ATOM 1647 HA ILE A 109 -51.572 -48.989 13.881 1.00 0.00 H +ATOM 1648 HB ILE A 109 -49.028 -49.193 12.214 1.00 0.00 H +ATOM 1649 HG12 ILE A 109 -49.345 -48.602 15.177 1.00 0.00 H +ATOM 1650 HG13 ILE A 109 -49.187 -50.272 14.533 1.00 0.00 H +ATOM 1651 HG21 ILE A 109 -48.581 -46.881 12.977 1.00 0.00 H +ATOM 1652 HG22 ILE A 109 -50.058 -46.979 11.959 1.00 0.00 H +ATOM 1653 HG23 ILE A 109 -50.205 -46.827 13.743 1.00 0.00 H +ATOM 1654 HD11 ILE A 109 -47.035 -49.402 15.311 1.00 0.00 H +ATOM 1655 HD12 ILE A 109 -47.021 -49.682 13.537 1.00 0.00 H +ATOM 1656 HD13 ILE A 109 -47.179 -48.013 14.181 1.00 0.00 H +ATOM 1657 N THR A 110 -52.857 -48.068 12.000 1.00 0.00 N +ATOM 1658 CA THR A 110 -53.682 -47.520 10.934 1.00 0.00 C +ATOM 1659 C THR A 110 -53.528 -46.017 10.764 1.00 0.00 C +ATOM 1660 O THR A 110 -53.912 -45.489 9.714 1.00 0.00 O +ATOM 1661 CB THR A 110 -55.158 -47.844 11.194 1.00 0.00 C +ATOM 1662 OG1 THR A 110 -55.517 -47.427 12.520 1.00 0.00 O +ATOM 1663 CG2 THR A 110 -55.396 -49.341 11.055 1.00 0.00 C +ATOM 1664 H THR A 110 -53.124 -47.900 12.949 1.00 0.00 H +ATOM 1665 HA THR A 110 -53.396 -47.998 9.986 1.00 0.00 H +ATOM 1666 HB THR A 110 -55.773 -47.314 10.451 1.00 0.00 H +ATOM 1667 HG1 THR A 110 -55.497 -46.479 12.570 1.00 0.00 H +ATOM 1668 HG21 THR A 110 -56.457 -49.563 11.243 1.00 0.00 H +ATOM 1669 HG22 THR A 110 -55.129 -49.662 10.037 1.00 0.00 H +ATOM 1670 HG23 THR A 110 -54.774 -49.880 11.784 1.00 0.00 H +ATOM 1671 N GLU A 111 -52.977 -45.323 11.758 1.00 0.00 N +ATOM 1672 CA GLU A 111 -52.821 -43.877 11.706 1.00 0.00 C +ATOM 1673 C GLU A 111 -51.667 -43.493 12.619 1.00 0.00 C +ATOM 1674 O GLU A 111 -51.497 -44.088 13.686 1.00 0.00 O +ATOM 1675 CB GLU A 111 -54.112 -43.169 12.133 1.00 0.00 C +ATOM 1676 CG GLU A 111 -54.168 -41.678 11.818 1.00 0.00 C +ATOM 1677 CD GLU A 111 -55.501 -41.057 12.204 1.00 0.00 C +ATOM 1678 OE1 GLU A 111 -56.523 -41.775 12.179 1.00 0.00 O +ATOM 1679 OE2 GLU A 111 -55.526 -39.855 12.540 1.00 0.00 O1- +ATOM 1680 H GLU A 111 -52.659 -45.813 12.569 1.00 0.00 H +ATOM 1681 HA GLU A 111 -52.576 -43.569 10.679 1.00 0.00 H +ATOM 1682 HB2 GLU A 111 -54.954 -43.657 11.620 1.00 0.00 H +ATOM 1683 HB3 GLU A 111 -54.224 -43.292 13.220 1.00 0.00 H +ATOM 1684 HG2 GLU A 111 -53.366 -41.169 12.373 1.00 0.00 H +ATOM 1685 HG3 GLU A 111 -54.012 -41.538 10.738 1.00 0.00 H +ATOM 1686 N SER A 112 -50.880 -42.505 12.198 1.00 0.00 N +ATOM 1687 CA SER A 112 -49.701 -42.130 12.967 1.00 0.00 C +ATOM 1688 C SER A 112 -49.227 -40.747 12.547 1.00 0.00 C +ATOM 1689 O SER A 112 -49.611 -40.228 11.496 1.00 0.00 O +ATOM 1690 CB SER A 112 -48.575 -43.155 12.795 1.00 0.00 C +ATOM 1691 OG SER A 112 -48.157 -43.223 11.443 1.00 0.00 O +ATOM 1692 H SER A 112 -51.099 -42.019 11.352 1.00 0.00 H +ATOM 1693 HA SER A 112 -49.967 -42.092 14.034 1.00 0.00 H +ATOM 1694 HB2 SER A 112 -47.721 -42.860 13.422 1.00 0.00 H +ATOM 1695 HB3 SER A 112 -48.937 -44.145 13.110 1.00 0.00 H +ATOM 1696 HG SER A 112 -48.871 -42.957 10.876 1.00 0.00 H +ATOM 1697 N ASP A 113 -48.383 -40.160 13.395 1.00 0.00 N +ATOM 1698 CA ASP A 113 -47.789 -38.855 13.138 1.00 0.00 C +ATOM 1699 C ASP A 113 -46.415 -38.818 13.787 1.00 0.00 C +ATOM 1700 O ASP A 113 -46.292 -39.064 14.990 1.00 0.00 O +ATOM 1701 CB ASP A 113 -48.673 -37.725 13.682 1.00 0.00 C +ATOM 1702 CG ASP A 113 -48.215 -36.350 13.223 1.00 0.00 C +ATOM 1703 OD1 ASP A 113 -48.079 -36.148 11.997 1.00 0.00 O +ATOM 1704 OD2 ASP A 113 -47.996 -35.470 14.084 1.00 0.00 O1- +ATOM 1705 H ASP A 113 -48.148 -40.635 14.243 1.00 0.00 H +ATOM 1706 HA ASP A 113 -47.670 -38.713 12.054 1.00 0.00 H +ATOM 1707 HB2 ASP A 113 -49.704 -37.887 13.335 1.00 0.00 H +ATOM 1708 HB3 ASP A 113 -48.647 -37.756 14.781 1.00 0.00 H +ATOM 1709 N LYS A 114 -45.391 -38.532 12.982 1.00 0.00 N +ATOM 1710 CA LYS A 114 -44.016 -38.385 13.466 1.00 0.00 C +ATOM 1711 C LYS A 114 -43.542 -39.637 14.203 1.00 0.00 C +ATOM 1712 O LYS A 114 -42.810 -39.565 15.192 1.00 0.00 O +ATOM 1713 CB LYS A 114 -43.872 -37.146 14.355 1.00 0.00 C +ATOM 1714 CG LYS A 114 -44.214 -35.833 13.668 1.00 0.00 C +ATOM 1715 CD LYS A 114 -43.988 -34.650 14.602 1.00 0.00 C +ATOM 1716 CE LYS A 114 -44.422 -33.341 13.958 1.00 0.00 C +ATOM 1717 NZ LYS A 114 -44.210 -32.174 14.861 1.00 0.00 N1+ +ATOM 1718 H LYS A 114 -45.569 -38.413 12.005 1.00 0.00 H +ATOM 1719 HA LYS A 114 -43.358 -38.245 12.596 1.00 0.00 H +ATOM 1720 HB2 LYS A 114 -44.540 -37.265 15.221 1.00 0.00 H +ATOM 1721 HB3 LYS A 114 -42.829 -37.091 14.701 1.00 0.00 H +ATOM 1722 HG2 LYS A 114 -43.577 -35.716 12.779 1.00 0.00 H +ATOM 1723 HG3 LYS A 114 -45.270 -35.852 13.362 1.00 0.00 H +ATOM 1724 HD2 LYS A 114 -44.569 -34.805 15.523 1.00 0.00 H +ATOM 1725 HD3 LYS A 114 -42.918 -34.589 14.850 1.00 0.00 H +ATOM 1726 HE2 LYS A 114 -43.839 -33.188 13.038 1.00 0.00 H +ATOM 1727 HE3 LYS A 114 -45.491 -33.406 13.708 1.00 0.00 H +ATOM 1728 HZ1 LYS A 114 -44.271 -31.327 14.332 1.00 0.00 H +ATOM 1729 HZ2 LYS A 114 -44.911 -32.173 15.574 1.00 0.00 H +ATOM 1730 HZ3 LYS A 114 -43.306 -32.239 15.283 1.00 0.00 H +ATOM 1731 N PHE A 115 -43.958 -40.803 13.711 1.00 0.00 N +ATOM 1732 CA PHE A 115 -43.556 -42.081 14.284 1.00 0.00 C +ATOM 1733 C PHE A 115 -42.662 -42.867 13.332 1.00 0.00 C +ATOM 1734 O PHE A 115 -41.532 -43.212 13.687 1.00 0.00 O +ATOM 1735 CB PHE A 115 -44.799 -42.901 14.668 1.00 0.00 C +ATOM 1736 CG PHE A 115 -44.493 -44.312 15.082 1.00 0.00 C +ATOM 1737 CD1 PHE A 115 -43.935 -44.585 16.321 1.00 0.00 C +ATOM 1738 CD2 PHE A 115 -44.767 -45.368 14.227 1.00 0.00 C +ATOM 1739 CE1 PHE A 115 -43.652 -45.886 16.696 1.00 0.00 C +ATOM 1740 CE2 PHE A 115 -44.488 -46.669 14.597 1.00 0.00 C +ATOM 1741 CZ PHE A 115 -43.932 -46.927 15.832 1.00 0.00 C +ATOM 1742 H PHE A 115 -44.569 -40.801 12.919 1.00 0.00 H +ATOM 1743 HA PHE A 115 -42.984 -41.891 15.204 1.00 0.00 H +ATOM 1744 HB2 PHE A 115 -45.302 -42.396 15.505 1.00 0.00 H +ATOM 1745 HB3 PHE A 115 -45.474 -42.933 13.800 1.00 0.00 H +ATOM 1746 HD1 PHE A 115 -43.719 -43.774 17.000 1.00 0.00 H +ATOM 1747 HD2 PHE A 115 -45.204 -45.171 13.259 1.00 0.00 H +ATOM 1748 HE1 PHE A 115 -43.213 -46.088 17.662 1.00 0.00 H +ATOM 1749 HE2 PHE A 115 -44.705 -47.482 13.920 1.00 0.00 H +ATOM 1750 HZ PHE A 115 -43.715 -47.944 16.124 1.00 0.00 H +ATOM 1751 N PHE A 116 -43.146 -43.167 12.130 1.00 0.00 N +ATOM 1752 CA PHE A 116 -42.295 -43.776 11.120 1.00 0.00 C +ATOM 1753 C PHE A 116 -41.271 -42.758 10.633 1.00 0.00 C +ATOM 1754 O PHE A 116 -41.589 -41.583 10.423 1.00 0.00 O +ATOM 1755 CB PHE A 116 -43.130 -44.291 9.947 1.00 0.00 C +ATOM 1756 CG PHE A 116 -44.081 -45.397 10.316 1.00 0.00 C +ATOM 1757 CD1 PHE A 116 -43.605 -46.629 10.737 1.00 0.00 C +ATOM 1758 CD2 PHE A 116 -45.452 -45.209 10.230 1.00 0.00 C +ATOM 1759 CE1 PHE A 116 -44.475 -47.647 11.075 1.00 0.00 C +ATOM 1760 CE2 PHE A 116 -46.329 -46.226 10.567 1.00 0.00 C +ATOM 1761 CZ PHE A 116 -45.841 -47.445 10.988 1.00 0.00 C +ATOM 1762 H PHE A 116 -44.104 -42.972 11.919 1.00 0.00 H +ATOM 1763 HA PHE A 116 -41.756 -44.627 11.562 1.00 0.00 H +ATOM 1764 HB2 PHE A 116 -43.715 -43.451 9.543 1.00 0.00 H +ATOM 1765 HB3 PHE A 116 -42.445 -44.667 9.173 1.00 0.00 H +ATOM 1766 HD1 PHE A 116 -42.540 -46.795 10.802 1.00 0.00 H +ATOM 1767 HD2 PHE A 116 -45.840 -44.258 9.896 1.00 0.00 H +ATOM 1768 HE1 PHE A 116 -44.090 -48.600 11.407 1.00 0.00 H +ATOM 1769 HE2 PHE A 116 -47.395 -46.065 10.500 1.00 0.00 H +ATOM 1770 HZ PHE A 116 -46.523 -48.240 11.249 1.00 0.00 H +ATOM 1771 N ILE A 117 -40.031 -43.211 10.465 1.00 0.00 N +ATOM 1772 CA ILE A 117 -38.932 -42.352 10.042 1.00 0.00 C +ATOM 1773 C ILE A 117 -38.684 -42.564 8.557 1.00 0.00 C +ATOM 1774 O ILE A 117 -38.546 -43.704 8.096 1.00 0.00 O +ATOM 1775 CB ILE A 117 -37.653 -42.629 10.851 1.00 0.00 C +ATOM 1776 CG1 ILE A 117 -37.920 -42.468 12.346 1.00 0.00 C +ATOM 1777 CG2 ILE A 117 -36.532 -41.694 10.413 1.00 0.00 C +ATOM 1778 CD1 ILE A 117 -36.704 -42.723 13.206 1.00 0.00 C +ATOM 1779 H ILE A 117 -39.846 -44.179 10.636 1.00 0.00 H +ATOM 1780 HA ILE A 117 -39.215 -41.301 10.199 1.00 0.00 H +ATOM 1781 HB ILE A 117 -37.339 -43.666 10.661 1.00 0.00 H +ATOM 1782 HG12 ILE A 117 -38.267 -41.440 12.529 1.00 0.00 H +ATOM 1783 HG13 ILE A 117 -38.708 -43.178 12.637 1.00 0.00 H +ATOM 1784 HG21 ILE A 117 -35.627 -41.906 11.001 1.00 0.00 H +ATOM 1785 HG22 ILE A 117 -36.320 -41.850 9.345 1.00 0.00 H +ATOM 1786 HG23 ILE A 117 -36.840 -40.651 10.577 1.00 0.00 H +ATOM 1787 HD11 ILE A 117 -36.969 -42.590 14.265 1.00 0.00 H +ATOM 1788 HD12 ILE A 117 -36.349 -43.751 13.043 1.00 0.00 H +ATOM 1789 HD13 ILE A 117 -35.908 -42.014 12.935 1.00 0.00 H +ATOM 1790 N ASN A 118 -38.621 -41.464 7.815 1.00 0.00 N +ATOM 1791 CA ASN A 118 -38.333 -41.517 6.390 1.00 0.00 C +ATOM 1792 C ASN A 118 -36.944 -42.098 6.152 1.00 0.00 C +ATOM 1793 O ASN A 118 -35.940 -41.530 6.593 1.00 0.00 O +ATOM 1794 CB ASN A 118 -38.440 -40.110 5.805 1.00 0.00 C +ATOM 1795 CG ASN A 118 -38.410 -40.099 4.294 1.00 0.00 C +ATOM 1796 OD1 ASN A 118 -38.334 -41.148 3.654 1.00 0.00 O +ATOM 1797 ND2 ASN A 118 -38.486 -38.908 3.712 1.00 0.00 N +ATOM 1798 H ASN A 118 -38.776 -40.576 8.248 1.00 0.00 H +ATOM 1799 HA ASN A 118 -39.072 -42.160 5.890 1.00 0.00 H +ATOM 1800 HB2 ASN A 118 -39.386 -39.661 6.141 1.00 0.00 H +ATOM 1801 HB3 ASN A 118 -37.597 -39.510 6.177 1.00 0.00 H +ATOM 1802 HD21 ASN A 118 -38.483 -38.838 2.714 1.00 0.00 H +ATOM 1803 HD22 ASN A 118 -38.546 -38.081 4.271 1.00 0.00 H +ATOM 1804 N GLY A 119 -36.888 -43.235 5.462 1.00 0.00 N +ATOM 1805 CA GLY A 119 -35.626 -43.862 5.121 1.00 0.00 C +ATOM 1806 C GLY A 119 -35.079 -44.833 6.144 1.00 0.00 C +ATOM 1807 O GLY A 119 -33.970 -45.349 5.951 1.00 0.00 O +ATOM 1808 H GLY A 119 -37.741 -43.669 5.171 1.00 0.00 H +ATOM 1809 HA2 GLY A 119 -35.764 -44.408 4.176 1.00 0.00 H +ATOM 1810 HA3 GLY A 119 -34.881 -43.066 4.978 1.00 0.00 H +ATOM 1811 N SER A 120 -35.817 -45.105 7.223 1.00 0.00 N +ATOM 1812 CA SER A 120 -35.316 -45.992 8.269 1.00 0.00 C +ATOM 1813 C SER A 120 -35.342 -47.456 7.861 1.00 0.00 C +ATOM 1814 O SER A 120 -34.631 -48.266 8.468 1.00 0.00 O +ATOM 1815 CB SER A 120 -36.127 -45.808 9.549 1.00 0.00 C +ATOM 1816 OG SER A 120 -37.437 -46.329 9.389 1.00 0.00 O +ATOM 1817 H SER A 120 -36.725 -44.696 7.316 1.00 0.00 H +ATOM 1818 HA SER A 120 -34.273 -45.721 8.490 1.00 0.00 H +ATOM 1819 HB2 SER A 120 -35.626 -46.336 10.374 1.00 0.00 H +ATOM 1820 HB3 SER A 120 -36.191 -44.736 9.786 1.00 0.00 H +ATOM 1821 HG SER A 120 -37.820 -45.985 8.591 1.00 0.00 H +ATOM 1822 N ASN A 121 -36.155 -47.810 6.867 1.00 0.00 N +ATOM 1823 CA ASN A 121 -36.289 -49.175 6.360 1.00 0.00 C +ATOM 1824 C ASN A 121 -36.898 -50.139 7.378 1.00 0.00 C +ATOM 1825 O ASN A 121 -36.837 -51.355 7.185 1.00 0.00 O +ATOM 1826 CB ASN A 121 -34.946 -49.728 5.856 1.00 0.00 C +ATOM 1827 CG ASN A 121 -35.122 -50.832 4.830 1.00 0.00 C +ATOM 1828 OD1 ASN A 121 -34.274 -51.714 4.707 1.00 0.00 O +ATOM 1829 ND2 ASN A 121 -36.218 -50.782 4.080 1.00 0.00 N +ATOM 1830 H ASN A 121 -36.710 -47.096 6.440 1.00 0.00 H +ATOM 1831 HA ASN A 121 -36.969 -49.148 5.496 1.00 0.00 H +ATOM 1832 HB2 ASN A 121 -34.376 -48.907 5.397 1.00 0.00 H +ATOM 1833 HB3 ASN A 121 -34.385 -50.129 6.713 1.00 0.00 H +ATOM 1834 HD21 ASN A 121 -36.378 -51.479 3.382 1.00 0.00 H +ATOM 1835 HD22 ASN A 121 -36.882 -50.046 4.214 1.00 0.00 H +ATOM 1836 N TRP A 122 -37.493 -49.653 8.464 1.00 0.00 N +ATOM 1837 CA TRP A 122 -38.294 -50.518 9.318 1.00 0.00 C +ATOM 1838 C TRP A 122 -39.759 -50.109 9.261 1.00 0.00 C +ATOM 1839 O TRP A 122 -40.087 -48.919 9.223 1.00 0.00 O +ATOM 1840 CB TRP A 122 -37.803 -50.559 10.777 1.00 0.00 C +ATOM 1841 CG TRP A 122 -37.618 -49.264 11.523 1.00 0.00 C +ATOM 1842 CD1 TRP A 122 -36.441 -48.600 11.712 1.00 0.00 C +ATOM 1843 CD2 TRP A 122 -38.614 -48.525 12.251 1.00 0.00 C +ATOM 1844 NE1 TRP A 122 -36.645 -47.480 12.478 1.00 0.00 N +ATOM 1845 CE2 TRP A 122 -37.968 -47.410 12.824 1.00 0.00 C +ATOM 1846 CE3 TRP A 122 -39.990 -48.687 12.457 1.00 0.00 C +ATOM 1847 CZ2 TRP A 122 -38.648 -46.461 13.586 1.00 0.00 C +ATOM 1848 CZ3 TRP A 122 -40.665 -47.739 13.218 1.00 0.00 C +ATOM 1849 CH2 TRP A 122 -39.992 -46.642 13.771 1.00 0.00 C +ATOM 1850 H TRP A 122 -37.389 -48.686 8.696 1.00 0.00 H +ATOM 1851 HA TRP A 122 -38.226 -51.543 8.925 1.00 0.00 H +ATOM 1852 HB2 TRP A 122 -38.531 -51.157 11.345 1.00 0.00 H +ATOM 1853 HB3 TRP A 122 -36.830 -51.071 10.775 1.00 0.00 H +ATOM 1854 HD1 TRP A 122 -35.486 -48.911 11.316 1.00 0.00 H +ATOM 1855 HE1 TRP A 122 -35.941 -46.821 12.741 1.00 0.00 H +ATOM 1856 HE3 TRP A 122 -40.515 -49.530 12.034 1.00 0.00 H +ATOM 1857 HZ2 TRP A 122 -38.133 -45.614 14.015 1.00 0.00 H +ATOM 1858 HZ3 TRP A 122 -41.726 -47.850 13.385 1.00 0.00 H +ATOM 1859 HH2 TRP A 122 -40.546 -45.923 14.356 1.00 0.00 H +ATOM 1860 N GLU A 123 -40.630 -51.117 9.239 1.00 0.00 N +ATOM 1861 CA GLU A 123 -42.065 -50.925 9.111 1.00 0.00 C +ATOM 1862 C GLU A 123 -42.819 -51.195 10.406 1.00 0.00 C +ATOM 1863 O GLU A 123 -44.053 -51.151 10.407 1.00 0.00 O +ATOM 1864 CB GLU A 123 -42.613 -51.820 7.990 1.00 0.00 C +ATOM 1865 CG GLU A 123 -42.153 -51.437 6.577 1.00 0.00 C +ATOM 1866 CD GLU A 123 -40.894 -52.170 6.124 1.00 0.00 C +ATOM 1867 OE1 GLU A 123 -40.723 -53.357 6.476 1.00 0.00 O +ATOM 1868 OE2 GLU A 123 -40.073 -51.551 5.413 1.00 0.00 O1- +ATOM 1869 H GLU A 123 -40.279 -52.051 9.314 1.00 0.00 H +ATOM 1870 HA GLU A 123 -42.255 -49.880 8.825 1.00 0.00 H +ATOM 1871 HB2 GLU A 123 -42.289 -52.853 8.187 1.00 0.00 H +ATOM 1872 HB3 GLU A 123 -43.711 -51.769 8.018 1.00 0.00 H +ATOM 1873 HG2 GLU A 123 -42.964 -51.671 5.872 1.00 0.00 H +ATOM 1874 HG3 GLU A 123 -41.951 -50.356 6.558 1.00 0.00 H +ATOM 1875 N GLY A 124 -42.118 -51.468 11.500 1.00 0.00 N +ATOM 1876 CA GLY A 124 -42.768 -51.731 12.771 1.00 0.00 C +ATOM 1877 C GLY A 124 -41.736 -51.844 13.870 1.00 0.00 C +ATOM 1878 O GLY A 124 -40.535 -51.667 13.647 1.00 0.00 O +ATOM 1879 H GLY A 124 -41.120 -51.493 11.447 1.00 0.00 H +ATOM 1880 HA2 GLY A 124 -43.458 -50.907 13.004 1.00 0.00 H +ATOM 1881 HA3 GLY A 124 -43.332 -52.673 12.703 1.00 0.00 H +ATOM 1882 N ILE A 125 -42.220 -52.148 15.073 1.00 0.00 N +ATOM 1883 CA ILE A 125 -41.374 -52.191 16.263 1.00 0.00 C +ATOM 1884 C ILE A 125 -41.658 -53.471 17.036 1.00 0.00 C +ATOM 1885 O ILE A 125 -42.822 -53.853 17.212 1.00 0.00 O +ATOM 1886 CB ILE A 125 -41.588 -50.965 17.175 1.00 0.00 C +ATOM 1887 CG1 ILE A 125 -41.191 -49.677 16.460 1.00 0.00 C +ATOM 1888 CG2 ILE A 125 -40.786 -51.099 18.461 1.00 0.00 C +ATOM 1889 CD1 ILE A 125 -41.216 -48.465 17.361 1.00 0.00 C +ATOM 1890 H ILE A 125 -43.194 -52.354 15.165 1.00 0.00 H +ATOM 1891 HA ILE A 125 -40.319 -52.203 15.953 1.00 0.00 H +ATOM 1892 HB ILE A 125 -42.656 -50.907 17.432 1.00 0.00 H +ATOM 1893 HG12 ILE A 125 -40.172 -49.797 16.063 1.00 0.00 H +ATOM 1894 HG13 ILE A 125 -41.891 -49.509 15.628 1.00 0.00 H +ATOM 1895 HG21 ILE A 125 -40.955 -50.215 19.093 1.00 0.00 H +ATOM 1896 HG22 ILE A 125 -41.107 -52.002 19.001 1.00 0.00 H +ATOM 1897 HG23 ILE A 125 -39.716 -51.178 18.219 1.00 0.00 H +ATOM 1898 HD11 ILE A 125 -40.921 -47.574 16.787 1.00 0.00 H +ATOM 1899 HD12 ILE A 125 -42.232 -48.325 17.759 1.00 0.00 H +ATOM 1900 HD13 ILE A 125 -40.513 -48.613 18.194 1.00 0.00 H +ATOM 1901 N LEU A 126 -40.597 -54.132 17.506 1.00 0.00 N +ATOM 1902 CA LEU A 126 -40.707 -55.291 18.389 1.00 0.00 C +ATOM 1903 C LEU A 126 -40.314 -54.837 19.790 1.00 0.00 C +ATOM 1904 O LEU A 126 -39.128 -54.724 20.108 1.00 0.00 O +ATOM 1905 CB LEU A 126 -39.827 -56.441 17.908 1.00 0.00 C +ATOM 1906 CG LEU A 126 -39.828 -57.690 18.790 1.00 0.00 C +ATOM 1907 CD1 LEU A 126 -41.246 -58.173 19.012 1.00 0.00 C +ATOM 1908 CD2 LEU A 126 -38.974 -58.797 18.185 1.00 0.00 C +ATOM 1909 H LEU A 126 -39.685 -53.819 17.241 1.00 0.00 H +ATOM 1910 HA LEU A 126 -41.751 -55.635 18.412 1.00 0.00 H +ATOM 1911 HB2 LEU A 126 -40.172 -56.735 16.906 1.00 0.00 H +ATOM 1912 HB3 LEU A 126 -38.793 -56.072 17.846 1.00 0.00 H +ATOM 1913 HG LEU A 126 -39.398 -57.420 19.766 1.00 0.00 H +ATOM 1914 HD11 LEU A 126 -41.233 -59.071 19.647 1.00 0.00 H +ATOM 1915 HD12 LEU A 126 -41.829 -57.382 19.507 1.00 0.00 H +ATOM 1916 HD13 LEU A 126 -41.706 -58.416 18.043 1.00 0.00 H +ATOM 1917 HD21 LEU A 126 -38.996 -59.679 18.842 1.00 0.00 H +ATOM 1918 HD22 LEU A 126 -39.371 -59.067 17.196 1.00 0.00 H +ATOM 1919 HD23 LEU A 126 -37.937 -58.444 18.080 1.00 0.00 H +ATOM 1920 N GLY A 127 -41.312 -54.571 20.626 1.00 0.00 N +ATOM 1921 CA GLY A 127 -41.053 -54.183 21.997 1.00 0.00 C +ATOM 1922 C GLY A 127 -40.744 -55.381 22.869 1.00 0.00 C +ATOM 1923 O GLY A 127 -41.611 -56.232 23.087 1.00 0.00 O +ATOM 1924 H GLY A 127 -42.256 -54.640 20.304 1.00 0.00 H +ATOM 1925 HA2 GLY A 127 -40.194 -53.496 22.017 1.00 0.00 H +ATOM 1926 HA3 GLY A 127 -41.941 -53.671 22.396 1.00 0.00 H +ATOM 1927 N LEU A 128 -39.513 -55.461 23.377 1.00 0.00 N +ATOM 1928 CA LEU A 128 -39.040 -56.633 24.103 1.00 0.00 C +ATOM 1929 C LEU A 128 -39.068 -56.464 25.617 1.00 0.00 C +ATOM 1930 O LEU A 128 -38.599 -57.355 26.331 1.00 0.00 O +ATOM 1931 CB LEU A 128 -37.617 -56.994 23.652 1.00 0.00 C +ATOM 1932 CG LEU A 128 -37.478 -57.460 22.199 1.00 0.00 C +ATOM 1933 CD1 LEU A 128 -36.022 -57.574 21.756 1.00 0.00 C +ATOM 1934 CD2 LEU A 128 -38.198 -58.786 22.004 1.00 0.00 C +ATOM 1935 H LEU A 128 -38.891 -54.687 23.255 1.00 0.00 H +ATOM 1936 HA LEU A 128 -39.691 -57.483 23.852 1.00 0.00 H +ATOM 1937 HB2 LEU A 128 -36.985 -56.103 23.783 1.00 0.00 H +ATOM 1938 HB3 LEU A 128 -37.251 -57.802 24.302 1.00 0.00 H +ATOM 1939 HG LEU A 128 -37.968 -56.712 21.558 1.00 0.00 H +ATOM 1940 HD11 LEU A 128 -35.982 -57.911 20.710 1.00 0.00 H +ATOM 1941 HD12 LEU A 128 -35.534 -56.592 21.844 1.00 0.00 H +ATOM 1942 HD13 LEU A 128 -35.500 -58.301 22.395 1.00 0.00 H +ATOM 1943 HD21 LEU A 128 -38.092 -59.111 20.958 1.00 0.00 H +ATOM 1944 HD22 LEU A 128 -37.758 -59.543 22.670 1.00 0.00 H +ATOM 1945 HD23 LEU A 128 -39.265 -58.663 22.243 1.00 0.00 H +ATOM 1946 N ALA A 129 -39.607 -55.361 26.130 1.00 0.00 N +ATOM 1947 CA ALA A 129 -39.695 -55.171 27.572 1.00 0.00 C +ATOM 1948 C ALA A 129 -40.911 -55.924 28.115 1.00 0.00 C +ATOM 1949 O ALA A 129 -41.520 -56.753 27.434 1.00 0.00 O +ATOM 1950 CB ALA A 129 -39.722 -53.684 27.911 1.00 0.00 C +ATOM 1951 H ALA A 129 -39.957 -54.652 25.517 1.00 0.00 H +ATOM 1952 HA ALA A 129 -38.798 -55.605 28.038 1.00 0.00 H +ATOM 1953 HB1 ALA A 129 -39.789 -53.557 29.002 1.00 0.00 H +ATOM 1954 HB2 ALA A 129 -38.802 -53.207 27.542 1.00 0.00 H +ATOM 1955 HB3 ALA A 129 -40.595 -53.215 27.434 1.00 0.00 H +ATOM 1956 N TYR A 130 -41.281 -55.636 29.357 1.00 0.00 N +ATOM 1957 CA TYR A 130 -42.249 -56.442 30.085 1.00 0.00 C +ATOM 1958 C TYR A 130 -43.664 -55.885 29.948 1.00 0.00 C +ATOM 1959 O TYR A 130 -43.881 -54.768 29.477 1.00 0.00 O +ATOM 1960 CB TYR A 130 -41.869 -56.523 31.560 1.00 0.00 C +ATOM 1961 CG TYR A 130 -40.562 -57.214 31.829 1.00 0.00 C +ATOM 1962 CD1 TYR A 130 -40.480 -58.598 31.839 1.00 0.00 C +ATOM 1963 CD2 TYR A 130 -39.416 -56.484 32.102 1.00 0.00 C +ATOM 1964 CE1 TYR A 130 -39.284 -59.239 32.100 1.00 0.00 C +ATOM 1965 CE2 TYR A 130 -38.216 -57.114 32.366 1.00 0.00 C +ATOM 1966 CZ TYR A 130 -38.155 -58.490 32.363 1.00 0.00 C +ATOM 1967 OH TYR A 130 -36.963 -59.132 32.629 1.00 0.00 O +ATOM 1968 H TYR A 130 -40.881 -54.838 29.808 1.00 0.00 H +ATOM 1969 HA TYR A 130 -42.244 -57.463 29.675 1.00 0.00 H +ATOM 1970 HB2 TYR A 130 -41.804 -55.498 31.955 1.00 0.00 H +ATOM 1971 HB3 TYR A 130 -42.663 -57.070 32.090 1.00 0.00 H +ATOM 1972 HD1 TYR A 130 -41.365 -59.184 31.640 1.00 0.00 H +ATOM 1973 HD2 TYR A 130 -39.462 -55.405 32.108 1.00 0.00 H +ATOM 1974 HE1 TYR A 130 -39.233 -60.318 32.098 1.00 0.00 H +ATOM 1975 HE2 TYR A 130 -37.331 -56.531 32.574 1.00 0.00 H +ATOM 1976 HH TYR A 130 -37.130 -59.907 33.153 1.00 0.00 H +ATOM 1977 N ALA A 131 -44.635 -56.673 30.417 1.00 0.00 N +ATOM 1978 CA ALA A 131 -46.036 -56.337 30.180 1.00 0.00 C +ATOM 1979 C ALA A 131 -46.437 -55.058 30.900 1.00 0.00 C +ATOM 1980 O ALA A 131 -47.375 -54.374 30.475 1.00 0.00 O +ATOM 1981 CB ALA A 131 -46.934 -57.498 30.605 1.00 0.00 C +ATOM 1982 H ALA A 131 -44.401 -57.497 30.933 1.00 0.00 H +ATOM 1983 HA ALA A 131 -46.182 -56.177 29.101 1.00 0.00 H +ATOM 1984 HB1 ALA A 131 -47.986 -57.234 30.422 1.00 0.00 H +ATOM 1985 HB2 ALA A 131 -46.674 -58.394 30.023 1.00 0.00 H +ATOM 1986 HB3 ALA A 131 -46.788 -57.701 31.676 1.00 0.00 H +ATOM 1987 N GLU A 132 -45.726 -54.704 31.977 1.00 0.00 N +ATOM 1988 CA GLU A 132 -46.155 -53.594 32.826 1.00 0.00 C +ATOM 1989 C GLU A 132 -46.263 -52.285 32.058 1.00 0.00 C +ATOM 1990 O GLU A 132 -47.029 -51.395 32.448 1.00 0.00 O +ATOM 1991 CB GLU A 132 -45.197 -53.429 34.010 1.00 0.00 C +ATOM 1992 CG GLU A 132 -45.823 -52.717 35.209 1.00 0.00 C +ATOM 1993 CD GLU A 132 -44.792 -52.243 36.209 1.00 0.00 C +ATOM 1994 OE1 GLU A 132 -44.155 -53.100 36.856 1.00 0.00 O +ATOM 1995 OE2 GLU A 132 -44.609 -51.013 36.328 1.00 0.00 O1- +ATOM 1996 H GLU A 132 -44.891 -55.205 32.204 1.00 0.00 H +ATOM 1997 HA GLU A 132 -47.150 -53.828 33.232 1.00 0.00 H +ATOM 1998 HB2 GLU A 132 -44.867 -54.428 34.332 1.00 0.00 H +ATOM 1999 HB3 GLU A 132 -44.327 -52.846 33.675 1.00 0.00 H +ATOM 2000 HG2 GLU A 132 -46.388 -51.846 34.846 1.00 0.00 H +ATOM 2001 HG3 GLU A 132 -46.509 -53.414 35.714 1.00 0.00 H +ATOM 2002 N ILE A 133 -45.519 -52.147 30.963 1.00 0.00 N +ATOM 2003 CA ILE A 133 -45.575 -50.946 30.147 1.00 0.00 C +ATOM 2004 C ILE A 133 -46.180 -51.212 28.771 1.00 0.00 C +ATOM 2005 O ILE A 133 -46.046 -50.386 27.866 1.00 0.00 O +ATOM 2006 CB ILE A 133 -44.181 -50.301 30.033 1.00 0.00 C +ATOM 2007 CG1 ILE A 133 -43.242 -51.201 29.238 1.00 0.00 C +ATOM 2008 CG2 ILE A 133 -43.606 -50.059 31.418 1.00 0.00 C +ATOM 2009 CD1 ILE A 133 -41.777 -50.920 29.479 1.00 0.00 C +ATOM 2010 H ILE A 133 -44.905 -52.889 30.695 1.00 0.00 H +ATOM 2011 HA ILE A 133 -46.226 -50.219 30.654 1.00 0.00 H +ATOM 2012 HB ILE A 133 -44.279 -49.337 29.512 1.00 0.00 H +ATOM 2013 HG12 ILE A 133 -43.445 -52.246 29.516 1.00 0.00 H +ATOM 2014 HG13 ILE A 133 -43.451 -51.060 28.167 1.00 0.00 H +ATOM 2015 HG21 ILE A 133 -42.611 -49.598 31.327 1.00 0.00 H +ATOM 2016 HG22 ILE A 133 -44.271 -49.386 31.979 1.00 0.00 H +ATOM 2017 HG23 ILE A 133 -43.519 -51.017 31.951 1.00 0.00 H +ATOM 2018 HD11 ILE A 133 -41.167 -51.606 28.873 1.00 0.00 H +ATOM 2019 HD12 ILE A 133 -41.551 -49.881 29.195 1.00 0.00 H +ATOM 2020 HD13 ILE A 133 -41.546 -51.067 30.544 1.00 0.00 H +ATOM 2021 N ALA A 134 -46.852 -52.348 28.593 1.00 0.00 N +ATOM 2022 CA ALA A 134 -47.554 -52.585 27.340 1.00 0.00 C +ATOM 2023 C ALA A 134 -48.800 -51.710 27.268 1.00 0.00 C +ATOM 2024 O ALA A 134 -49.457 -51.452 28.280 1.00 0.00 O +ATOM 2025 CB ALA A 134 -47.922 -54.062 27.199 1.00 0.00 C +ATOM 2026 H ALA A 134 -46.874 -53.036 29.318 1.00 0.00 H +ATOM 2027 HA ALA A 134 -46.895 -52.314 26.502 1.00 0.00 H +ATOM 2028 HB1 ALA A 134 -48.451 -54.220 26.247 1.00 0.00 H +ATOM 2029 HB2 ALA A 134 -47.006 -54.671 27.215 1.00 0.00 H +ATOM 2030 HB3 ALA A 134 -48.574 -54.359 28.034 1.00 0.00 H +ATOM 2031 N ARG A 135 -49.098 -51.215 26.068 1.00 0.00 N +ATOM 2032 CA ARG A 135 -50.337 -50.478 25.845 1.00 0.00 C +ATOM 2033 C ARG A 135 -51.361 -51.383 25.170 1.00 0.00 C +ATOM 2034 O ARG A 135 -51.002 -52.161 24.279 1.00 0.00 O +ATOM 2035 CB ARG A 135 -50.100 -49.245 24.968 1.00 0.00 C +ATOM 2036 CG ARG A 135 -49.106 -48.235 25.530 1.00 0.00 C +ATOM 2037 CD ARG A 135 -49.634 -47.564 26.782 1.00 0.00 C +ATOM 2038 NE ARG A 135 -50.724 -46.625 26.514 1.00 0.00 N +ATOM 2039 CZ ARG A 135 -50.561 -45.313 26.370 1.00 0.00 C +ATOM 2040 NH1 ARG A 135 -51.608 -44.534 26.135 1.00 0.00 N +ATOM 2041 NH2 ARG A 135 -49.350 -44.780 26.464 1.00 0.00 N1+ +ATOM 2042 H ARG A 135 -48.462 -51.351 25.308 1.00 0.00 H +ATOM 2043 HA ARG A 135 -50.745 -50.146 26.811 1.00 0.00 H +ATOM 2044 HB2 ARG A 135 -49.724 -49.587 23.993 1.00 0.00 H +ATOM 2045 HB3 ARG A 135 -51.064 -48.734 24.830 1.00 0.00 H +ATOM 2046 HG2 ARG A 135 -48.168 -48.756 25.773 1.00 0.00 H +ATOM 2047 HG3 ARG A 135 -48.911 -47.465 24.769 1.00 0.00 H +ATOM 2048 HD2 ARG A 135 -50.002 -48.342 27.467 1.00 0.00 H +ATOM 2049 HD3 ARG A 135 -48.809 -47.015 27.260 1.00 0.00 H +ATOM 2050 HE ARG A 135 -51.650 -46.993 26.434 1.00 0.00 H +ATOM 2051 HH11 ARG A 135 -51.484 -43.547 26.029 1.00 0.00 H +ATOM 2052 HH12 ARG A 135 -52.522 -44.933 26.064 1.00 0.00 H +ATOM 2053 HH21 ARG A 135 -49.228 -43.793 26.358 1.00 0.00 H +ATOM 2054 HH22 ARG A 135 -48.559 -45.365 26.641 1.00 0.00 H +ATOM 2055 N PRO A 136 -52.656 -51.304 25.532 1.00 0.00 N +ATOM 2056 CA PRO A 136 -53.254 -50.356 26.482 1.00 0.00 C +ATOM 2057 C PRO A 136 -52.975 -50.664 27.961 1.00 0.00 C +ATOM 2058 O PRO A 136 -53.034 -49.740 28.777 1.00 0.00 O +ATOM 2059 CB PRO A 136 -54.754 -50.475 26.189 1.00 0.00 C +ATOM 2060 CG PRO A 136 -54.921 -51.853 25.663 1.00 0.00 C +ATOM 2061 CD PRO A 136 -53.683 -52.116 24.852 1.00 0.00 C +ATOM 2062 HA PRO A 136 -52.920 -49.333 26.256 1.00 0.00 H +ATOM 2063 HB2 PRO A 136 -55.342 -50.336 27.108 1.00 0.00 H +ATOM 2064 HB3 PRO A 136 -55.064 -49.732 25.439 1.00 0.00 H +ATOM 2065 HG2 PRO A 136 -55.002 -52.575 26.489 1.00 0.00 H +ATOM 2066 HG3 PRO A 136 -55.818 -51.918 25.030 1.00 0.00 H +ATOM 2067 HD2 PRO A 136 -53.418 -53.183 24.871 1.00 0.00 H +ATOM 2068 HD3 PRO A 136 -53.817 -51.790 23.810 1.00 0.00 H +ATOM 2069 N ASP A 137 -52.688 -51.919 28.299 1.00 0.00 N +ATOM 2070 CA ASP A 137 -52.343 -52.288 29.674 1.00 0.00 C +ATOM 2071 C ASP A 137 -51.612 -53.637 29.669 1.00 0.00 C +ATOM 2072 O ASP A 137 -51.280 -54.185 28.614 1.00 0.00 O +ATOM 2073 CB ASP A 137 -53.583 -52.320 30.566 1.00 0.00 C +ATOM 2074 CG ASP A 137 -54.677 -53.208 30.016 1.00 0.00 C +ATOM 2075 OD1 ASP A 137 -54.460 -54.428 29.884 1.00 0.00 O +ATOM 2076 OD2 ASP A 137 -55.769 -52.682 29.722 1.00 0.00 O1- +ATOM 2077 H ASP A 137 -52.708 -52.629 27.595 1.00 0.00 H +ATOM 2078 HA ASP A 137 -51.654 -51.533 30.080 1.00 0.00 H +ATOM 2079 HB2 ASP A 137 -53.292 -52.694 31.559 1.00 0.00 H +ATOM 2080 HB3 ASP A 137 -53.975 -51.297 30.660 1.00 0.00 H +ATOM 2081 N ASP A 138 -51.377 -54.178 30.873 1.00 0.00 N +ATOM 2082 CA ASP A 138 -50.557 -55.371 31.068 1.00 0.00 C +ATOM 2083 C ASP A 138 -51.290 -56.670 30.741 1.00 0.00 C +ATOM 2084 O ASP A 138 -50.681 -57.748 30.833 1.00 0.00 O +ATOM 2085 CB ASP A 138 -49.983 -55.401 32.508 1.00 0.00 C +ATOM 2086 CG ASP A 138 -51.020 -55.821 33.579 1.00 0.00 C +ATOM 2087 OD1 ASP A 138 -52.214 -56.017 33.237 1.00 0.00 O +ATOM 2088 OD2 ASP A 138 -50.625 -55.966 34.758 1.00 0.00 O1- +ATOM 2089 H ASP A 138 -51.783 -53.743 31.677 1.00 0.00 H +ATOM 2090 HA ASP A 138 -49.697 -55.305 30.385 1.00 0.00 H +ATOM 2091 HB2 ASP A 138 -49.147 -56.115 32.534 1.00 0.00 H +ATOM 2092 HB3 ASP A 138 -49.614 -54.395 32.757 1.00 0.00 H +ATOM 2093 N SER A 139 -52.555 -56.596 30.293 1.00 0.00 N +ATOM 2094 CA SER A 139 -53.247 -57.793 29.838 1.00 0.00 C +ATOM 2095 C SER A 139 -52.867 -58.178 28.419 1.00 0.00 C +ATOM 2096 O SER A 139 -53.139 -59.314 28.004 1.00 0.00 O +ATOM 2097 CB SER A 139 -54.764 -57.607 29.916 1.00 0.00 C +ATOM 2098 OG SER A 139 -55.188 -56.531 29.090 1.00 0.00 O +ATOM 2099 H SER A 139 -53.024 -55.713 30.272 1.00 0.00 H +ATOM 2100 HA SER A 139 -52.977 -58.630 30.499 1.00 0.00 H +ATOM 2101 HB2 SER A 139 -55.257 -58.532 29.583 1.00 0.00 H +ATOM 2102 HB3 SER A 139 -55.047 -57.394 30.957 1.00 0.00 H +ATOM 2103 HG SER A 139 -55.050 -55.708 29.545 1.00 0.00 H +ATOM 2104 N LEU A 140 -52.261 -57.255 27.670 1.00 0.00 N +ATOM 2105 CA LEU A 140 -51.757 -57.532 26.328 1.00 0.00 C +ATOM 2106 C LEU A 140 -50.354 -58.103 26.459 1.00 0.00 C +ATOM 2107 O LEU A 140 -49.389 -57.374 26.707 1.00 0.00 O +ATOM 2108 CB LEU A 140 -51.744 -56.271 25.481 1.00 0.00 C +ATOM 2109 CG LEU A 140 -51.797 -56.578 23.987 1.00 0.00 C +ATOM 2110 CD1 LEU A 140 -53.223 -56.897 23.589 1.00 0.00 C +ATOM 2111 CD2 LEU A 140 -51.234 -55.446 23.155 1.00 0.00 C +ATOM 2112 H LEU A 140 -52.147 -56.334 28.044 1.00 0.00 H +ATOM 2113 HA LEU A 140 -52.399 -58.280 25.840 1.00 0.00 H +ATOM 2114 HB2 LEU A 140 -52.617 -55.657 25.747 1.00 0.00 H +ATOM 2115 HB3 LEU A 140 -50.822 -55.710 25.695 1.00 0.00 H +ATOM 2116 HG LEU A 140 -51.184 -57.473 23.804 1.00 0.00 H +ATOM 2117 HD11 LEU A 140 -53.263 -57.118 22.512 1.00 0.00 H +ATOM 2118 HD12 LEU A 140 -53.575 -57.771 24.156 1.00 0.00 H +ATOM 2119 HD13 LEU A 140 -53.867 -56.033 23.810 1.00 0.00 H +ATOM 2120 HD21 LEU A 140 -51.292 -55.710 22.089 1.00 0.00 H +ATOM 2121 HD22 LEU A 140 -51.817 -54.531 23.339 1.00 0.00 H +ATOM 2122 HD23 LEU A 140 -50.184 -55.274 23.433 1.00 0.00 H +ATOM 2123 N GLU A 141 -50.239 -59.409 26.310 1.00 0.00 N +ATOM 2124 CA GLU A 141 -48.981 -60.106 26.494 1.00 0.00 C +ATOM 2125 C GLU A 141 -47.987 -59.697 25.404 1.00 0.00 C +ATOM 2126 O GLU A 141 -48.358 -59.683 24.217 1.00 0.00 O +ATOM 2127 CB GLU A 141 -49.258 -61.605 26.468 1.00 0.00 C +ATOM 2128 CG GLU A 141 -48.079 -62.514 26.639 1.00 0.00 C +ATOM 2129 CD GLU A 141 -48.537 -63.942 26.833 1.00 0.00 C +ATOM 2130 OE1 GLU A 141 -48.378 -64.487 27.946 1.00 0.00 O +ATOM 2131 OE2 GLU A 141 -49.077 -64.514 25.864 1.00 0.00 O1- +ATOM 2132 H GLU A 141 -51.050 -59.938 26.061 1.00 0.00 H +ATOM 2133 HA GLU A 141 -48.554 -59.845 27.473 1.00 0.00 H +ATOM 2134 HB2 GLU A 141 -49.969 -61.826 27.278 1.00 0.00 H +ATOM 2135 HB3 GLU A 141 -49.722 -61.841 25.499 1.00 0.00 H +ATOM 2136 HG2 GLU A 141 -47.444 -62.456 25.743 1.00 0.00 H +ATOM 2137 HG3 GLU A 141 -47.501 -62.196 27.519 1.00 0.00 H +ATOM 2138 N PRO A 142 -46.754 -59.334 25.755 1.00 0.00 N +ATOM 2139 CA PRO A 142 -45.781 -58.953 24.725 1.00 0.00 C +ATOM 2140 C PRO A 142 -45.341 -60.157 23.909 1.00 0.00 C +ATOM 2141 O PRO A 142 -45.646 -61.307 24.228 1.00 0.00 O +ATOM 2142 CB PRO A 142 -44.609 -58.363 25.524 1.00 0.00 C +ATOM 2143 CG PRO A 142 -45.147 -58.102 26.899 1.00 0.00 C +ATOM 2144 CD PRO A 142 -46.208 -59.146 27.112 1.00 0.00 C +ATOM 2145 HA PRO A 142 -46.201 -58.183 24.060 1.00 0.00 H +ATOM 2146 HB2 PRO A 142 -43.775 -59.079 25.568 1.00 0.00 H +ATOM 2147 HB3 PRO A 142 -44.264 -57.426 25.063 1.00 0.00 H +ATOM 2148 HG2 PRO A 142 -44.350 -58.203 27.651 1.00 0.00 H +ATOM 2149 HG3 PRO A 142 -45.581 -57.093 26.958 1.00 0.00 H +ATOM 2150 HD2 PRO A 142 -46.983 -58.788 27.806 1.00 0.00 H +ATOM 2151 HD3 PRO A 142 -45.774 -60.080 27.498 1.00 0.00 H +ATOM 2152 N PHE A 143 -44.596 -59.874 22.839 1.00 0.00 N +ATOM 2153 CA PHE A 143 -44.208 -60.938 21.917 1.00 0.00 C +ATOM 2154 C PHE A 143 -43.362 -62.002 22.605 1.00 0.00 C +ATOM 2155 O PHE A 143 -43.620 -63.203 22.467 1.00 0.00 O +ATOM 2156 CB PHE A 143 -43.454 -60.358 20.719 1.00 0.00 C +ATOM 2157 CG PHE A 143 -42.898 -61.407 19.788 1.00 0.00 C +ATOM 2158 CD1 PHE A 143 -43.698 -61.976 18.806 1.00 0.00 C +ATOM 2159 CD2 PHE A 143 -41.576 -61.823 19.892 1.00 0.00 C +ATOM 2160 CE1 PHE A 143 -43.194 -62.941 17.957 1.00 0.00 C +ATOM 2161 CE2 PHE A 143 -41.069 -62.790 19.044 1.00 0.00 C +ATOM 2162 CZ PHE A 143 -41.880 -63.347 18.074 1.00 0.00 C +ATOM 2163 H PHE A 143 -44.303 -58.933 22.668 1.00 0.00 H +ATOM 2164 HA PHE A 143 -45.119 -61.424 21.538 1.00 0.00 H +ATOM 2165 HB2 PHE A 143 -44.145 -59.719 20.150 1.00 0.00 H +ATOM 2166 HB3 PHE A 143 -42.618 -59.750 21.096 1.00 0.00 H +ATOM 2167 HD1 PHE A 143 -44.726 -61.660 18.705 1.00 0.00 H +ATOM 2168 HD2 PHE A 143 -40.937 -61.385 20.645 1.00 0.00 H +ATOM 2169 HE1 PHE A 143 -43.828 -63.379 17.201 1.00 0.00 H +ATOM 2170 HE2 PHE A 143 -40.042 -63.109 19.139 1.00 0.00 H +ATOM 2171 HZ PHE A 143 -41.486 -64.100 17.407 1.00 0.00 H +ATOM 2172 N PHE A 144 -42.340 -61.578 23.348 1.00 0.00 N +ATOM 2173 CA PHE A 144 -41.395 -62.543 23.895 1.00 0.00 C +ATOM 2174 C PHE A 144 -42.044 -63.422 24.952 1.00 0.00 C +ATOM 2175 O PHE A 144 -41.746 -64.617 25.035 1.00 0.00 O +ATOM 2176 CB PHE A 144 -40.170 -61.829 24.468 1.00 0.00 C +ATOM 2177 CG PHE A 144 -38.958 -62.703 24.552 1.00 0.00 C +ATOM 2178 CD1 PHE A 144 -38.136 -62.871 23.451 1.00 0.00 C +ATOM 2179 CD2 PHE A 144 -38.659 -63.383 25.720 1.00 0.00 C +ATOM 2180 CE1 PHE A 144 -37.028 -63.689 23.517 1.00 0.00 C +ATOM 2181 CE2 PHE A 144 -37.547 -64.203 25.791 1.00 0.00 C +ATOM 2182 CZ PHE A 144 -36.733 -64.355 24.688 1.00 0.00 C +ATOM 2183 H PHE A 144 -42.222 -60.602 23.529 1.00 0.00 H +ATOM 2184 HA PHE A 144 -41.050 -63.196 23.080 1.00 0.00 H +ATOM 2185 HB2 PHE A 144 -39.935 -60.968 23.824 1.00 0.00 H +ATOM 2186 HB3 PHE A 144 -40.415 -61.474 25.480 1.00 0.00 H +ATOM 2187 HD1 PHE A 144 -38.365 -62.356 22.530 1.00 0.00 H +ATOM 2188 HD2 PHE A 144 -39.299 -63.272 26.583 1.00 0.00 H +ATOM 2189 HE1 PHE A 144 -36.392 -63.808 22.653 1.00 0.00 H +ATOM 2190 HE2 PHE A 144 -37.317 -64.723 26.709 1.00 0.00 H +ATOM 2191 HZ PHE A 144 -35.865 -64.995 24.741 1.00 0.00 H +ATOM 2192 N ASP A 145 -42.920 -62.850 25.778 1.00 0.00 N +ATOM 2193 CA ASP A 145 -43.678 -63.667 26.717 1.00 0.00 C +ATOM 2194 C ASP A 145 -44.416 -64.783 25.992 1.00 0.00 C +ATOM 2195 O ASP A 145 -44.387 -65.943 26.418 1.00 0.00 O +ATOM 2196 CB ASP A 145 -44.652 -62.794 27.505 1.00 0.00 C +ATOM 2197 CG ASP A 145 -43.957 -61.952 28.547 1.00 0.00 C +ATOM 2198 OD1 ASP A 145 -43.978 -62.334 29.739 1.00 0.00 O +ATOM 2199 OD2 ASP A 145 -43.364 -60.924 28.165 1.00 0.00 O1- +ATOM 2200 H ASP A 145 -43.057 -61.860 25.753 1.00 0.00 H +ATOM 2201 HA ASP A 145 -42.980 -64.128 27.432 1.00 0.00 H +ATOM 2202 HB2 ASP A 145 -45.175 -62.127 26.804 1.00 0.00 H +ATOM 2203 HB3 ASP A 145 -45.383 -63.445 28.007 1.00 0.00 H +ATOM 2204 N SER A 146 -45.056 -64.453 24.868 1.00 0.00 N +ATOM 2205 CA SER A 146 -45.813 -65.455 24.126 1.00 0.00 C +ATOM 2206 C SER A 146 -44.898 -66.487 23.481 1.00 0.00 C +ATOM 2207 O SER A 146 -45.238 -67.674 23.417 1.00 0.00 O +ATOM 2208 CB SER A 146 -46.677 -64.777 23.065 1.00 0.00 C +ATOM 2209 OG SER A 146 -47.694 -64.007 23.669 1.00 0.00 O +ATOM 2210 H SER A 146 -45.016 -63.512 24.532 1.00 0.00 H +ATOM 2211 HA SER A 146 -46.482 -65.982 24.822 1.00 0.00 H +ATOM 2212 HB2 SER A 146 -46.044 -64.121 22.449 1.00 0.00 H +ATOM 2213 HB3 SER A 146 -47.137 -65.547 22.428 1.00 0.00 H +ATOM 2214 HG SER A 146 -48.081 -64.498 24.384 1.00 0.00 H +ATOM 2215 N LEU A 147 -43.746 -66.049 22.971 1.00 0.00 N +ATOM 2216 CA LEU A 147 -42.818 -66.968 22.322 1.00 0.00 C +ATOM 2217 C LEU A 147 -42.324 -68.029 23.294 1.00 0.00 C +ATOM 2218 O LEU A 147 -42.267 -69.218 22.964 1.00 0.00 O +ATOM 2219 CB LEU A 147 -41.641 -66.188 21.740 1.00 0.00 C +ATOM 2220 CG LEU A 147 -40.516 -67.022 21.139 1.00 0.00 C +ATOM 2221 CD1 LEU A 147 -40.867 -67.454 19.727 1.00 0.00 C +ATOM 2222 CD2 LEU A 147 -39.225 -66.232 21.164 1.00 0.00 C +ATOM 2223 H LEU A 147 -43.516 -65.078 23.035 1.00 0.00 H +ATOM 2224 HA LEU A 147 -43.336 -67.475 21.494 1.00 0.00 H +ATOM 2225 HB2 LEU A 147 -42.032 -65.531 20.949 1.00 0.00 H +ATOM 2226 HB3 LEU A 147 -41.212 -65.576 22.547 1.00 0.00 H +ATOM 2227 HG LEU A 147 -40.385 -67.923 21.756 1.00 0.00 H +ATOM 2228 HD11 LEU A 147 -40.044 -68.054 19.311 1.00 0.00 H +ATOM 2229 HD12 LEU A 147 -41.787 -68.057 19.746 1.00 0.00 H +ATOM 2230 HD13 LEU A 147 -41.025 -66.564 19.100 1.00 0.00 H +ATOM 2231 HD21 LEU A 147 -38.416 -66.837 20.729 1.00 0.00 H +ATOM 2232 HD22 LEU A 147 -39.348 -65.310 20.577 1.00 0.00 H +ATOM 2233 HD23 LEU A 147 -38.973 -65.975 22.203 1.00 0.00 H +ATOM 2234 N VAL A 148 -41.965 -67.610 24.506 1.00 0.00 N +ATOM 2235 CA VAL A 148 -41.437 -68.547 25.493 1.00 0.00 C +ATOM 2236 C VAL A 148 -42.516 -69.523 25.949 1.00 0.00 C +ATOM 2237 O VAL A 148 -42.264 -70.725 26.095 1.00 0.00 O +ATOM 2238 CB VAL A 148 -40.822 -67.781 26.678 1.00 0.00 C +ATOM 2239 CG1 VAL A 148 -40.595 -68.714 27.856 1.00 0.00 C +ATOM 2240 CG2 VAL A 148 -39.522 -67.130 26.249 1.00 0.00 C +ATOM 2241 H VAL A 148 -42.058 -66.643 24.743 1.00 0.00 H +ATOM 2242 HA VAL A 148 -40.633 -69.133 25.024 1.00 0.00 H +ATOM 2243 HB VAL A 148 -41.526 -66.993 26.985 1.00 0.00 H +ATOM 2244 HG11 VAL A 148 -40.156 -68.150 28.692 1.00 0.00 H +ATOM 2245 HG12 VAL A 148 -41.556 -69.147 28.171 1.00 0.00 H +ATOM 2246 HG13 VAL A 148 -39.909 -69.521 27.558 1.00 0.00 H +ATOM 2247 HG21 VAL A 148 -39.087 -66.584 27.099 1.00 0.00 H +ATOM 2248 HG22 VAL A 148 -38.818 -67.905 25.912 1.00 0.00 H +ATOM 2249 HG23 VAL A 148 -39.718 -66.429 25.425 1.00 0.00 H +ATOM 2250 N LYS A 149 -43.731 -69.028 26.183 1.00 0.00 N +ATOM 2251 CA LYS A 149 -44.802 -69.905 26.647 1.00 0.00 C +ATOM 2252 C LYS A 149 -45.173 -70.950 25.600 1.00 0.00 C +ATOM 2253 O LYS A 149 -45.494 -72.094 25.943 1.00 0.00 O +ATOM 2254 CB LYS A 149 -46.033 -69.083 27.027 1.00 0.00 C +ATOM 2255 CG LYS A 149 -45.926 -68.343 28.349 1.00 0.00 C +ATOM 2256 CD LYS A 149 -47.288 -67.791 28.741 1.00 0.00 C +ATOM 2257 CE LYS A 149 -47.212 -66.857 29.937 1.00 0.00 C +ATOM 2258 NZ LYS A 149 -46.577 -65.553 29.594 1.00 0.00 N1+ +ATOM 2259 H LYS A 149 -43.909 -68.054 26.041 1.00 0.00 H +ATOM 2260 HA LYS A 149 -44.459 -70.435 27.548 1.00 0.00 H +ATOM 2261 HB2 LYS A 149 -46.207 -68.341 26.233 1.00 0.00 H +ATOM 2262 HB3 LYS A 149 -46.894 -69.765 27.086 1.00 0.00 H +ATOM 2263 HG2 LYS A 149 -45.575 -69.036 29.128 1.00 0.00 H +ATOM 2264 HG3 LYS A 149 -45.211 -67.513 28.246 1.00 0.00 H +ATOM 2265 HD2 LYS A 149 -47.704 -67.237 27.886 1.00 0.00 H +ATOM 2266 HD3 LYS A 149 -47.951 -68.632 28.991 1.00 0.00 H +ATOM 2267 HE2 LYS A 149 -48.231 -66.669 30.305 1.00 0.00 H +ATOM 2268 HE3 LYS A 149 -46.621 -67.342 30.728 1.00 0.00 H +ATOM 2269 HZ1 LYS A 149 -46.437 -65.020 30.429 1.00 0.00 H +ATOM 2270 HZ2 LYS A 149 -45.695 -65.718 29.152 1.00 0.00 H +ATOM 2271 HZ3 LYS A 149 -47.173 -65.046 28.971 1.00 0.00 H +ATOM 2272 N GLN A 150 -45.133 -70.579 24.318 1.00 0.00 N +ATOM 2273 CA GLN A 150 -45.636 -71.466 23.273 1.00 0.00 C +ATOM 2274 C GLN A 150 -44.581 -72.444 22.776 1.00 0.00 C +ATOM 2275 O GLN A 150 -44.930 -73.482 22.203 1.00 0.00 O +ATOM 2276 CB GLN A 150 -46.181 -70.640 22.104 1.00 0.00 C +ATOM 2277 CG GLN A 150 -47.472 -69.895 22.419 1.00 0.00 C +ATOM 2278 CD GLN A 150 -47.888 -68.949 21.308 1.00 0.00 C +ATOM 2279 OE1 GLN A 150 -47.733 -69.254 20.125 1.00 0.00 O +ATOM 2280 NE2 GLN A 150 -48.414 -67.790 21.685 1.00 0.00 N +ATOM 2281 H GLN A 150 -44.756 -69.686 24.072 1.00 0.00 H +ATOM 2282 HA GLN A 150 -46.471 -72.053 23.684 1.00 0.00 H +ATOM 2283 HB2 GLN A 150 -45.417 -69.902 21.816 1.00 0.00 H +ATOM 2284 HB3 GLN A 150 -46.371 -71.319 21.260 1.00 0.00 H +ATOM 2285 HG2 GLN A 150 -48.275 -70.631 22.573 1.00 0.00 H +ATOM 2286 HG3 GLN A 150 -47.326 -69.313 23.341 1.00 0.00 H +ATOM 2287 HE21 GLN A 150 -48.702 -67.124 20.997 1.00 0.00 H +ATOM 2288 HE22 GLN A 150 -48.522 -67.583 22.657 1.00 0.00 H +ATOM 2289 N THR A 151 -43.307 -72.142 22.982 1.00 0.00 N +ATOM 2290 CA THR A 151 -42.207 -72.965 22.500 1.00 0.00 C +ATOM 2291 C THR A 151 -41.348 -73.429 23.672 1.00 0.00 C +ATOM 2292 O THR A 151 -41.674 -73.214 24.842 1.00 0.00 O +ATOM 2293 CB THR A 151 -41.355 -72.195 21.488 1.00 0.00 C +ATOM 2294 OG1 THR A 151 -40.722 -71.094 22.151 1.00 0.00 O +ATOM 2295 CG2 THR A 151 -42.221 -71.661 20.367 1.00 0.00 C +ATOM 2296 H THR A 151 -43.091 -71.310 23.493 1.00 0.00 H +ATOM 2297 HA THR A 151 -42.615 -73.855 21.999 1.00 0.00 H +ATOM 2298 HB THR A 151 -40.598 -72.874 21.068 1.00 0.00 H +ATOM 2299 HG1 THR A 151 -41.363 -70.628 22.674 1.00 0.00 H +ATOM 2300 HG21 THR A 151 -41.595 -71.111 19.649 1.00 0.00 H +ATOM 2301 HG22 THR A 151 -42.716 -72.499 19.855 1.00 0.00 H +ATOM 2302 HG23 THR A 151 -42.982 -70.985 20.782 1.00 0.00 H +ATOM 2303 N HIS A 152 -40.232 -74.074 23.334 1.00 0.00 N +ATOM 2304 CA HIS A 152 -39.227 -74.472 24.309 1.00 0.00 C +ATOM 2305 C HIS A 152 -38.047 -73.510 24.348 1.00 0.00 C +ATOM 2306 O HIS A 152 -36.966 -73.883 24.819 1.00 0.00 O +ATOM 2307 CB HIS A 152 -38.738 -75.891 24.018 1.00 0.00 C +ATOM 2308 CG HIS A 152 -39.740 -76.954 24.343 1.00 0.00 C +ATOM 2309 ND1 HIS A 152 -40.578 -77.503 23.397 1.00 0.00 N +ATOM 2310 CD2 HIS A 152 -40.040 -77.564 25.513 1.00 0.00 C +ATOM 2311 CE1 HIS A 152 -41.349 -78.410 23.970 1.00 0.00 C +ATOM 2312 NE2 HIS A 152 -41.044 -78.466 25.254 1.00 0.00 N +ATOM 2313 H HIS A 152 -40.078 -74.294 22.371 1.00 0.00 H +ATOM 2314 HA HIS A 152 -39.689 -74.476 25.307 1.00 0.00 H +ATOM 2315 HB2 HIS A 152 -38.493 -75.961 22.948 1.00 0.00 H +ATOM 2316 HB3 HIS A 152 -37.832 -76.075 24.614 1.00 0.00 H +ATOM 2317 HD1 HIS A 152 -40.599 -77.254 22.429 1.00 0.00 H +ATOM 2318 HD2 HIS A 152 -39.577 -77.377 26.471 1.00 0.00 H +ATOM 2319 HE1 HIS A 152 -42.101 -79.005 23.473 1.00 0.00 H +ATOM 2320 N VAL A 153 -38.232 -72.292 23.859 1.00 0.00 N +ATOM 2321 CA VAL A 153 -37.138 -71.314 23.805 1.00 0.00 C +ATOM 2322 C VAL A 153 -36.826 -70.830 25.212 1.00 0.00 C +ATOM 2323 O VAL A 153 -37.748 -70.429 25.947 1.00 0.00 O +ATOM 2324 CB VAL A 153 -37.516 -70.149 22.891 1.00 0.00 C +ATOM 2325 CG1 VAL A 153 -36.505 -69.027 23.018 1.00 0.00 C +ATOM 2326 CG2 VAL A 153 -37.622 -70.621 21.446 1.00 0.00 C +ATOM 2327 H VAL A 153 -39.136 -72.033 23.519 1.00 0.00 H +ATOM 2328 HA VAL A 153 -36.240 -71.799 23.396 1.00 0.00 H +ATOM 2329 HB VAL A 153 -38.500 -69.769 23.204 1.00 0.00 H +ATOM 2330 HG11 VAL A 153 -36.792 -68.198 22.355 1.00 0.00 H +ATOM 2331 HG12 VAL A 153 -36.480 -68.672 24.059 1.00 0.00 H +ATOM 2332 HG13 VAL A 153 -35.509 -69.397 22.733 1.00 0.00 H +ATOM 2333 HG21 VAL A 153 -37.894 -69.772 20.802 1.00 0.00 H +ATOM 2334 HG22 VAL A 153 -36.654 -71.031 21.122 1.00 0.00 H +ATOM 2335 HG23 VAL A 153 -38.394 -71.401 21.371 1.00 0.00 H +ATOM 2336 N PRO A 154 -35.563 -70.847 25.641 1.00 0.00 N +ATOM 2337 CA PRO A 154 -35.229 -70.340 26.975 1.00 0.00 C +ATOM 2338 C PRO A 154 -35.604 -68.873 27.127 1.00 0.00 C +ATOM 2339 O PRO A 154 -35.539 -68.092 26.176 1.00 0.00 O +ATOM 2340 CB PRO A 154 -33.715 -70.547 27.065 1.00 0.00 C +ATOM 2341 CG PRO A 154 -33.449 -71.694 26.146 1.00 0.00 C +ATOM 2342 CD PRO A 154 -34.414 -71.515 25.002 1.00 0.00 C +ATOM 2343 HA PRO A 154 -35.731 -70.935 27.752 1.00 0.00 H +ATOM 2344 HB2 PRO A 154 -33.177 -69.647 26.732 1.00 0.00 H +ATOM 2345 HB3 PRO A 154 -33.415 -70.794 28.094 1.00 0.00 H +ATOM 2346 HG2 PRO A 154 -32.411 -71.665 25.783 1.00 0.00 H +ATOM 2347 HG3 PRO A 154 -33.632 -72.650 26.659 1.00 0.00 H +ATOM 2348 HD2 PRO A 154 -34.707 -72.486 24.576 1.00 0.00 H +ATOM 2349 HD3 PRO A 154 -33.979 -70.885 24.212 1.00 0.00 H +ATOM 2350 N ASN A 155 -36.006 -68.509 28.345 1.00 0.00 N +ATOM 2351 CA ASN A 155 -36.484 -67.161 28.650 1.00 0.00 C +ATOM 2352 C ASN A 155 -35.297 -66.198 28.725 1.00 0.00 C +ATOM 2353 O ASN A 155 -34.923 -65.692 29.784 1.00 0.00 O +ATOM 2354 CB ASN A 155 -37.284 -67.176 29.945 1.00 0.00 C +ATOM 2355 CG ASN A 155 -38.023 -65.880 30.191 1.00 0.00 C +ATOM 2356 OD1 ASN A 155 -38.181 -65.058 29.287 1.00 0.00 O +ATOM 2357 ND2 ASN A 155 -38.490 -65.692 31.420 1.00 0.00 N +ATOM 2358 H ASN A 155 -35.980 -69.186 29.081 1.00 0.00 H +ATOM 2359 HA ASN A 155 -37.147 -66.824 27.840 1.00 0.00 H +ATOM 2360 HB2 ASN A 155 -38.017 -67.995 29.896 1.00 0.00 H +ATOM 2361 HB3 ASN A 155 -36.593 -67.352 30.783 1.00 0.00 H +ATOM 2362 HD21 ASN A 155 -38.993 -64.856 31.642 1.00 0.00 H +ATOM 2363 HD22 ASN A 155 -38.339 -66.386 32.124 1.00 0.00 H +ATOM 2364 N LEU A 156 -34.711 -65.935 27.558 1.00 0.00 N +ATOM 2365 CA LEU A 156 -33.481 -65.158 27.468 1.00 0.00 C +ATOM 2366 C LEU A 156 -33.276 -64.719 26.026 1.00 0.00 C +ATOM 2367 O LEU A 156 -33.540 -65.485 25.099 1.00 0.00 O +ATOM 2368 CB LEU A 156 -32.276 -65.985 27.952 1.00 0.00 C +ATOM 2369 CG LEU A 156 -30.831 -65.462 28.002 1.00 0.00 C +ATOM 2370 CD1 LEU A 156 -29.990 -66.378 28.883 1.00 0.00 C +ATOM 2371 CD2 LEU A 156 -30.175 -65.345 26.629 1.00 0.00 C +ATOM 2372 H LEU A 156 -35.128 -66.281 26.718 1.00 0.00 H +ATOM 2373 HA LEU A 156 -33.565 -64.262 28.101 1.00 0.00 H +ATOM 2374 HB2 LEU A 156 -32.518 -66.288 28.982 1.00 0.00 H +ATOM 2375 HB3 LEU A 156 -32.248 -66.874 27.306 1.00 0.00 H +ATOM 2376 HG LEU A 156 -30.846 -64.463 28.461 1.00 0.00 H +ATOM 2377 HD11 LEU A 156 -28.956 -66.005 28.919 1.00 0.00 H +ATOM 2378 HD12 LEU A 156 -30.410 -66.395 29.900 1.00 0.00 H +ATOM 2379 HD13 LEU A 156 -29.998 -67.396 28.466 1.00 0.00 H +ATOM 2380 HD21 LEU A 156 -29.149 -64.967 26.744 1.00 0.00 H +ATOM 2381 HD22 LEU A 156 -30.149 -66.335 26.149 1.00 0.00 H +ATOM 2382 HD23 LEU A 156 -30.754 -64.649 26.004 1.00 0.00 H +ATOM 2383 N PHE A 157 -32.802 -63.489 25.844 1.00 0.00 N +ATOM 2384 CA PHE A 157 -32.287 -63.057 24.553 1.00 0.00 C +ATOM 2385 C PHE A 157 -31.067 -62.178 24.787 1.00 0.00 C +ATOM 2386 O PHE A 157 -30.841 -61.671 25.886 1.00 0.00 O +ATOM 2387 CB PHE A 157 -33.349 -62.327 23.703 1.00 0.00 C +ATOM 2388 CG PHE A 157 -33.805 -61.009 24.270 1.00 0.00 C +ATOM 2389 CD1 PHE A 157 -34.873 -60.951 25.149 1.00 0.00 C +ATOM 2390 CD2 PHE A 157 -33.186 -59.825 23.900 1.00 0.00 C +ATOM 2391 CE1 PHE A 157 -35.303 -59.742 25.664 1.00 0.00 C +ATOM 2392 CE2 PHE A 157 -33.609 -58.615 24.413 1.00 0.00 C +ATOM 2393 CZ PHE A 157 -34.669 -58.573 25.296 1.00 0.00 C +ATOM 2394 H PHE A 157 -32.800 -62.847 26.611 1.00 0.00 H +ATOM 2395 HA PHE A 157 -31.961 -63.943 23.989 1.00 0.00 H +ATOM 2396 HB2 PHE A 157 -32.923 -62.143 22.705 1.00 0.00 H +ATOM 2397 HB3 PHE A 157 -34.227 -62.984 23.612 1.00 0.00 H +ATOM 2398 HD1 PHE A 157 -35.377 -61.862 25.436 1.00 0.00 H +ATOM 2399 HD2 PHE A 157 -32.362 -59.849 23.202 1.00 0.00 H +ATOM 2400 HE1 PHE A 157 -36.134 -59.713 26.353 1.00 0.00 H +ATOM 2401 HE2 PHE A 157 -33.111 -57.702 24.124 1.00 0.00 H +ATOM 2402 HZ PHE A 157 -35.001 -57.627 25.698 1.00 0.00 H +ATOM 2403 N SER A 158 -30.268 -62.021 23.738 1.00 0.00 N +ATOM 2404 CA SER A 158 -29.027 -61.270 23.822 1.00 0.00 C +ATOM 2405 C SER A 158 -28.860 -60.442 22.558 1.00 0.00 C +ATOM 2406 O SER A 158 -29.254 -60.861 21.468 1.00 0.00 O +ATOM 2407 CB SER A 158 -27.823 -62.201 24.022 1.00 0.00 C +ATOM 2408 OG SER A 158 -27.693 -63.104 22.939 1.00 0.00 O +ATOM 2409 H SER A 158 -30.528 -62.432 22.864 1.00 0.00 H +ATOM 2410 HA SER A 158 -29.076 -60.584 24.681 1.00 0.00 H +ATOM 2411 HB2 SER A 158 -26.908 -61.595 24.096 1.00 0.00 H +ATOM 2412 HB3 SER A 158 -27.961 -62.772 24.952 1.00 0.00 H +ATOM 2413 HG SER A 158 -28.050 -63.949 23.186 1.00 0.00 H +ATOM 2414 N LEU A 159 -28.270 -59.259 22.717 1.00 0.00 N +ATOM 2415 CA LEU A 159 -28.136 -58.292 21.635 1.00 0.00 C +ATOM 2416 C LEU A 159 -26.680 -57.896 21.451 1.00 0.00 C +ATOM 2417 O LEU A 159 -26.016 -57.496 22.412 1.00 0.00 O +ATOM 2418 CB LEU A 159 -28.973 -57.042 21.912 1.00 0.00 C +ATOM 2419 CG LEU A 159 -30.487 -57.230 21.871 1.00 0.00 C +ATOM 2420 CD1 LEU A 159 -31.194 -55.891 21.973 1.00 0.00 C +ATOM 2421 CD2 LEU A 159 -30.859 -57.935 20.590 1.00 0.00 C +ATOM 2422 H LEU A 159 -27.903 -59.024 23.617 1.00 0.00 H +ATOM 2423 HA LEU A 159 -28.493 -58.745 20.698 1.00 0.00 H +ATOM 2424 HB2 LEU A 159 -28.706 -56.673 22.913 1.00 0.00 H +ATOM 2425 HB3 LEU A 159 -28.707 -56.285 21.160 1.00 0.00 H +ATOM 2426 HG LEU A 159 -30.788 -57.856 22.724 1.00 0.00 H +ATOM 2427 HD11 LEU A 159 -32.282 -56.047 21.942 1.00 0.00 H +ATOM 2428 HD12 LEU A 159 -30.921 -55.403 22.920 1.00 0.00 H +ATOM 2429 HD13 LEU A 159 -30.891 -55.251 21.131 1.00 0.00 H +ATOM 2430 HD21 LEU A 159 -31.949 -58.075 20.551 1.00 0.00 H +ATOM 2431 HD22 LEU A 159 -30.537 -57.328 19.731 1.00 0.00 H +ATOM 2432 HD23 LEU A 159 -30.362 -58.916 20.554 1.00 0.00 H +ATOM 2433 N GLN A 160 -26.196 -57.992 20.215 1.00 0.00 N +ATOM 2434 CA GLN A 160 -24.906 -57.442 19.814 1.00 0.00 C +ATOM 2435 C GLN A 160 -25.162 -56.416 18.716 1.00 0.00 C +ATOM 2436 O GLN A 160 -25.475 -56.788 17.582 1.00 0.00 O +ATOM 2437 CB GLN A 160 -23.967 -58.547 19.329 1.00 0.00 C +ATOM 2438 CG GLN A 160 -22.534 -58.095 19.074 1.00 0.00 C +ATOM 2439 CD GLN A 160 -21.837 -58.927 18.011 1.00 0.00 C +ATOM 2440 OE1 GLN A 160 -22.424 -59.244 16.974 1.00 0.00 O +ATOM 2441 NE2 GLN A 160 -20.583 -59.288 18.264 1.00 0.00 N +ATOM 2442 H GLN A 160 -26.745 -58.465 19.526 1.00 0.00 H +ATOM 2443 HA GLN A 160 -24.439 -56.934 20.670 1.00 0.00 H +ATOM 2444 HB2 GLN A 160 -23.946 -59.339 20.092 1.00 0.00 H +ATOM 2445 HB3 GLN A 160 -24.371 -58.953 18.390 1.00 0.00 H +ATOM 2446 HG2 GLN A 160 -22.549 -57.045 18.745 1.00 0.00 H +ATOM 2447 HG3 GLN A 160 -21.967 -58.177 20.013 1.00 0.00 H +ATOM 2448 HE21 GLN A 160 -20.078 -59.839 17.600 1.00 0.00 H +ATOM 2449 HE22 GLN A 160 -20.145 -59.009 19.118 1.00 0.00 H +ATOM 2450 N LEU A 161 -25.056 -55.130 19.054 1.00 0.00 N +ATOM 2451 CA LEU A 161 -25.202 -54.044 18.089 1.00 0.00 C +ATOM 2452 C LEU A 161 -23.815 -53.524 17.732 1.00 0.00 C +ATOM 2453 O LEU A 161 -23.065 -53.099 18.618 1.00 0.00 O +ATOM 2454 CB LEU A 161 -26.068 -52.917 18.651 1.00 0.00 C +ATOM 2455 CG LEU A 161 -27.347 -53.318 19.390 1.00 0.00 C +ATOM 2456 CD1 LEU A 161 -28.003 -52.098 20.013 1.00 0.00 C +ATOM 2457 CD2 LEU A 161 -28.306 -54.036 18.452 1.00 0.00 C +ATOM 2458 H LEU A 161 -24.869 -54.900 20.009 1.00 0.00 H +ATOM 2459 HA LEU A 161 -25.678 -54.428 17.175 1.00 0.00 H +ATOM 2460 HB2 LEU A 161 -25.447 -52.341 19.353 1.00 0.00 H +ATOM 2461 HB3 LEU A 161 -26.361 -52.273 17.808 1.00 0.00 H +ATOM 2462 HG LEU A 161 -27.072 -54.012 20.198 1.00 0.00 H +ATOM 2463 HD11 LEU A 161 -28.920 -52.403 20.540 1.00 0.00 H +ATOM 2464 HD12 LEU A 161 -27.308 -51.632 20.727 1.00 0.00 H +ATOM 2465 HD13 LEU A 161 -28.257 -51.375 19.224 1.00 0.00 H +ATOM 2466 HD21 LEU A 161 -29.217 -54.315 19.002 1.00 0.00 H +ATOM 2467 HD22 LEU A 161 -28.571 -53.369 17.618 1.00 0.00 H +ATOM 2468 HD23 LEU A 161 -27.823 -54.943 18.058 1.00 0.00 H +ATOM 2469 N CYS A 162 -23.483 -53.538 16.439 1.00 0.00 N +ATOM 2470 CA CYS A 162 -22.088 -53.365 16.048 1.00 0.00 C +ATOM 2471 C CYS A 162 -21.714 -51.919 15.735 1.00 0.00 C +ATOM 2472 O CYS A 162 -20.612 -51.480 16.084 1.00 0.00 O +ATOM 2473 CB CYS A 162 -21.769 -54.268 14.855 1.00 0.00 C +ATOM 2474 SG CYS A 162 -21.705 -56.018 15.318 1.00 0.00 S +ATOM 2475 H CYS A 162 -24.188 -53.667 15.741 1.00 0.00 H +ATOM 2476 HA CYS A 162 -21.454 -53.693 16.885 1.00 0.00 H +ATOM 2477 HB2 CYS A 162 -22.548 -54.132 14.090 1.00 0.00 H +ATOM 2478 HB3 CYS A 162 -20.793 -53.975 14.441 1.00 0.00 H +ATOM 2479 N GLY A 163 -22.600 -51.200 14.978 1.00 0.00 N +ATOM 2480 CA GLY A 163 -22.309 -49.806 14.721 1.00 0.00 C +ATOM 2481 C GLY A 163 -21.360 -49.613 13.554 1.00 0.00 C +ATOM 2482 O GLY A 163 -20.842 -50.518 12.890 1.00 0.00 O +ATOM 2483 H GLY A 163 -23.427 -51.627 14.612 1.00 0.00 H +ATOM 2484 HA2 GLY A 163 -23.252 -49.285 14.498 1.00 0.00 H +ATOM 2485 HA3 GLY A 163 -21.854 -49.369 15.622 1.00 0.00 H +ATOM 2486 N ALA A 164 -21.167 -48.263 13.319 1.00 0.00 N +ATOM 2487 CA ALA A 164 -20.290 -47.810 12.287 1.00 0.00 C +ATOM 2488 C ALA A 164 -18.892 -47.348 12.625 1.00 0.00 C +ATOM 2489 O ALA A 164 -18.589 -46.871 13.721 1.00 0.00 O +ATOM 2490 CB ALA A 164 -20.809 -47.768 10.878 1.00 0.00 C +ATOM 2491 H ALA A 164 -21.650 -47.593 13.882 1.00 0.00 H +ATOM 2492 HA ALA A 164 -19.924 -48.835 12.132 1.00 0.00 H +ATOM 2493 HB1 ALA A 164 -20.020 -47.399 10.206 1.00 0.00 H +ATOM 2494 HB2 ALA A 164 -21.111 -48.780 10.568 1.00 0.00 H +ATOM 2495 HB3 ALA A 164 -21.678 -47.095 10.827 1.00 0.00 H +ATOM 2496 N GLY A 165 -18.004 -47.471 11.573 1.00 0.00 N +ATOM 2497 CA GLY A 165 -16.651 -47.002 11.779 1.00 0.00 C +ATOM 2498 C GLY A 165 -15.859 -47.086 10.494 1.00 0.00 C +ATOM 2499 O GLY A 165 -16.088 -47.853 9.559 1.00 0.00 O +ATOM 2500 H GLY A 165 -18.287 -47.870 10.701 1.00 0.00 H +ATOM 2501 HA2 GLY A 165 -16.678 -45.956 12.119 1.00 0.00 H +ATOM 2502 HA3 GLY A 165 -16.165 -47.624 12.545 1.00 0.00 H +ATOM 2503 N PHE A 166 -14.780 -46.223 10.547 1.00 0.00 N +ATOM 2504 CA PHE A 166 -13.845 -46.160 9.462 1.00 0.00 C +ATOM 2505 C PHE A 166 -13.012 -47.376 9.114 1.00 0.00 C +ATOM 2506 O PHE A 166 -12.491 -48.066 10.002 1.00 0.00 O +ATOM 2507 CB PHE A 166 -13.392 -44.808 8.985 1.00 0.00 C +ATOM 2508 CG PHE A 166 -14.375 -44.007 8.200 1.00 0.00 C +ATOM 2509 CD1 PHE A 166 -14.043 -43.603 6.909 1.00 0.00 C +ATOM 2510 CD2 PHE A 166 -15.582 -43.598 8.762 1.00 0.00 C +ATOM 2511 CE1 PHE A 166 -14.939 -42.833 6.167 1.00 0.00 C +ATOM 2512 CE2 PHE A 166 -16.472 -42.818 8.033 1.00 0.00 C +ATOM 2513 CZ PHE A 166 -16.156 -42.442 6.731 1.00 0.00 C +ATOM 2514 H PHE A 166 -14.650 -45.640 11.348 1.00 0.00 H +ATOM 2515 HA PHE A 166 -14.589 -46.310 8.665 1.00 0.00 H +ATOM 2516 HB2 PHE A 166 -13.115 -44.218 9.872 1.00 0.00 H +ATOM 2517 HB3 PHE A 166 -12.505 -44.958 8.352 1.00 0.00 H +ATOM 2518 HD1 PHE A 166 -13.093 -43.887 6.481 1.00 0.00 H +ATOM 2519 HD2 PHE A 166 -15.828 -43.889 9.773 1.00 0.00 H +ATOM 2520 HE1 PHE A 166 -14.693 -42.540 5.157 1.00 0.00 H +ATOM 2521 HE2 PHE A 166 -17.407 -42.506 8.475 1.00 0.00 H +ATOM 2522 HZ PHE A 166 -16.850 -41.847 6.157 1.00 0.00 H +ATOM 2523 N PRO A 167 -12.799 -47.662 7.792 1.00 0.00 N +ATOM 2524 CA PRO A 167 -13.424 -46.994 6.664 1.00 0.00 C +ATOM 2525 C PRO A 167 -14.860 -47.414 6.369 1.00 0.00 C +ATOM 2526 O PRO A 167 -15.307 -48.543 6.564 1.00 0.00 O +ATOM 2527 CB PRO A 167 -12.546 -47.356 5.473 1.00 0.00 C +ATOM 2528 CG PRO A 167 -11.932 -48.697 5.861 1.00 0.00 C +ATOM 2529 CD PRO A 167 -11.864 -48.683 7.374 1.00 0.00 C +ATOM 2530 HA PRO A 167 -13.394 -45.904 6.811 1.00 0.00 H +ATOM 2531 HB2 PRO A 167 -13.149 -47.452 4.558 1.00 0.00 H +ATOM 2532 HB3 PRO A 167 -11.764 -46.598 5.319 1.00 0.00 H +ATOM 2533 HG2 PRO A 167 -12.565 -49.526 5.512 1.00 0.00 H +ATOM 2534 HG3 PRO A 167 -10.924 -48.799 5.432 1.00 0.00 H +ATOM 2535 HD2 PRO A 167 -10.847 -48.434 7.711 1.00 0.00 H +ATOM 2536 HD3 PRO A 167 -12.156 -49.662 7.781 1.00 0.00 H +ATOM 2537 N LEU A 168 -15.563 -46.374 5.799 1.00 0.00 N +ATOM 2538 CA LEU A 168 -16.890 -46.574 5.313 1.00 0.00 C +ATOM 2539 C LEU A 168 -17.116 -47.210 3.975 1.00 0.00 C +ATOM 2540 O LEU A 168 -16.362 -47.029 3.007 1.00 0.00 O +ATOM 2541 CB LEU A 168 -18.027 -45.924 6.034 1.00 0.00 C +ATOM 2542 CG LEU A 168 -18.361 -46.571 7.334 1.00 0.00 C +ATOM 2543 CD1 LEU A 168 -18.484 -45.716 8.554 1.00 0.00 C +ATOM 2544 CD2 LEU A 168 -18.632 -48.041 7.438 1.00 0.00 C +ATOM 2545 H LEU A 168 -15.139 -45.472 5.724 1.00 0.00 H +ATOM 2546 HA LEU A 168 -17.012 -47.525 5.853 1.00 0.00 H +ATOM 2547 HB2 LEU A 168 -17.760 -44.874 6.226 1.00 0.00 H +ATOM 2548 HB3 LEU A 168 -18.916 -45.964 5.387 1.00 0.00 H +ATOM 2549 HG LEU A 168 -17.294 -46.738 7.542 1.00 0.00 H +ATOM 2550 HD11 LEU A 168 -18.734 -46.347 9.421 1.00 0.00 H +ATOM 2551 HD12 LEU A 168 -17.530 -45.201 8.739 1.00 0.00 H +ATOM 2552 HD13 LEU A 168 -19.280 -44.972 8.402 1.00 0.00 H +ATOM 2553 HD21 LEU A 168 -18.856 -48.301 8.483 1.00 0.00 H +ATOM 2554 HD22 LEU A 168 -19.491 -48.302 6.803 1.00 0.00 H +ATOM 2555 HD23 LEU A 168 -17.746 -48.601 7.103 1.00 0.00 H +ATOM 2556 N ASN A 169 -18.283 -47.946 3.904 1.00 0.00 N +ATOM 2557 CA ASN A 169 -18.733 -48.393 2.627 1.00 0.00 C +ATOM 2558 C ASN A 169 -19.399 -47.410 1.702 1.00 0.00 C +ATOM 2559 O ASN A 169 -19.059 -47.215 0.531 1.00 0.00 O +ATOM 2560 CB ASN A 169 -18.628 -49.842 2.319 1.00 0.00 C +ATOM 2561 CG ASN A 169 -18.000 -50.068 0.978 1.00 0.00 C +ATOM 2562 OD1 ASN A 169 -16.774 -50.141 0.809 1.00 0.00 O +ATOM 2563 ND2 ASN A 169 -18.941 -50.225 -0.019 1.00 0.00 N +ATOM 2564 H ASN A 169 -18.802 -48.160 4.731 1.00 0.00 H +ATOM 2565 HA ASN A 169 -17.749 -48.195 2.177 1.00 0.00 H +ATOM 2566 HB2 ASN A 169 -18.012 -50.329 3.089 1.00 0.00 H +ATOM 2567 HB3 ASN A 169 -19.635 -50.284 2.322 1.00 0.00 H +ATOM 2568 HD21 ASN A 169 -18.649 -50.412 -0.957 1.00 0.00 H +ATOM 2569 HD22 ASN A 169 -19.915 -50.151 0.196 1.00 0.00 H +ATOM 2570 N GLN A 170 -20.470 -46.774 2.305 1.00 0.00 N +ATOM 2571 CA GLN A 170 -21.244 -45.820 1.575 1.00 0.00 C +ATOM 2572 C GLN A 170 -20.887 -44.359 1.641 1.00 0.00 C +ATOM 2573 O GLN A 170 -20.672 -43.760 2.695 1.00 0.00 O +ATOM 2574 CB GLN A 170 -22.472 -46.245 0.817 1.00 0.00 C +ATOM 2575 CG GLN A 170 -22.353 -47.575 0.105 1.00 0.00 C +ATOM 2576 CD GLN A 170 -22.719 -48.695 1.039 1.00 0.00 C +ATOM 2577 OE1 GLN A 170 -21.897 -49.535 1.408 1.00 0.00 O +ATOM 2578 NE2 GLN A 170 -24.039 -48.653 1.438 1.00 0.00 N +ATOM 2579 H GLN A 170 -20.703 -46.982 3.255 1.00 0.00 H +ATOM 2580 HA GLN A 170 -20.617 -45.958 0.682 1.00 0.00 H +ATOM 2581 HB2 GLN A 170 -23.306 -46.313 1.531 1.00 0.00 H +ATOM 2582 HB3 GLN A 170 -22.694 -45.473 0.066 1.00 0.00 H +ATOM 2583 HG2 GLN A 170 -23.032 -47.586 -0.761 1.00 0.00 H +ATOM 2584 HG3 GLN A 170 -21.317 -47.712 -0.240 1.00 0.00 H +ATOM 2585 HE21 GLN A 170 -24.391 -49.340 2.073 1.00 0.00 H +ATOM 2586 HE22 GLN A 170 -24.643 -47.935 1.092 1.00 0.00 H +ATOM 2587 N SER A 171 -20.870 -43.765 0.398 1.00 0.00 N +ATOM 2588 CA SER A 171 -20.811 -42.341 0.294 1.00 0.00 C +ATOM 2589 C SER A 171 -22.080 -41.540 0.309 1.00 0.00 C +ATOM 2590 O SER A 171 -22.314 -40.635 1.120 1.00 0.00 O +ATOM 2591 CB SER A 171 -19.497 -41.650 0.136 1.00 0.00 C +ATOM 2592 OG SER A 171 -19.046 -41.801 -1.214 1.00 0.00 O +ATOM 2593 H SER A 171 -20.898 -44.328 -0.429 1.00 0.00 H +ATOM 2594 HA SER A 171 -20.734 -42.256 1.388 1.00 0.00 H +ATOM 2595 HB2 SER A 171 -19.612 -40.581 0.368 1.00 0.00 H +ATOM 2596 HB3 SER A 171 -18.761 -42.097 0.821 1.00 0.00 H +ATOM 2597 HG SER A 171 -18.707 -40.972 -1.530 1.00 0.00 H +ATOM 2598 N GLU A 172 -22.936 -41.941 -0.688 1.00 0.00 N +ATOM 2599 CA GLU A 172 -24.225 -41.358 -0.851 1.00 0.00 C +ATOM 2600 C GLU A 172 -25.347 -41.603 0.146 1.00 0.00 C +ATOM 2601 O GLU A 172 -26.307 -40.845 0.308 1.00 0.00 O +ATOM 2602 CB GLU A 172 -24.486 -40.472 -2.032 1.00 0.00 C +ATOM 2603 CG GLU A 172 -23.588 -40.816 -3.202 1.00 0.00 C +ATOM 2604 CD GLU A 172 -23.933 -39.757 -4.199 1.00 0.00 C +ATOM 2605 OE1 GLU A 172 -23.211 -38.711 -4.203 1.00 0.00 O +ATOM 2606 OE2 GLU A 172 -24.926 -40.000 -4.963 1.00 0.00 O1- +ATOM 2607 H GLU A 172 -22.646 -42.663 -1.316 1.00 0.00 H +ATOM 2608 HA GLU A 172 -24.549 -42.233 -1.434 1.00 0.00 H +ATOM 2609 HB2 GLU A 172 -25.535 -40.591 -2.341 1.00 0.00 H +ATOM 2610 HB3 GLU A 172 -24.307 -39.426 -1.740 1.00 0.00 H +ATOM 2611 HG2 GLU A 172 -22.527 -40.762 -2.918 1.00 0.00 H +ATOM 2612 HG3 GLU A 172 -23.812 -41.819 -3.594 1.00 0.00 H +ATOM 2613 N VAL A 173 -25.216 -42.820 0.798 1.00 0.00 N +ATOM 2614 CA VAL A 173 -26.114 -43.202 1.844 1.00 0.00 C +ATOM 2615 C VAL A 173 -25.627 -43.123 3.261 1.00 0.00 C +ATOM 2616 O VAL A 173 -24.597 -42.517 3.606 1.00 0.00 O +ATOM 2617 CB VAL A 173 -27.448 -43.718 1.474 1.00 0.00 C +ATOM 2618 CG1 VAL A 173 -28.668 -42.852 1.405 1.00 0.00 C +ATOM 2619 CG2 VAL A 173 -27.582 -45.168 1.141 1.00 0.00 C +ATOM 2620 H VAL A 173 -24.480 -43.442 0.532 1.00 0.00 H +ATOM 2621 HA VAL A 173 -26.508 -42.177 1.905 1.00 0.00 H +ATOM 2622 HB VAL A 173 -27.660 -43.938 2.531 1.00 0.00 H +ATOM 2623 HG11 VAL A 173 -29.535 -43.462 1.109 1.00 0.00 H +ATOM 2624 HG12 VAL A 173 -28.855 -42.403 2.392 1.00 0.00 H +ATOM 2625 HG13 VAL A 173 -28.510 -42.055 0.664 1.00 0.00 H +ATOM 2626 HG21 VAL A 173 -28.629 -45.391 0.888 1.00 0.00 H +ATOM 2627 HG22 VAL A 173 -26.939 -45.407 0.281 1.00 0.00 H +ATOM 2628 HG23 VAL A 173 -27.277 -45.773 2.007 1.00 0.00 H +ATOM 2629 N LEU A 174 -26.404 -43.863 4.148 1.00 0.00 N +ATOM 2630 CA LEU A 174 -25.934 -44.091 5.482 1.00 0.00 C +ATOM 2631 C LEU A 174 -24.743 -44.993 5.722 1.00 0.00 C +ATOM 2632 O LEU A 174 -24.423 -45.904 4.948 1.00 0.00 O +ATOM 2633 CB LEU A 174 -26.769 -43.618 6.625 1.00 0.00 C +ATOM 2634 CG LEU A 174 -27.006 -42.134 6.616 1.00 0.00 C +ATOM 2635 CD1 LEU A 174 -28.406 -41.617 6.619 1.00 0.00 C +ATOM 2636 CD2 LEU A 174 -25.859 -41.177 6.626 1.00 0.00 C +ATOM 2637 H LEU A 174 -27.286 -44.234 3.855 1.00 0.00 H +ATOM 2638 HA LEU A 174 -25.316 -43.181 5.483 1.00 0.00 H +ATOM 2639 HB2 LEU A 174 -27.743 -44.127 6.580 1.00 0.00 H +ATOM 2640 HB3 LEU A 174 -26.260 -43.884 7.563 1.00 0.00 H +ATOM 2641 HG LEU A 174 -27.003 -42.135 5.516 1.00 0.00 H +ATOM 2642 HD11 LEU A 174 -28.392 -40.517 6.611 1.00 0.00 H +ATOM 2643 HD12 LEU A 174 -28.934 -41.982 5.726 1.00 0.00 H +ATOM 2644 HD13 LEU A 174 -28.926 -41.970 7.522 1.00 0.00 H +ATOM 2645 HD21 LEU A 174 -26.242 -40.145 6.617 1.00 0.00 H +ATOM 2646 HD22 LEU A 174 -25.256 -41.336 7.532 1.00 0.00 H +ATOM 2647 HD23 LEU A 174 -25.234 -41.344 5.736 1.00 0.00 H +ATOM 2648 N ALA A 175 -24.097 -44.754 6.915 1.00 0.00 N +ATOM 2649 CA ALA A 175 -23.115 -45.691 7.358 1.00 0.00 C +ATOM 2650 C ALA A 175 -23.587 -47.030 7.869 1.00 0.00 C +ATOM 2651 O ALA A 175 -24.581 -47.155 8.588 1.00 0.00 O +ATOM 2652 CB ALA A 175 -21.669 -45.324 7.285 1.00 0.00 C +ATOM 2653 H ALA A 175 -24.311 -43.942 7.458 1.00 0.00 H +ATOM 2654 HA ALA A 175 -23.173 -45.277 8.376 1.00 0.00 H +ATOM 2655 HB1 ALA A 175 -21.059 -46.157 7.665 1.00 0.00 H +ATOM 2656 HB2 ALA A 175 -21.396 -45.117 6.240 1.00 0.00 H +ATOM 2657 HB3 ALA A 175 -21.486 -44.428 7.896 1.00 0.00 H +ATOM 2658 N SER A 176 -22.820 -48.088 7.405 1.00 0.00 N +ATOM 2659 CA SER A 176 -23.369 -49.405 7.502 1.00 0.00 C +ATOM 2660 C SER A 176 -23.466 -50.128 8.812 1.00 0.00 C +ATOM 2661 O SER A 176 -22.491 -50.465 9.486 1.00 0.00 O +ATOM 2662 CB SER A 176 -24.021 -50.058 6.309 1.00 0.00 C +ATOM 2663 OG SER A 176 -23.423 -49.626 5.077 1.00 0.00 O +ATOM 2664 H SER A 176 -21.912 -47.935 7.016 1.00 0.00 H +ATOM 2665 HA SER A 176 -22.399 -49.870 7.274 1.00 0.00 H +ATOM 2666 HB2 SER A 176 -23.912 -51.149 6.395 1.00 0.00 H +ATOM 2667 HB3 SER A 176 -25.089 -49.796 6.298 1.00 0.00 H +ATOM 2668 HG SER A 176 -23.135 -48.725 5.162 1.00 0.00 H +ATOM 2669 N VAL A 177 -24.781 -50.324 9.194 1.00 0.00 N +ATOM 2670 CA VAL A 177 -25.105 -50.973 10.424 1.00 0.00 C +ATOM 2671 C VAL A 177 -25.312 -52.465 10.423 1.00 0.00 C +ATOM 2672 O VAL A 177 -25.808 -53.049 9.453 1.00 0.00 O +ATOM 2673 CB VAL A 177 -25.431 -50.111 11.582 1.00 0.00 C +ATOM 2674 CG1 VAL A 177 -24.381 -49.328 12.299 1.00 0.00 C +ATOM 2675 CG2 VAL A 177 -26.860 -49.934 11.994 1.00 0.00 C +ATOM 2676 H VAL A 177 -25.518 -50.005 8.598 1.00 0.00 H +ATOM 2677 HA VAL A 177 -24.050 -51.066 10.720 1.00 0.00 H +ATOM 2678 HB VAL A 177 -25.310 -50.956 12.276 1.00 0.00 H +ATOM 2679 HG11 VAL A 177 -24.842 -48.762 13.121 1.00 0.00 H +ATOM 2680 HG12 VAL A 177 -23.625 -50.016 12.706 1.00 0.00 H +ATOM 2681 HG13 VAL A 177 -23.901 -48.631 11.597 1.00 0.00 H +ATOM 2682 HG21 VAL A 177 -26.910 -49.265 12.866 1.00 0.00 H +ATOM 2683 HG22 VAL A 177 -27.430 -49.494 11.162 1.00 0.00 H +ATOM 2684 HG23 VAL A 177 -27.290 -50.911 12.257 1.00 0.00 H +ATOM 2685 N GLY A 178 -24.988 -53.109 11.600 1.00 0.00 N +ATOM 2686 CA GLY A 178 -25.434 -54.483 11.668 1.00 0.00 C +ATOM 2687 C GLY A 178 -25.291 -55.009 13.080 1.00 0.00 C +ATOM 2688 O GLY A 178 -24.871 -54.294 13.991 1.00 0.00 O +ATOM 2689 H GLY A 178 -24.488 -52.663 12.342 1.00 0.00 H +ATOM 2690 HA2 GLY A 178 -26.490 -54.538 11.365 1.00 0.00 H +ATOM 2691 HA3 GLY A 178 -24.825 -55.097 10.988 1.00 0.00 H +ATOM 2692 N GLY A 179 -25.650 -56.273 13.260 1.00 0.00 N +ATOM 2693 CA GLY A 179 -25.520 -56.896 14.566 1.00 0.00 C +ATOM 2694 C GLY A 179 -26.268 -58.219 14.629 1.00 0.00 C +ATOM 2695 O GLY A 179 -26.595 -58.811 13.605 1.00 0.00 O +ATOM 2696 H GLY A 179 -26.013 -56.801 12.492 1.00 0.00 H +ATOM 2697 HA2 GLY A 179 -24.455 -57.077 14.771 1.00 0.00 H +ATOM 2698 HA3 GLY A 179 -25.930 -56.217 15.328 1.00 0.00 H +ATOM 2699 N SER A 180 -26.523 -58.660 15.865 1.00 0.00 N +ATOM 2700 CA SER A 180 -27.141 -59.956 16.128 1.00 0.00 C +ATOM 2701 C SER A 180 -28.148 -59.854 17.263 1.00 0.00 C +ATOM 2702 O SER A 180 -27.876 -59.219 18.287 1.00 0.00 O +ATOM 2703 CB SER A 180 -26.093 -61.015 16.492 1.00 0.00 C +ATOM 2704 OG SER A 180 -25.216 -61.272 15.422 1.00 0.00 O +ATOM 2705 H SER A 180 -26.280 -58.078 16.641 1.00 0.00 H +ATOM 2706 HA SER A 180 -27.670 -60.293 15.225 1.00 0.00 H +ATOM 2707 HB2 SER A 180 -25.509 -60.657 17.353 1.00 0.00 H +ATOM 2708 HB3 SER A 180 -26.609 -61.948 16.762 1.00 0.00 H +ATOM 2709 HG SER A 180 -24.369 -60.882 15.605 1.00 0.00 H +ATOM 2710 N MET A 181 -29.298 -60.507 17.086 1.00 0.00 N +ATOM 2711 CA MET A 181 -30.249 -60.762 18.164 1.00 0.00 C +ATOM 2712 C MET A 181 -30.375 -62.272 18.321 1.00 0.00 C +ATOM 2713 O MET A 181 -30.994 -62.937 17.485 1.00 0.00 O +ATOM 2714 CB MET A 181 -31.611 -60.133 17.880 1.00 0.00 C +ATOM 2715 CG MET A 181 -32.661 -60.528 18.919 1.00 0.00 C +ATOM 2716 SD MET A 181 -34.241 -59.675 18.777 1.00 0.00 S +ATOM 2717 CE MET A 181 -34.751 -60.166 17.135 1.00 0.00 C +ATOM 2718 H MET A 181 -29.520 -60.838 16.169 1.00 0.00 H +ATOM 2719 HA MET A 181 -29.859 -60.343 19.103 1.00 0.00 H +ATOM 2720 HB2 MET A 181 -31.503 -59.038 17.885 1.00 0.00 H +ATOM 2721 HB3 MET A 181 -31.953 -60.463 16.888 1.00 0.00 H +ATOM 2722 HG2 MET A 181 -32.847 -61.608 18.821 1.00 0.00 H +ATOM 2723 HG3 MET A 181 -32.249 -60.316 19.917 1.00 0.00 H +ATOM 2724 HE1 MET A 181 -35.849 -60.164 17.076 1.00 0.00 H +ATOM 2725 HE2 MET A 181 -34.343 -59.459 16.398 1.00 0.00 H +ATOM 2726 HE3 MET A 181 -34.374 -61.177 16.921 1.00 0.00 H +ATOM 2727 N ILE A 182 -29.782 -62.812 19.379 1.00 0.00 N +ATOM 2728 CA ILE A 182 -29.830 -64.245 19.638 1.00 0.00 C +ATOM 2729 C ILE A 182 -31.053 -64.514 20.501 1.00 0.00 C +ATOM 2730 O ILE A 182 -31.092 -64.144 21.677 1.00 0.00 O +ATOM 2731 CB ILE A 182 -28.551 -64.747 20.315 1.00 0.00 C +ATOM 2732 CG1 ILE A 182 -27.321 -64.255 19.556 1.00 0.00 C +ATOM 2733 CG2 ILE A 182 -28.561 -66.270 20.396 1.00 0.00 C +ATOM 2734 CD1 ILE A 182 -27.263 -64.732 18.125 1.00 0.00 C +ATOM 2735 H ILE A 182 -29.289 -62.219 20.016 1.00 0.00 H +ATOM 2736 HA ILE A 182 -29.951 -64.782 18.686 1.00 0.00 H +ATOM 2737 HB ILE A 182 -28.516 -64.342 21.337 1.00 0.00 H +ATOM 2738 HG12 ILE A 182 -27.330 -63.155 19.556 1.00 0.00 H +ATOM 2739 HG13 ILE A 182 -26.423 -64.615 20.079 1.00 0.00 H +ATOM 2740 HG21 ILE A 182 -27.638 -66.618 20.884 1.00 0.00 H +ATOM 2741 HG22 ILE A 182 -29.432 -66.600 20.981 1.00 0.00 H +ATOM 2742 HG23 ILE A 182 -28.620 -66.691 19.381 1.00 0.00 H +ATOM 2743 HD11 ILE A 182 -26.355 -64.340 17.644 1.00 0.00 H +ATOM 2744 HD12 ILE A 182 -27.243 -65.832 18.106 1.00 0.00 H +ATOM 2745 HD13 ILE A 182 -28.150 -64.372 17.582 1.00 0.00 H +ATOM 2746 N ILE A 183 -32.059 -65.148 19.916 1.00 0.00 N +ATOM 2747 CA ILE A 183 -33.278 -65.476 20.644 1.00 0.00 C +ATOM 2748 C ILE A 183 -33.074 -66.803 21.355 1.00 0.00 C +ATOM 2749 O ILE A 183 -32.809 -67.830 20.716 1.00 0.00 O +ATOM 2750 CB ILE A 183 -34.489 -65.533 19.704 1.00 0.00 C +ATOM 2751 CG1 ILE A 183 -34.753 -64.159 19.084 1.00 0.00 C +ATOM 2752 CG2 ILE A 183 -35.706 -66.040 20.453 1.00 0.00 C +ATOM 2753 CD1 ILE A 183 -35.349 -63.160 20.052 1.00 0.00 C +ATOM 2754 H ILE A 183 -31.980 -65.407 18.953 1.00 0.00 H +ATOM 2755 HA ILE A 183 -33.467 -64.701 21.402 1.00 0.00 H +ATOM 2756 HB ILE A 183 -34.261 -66.239 18.892 1.00 0.00 H +ATOM 2757 HG12 ILE A 183 -33.799 -63.756 18.713 1.00 0.00 H +ATOM 2758 HG13 ILE A 183 -35.450 -64.286 18.243 1.00 0.00 H +ATOM 2759 HG21 ILE A 183 -36.568 -66.077 19.771 1.00 0.00 H +ATOM 2760 HG22 ILE A 183 -35.503 -67.049 20.842 1.00 0.00 H +ATOM 2761 HG23 ILE A 183 -35.930 -65.362 21.290 1.00 0.00 H +ATOM 2762 HD11 ILE A 183 -35.510 -62.201 19.538 1.00 0.00 H +ATOM 2763 HD12 ILE A 183 -36.311 -63.541 20.426 1.00 0.00 H +ATOM 2764 HD13 ILE A 183 -34.660 -63.012 20.896 1.00 0.00 H +ATOM 2765 N GLY A 184 -33.183 -66.789 22.677 1.00 0.00 N +ATOM 2766 CA GLY A 184 -33.095 -67.999 23.460 1.00 0.00 C +ATOM 2767 C GLY A 184 -31.705 -68.444 23.848 1.00 0.00 C +ATOM 2768 O GLY A 184 -31.552 -69.575 24.322 1.00 0.00 O +ATOM 2769 H GLY A 184 -33.330 -65.917 23.144 1.00 0.00 H +ATOM 2770 HA2 GLY A 184 -33.667 -67.842 24.386 1.00 0.00 H +ATOM 2771 HA3 GLY A 184 -33.556 -68.810 22.877 1.00 0.00 H +ATOM 2772 N GLY A 185 -30.686 -67.607 23.675 1.00 0.00 N +ATOM 2773 CA GLY A 185 -29.368 -68.096 24.025 1.00 0.00 C +ATOM 2774 C GLY A 185 -28.309 -67.018 24.079 1.00 0.00 C +ATOM 2775 O GLY A 185 -28.577 -65.831 23.884 1.00 0.00 O +ATOM 2776 H GLY A 185 -30.823 -66.682 23.320 1.00 0.00 H +ATOM 2777 HA2 GLY A 185 -29.427 -68.573 25.014 1.00 0.00 H +ATOM 2778 HA3 GLY A 185 -29.065 -68.842 23.275 1.00 0.00 H +ATOM 2779 N ILE A 186 -27.089 -67.470 24.359 1.00 0.00 N +ATOM 2780 CA ILE A 186 -25.893 -66.639 24.405 1.00 0.00 C +ATOM 2781 C ILE A 186 -24.901 -67.254 23.426 1.00 0.00 C +ATOM 2782 O ILE A 186 -24.471 -68.400 23.614 1.00 0.00 O +ATOM 2783 CB ILE A 186 -25.289 -66.567 25.816 1.00 0.00 C +ATOM 2784 CG1 ILE A 186 -26.330 -66.103 26.839 1.00 0.00 C +ATOM 2785 CG2 ILE A 186 -24.067 -65.660 25.829 1.00 0.00 C +ATOM 2786 CD1 ILE A 186 -25.827 -66.137 28.272 1.00 0.00 C +ATOM 2787 H ILE A 186 -26.986 -68.446 24.551 1.00 0.00 H +ATOM 2788 HA ILE A 186 -26.135 -65.619 24.072 1.00 0.00 H +ATOM 2789 HB ILE A 186 -24.965 -67.580 26.097 1.00 0.00 H +ATOM 2790 HG12 ILE A 186 -26.621 -65.070 26.596 1.00 0.00 H +ATOM 2791 HG13 ILE A 186 -27.210 -66.759 26.763 1.00 0.00 H +ATOM 2792 HG21 ILE A 186 -23.649 -65.621 26.846 1.00 0.00 H +ATOM 2793 HG22 ILE A 186 -23.309 -66.056 25.136 1.00 0.00 H +ATOM 2794 HG23 ILE A 186 -24.359 -64.647 25.514 1.00 0.00 H +ATOM 2795 HD11 ILE A 186 -26.623 -65.793 28.949 1.00 0.00 H +ATOM 2796 HD12 ILE A 186 -25.541 -67.166 28.536 1.00 0.00 H +ATOM 2797 HD13 ILE A 186 -24.952 -65.477 28.369 1.00 0.00 H +ATOM 2798 N ASP A 187 -24.537 -66.509 22.385 1.00 0.00 N +ATOM 2799 CA ASP A 187 -23.600 -67.004 21.383 1.00 0.00 C +ATOM 2800 C ASP A 187 -22.177 -66.664 21.809 1.00 0.00 C +ATOM 2801 O ASP A 187 -21.836 -65.488 21.980 1.00 0.00 O +ATOM 2802 CB ASP A 187 -23.894 -66.423 20.002 1.00 0.00 C +ATOM 2803 CG ASP A 187 -23.089 -67.105 18.907 1.00 0.00 C +ATOM 2804 OD1 ASP A 187 -23.665 -67.920 18.155 1.00 0.00 O +ATOM 2805 OD2 ASP A 187 -21.870 -66.847 18.812 1.00 0.00 O1- +ATOM 2806 H ASP A 187 -24.915 -65.588 22.288 1.00 0.00 H +ATOM 2807 HA ASP A 187 -23.687 -68.099 21.320 1.00 0.00 H +ATOM 2808 HB2 ASP A 187 -24.965 -66.550 19.785 1.00 0.00 H +ATOM 2809 HB3 ASP A 187 -23.646 -65.351 20.009 1.00 0.00 H +ATOM 2810 N HIS A 188 -21.343 -67.698 21.939 1.00 0.00 N +ATOM 2811 CA HIS A 188 -20.007 -67.523 22.498 1.00 0.00 C +ATOM 2812 C HIS A 188 -19.095 -66.734 21.570 1.00 0.00 C +ATOM 2813 O HIS A 188 -18.102 -66.157 22.024 1.00 0.00 O +ATOM 2814 CB HIS A 188 -19.395 -68.892 22.798 1.00 0.00 C +ATOM 2815 CG HIS A 188 -18.548 -68.923 24.031 1.00 0.00 C +ATOM 2816 ND1 HIS A 188 -18.765 -68.535 25.336 1.00 0.00 N +ATOM 2817 CD2 HIS A 188 -17.289 -69.419 23.990 1.00 0.00 C +ATOM 2818 CE1 HIS A 188 -17.685 -68.788 26.054 1.00 0.00 C +ATOM 2819 NE2 HIS A 188 -16.775 -69.324 25.261 1.00 0.00 N +ATOM 2820 H HIS A 188 -21.639 -68.608 21.648 1.00 0.00 H +ATOM 2821 HA HIS A 188 -20.088 -66.972 23.447 1.00 0.00 H +ATOM 2822 HB2 HIS A 188 -20.213 -69.617 22.922 1.00 0.00 H +ATOM 2823 HB3 HIS A 188 -18.770 -69.187 21.942 1.00 0.00 H +ATOM 2824 HD1 HIS A 188 -19.608 -68.125 25.685 1.00 0.00 H +ATOM 2825 HD2 HIS A 188 -16.783 -69.814 23.122 1.00 0.00 H +ATOM 2826 HE1 HIS A 188 -17.567 -68.590 27.109 1.00 0.00 H +ATOM 2827 N SER A 189 -19.402 -66.700 20.276 1.00 0.00 N +ATOM 2828 CA SER A 189 -18.508 -66.060 19.321 1.00 0.00 C +ATOM 2829 C SER A 189 -18.690 -64.551 19.243 1.00 0.00 C +ATOM 2830 O SER A 189 -17.899 -63.887 18.566 1.00 0.00 O +ATOM 2831 CB SER A 189 -18.707 -66.662 17.932 1.00 0.00 C +ATOM 2832 OG SER A 189 -19.924 -66.210 17.363 1.00 0.00 O +ATOM 2833 H SER A 189 -20.253 -67.117 19.957 1.00 0.00 H +ATOM 2834 HA SER A 189 -17.470 -66.258 19.626 1.00 0.00 H +ATOM 2835 HB2 SER A 189 -17.871 -66.361 17.284 1.00 0.00 H +ATOM 2836 HB3 SER A 189 -18.731 -67.759 18.013 1.00 0.00 H +ATOM 2837 HG SER A 189 -20.649 -66.460 17.924 1.00 0.00 H +ATOM 2838 N LEU A 190 -19.695 -63.999 19.914 1.00 0.00 N +ATOM 2839 CA LEU A 190 -19.984 -62.573 19.832 1.00 0.00 C +ATOM 2840 C LEU A 190 -19.358 -61.758 20.958 1.00 0.00 C +ATOM 2841 O LEU A 190 -19.558 -60.540 20.999 1.00 0.00 O +ATOM 2842 CB LEU A 190 -21.501 -62.341 19.810 1.00 0.00 C +ATOM 2843 CG LEU A 190 -22.290 -63.130 18.762 1.00 0.00 C +ATOM 2844 CD1 LEU A 190 -23.763 -62.747 18.805 1.00 0.00 C +ATOM 2845 CD2 LEU A 190 -21.707 -62.907 17.377 1.00 0.00 C +ATOM 2846 H LEU A 190 -20.270 -64.579 20.492 1.00 0.00 H +ATOM 2847 HA LEU A 190 -19.579 -62.192 18.883 1.00 0.00 H +ATOM 2848 HB2 LEU A 190 -21.896 -62.610 20.801 1.00 0.00 H +ATOM 2849 HB3 LEU A 190 -21.674 -61.271 19.625 1.00 0.00 H +ATOM 2850 HG LEU A 190 -22.205 -64.200 19.004 1.00 0.00 H +ATOM 2851 HD11 LEU A 190 -24.314 -63.322 18.047 1.00 0.00 H +ATOM 2852 HD12 LEU A 190 -24.171 -62.970 19.802 1.00 0.00 H +ATOM 2853 HD13 LEU A 190 -23.868 -61.672 18.597 1.00 0.00 H +ATOM 2854 HD21 LEU A 190 -22.286 -63.481 16.638 1.00 0.00 H +ATOM 2855 HD22 LEU A 190 -21.753 -61.837 17.128 1.00 0.00 H +ATOM 2856 HD23 LEU A 190 -20.659 -63.242 17.362 1.00 0.00 H +ATOM 2857 N TYR A 191 -18.604 -62.378 21.864 1.00 0.00 N +ATOM 2858 CA TYR A 191 -17.946 -61.632 22.929 1.00 0.00 C +ATOM 2859 C TYR A 191 -16.613 -62.276 23.288 1.00 0.00 C +ATOM 2860 O TYR A 191 -16.322 -63.414 22.917 1.00 0.00 O +ATOM 2861 CB TYR A 191 -18.840 -61.504 24.178 1.00 0.00 C +ATOM 2862 CG TYR A 191 -19.075 -62.777 24.970 1.00 0.00 C +ATOM 2863 CD1 TYR A 191 -19.981 -63.738 24.535 1.00 0.00 C +ATOM 2864 CD2 TYR A 191 -18.423 -62.997 26.177 1.00 0.00 C +ATOM 2865 CE1 TYR A 191 -20.211 -64.892 25.266 1.00 0.00 C +ATOM 2866 CE2 TYR A 191 -18.648 -64.149 26.918 1.00 0.00 C +ATOM 2867 CZ TYR A 191 -19.544 -65.095 26.455 1.00 0.00 C +ATOM 2868 OH TYR A 191 -19.780 -66.246 27.175 1.00 0.00 O +ATOM 2869 H TYR A 191 -18.488 -63.370 21.813 1.00 0.00 H +ATOM 2870 HA TYR A 191 -17.738 -60.615 22.566 1.00 0.00 H +ATOM 2871 HB2 TYR A 191 -18.371 -60.772 24.852 1.00 0.00 H +ATOM 2872 HB3 TYR A 191 -19.820 -61.127 23.851 1.00 0.00 H +ATOM 2873 HD1 TYR A 191 -20.515 -63.582 23.610 1.00 0.00 H +ATOM 2874 HD2 TYR A 191 -17.728 -62.257 26.546 1.00 0.00 H +ATOM 2875 HE1 TYR A 191 -20.911 -65.631 24.905 1.00 0.00 H +ATOM 2876 HE2 TYR A 191 -18.126 -64.305 27.851 1.00 0.00 H +ATOM 2877 HH TYR A 191 -19.851 -66.034 28.098 1.00 0.00 H +ATOM 2878 N THR A 192 -15.790 -61.511 24.002 1.00 0.00 N +ATOM 2879 CA THR A 192 -14.557 -62.002 24.599 1.00 0.00 C +ATOM 2880 C THR A 192 -14.568 -61.658 26.081 1.00 0.00 C +ATOM 2881 O THR A 192 -15.286 -60.759 26.524 1.00 0.00 O +ATOM 2882 CB THR A 192 -13.298 -61.404 23.939 1.00 0.00 C +ATOM 2883 OG1 THR A 192 -13.293 -59.980 24.102 1.00 0.00 O +ATOM 2884 CG2 THR A 192 -13.247 -61.743 22.455 1.00 0.00 C +ATOM 2885 H THR A 192 -16.031 -60.550 24.135 1.00 0.00 H +ATOM 2886 HA THR A 192 -14.513 -63.096 24.496 1.00 0.00 H +ATOM 2887 HB THR A 192 -12.413 -61.837 24.427 1.00 0.00 H +ATOM 2888 HG1 THR A 192 -14.040 -59.718 24.628 1.00 0.00 H +ATOM 2889 HG21 THR A 192 -12.341 -61.306 22.009 1.00 0.00 H +ATOM 2890 HG22 THR A 192 -13.226 -62.836 22.329 1.00 0.00 H +ATOM 2891 HG23 THR A 192 -14.137 -61.333 21.955 1.00 0.00 H +ATOM 2892 N GLY A 193 -13.772 -62.390 26.852 1.00 0.00 N +ATOM 2893 CA GLY A 193 -13.728 -62.082 28.265 1.00 0.00 C +ATOM 2894 C GLY A 193 -14.919 -62.652 29.021 1.00 0.00 C +ATOM 2895 O GLY A 193 -15.637 -63.534 28.550 1.00 0.00 O +ATOM 2896 H GLY A 193 -13.220 -63.131 26.468 1.00 0.00 H +ATOM 2897 HA2 GLY A 193 -12.805 -62.504 28.690 1.00 0.00 H +ATOM 2898 HA3 GLY A 193 -13.722 -60.989 28.388 1.00 0.00 H +ATOM 2899 N SER A 194 -15.122 -62.120 30.223 1.00 0.00 N +ATOM 2900 CA SER A 194 -16.181 -62.600 31.097 1.00 0.00 C +ATOM 2901 C SER A 194 -17.483 -61.849 30.844 1.00 0.00 C +ATOM 2902 O SER A 194 -17.491 -60.696 30.404 1.00 0.00 O +ATOM 2903 CB SER A 194 -15.784 -62.449 32.568 1.00 0.00 C +ATOM 2904 OG SER A 194 -14.719 -63.321 32.913 1.00 0.00 O +ATOM 2905 H SER A 194 -14.533 -61.373 30.533 1.00 0.00 H +ATOM 2906 HA SER A 194 -16.356 -63.668 30.898 1.00 0.00 H +ATOM 2907 HB2 SER A 194 -15.468 -61.411 32.748 1.00 0.00 H +ATOM 2908 HB3 SER A 194 -16.655 -62.682 33.199 1.00 0.00 H +ATOM 2909 HG SER A 194 -14.189 -63.494 32.144 1.00 0.00 H +ATOM 2910 N LEU A 195 -18.591 -62.525 31.128 1.00 0.00 N +ATOM 2911 CA LEU A 195 -19.899 -61.887 31.187 1.00 0.00 C +ATOM 2912 C LEU A 195 -20.156 -61.474 32.630 1.00 0.00 C +ATOM 2913 O LEU A 195 -20.093 -62.310 33.539 1.00 0.00 O +ATOM 2914 CB LEU A 195 -20.995 -62.832 30.691 1.00 0.00 C +ATOM 2915 CG LEU A 195 -21.530 -62.692 29.264 1.00 0.00 C +ATOM 2916 CD1 LEU A 195 -22.331 -63.930 28.889 1.00 0.00 C +ATOM 2917 CD2 LEU A 195 -22.391 -61.447 29.146 1.00 0.00 C +ATOM 2918 H LEU A 195 -18.524 -63.507 31.308 1.00 0.00 H +ATOM 2919 HA LEU A 195 -19.899 -60.986 30.556 1.00 0.00 H +ATOM 2920 HB2 LEU A 195 -20.601 -63.855 30.785 1.00 0.00 H +ATOM 2921 HB3 LEU A 195 -21.854 -62.707 31.367 1.00 0.00 H +ATOM 2922 HG LEU A 195 -20.677 -62.600 28.576 1.00 0.00 H +ATOM 2923 HD11 LEU A 195 -22.713 -63.824 27.863 1.00 0.00 H +ATOM 2924 HD12 LEU A 195 -21.684 -64.817 28.949 1.00 0.00 H +ATOM 2925 HD13 LEU A 195 -23.176 -64.045 29.584 1.00 0.00 H +ATOM 2926 HD21 LEU A 195 -22.769 -61.358 28.117 1.00 0.00 H +ATOM 2927 HD22 LEU A 195 -23.239 -61.520 29.843 1.00 0.00 H +ATOM 2928 HD23 LEU A 195 -21.789 -60.560 29.393 1.00 0.00 H +ATOM 2929 N TRP A 196 -20.423 -60.191 32.846 1.00 0.00 N +ATOM 2930 CA TRP A 196 -20.711 -59.667 34.173 1.00 0.00 C +ATOM 2931 C TRP A 196 -22.168 -59.231 34.241 1.00 0.00 C +ATOM 2932 O TRP A 196 -22.636 -58.476 33.384 1.00 0.00 O +ATOM 2933 CB TRP A 196 -19.782 -58.504 34.520 1.00 0.00 C +ATOM 2934 CG TRP A 196 -18.433 -58.958 35.005 1.00 0.00 C +ATOM 2935 CD1 TRP A 196 -17.326 -59.197 34.246 1.00 0.00 C +ATOM 2936 CD2 TRP A 196 -18.056 -59.235 36.360 1.00 0.00 C +ATOM 2937 NE1 TRP A 196 -16.281 -59.599 35.043 1.00 0.00 N +ATOM 2938 CE2 TRP A 196 -16.704 -59.631 36.346 1.00 0.00 C +ATOM 2939 CE3 TRP A 196 -18.731 -59.182 37.584 1.00 0.00 C +ATOM 2940 CZ2 TRP A 196 -16.013 -59.973 37.508 1.00 0.00 C +ATOM 2941 CZ3 TRP A 196 -18.042 -59.523 38.737 1.00 0.00 C +ATOM 2942 CH2 TRP A 196 -16.698 -59.912 38.690 1.00 0.00 C +ATOM 2943 H TRP A 196 -20.426 -59.562 32.069 1.00 0.00 H +ATOM 2944 HA TRP A 196 -20.555 -60.463 34.916 1.00 0.00 H +ATOM 2945 HB2 TRP A 196 -19.642 -57.886 33.621 1.00 0.00 H +ATOM 2946 HB3 TRP A 196 -20.254 -57.901 35.310 1.00 0.00 H +ATOM 2947 HD1 TRP A 196 -17.277 -59.086 33.173 1.00 0.00 H +ATOM 2948 HE1 TRP A 196 -15.362 -59.830 34.724 1.00 0.00 H +ATOM 2949 HE3 TRP A 196 -19.767 -58.882 37.630 1.00 0.00 H +ATOM 2950 HZ2 TRP A 196 -14.976 -60.275 37.476 1.00 0.00 H +ATOM 2951 HZ3 TRP A 196 -18.551 -59.488 39.689 1.00 0.00 H +ATOM 2952 HH2 TRP A 196 -16.191 -60.170 39.608 1.00 0.00 H +ATOM 2953 N TYR A 197 -22.869 -59.691 35.273 1.00 0.00 N +ATOM 2954 CA TYR A 197 -24.317 -59.570 35.373 1.00 0.00 C +ATOM 2955 C TYR A 197 -24.707 -58.587 36.468 1.00 0.00 C +ATOM 2956 O TYR A 197 -24.194 -58.661 37.590 1.00 0.00 O +ATOM 2957 CB TYR A 197 -24.951 -60.933 35.657 1.00 0.00 C +ATOM 2958 CG TYR A 197 -24.939 -61.883 34.485 1.00 0.00 C +ATOM 2959 CD1 TYR A 197 -25.965 -61.869 33.546 1.00 0.00 C +ATOM 2960 CD2 TYR A 197 -23.914 -62.803 34.321 1.00 0.00 C +ATOM 2961 CE1 TYR A 197 -25.963 -62.734 32.474 1.00 0.00 C +ATOM 2962 CE2 TYR A 197 -23.905 -63.676 33.250 1.00 0.00 C +ATOM 2963 CZ TYR A 197 -24.934 -63.638 32.330 1.00 0.00 C +ATOM 2964 OH TYR A 197 -24.934 -64.503 31.260 1.00 0.00 O +ATOM 2965 H TYR A 197 -22.378 -60.143 36.018 1.00 0.00 H +ATOM 2966 HA TYR A 197 -24.715 -59.198 34.417 1.00 0.00 H +ATOM 2967 HB2 TYR A 197 -24.401 -61.402 36.486 1.00 0.00 H +ATOM 2968 HB3 TYR A 197 -25.997 -60.770 35.957 1.00 0.00 H +ATOM 2969 HD1 TYR A 197 -26.778 -61.167 33.660 1.00 0.00 H +ATOM 2970 HD2 TYR A 197 -23.110 -62.838 35.042 1.00 0.00 H +ATOM 2971 HE1 TYR A 197 -26.764 -62.703 31.750 1.00 0.00 H +ATOM 2972 HE2 TYR A 197 -23.098 -64.384 33.133 1.00 0.00 H +ATOM 2973 HH TYR A 197 -25.294 -64.065 30.498 1.00 0.00 H +ATOM 2974 N THR A 198 -25.625 -57.684 36.136 1.00 0.00 N +ATOM 2975 CA THR A 198 -26.280 -56.769 37.047 1.00 0.00 C +ATOM 2976 C THR A 198 -27.750 -57.150 37.196 1.00 0.00 C +ATOM 2977 O THR A 198 -28.369 -57.638 36.245 1.00 0.00 O +ATOM 2978 CB THR A 198 -26.167 -55.314 36.565 1.00 0.00 C +ATOM 2979 OG1 THR A 198 -26.521 -54.425 37.632 1.00 0.00 O +ATOM 2980 CG2 THR A 198 -27.089 -55.049 35.379 1.00 0.00 C +ATOM 2981 H THR A 198 -25.886 -57.632 35.172 1.00 0.00 H +ATOM 2982 HA THR A 198 -25.804 -56.839 38.036 1.00 0.00 H +ATOM 2983 HB THR A 198 -25.129 -55.129 36.252 1.00 0.00 H +ATOM 2984 HG1 THR A 198 -25.940 -54.568 38.370 1.00 0.00 H +ATOM 2985 HG21 THR A 198 -26.984 -54.002 35.059 1.00 0.00 H +ATOM 2986 HG22 THR A 198 -26.817 -55.715 34.547 1.00 0.00 H +ATOM 2987 HG23 THR A 198 -28.131 -55.239 35.675 1.00 0.00 H +ATOM 2988 N PRO A 199 -28.333 -56.965 38.377 1.00 0.00 N +ATOM 2989 CA PRO A 199 -29.731 -57.356 38.573 1.00 0.00 C +ATOM 2990 C PRO A 199 -30.691 -56.462 37.805 1.00 0.00 C +ATOM 2991 O PRO A 199 -30.479 -55.255 37.659 1.00 0.00 O +ATOM 2992 CB PRO A 199 -29.936 -57.207 40.086 1.00 0.00 C +ATOM 2993 CG PRO A 199 -28.565 -57.245 40.665 1.00 0.00 C +ATOM 2994 CD PRO A 199 -27.689 -56.591 39.647 1.00 0.00 C +ATOM 2995 HA PRO A 199 -29.884 -58.405 38.281 1.00 0.00 H +ATOM 2996 HB2 PRO A 199 -30.427 -56.250 40.317 1.00 0.00 H +ATOM 2997 HB3 PRO A 199 -30.544 -58.036 40.477 1.00 0.00 H +ATOM 2998 HG2 PRO A 199 -28.532 -56.691 41.615 1.00 0.00 H +ATOM 2999 HG3 PRO A 199 -28.248 -58.284 40.836 1.00 0.00 H +ATOM 3000 HD2 PRO A 199 -26.661 -56.979 39.700 1.00 0.00 H +ATOM 3001 HD3 PRO A 199 -27.676 -55.499 39.778 1.00 0.00 H +ATOM 3002 N ILE A 200 -31.750 -57.080 37.292 1.00 0.00 N +ATOM 3003 CA ILE A 200 -32.907 -56.329 36.823 1.00 0.00 C +ATOM 3004 C ILE A 200 -33.679 -55.905 38.067 1.00 0.00 C +ATOM 3005 O ILE A 200 -34.234 -56.741 38.782 1.00 0.00 O +ATOM 3006 CB ILE A 200 -33.780 -57.149 35.868 1.00 0.00 C +ATOM 3007 CG1 ILE A 200 -32.980 -57.544 34.625 1.00 0.00 C +ATOM 3008 CG2 ILE A 200 -35.010 -56.345 35.471 1.00 0.00 C +ATOM 3009 CD1 ILE A 200 -33.736 -58.442 33.668 1.00 0.00 C +ATOM 3010 H ILE A 200 -31.753 -58.078 37.227 1.00 0.00 H +ATOM 3011 HA ILE A 200 -32.568 -55.425 36.296 1.00 0.00 H +ATOM 3012 HB ILE A 200 -34.105 -58.064 36.385 1.00 0.00 H +ATOM 3013 HG12 ILE A 200 -32.698 -56.626 34.089 1.00 0.00 H +ATOM 3014 HG13 ILE A 200 -32.072 -58.072 34.951 1.00 0.00 H +ATOM 3015 HG21 ILE A 200 -35.632 -56.940 34.786 1.00 0.00 H +ATOM 3016 HG22 ILE A 200 -35.591 -56.096 36.371 1.00 0.00 H +ATOM 3017 HG23 ILE A 200 -34.696 -55.418 34.970 1.00 0.00 H +ATOM 3018 HD11 ILE A 200 -33.097 -58.680 32.805 1.00 0.00 H +ATOM 3019 HD12 ILE A 200 -34.017 -59.372 34.183 1.00 0.00 H +ATOM 3020 HD13 ILE A 200 -34.643 -57.926 33.321 1.00 0.00 H +ATOM 3021 N ARG A 201 -33.694 -54.600 38.337 1.00 0.00 N +ATOM 3022 CA ARG A 201 -34.255 -54.106 39.589 1.00 0.00 C +ATOM 3023 C ARG A 201 -35.762 -54.324 39.656 1.00 0.00 C +ATOM 3024 O ARG A 201 -36.288 -54.741 40.694 1.00 0.00 O +ATOM 3025 CB ARG A 201 -33.923 -52.627 39.749 1.00 0.00 C +ATOM 3026 CG ARG A 201 -34.136 -52.096 41.141 1.00 0.00 C +ATOM 3027 CD ARG A 201 -34.196 -50.590 41.099 1.00 0.00 C +ATOM 3028 NE ARG A 201 -34.457 -50.016 42.411 1.00 0.00 N +ATOM 3029 CZ ARG A 201 -34.885 -48.774 42.601 1.00 0.00 C +ATOM 3030 NH1 ARG A 201 -35.107 -47.985 41.558 1.00 0.00 N +ATOM 3031 NH2 ARG A 201 -35.098 -48.324 43.830 1.00 0.00 N1+ +ATOM 3032 H ARG A 201 -33.317 -53.953 37.674 1.00 0.00 H +ATOM 3033 HA ARG A 201 -33.794 -54.651 40.426 1.00 0.00 H +ATOM 3034 HB2 ARG A 201 -32.866 -52.479 39.481 1.00 0.00 H +ATOM 3035 HB3 ARG A 201 -34.560 -52.054 39.059 1.00 0.00 H +ATOM 3036 HG2 ARG A 201 -35.081 -52.490 41.543 1.00 0.00 H +ATOM 3037 HG3 ARG A 201 -33.303 -52.412 41.786 1.00 0.00 H +ATOM 3038 HD2 ARG A 201 -33.234 -50.207 40.729 1.00 0.00 H +ATOM 3039 HD3 ARG A 201 -35.000 -50.286 40.412 1.00 0.00 H +ATOM 3040 HE ARG A 201 -34.305 -50.590 43.215 1.00 0.00 H +ATOM 3041 HH11 ARG A 201 -35.435 -47.051 41.699 1.00 0.00 H +ATOM 3042 HH12 ARG A 201 -34.946 -48.324 40.631 1.00 0.00 H +ATOM 3043 HH21 ARG A 201 -35.426 -47.390 43.971 1.00 0.00 H +ATOM 3044 HH22 ARG A 201 -34.931 -48.919 44.616 1.00 0.00 H +ATOM 3045 N ARG A 202 -36.475 -54.027 38.573 1.00 0.00 N +ATOM 3046 CA ARG A 202 -37.916 -54.232 38.524 1.00 0.00 C +ATOM 3047 C ARG A 202 -38.298 -54.618 37.105 1.00 0.00 C +ATOM 3048 O ARG A 202 -37.727 -54.102 36.141 1.00 0.00 O +ATOM 3049 CB ARG A 202 -38.674 -52.973 38.967 1.00 0.00 C +ATOM 3050 CG ARG A 202 -40.194 -53.086 38.916 1.00 0.00 C +ATOM 3051 CD ARG A 202 -40.849 -52.115 39.892 1.00 0.00 C +ATOM 3052 NE ARG A 202 -40.882 -50.744 39.392 1.00 0.00 N +ATOM 3053 CZ ARG A 202 -41.907 -50.224 38.724 1.00 0.00 C +ATOM 3054 NH1 ARG A 202 -41.864 -48.967 38.305 1.00 0.00 N +ATOM 3055 NH2 ARG A 202 -42.976 -50.966 38.472 1.00 0.00 N1+ +ATOM 3056 H ARG A 202 -36.009 -53.653 37.771 1.00 0.00 H +ATOM 3057 HA ARG A 202 -38.191 -55.057 39.197 1.00 0.00 H +ATOM 3058 HB2 ARG A 202 -38.383 -52.746 40.003 1.00 0.00 H +ATOM 3059 HB3 ARG A 202 -38.371 -52.144 38.311 1.00 0.00 H +ATOM 3060 HG2 ARG A 202 -40.537 -52.857 37.896 1.00 0.00 H +ATOM 3061 HG3 ARG A 202 -40.487 -54.113 39.180 1.00 0.00 H +ATOM 3062 HD2 ARG A 202 -41.882 -52.446 40.077 1.00 0.00 H +ATOM 3063 HD3 ARG A 202 -40.284 -52.131 40.836 1.00 0.00 H +ATOM 3064 HE ARG A 202 -40.087 -50.162 39.562 1.00 0.00 H +ATOM 3065 HH11 ARG A 202 -42.637 -48.581 37.800 1.00 0.00 H +ATOM 3066 HH12 ARG A 202 -41.059 -48.404 38.493 1.00 0.00 H +ATOM 3067 HH21 ARG A 202 -43.747 -50.577 37.967 1.00 0.00 H +ATOM 3068 HH22 ARG A 202 -43.012 -51.915 38.787 1.00 0.00 H +ATOM 3069 N GLU A 203 -39.254 -55.535 36.978 1.00 0.00 N +ATOM 3070 CA GLU A 203 -39.646 -56.038 35.663 1.00 0.00 C +ATOM 3071 C GLU A 203 -40.675 -55.097 35.049 1.00 0.00 C +ATOM 3072 O GLU A 203 -41.884 -55.307 35.151 1.00 0.00 O +ATOM 3073 CB GLU A 203 -40.187 -57.459 35.765 1.00 0.00 C +ATOM 3074 CG GLU A 203 -39.216 -58.461 36.370 1.00 0.00 C +ATOM 3075 CD GLU A 203 -39.683 -59.894 36.196 1.00 0.00 C +ATOM 3076 OE1 GLU A 203 -40.898 -60.107 35.987 1.00 0.00 O +ATOM 3077 OE2 GLU A 203 -38.835 -60.808 36.255 1.00 0.00 O1- +ATOM 3078 H GLU A 203 -39.711 -55.885 37.795 1.00 0.00 H +ATOM 3079 HA GLU A 203 -38.762 -56.054 35.008 1.00 0.00 H +ATOM 3080 HB2 GLU A 203 -41.092 -57.438 36.389 1.00 0.00 H +ATOM 3081 HB3 GLU A 203 -40.447 -57.801 34.752 1.00 0.00 H +ATOM 3082 HG2 GLU A 203 -38.238 -58.346 35.880 1.00 0.00 H +ATOM 3083 HG3 GLU A 203 -39.114 -58.250 37.445 1.00 0.00 H +ATOM 3084 N TRP A 204 -40.191 -54.035 34.408 1.00 0.00 N +ATOM 3085 CA TRP A 204 -41.043 -53.213 33.554 1.00 0.00 C +ATOM 3086 C TRP A 204 -40.239 -52.771 32.337 1.00 0.00 C +ATOM 3087 O TRP A 204 -40.254 -53.445 31.303 1.00 0.00 O +ATOM 3088 CB TRP A 204 -41.640 -52.025 34.323 1.00 0.00 C +ATOM 3089 CG TRP A 204 -40.687 -51.038 34.955 1.00 0.00 C +ATOM 3090 CD1 TRP A 204 -39.416 -51.268 35.403 1.00 0.00 C +ATOM 3091 CD2 TRP A 204 -40.950 -49.654 35.193 1.00 0.00 C +ATOM 3092 NE1 TRP A 204 -38.877 -50.109 35.912 1.00 0.00 N +ATOM 3093 CE2 TRP A 204 -39.800 -49.103 35.792 1.00 0.00 C +ATOM 3094 CE3 TRP A 204 -42.052 -48.826 34.959 1.00 0.00 C +ATOM 3095 CZ2 TRP A 204 -39.723 -47.762 36.159 1.00 0.00 C +ATOM 3096 CZ3 TRP A 204 -41.975 -47.500 35.323 1.00 0.00 C +ATOM 3097 CH2 TRP A 204 -40.821 -46.979 35.918 1.00 0.00 C +ATOM 3098 H TRP A 204 -39.226 -53.796 34.514 1.00 0.00 H +ATOM 3099 HA TRP A 204 -41.880 -53.831 33.196 1.00 0.00 H +ATOM 3100 HB2 TRP A 204 -42.272 -51.464 33.619 1.00 0.00 H +ATOM 3101 HB3 TRP A 204 -42.265 -52.438 35.129 1.00 0.00 H +ATOM 3102 HD1 TRP A 204 -38.909 -52.221 35.363 1.00 0.00 H +ATOM 3103 HE1 TRP A 204 -37.962 -50.016 36.305 1.00 0.00 H +ATOM 3104 HE3 TRP A 204 -42.948 -49.219 34.501 1.00 0.00 H +ATOM 3105 HZ2 TRP A 204 -38.833 -47.355 36.616 1.00 0.00 H +ATOM 3106 HZ3 TRP A 204 -42.820 -46.851 35.146 1.00 0.00 H +ATOM 3107 HH2 TRP A 204 -40.795 -45.935 36.194 1.00 0.00 H +ATOM 3108 N TYR A 205 -39.533 -51.654 32.437 1.00 0.00 N +ATOM 3109 CA TYR A 205 -38.348 -51.514 31.619 1.00 0.00 C +ATOM 3110 C TYR A 205 -37.288 -52.490 32.126 1.00 0.00 C +ATOM 3111 O TYR A 205 -37.463 -53.173 33.140 1.00 0.00 O +ATOM 3112 CB TYR A 205 -37.804 -50.087 31.671 1.00 0.00 C +ATOM 3113 CG TYR A 205 -38.716 -48.991 31.168 1.00 0.00 C +ATOM 3114 CD1 TYR A 205 -38.757 -48.656 29.821 1.00 0.00 C +ATOM 3115 CD2 TYR A 205 -39.498 -48.257 32.047 1.00 0.00 C +ATOM 3116 CE1 TYR A 205 -39.574 -47.635 29.361 1.00 0.00 C +ATOM 3117 CE2 TYR A 205 -40.316 -47.237 31.599 1.00 0.00 C +ATOM 3118 CZ TYR A 205 -40.353 -46.930 30.258 1.00 0.00 C +ATOM 3119 OH TYR A 205 -41.170 -45.913 29.823 1.00 0.00 O +ATOM 3120 H TYR A 205 -39.812 -50.926 33.063 1.00 0.00 H +ATOM 3121 HA TYR A 205 -38.585 -51.763 30.574 1.00 0.00 H +ATOM 3122 HB2 TYR A 205 -37.560 -49.862 32.720 1.00 0.00 H +ATOM 3123 HB3 TYR A 205 -36.885 -50.059 31.067 1.00 0.00 H +ATOM 3124 HD1 TYR A 205 -38.142 -49.200 29.120 1.00 0.00 H +ATOM 3125 HD2 TYR A 205 -39.467 -48.487 33.102 1.00 0.00 H +ATOM 3126 HE1 TYR A 205 -39.601 -47.392 28.309 1.00 0.00 H +ATOM 3127 HE2 TYR A 205 -40.924 -46.683 32.299 1.00 0.00 H +ATOM 3128 HH TYR A 205 -41.461 -46.097 28.937 1.00 0.00 H +ATOM 3129 N TYR A 206 -36.170 -52.557 31.420 1.00 0.00 N +ATOM 3130 CA TYR A 206 -35.031 -53.303 31.945 1.00 0.00 C +ATOM 3131 C TYR A 206 -34.244 -52.338 32.820 1.00 0.00 C +ATOM 3132 O TYR A 206 -33.322 -51.659 32.368 1.00 0.00 O +ATOM 3133 CB TYR A 206 -34.198 -53.889 30.815 1.00 0.00 C +ATOM 3134 CG TYR A 206 -34.896 -55.015 30.093 1.00 0.00 C +ATOM 3135 CD1 TYR A 206 -34.912 -56.295 30.623 1.00 0.00 C +ATOM 3136 CD2 TYR A 206 -35.546 -54.796 28.884 1.00 0.00 C +ATOM 3137 CE1 TYR A 206 -35.549 -57.332 29.969 1.00 0.00 C +ATOM 3138 CE2 TYR A 206 -36.185 -55.827 28.221 1.00 0.00 C +ATOM 3139 CZ TYR A 206 -36.187 -57.093 28.769 1.00 0.00 C +ATOM 3140 OH TYR A 206 -36.822 -58.118 28.108 1.00 0.00 O +ATOM 3141 H TYR A 206 -36.106 -52.099 30.533 1.00 0.00 H +ATOM 3142 HA TYR A 206 -35.395 -54.128 32.575 1.00 0.00 H +ATOM 3143 HB2 TYR A 206 -33.978 -53.091 30.091 1.00 0.00 H +ATOM 3144 HB3 TYR A 206 -33.257 -54.272 31.235 1.00 0.00 H +ATOM 3145 HD1 TYR A 206 -34.418 -56.486 31.564 1.00 0.00 H +ATOM 3146 HD2 TYR A 206 -35.552 -53.805 28.456 1.00 0.00 H +ATOM 3147 HE1 TYR A 206 -35.548 -58.325 30.395 1.00 0.00 H +ATOM 3148 HE2 TYR A 206 -36.680 -55.642 27.279 1.00 0.00 H +ATOM 3149 HH TYR A 206 -37.345 -57.762 27.399 1.00 0.00 H +ATOM 3150 N GLU A 207 -34.636 -52.259 34.089 1.00 0.00 N +ATOM 3151 CA GLU A 207 -34.097 -51.261 35.002 1.00 0.00 C +ATOM 3152 C GLU A 207 -32.887 -51.817 35.740 1.00 0.00 C +ATOM 3153 O GLU A 207 -32.925 -52.935 36.266 1.00 0.00 O +ATOM 3154 CB GLU A 207 -35.156 -50.799 36.004 1.00 0.00 C +ATOM 3155 CG GLU A 207 -34.818 -49.473 36.677 1.00 0.00 C +ATOM 3156 CD GLU A 207 -35.686 -49.177 37.885 1.00 0.00 C +ATOM 3157 OE1 GLU A 207 -36.714 -49.864 38.075 1.00 0.00 O +ATOM 3158 OE2 GLU A 207 -35.333 -48.257 38.651 1.00 0.00 O1- +ATOM 3159 H GLU A 207 -35.321 -52.904 34.426 1.00 0.00 H +ATOM 3160 HA GLU A 207 -33.771 -50.385 34.422 1.00 0.00 H +ATOM 3161 HB2 GLU A 207 -36.112 -50.686 35.473 1.00 0.00 H +ATOM 3162 HB3 GLU A 207 -35.258 -51.569 36.783 1.00 0.00 H +ATOM 3163 HG2 GLU A 207 -33.767 -49.503 37.001 1.00 0.00 H +ATOM 3164 HG3 GLU A 207 -34.953 -48.665 35.943 1.00 0.00 H +ATOM 3165 N VAL A 208 -31.817 -51.025 35.773 1.00 0.00 N +ATOM 3166 CA VAL A 208 -30.579 -51.390 36.452 1.00 0.00 C +ATOM 3167 C VAL A 208 -30.219 -50.276 37.425 1.00 0.00 C +ATOM 3168 O VAL A 208 -30.926 -49.266 37.521 1.00 0.00 O +ATOM 3169 CB VAL A 208 -29.439 -51.636 35.447 1.00 0.00 C +ATOM 3170 CG1 VAL A 208 -29.822 -52.729 34.461 1.00 0.00 C +ATOM 3171 CG2 VAL A 208 -29.087 -50.346 34.715 1.00 0.00 C +ATOM 3172 H VAL A 208 -31.865 -50.139 35.311 1.00 0.00 H +ATOM 3173 HA VAL A 208 -30.738 -52.315 37.025 1.00 0.00 H +ATOM 3174 HB VAL A 208 -28.553 -51.969 36.008 1.00 0.00 H +ATOM 3175 HG11 VAL A 208 -28.996 -52.890 33.752 1.00 0.00 H +ATOM 3176 HG12 VAL A 208 -30.023 -53.662 35.008 1.00 0.00 H +ATOM 3177 HG13 VAL A 208 -30.724 -52.426 33.910 1.00 0.00 H +ATOM 3178 HG21 VAL A 208 -28.272 -50.539 34.002 1.00 0.00 H +ATOM 3179 HG22 VAL A 208 -29.971 -49.980 34.172 1.00 0.00 H +ATOM 3180 HG23 VAL A 208 -28.764 -49.588 35.444 1.00 0.00 H +ATOM 3181 N ILE A 209 -29.112 -50.443 38.143 1.00 0.00 N +ATOM 3182 CA ILE A 209 -28.649 -49.466 39.122 1.00 0.00 C +ATOM 3183 C ILE A 209 -27.198 -49.130 38.814 1.00 0.00 C +ATOM 3184 O ILE A 209 -26.339 -50.020 38.796 1.00 0.00 O +ATOM 3185 CB ILE A 209 -28.784 -49.985 40.562 1.00 0.00 C +ATOM 3186 CG1 ILE A 209 -30.243 -50.276 40.902 1.00 0.00 C +ATOM 3187 CG2 ILE A 209 -28.191 -48.987 41.540 1.00 0.00 C +ATOM 3188 CD1 ILE A 209 -30.453 -50.696 42.336 1.00 0.00 C +ATOM 3189 H ILE A 209 -28.574 -51.275 38.006 1.00 0.00 H +ATOM 3190 HA ILE A 209 -29.247 -48.547 39.029 1.00 0.00 H +ATOM 3191 HB ILE A 209 -28.218 -50.925 40.641 1.00 0.00 H +ATOM 3192 HG12 ILE A 209 -30.832 -49.366 40.715 1.00 0.00 H +ATOM 3193 HG13 ILE A 209 -30.599 -51.084 40.246 1.00 0.00 H +ATOM 3194 HG21 ILE A 209 -28.295 -49.372 42.565 1.00 0.00 H +ATOM 3195 HG22 ILE A 209 -27.126 -48.838 41.311 1.00 0.00 H +ATOM 3196 HG23 ILE A 209 -28.723 -48.028 41.453 1.00 0.00 H +ATOM 3197 HD11 ILE A 209 -31.522 -50.889 42.509 1.00 0.00 H +ATOM 3198 HD12 ILE A 209 -29.878 -51.612 42.538 1.00 0.00 H +ATOM 3199 HD13 ILE A 209 -30.111 -49.894 43.007 1.00 0.00 H +ATOM 3200 N ILE A 210 -26.928 -47.855 38.572 1.00 0.00 N +ATOM 3201 CA ILE A 210 -25.560 -47.368 38.431 1.00 0.00 C +ATOM 3202 C ILE A 210 -25.057 -46.943 39.805 1.00 0.00 C +ATOM 3203 O ILE A 210 -25.744 -46.215 40.528 1.00 0.00 O +ATOM 3204 CB ILE A 210 -25.505 -46.200 37.436 1.00 0.00 C +ATOM 3205 CG1 ILE A 210 -26.011 -46.645 36.063 1.00 0.00 C +ATOM 3206 CG2 ILE A 210 -24.103 -45.626 37.368 1.00 0.00 C +ATOM 3207 CD1 ILE A 210 -26.474 -45.502 35.198 1.00 0.00 C +ATOM 3208 H ILE A 210 -27.685 -47.207 38.483 1.00 0.00 H +ATOM 3209 HA ILE A 210 -24.918 -48.178 38.055 1.00 0.00 H +ATOM 3210 HB ILE A 210 -26.178 -45.412 37.805 1.00 0.00 H +ATOM 3211 HG12 ILE A 210 -25.195 -47.168 35.543 1.00 0.00 H +ATOM 3212 HG13 ILE A 210 -26.854 -47.336 36.209 1.00 0.00 H +ATOM 3213 HG21 ILE A 210 -24.082 -44.792 36.652 1.00 0.00 H +ATOM 3214 HG22 ILE A 210 -23.807 -45.262 38.363 1.00 0.00 H +ATOM 3215 HG23 ILE A 210 -23.402 -46.408 37.040 1.00 0.00 H +ATOM 3216 HD11 ILE A 210 -26.823 -45.892 34.230 1.00 0.00 H +ATOM 3217 HD12 ILE A 210 -27.298 -44.974 35.699 1.00 0.00 H +ATOM 3218 HD13 ILE A 210 -25.639 -44.806 35.033 1.00 0.00 H +ATOM 3219 N VAL A 211 -23.863 -47.395 40.175 1.00 0.00 N +ATOM 3220 CA VAL A 211 -23.328 -47.084 41.498 1.00 0.00 C +ATOM 3221 C VAL A 211 -22.129 -46.146 41.448 1.00 0.00 C +ATOM 3222 O VAL A 211 -21.751 -45.610 42.507 1.00 0.00 O +ATOM 3223 CB VAL A 211 -22.975 -48.365 42.280 1.00 0.00 C +ATOM 3224 CG1 VAL A 211 -24.165 -49.315 42.315 1.00 0.00 C +ATOM 3225 CG2 VAL A 211 -21.754 -49.045 41.693 1.00 0.00 C +ATOM 3226 H VAL A 211 -23.326 -47.953 39.542 1.00 0.00 H +ATOM 3227 HA VAL A 211 -24.116 -46.570 42.068 1.00 0.00 H +ATOM 3228 HB VAL A 211 -22.741 -48.074 43.315 1.00 0.00 H +ATOM 3229 HG11 VAL A 211 -23.895 -50.222 42.876 1.00 0.00 H +ATOM 3230 HG12 VAL A 211 -25.015 -48.819 42.807 1.00 0.00 H +ATOM 3231 HG13 VAL A 211 -24.445 -49.590 41.287 1.00 0.00 H +ATOM 3232 HG21 VAL A 211 -21.527 -49.953 42.270 1.00 0.00 H +ATOM 3233 HG22 VAL A 211 -21.953 -49.316 40.646 1.00 0.00 H +ATOM 3234 HG23 VAL A 211 -20.896 -48.359 41.737 1.00 0.00 H +ATOM 3235 N ARG A 212 -21.534 -45.907 40.284 1.00 0.00 N +ATOM 3236 CA ARG A 212 -20.358 -45.054 40.175 1.00 0.00 C +ATOM 3237 C ARG A 212 -20.123 -44.721 38.708 1.00 0.00 C +ATOM 3238 O ARG A 212 -20.397 -45.537 37.825 1.00 0.00 O +ATOM 3239 CB ARG A 212 -19.119 -45.732 40.784 1.00 0.00 C +ATOM 3240 CG ARG A 212 -17.827 -44.930 40.692 1.00 0.00 C +ATOM 3241 CD ARG A 212 -16.675 -45.684 41.356 1.00 0.00 C +ATOM 3242 NE ARG A 212 -15.361 -45.111 41.060 1.00 0.00 N +ATOM 3243 CZ ARG A 212 -14.693 -44.293 41.871 1.00 0.00 C +ATOM 3244 NH1 ARG A 212 -15.209 -43.935 43.040 1.00 0.00 N +ATOM 3245 NH2 ARG A 212 -13.504 -43.830 41.513 1.00 0.00 N1+ +ATOM 3246 H ARG A 212 -21.906 -46.326 39.456 1.00 0.00 H +ATOM 3247 HA ARG A 212 -20.539 -44.115 40.720 1.00 0.00 H +ATOM 3248 HB2 ARG A 212 -19.325 -45.924 41.847 1.00 0.00 H +ATOM 3249 HB3 ARG A 212 -18.964 -46.688 40.262 1.00 0.00 H +ATOM 3250 HG2 ARG A 212 -17.583 -44.758 39.633 1.00 0.00 H +ATOM 3251 HG3 ARG A 212 -17.965 -43.963 41.198 1.00 0.00 H +ATOM 3252 HD2 ARG A 212 -16.827 -45.665 42.445 1.00 0.00 H +ATOM 3253 HD3 ARG A 212 -16.690 -46.725 41.002 1.00 0.00 H +ATOM 3254 HE ARG A 212 -14.935 -45.351 40.188 1.00 0.00 H +ATOM 3255 HH11 ARG A 212 -14.701 -43.320 43.644 1.00 0.00 H +ATOM 3256 HH12 ARG A 212 -16.106 -44.280 43.318 1.00 0.00 H +ATOM 3257 HH21 ARG A 212 -13.002 -43.216 42.122 1.00 0.00 H +ATOM 3258 HH22 ARG A 212 -13.108 -44.095 40.634 1.00 0.00 H +ATOM 3259 N VAL A 213 -19.617 -43.512 38.460 1.00 0.00 N +ATOM 3260 CA VAL A 213 -19.318 -43.039 37.112 1.00 0.00 C +ATOM 3261 C VAL A 213 -17.929 -42.411 37.103 1.00 0.00 C +ATOM 3262 O VAL A 213 -17.598 -41.616 37.990 1.00 0.00 O +ATOM 3263 CB VAL A 213 -20.367 -42.026 36.614 1.00 0.00 C +ATOM 3264 CG1 VAL A 213 -19.994 -41.513 35.232 1.00 0.00 C +ATOM 3265 CG2 VAL A 213 -21.756 -42.653 36.598 1.00 0.00 C +ATOM 3266 H VAL A 213 -19.434 -42.903 39.232 1.00 0.00 H +ATOM 3267 HA VAL A 213 -19.315 -43.895 36.421 1.00 0.00 H +ATOM 3268 HB VAL A 213 -20.379 -41.174 37.310 1.00 0.00 H +ATOM 3269 HG11 VAL A 213 -20.752 -40.792 34.891 1.00 0.00 H +ATOM 3270 HG12 VAL A 213 -19.012 -41.019 35.277 1.00 0.00 H +ATOM 3271 HG13 VAL A 213 -19.948 -42.357 34.528 1.00 0.00 H +ATOM 3272 HG21 VAL A 213 -22.488 -41.914 36.240 1.00 0.00 H +ATOM 3273 HG22 VAL A 213 -21.757 -43.525 35.927 1.00 0.00 H +ATOM 3274 HG23 VAL A 213 -22.025 -42.973 37.615 1.00 0.00 H +ATOM 3275 N GLU A 214 -17.127 -42.752 36.091 1.00 0.00 N +ATOM 3276 CA GLU A 214 -15.802 -42.179 35.908 1.00 0.00 C +ATOM 3277 C GLU A 214 -15.653 -41.631 34.497 1.00 0.00 C +ATOM 3278 O GLU A 214 -16.274 -42.124 33.551 1.00 0.00 O +ATOM 3279 CB GLU A 214 -14.694 -43.209 36.135 1.00 0.00 C +ATOM 3280 CG GLU A 214 -14.607 -43.807 37.516 1.00 0.00 C +ATOM 3281 CD GLU A 214 -13.621 -44.955 37.546 1.00 0.00 C +ATOM 3282 OE1 GLU A 214 -13.004 -45.215 36.493 1.00 0.00 O +ATOM 3283 OE2 GLU A 214 -13.468 -45.597 38.604 1.00 0.00 O1- +ATOM 3284 H GLU A 214 -17.451 -43.430 35.431 1.00 0.00 H +ATOM 3285 HA GLU A 214 -15.660 -41.353 36.620 1.00 0.00 H +ATOM 3286 HB2 GLU A 214 -14.852 -44.033 35.423 1.00 0.00 H +ATOM 3287 HB3 GLU A 214 -13.733 -42.719 35.922 1.00 0.00 H +ATOM 3288 HG2 GLU A 214 -14.279 -43.032 38.224 1.00 0.00 H +ATOM 3289 HG3 GLU A 214 -15.600 -44.177 37.812 1.00 0.00 H +ATOM 3290 N ILE A 215 -14.800 -40.620 34.362 1.00 0.00 N +ATOM 3291 CA ILE A 215 -14.356 -40.108 33.068 1.00 0.00 C +ATOM 3292 C ILE A 215 -12.838 -40.221 33.048 1.00 0.00 C +ATOM 3293 O ILE A 215 -12.146 -39.470 33.749 1.00 0.00 O +ATOM 3294 CB ILE A 215 -14.800 -38.659 32.824 1.00 0.00 C +ATOM 3295 CG1 ILE A 215 -16.311 -38.512 33.012 1.00 0.00 C +ATOM 3296 CG2 ILE A 215 -14.373 -38.206 31.433 1.00 0.00 C +ATOM 3297 CD1 ILE A 215 -17.134 -39.333 32.046 1.00 0.00 C +ATOM 3298 H ILE A 215 -14.444 -40.187 35.190 1.00 0.00 H +ATOM 3299 HA ILE A 215 -14.766 -40.738 32.265 1.00 0.00 H +ATOM 3300 HB ILE A 215 -14.298 -38.018 33.564 1.00 0.00 H +ATOM 3301 HG12 ILE A 215 -16.564 -38.826 34.035 1.00 0.00 H +ATOM 3302 HG13 ILE A 215 -16.575 -37.453 32.877 1.00 0.00 H +ATOM 3303 HG21 ILE A 215 -14.696 -37.167 31.270 1.00 0.00 H +ATOM 3304 HG22 ILE A 215 -13.278 -38.267 31.348 1.00 0.00 H +ATOM 3305 HG23 ILE A 215 -14.836 -38.857 30.677 1.00 0.00 H +ATOM 3306 HD11 ILE A 215 -18.204 -39.172 32.246 1.00 0.00 H +ATOM 3307 HD12 ILE A 215 -16.904 -39.026 31.015 1.00 0.00 H +ATOM 3308 HD13 ILE A 215 -16.893 -40.399 32.173 1.00 0.00 H +ATOM 3309 N ASN A 216 -12.319 -41.146 32.238 1.00 0.00 N +ATOM 3310 CA ASN A 216 -10.882 -41.432 32.192 1.00 0.00 C +ATOM 3311 C ASN A 216 -10.335 -41.713 33.588 1.00 0.00 C +ATOM 3312 O ASN A 216 -9.271 -41.227 33.974 1.00 0.00 O +ATOM 3313 CB ASN A 216 -10.111 -40.293 31.522 1.00 0.00 C +ATOM 3314 CG ASN A 216 -9.578 -40.677 30.159 1.00 0.00 C +ATOM 3315 OD1 ASN A 216 -9.904 -41.738 29.627 1.00 0.00 O +ATOM 3316 ND2 ASN A 216 -8.760 -39.810 29.580 1.00 0.00 N +ATOM 3317 H ASN A 216 -12.933 -41.663 31.641 1.00 0.00 H +ATOM 3318 HA ASN A 216 -10.729 -42.337 31.586 1.00 0.00 H +ATOM 3319 HB2 ASN A 216 -10.784 -39.431 31.407 1.00 0.00 H +ATOM 3320 HB3 ASN A 216 -9.264 -40.014 32.166 1.00 0.00 H +ATOM 3321 HD21 ASN A 216 -8.382 -40.007 28.675 1.00 0.00 H +ATOM 3322 HD22 ASN A 216 -8.521 -38.959 30.048 1.00 0.00 H +ATOM 3323 N GLY A 217 -11.078 -42.508 34.356 1.00 0.00 N +ATOM 3324 CA GLY A 217 -10.691 -42.865 35.701 1.00 0.00 C +ATOM 3325 C GLY A 217 -11.070 -41.871 36.775 1.00 0.00 C +ATOM 3326 O GLY A 217 -11.057 -42.232 37.959 1.00 0.00 O +ATOM 3327 H GLY A 217 -11.934 -42.871 33.988 1.00 0.00 H +ATOM 3328 HA2 GLY A 217 -11.167 -43.826 35.946 1.00 0.00 H +ATOM 3329 HA3 GLY A 217 -9.597 -42.982 35.719 1.00 0.00 H +ATOM 3330 N GLN A 218 -11.417 -40.640 36.413 1.00 0.00 N +ATOM 3331 CA GLN A 218 -11.710 -39.596 37.388 1.00 0.00 C +ATOM 3332 C GLN A 218 -13.161 -39.707 37.843 1.00 0.00 C +ATOM 3333 O GLN A 218 -14.084 -39.601 37.028 1.00 0.00 O +ATOM 3334 CB GLN A 218 -11.430 -38.216 36.795 1.00 0.00 C +ATOM 3335 CG GLN A 218 -11.600 -37.075 37.774 1.00 0.00 C +ATOM 3336 CD GLN A 218 -10.892 -35.818 37.316 1.00 0.00 C +ATOM 3337 OE1 GLN A 218 -10.439 -35.015 38.131 1.00 0.00 O +ATOM 3338 NE2 GLN A 218 -10.785 -35.644 36.004 1.00 0.00 N +ATOM 3339 H GLN A 218 -11.479 -40.422 35.439 1.00 0.00 H +ATOM 3340 HA GLN A 218 -11.062 -39.731 38.266 1.00 0.00 H +ATOM 3341 HB2 GLN A 218 -10.394 -38.203 36.426 1.00 0.00 H +ATOM 3342 HB3 GLN A 218 -12.121 -38.055 35.954 1.00 0.00 H +ATOM 3343 HG2 GLN A 218 -12.673 -36.858 37.882 1.00 0.00 H +ATOM 3344 HG3 GLN A 218 -11.188 -37.379 38.747 1.00 0.00 H +ATOM 3345 HE21 GLN A 218 -10.321 -34.835 35.643 1.00 0.00 H +ATOM 3346 HE22 GLN A 218 -11.168 -36.322 35.377 1.00 0.00 H +ATOM 3347 N ASP A 219 -13.353 -39.912 39.145 1.00 0.00 N +ATOM 3348 CA ASP A 219 -14.691 -40.044 39.709 1.00 0.00 C +ATOM 3349 C ASP A 219 -15.498 -38.766 39.501 1.00 0.00 C +ATOM 3350 O ASP A 219 -15.020 -37.663 39.785 1.00 0.00 O +ATOM 3351 CB ASP A 219 -14.583 -40.371 41.199 1.00 0.00 C +ATOM 3352 CG ASP A 219 -15.930 -40.584 41.850 1.00 0.00 C +ATOM 3353 OD1 ASP A 219 -16.911 -40.838 41.122 1.00 0.00 O +ATOM 3354 OD2 ASP A 219 -16.008 -40.495 43.094 1.00 0.00 O1- +ATOM 3355 H ASP A 219 -12.560 -39.977 39.751 1.00 0.00 H +ATOM 3356 HA ASP A 219 -15.216 -40.872 39.210 1.00 0.00 H +ATOM 3357 HB2 ASP A 219 -13.987 -41.288 41.315 1.00 0.00 H +ATOM 3358 HB3 ASP A 219 -14.075 -39.537 41.706 1.00 0.00 H +ATOM 3359 N LEU A 220 -16.735 -38.918 39.007 1.00 0.00 N +ATOM 3360 CA LEU A 220 -17.616 -37.765 38.845 1.00 0.00 C +ATOM 3361 C LEU A 220 -18.100 -37.240 40.192 1.00 0.00 C +ATOM 3362 O LEU A 220 -18.390 -36.044 40.319 1.00 0.00 O +ATOM 3363 CB LEU A 220 -18.801 -38.129 37.943 1.00 0.00 C +ATOM 3364 CG LEU A 220 -19.300 -37.073 36.950 1.00 0.00 C +ATOM 3365 CD1 LEU A 220 -18.194 -36.649 35.997 1.00 0.00 C +ATOM 3366 CD2 LEU A 220 -20.495 -37.608 36.171 1.00 0.00 C +ATOM 3367 H LEU A 220 -17.057 -39.828 38.747 1.00 0.00 H +ATOM 3368 HA LEU A 220 -17.054 -36.959 38.351 1.00 0.00 H +ATOM 3369 HB2 LEU A 220 -18.508 -39.014 37.360 1.00 0.00 H +ATOM 3370 HB3 LEU A 220 -19.645 -38.386 38.600 1.00 0.00 H +ATOM 3371 HG LEU A 220 -19.625 -36.190 37.520 1.00 0.00 H +ATOM 3372 HD11 LEU A 220 -18.581 -35.893 35.298 1.00 0.00 H +ATOM 3373 HD12 LEU A 220 -17.358 -36.223 36.572 1.00 0.00 H +ATOM 3374 HD13 LEU A 220 -17.842 -37.524 35.431 1.00 0.00 H +ATOM 3375 HD21 LEU A 220 -20.844 -36.842 35.462 1.00 0.00 H +ATOM 3376 HD22 LEU A 220 -20.197 -38.511 35.618 1.00 0.00 H +ATOM 3377 HD23 LEU A 220 -21.307 -37.857 36.870 1.00 0.00 H +ATOM 3378 N LYS A 221 -18.223 -38.124 41.189 1.00 0.00 N +ATOM 3379 CA LYS A 221 -18.277 -37.745 42.607 1.00 0.00 C +ATOM 3380 C LYS A 221 -19.606 -37.084 42.988 1.00 0.00 C +ATOM 3381 O LYS A 221 -19.640 -35.979 43.530 1.00 0.00 O +ATOM 3382 CB LYS A 221 -17.092 -36.842 42.971 1.00 0.00 C +ATOM 3383 CG LYS A 221 -16.706 -36.870 44.437 1.00 0.00 C +ATOM 3384 CD LYS A 221 -15.474 -36.016 44.691 1.00 0.00 C +ATOM 3385 CE LYS A 221 -15.097 -36.022 46.164 1.00 0.00 C +ATOM 3386 NZ LYS A 221 -13.904 -35.176 46.444 1.00 0.00 N1+ +ATOM 3387 H LYS A 221 -18.281 -39.095 40.956 1.00 0.00 H +ATOM 3388 HA LYS A 221 -18.185 -38.662 43.208 1.00 0.00 H +ATOM 3389 HB2 LYS A 221 -16.221 -37.163 42.381 1.00 0.00 H +ATOM 3390 HB3 LYS A 221 -17.354 -35.807 42.705 1.00 0.00 H +ATOM 3391 HG2 LYS A 221 -17.543 -36.482 45.037 1.00 0.00 H +ATOM 3392 HG3 LYS A 221 -16.492 -37.908 44.733 1.00 0.00 H +ATOM 3393 HD2 LYS A 221 -14.634 -36.414 44.104 1.00 0.00 H +ATOM 3394 HD3 LYS A 221 -15.683 -34.982 44.378 1.00 0.00 H +ATOM 3395 HE2 LYS A 221 -15.947 -35.640 46.749 1.00 0.00 H +ATOM 3396 HE3 LYS A 221 -14.877 -37.056 46.469 1.00 0.00 H +ATOM 3397 HZ1 LYS A 221 -13.693 -35.209 47.421 1.00 0.00 H +ATOM 3398 HZ2 LYS A 221 -13.123 -35.519 45.922 1.00 0.00 H +ATOM 3399 HZ3 LYS A 221 -14.095 -34.232 46.176 1.00 0.00 H +ATOM 3400 N MET A 222 -20.706 -37.783 42.724 1.00 0.00 N +ATOM 3401 CA MET A 222 -22.030 -37.323 43.112 1.00 0.00 C +ATOM 3402 C MET A 222 -22.695 -38.353 44.014 1.00 0.00 C +ATOM 3403 O MET A 222 -22.234 -39.492 44.142 1.00 0.00 O +ATOM 3404 CB MET A 222 -22.914 -37.062 41.884 1.00 0.00 C +ATOM 3405 CG MET A 222 -22.298 -36.116 40.865 1.00 0.00 C +ATOM 3406 SD MET A 222 -23.199 -36.039 39.304 1.00 0.00 S +ATOM 3407 CE MET A 222 -24.778 -35.370 39.830 1.00 0.00 C +ATOM 3408 H MET A 222 -20.620 -38.655 42.242 1.00 0.00 H +ATOM 3409 HA MET A 222 -21.936 -36.382 43.673 1.00 0.00 H +ATOM 3410 HB2 MET A 222 -23.109 -38.024 41.388 1.00 0.00 H +ATOM 3411 HB3 MET A 222 -23.864 -36.628 42.228 1.00 0.00 H +ATOM 3412 HG2 MET A 222 -22.272 -35.106 41.301 1.00 0.00 H +ATOM 3413 HG3 MET A 222 -21.271 -36.452 40.657 1.00 0.00 H +ATOM 3414 HE1 MET A 222 -25.423 -35.217 38.952 1.00 0.00 H +ATOM 3415 HE2 MET A 222 -25.262 -36.075 40.522 1.00 0.00 H +ATOM 3416 HE3 MET A 222 -24.617 -34.408 40.339 1.00 0.00 H +ATOM 3417 N ASP A 223 -23.774 -37.930 44.666 1.00 0.00 N +ATOM 3418 CA ASP A 223 -24.653 -38.885 45.320 1.00 0.00 C +ATOM 3419 C ASP A 223 -25.124 -39.890 44.278 1.00 0.00 C +ATOM 3420 O ASP A 223 -25.668 -39.509 43.237 1.00 0.00 O +ATOM 3421 CB ASP A 223 -25.842 -38.164 45.953 1.00 0.00 C +ATOM 3422 CG ASP A 223 -26.550 -39.004 46.997 1.00 0.00 C +ATOM 3423 OD1 ASP A 223 -26.513 -40.247 46.892 1.00 0.00 O +ATOM 3424 OD2 ASP A 223 -27.145 -38.415 47.925 1.00 0.00 O1- +ATOM 3425 H ASP A 223 -23.981 -36.952 44.706 1.00 0.00 H +ATOM 3426 HA ASP A 223 -24.102 -39.420 46.108 1.00 0.00 H +ATOM 3427 HB2 ASP A 223 -25.480 -37.242 46.431 1.00 0.00 H +ATOM 3428 HB3 ASP A 223 -26.561 -37.909 45.160 1.00 0.00 H +ATOM 3429 N CYS A 224 -24.893 -41.179 44.541 1.00 0.00 N +ATOM 3430 CA CYS A 224 -25.154 -42.168 43.500 1.00 0.00 C +ATOM 3431 C CYS A 224 -26.634 -42.299 43.166 1.00 0.00 C +ATOM 3432 O CYS A 224 -26.969 -42.921 42.155 1.00 0.00 O +ATOM 3433 CB CYS A 224 -24.579 -43.533 43.885 1.00 0.00 C +ATOM 3434 SG CYS A 224 -25.288 -44.308 45.346 1.00 0.00 S +ATOM 3435 H CYS A 224 -24.547 -41.461 45.436 1.00 0.00 H +ATOM 3436 HA CYS A 224 -24.639 -41.844 42.584 1.00 0.00 H +ATOM 3437 HB2 CYS A 224 -24.733 -44.214 43.035 1.00 0.00 H +ATOM 3438 HB3 CYS A 224 -23.501 -43.405 44.062 1.00 0.00 H +ATOM 3439 N LYS A 225 -27.528 -41.730 43.976 1.00 0.00 N +ATOM 3440 CA LYS A 225 -28.931 -41.671 43.573 1.00 0.00 C +ATOM 3441 C LYS A 225 -29.126 -40.799 42.341 1.00 0.00 C +ATOM 3442 O LYS A 225 -30.038 -41.047 41.545 1.00 0.00 O +ATOM 3443 CB LYS A 225 -29.800 -41.150 44.717 1.00 0.00 C +ATOM 3444 CG LYS A 225 -30.060 -42.134 45.839 1.00 0.00 C +ATOM 3445 CD LYS A 225 -31.003 -41.508 46.857 1.00 0.00 C +ATOM 3446 CE LYS A 225 -31.379 -42.467 47.974 1.00 0.00 C +ATOM 3447 NZ LYS A 225 -30.199 -42.876 48.785 1.00 0.00 N1+ +ATOM 3448 H LYS A 225 -27.240 -41.346 44.853 1.00 0.00 H +ATOM 3449 HA LYS A 225 -29.271 -42.688 43.326 1.00 0.00 H +ATOM 3450 HB2 LYS A 225 -29.301 -40.269 45.147 1.00 0.00 H +ATOM 3451 HB3 LYS A 225 -30.771 -40.852 44.296 1.00 0.00 H +ATOM 3452 HG2 LYS A 225 -30.518 -43.046 45.428 1.00 0.00 H +ATOM 3453 HG3 LYS A 225 -29.109 -42.390 46.329 1.00 0.00 H +ATOM 3454 HD2 LYS A 225 -30.511 -40.629 47.299 1.00 0.00 H +ATOM 3455 HD3 LYS A 225 -31.921 -41.193 46.339 1.00 0.00 H +ATOM 3456 HE2 LYS A 225 -32.109 -41.974 48.633 1.00 0.00 H +ATOM 3457 HE3 LYS A 225 -31.833 -43.366 47.531 1.00 0.00 H +ATOM 3458 HZ1 LYS A 225 -30.489 -43.068 49.723 1.00 0.00 H +ATOM 3459 HZ2 LYS A 225 -29.791 -43.698 48.388 1.00 0.00 H +ATOM 3460 HZ3 LYS A 225 -29.525 -42.137 48.789 1.00 0.00 H +ATOM 3461 N GLU A 226 -28.288 -39.774 42.167 1.00 0.00 N +ATOM 3462 CA GLU A 226 -28.431 -38.894 41.011 1.00 0.00 C +ATOM 3463 C GLU A 226 -28.237 -39.651 39.707 1.00 0.00 C +ATOM 3464 O GLU A 226 -28.841 -39.297 38.688 1.00 0.00 O +ATOM 3465 CB GLU A 226 -27.435 -37.738 41.102 1.00 0.00 C +ATOM 3466 CG GLU A 226 -27.584 -36.900 42.355 1.00 0.00 C +ATOM 3467 CD GLU A 226 -28.835 -36.047 42.342 1.00 0.00 C +ATOM 3468 OE1 GLU A 226 -29.239 -35.598 41.248 1.00 0.00 O +ATOM 3469 OE2 GLU A 226 -29.418 -35.833 43.424 1.00 0.00 O1- +ATOM 3470 H GLU A 226 -27.559 -39.609 42.832 1.00 0.00 H +ATOM 3471 HA GLU A 226 -29.445 -38.469 41.011 1.00 0.00 H +ATOM 3472 HB2 GLU A 226 -26.417 -38.155 41.083 1.00 0.00 H +ATOM 3473 HB3 GLU A 226 -27.581 -37.085 40.229 1.00 0.00 H +ATOM 3474 HG2 GLU A 226 -27.625 -37.572 43.225 1.00 0.00 H +ATOM 3475 HG3 GLU A 226 -26.709 -36.240 42.443 1.00 0.00 H +ATOM 3476 N TYR A 227 -27.402 -40.692 39.718 1.00 0.00 N +ATOM 3477 CA TYR A 227 -27.216 -41.501 38.522 1.00 0.00 C +ATOM 3478 C TYR A 227 -28.469 -42.288 38.151 1.00 0.00 C +ATOM 3479 O TYR A 227 -28.612 -42.685 36.990 1.00 0.00 O +ATOM 3480 CB TYR A 227 -26.042 -42.462 38.716 1.00 0.00 C +ATOM 3481 CG TYR A 227 -24.726 -41.797 39.077 1.00 0.00 C +ATOM 3482 CD1 TYR A 227 -24.279 -40.666 38.406 1.00 0.00 C +ATOM 3483 CD2 TYR A 227 -23.930 -42.309 40.092 1.00 0.00 C +ATOM 3484 CE1 TYR A 227 -23.072 -40.063 38.737 1.00 0.00 C +ATOM 3485 CE2 TYR A 227 -22.726 -41.714 40.430 1.00 0.00 C +ATOM 3486 CZ TYR A 227 -22.301 -40.592 39.751 1.00 0.00 C +ATOM 3487 OH TYR A 227 -21.101 -40.001 40.087 1.00 0.00 O +ATOM 3488 H TYR A 227 -26.901 -40.918 40.553 1.00 0.00 H +ATOM 3489 HA TYR A 227 -26.972 -40.837 37.680 1.00 0.00 H +ATOM 3490 HB2 TYR A 227 -26.303 -43.163 39.522 1.00 0.00 H +ATOM 3491 HB3 TYR A 227 -25.898 -43.019 37.779 1.00 0.00 H +ATOM 3492 HD1 TYR A 227 -24.881 -40.248 37.613 1.00 0.00 H +ATOM 3493 HD2 TYR A 227 -24.256 -43.188 40.628 1.00 0.00 H +ATOM 3494 HE1 TYR A 227 -22.739 -39.185 38.203 1.00 0.00 H +ATOM 3495 HE2 TYR A 227 -22.122 -42.128 41.223 1.00 0.00 H +ATOM 3496 HH TYR A 227 -21.196 -39.056 40.065 1.00 0.00 H +ATOM 3497 N ASN A 228 -29.377 -42.525 39.100 1.00 0.00 N +ATOM 3498 CA ASN A 228 -30.558 -43.349 38.860 1.00 0.00 C +ATOM 3499 C ASN A 228 -31.856 -42.607 39.171 1.00 0.00 C +ATOM 3500 O ASN A 228 -32.839 -43.222 39.588 1.00 0.00 O +ATOM 3501 CB ASN A 228 -30.498 -44.646 39.670 1.00 0.00 C +ATOM 3502 CG ASN A 228 -29.164 -45.358 39.549 1.00 0.00 C +ATOM 3503 OD1 ASN A 228 -28.828 -45.904 38.498 1.00 0.00 O +ATOM 3504 ND2 ASN A 228 -28.401 -45.367 40.636 1.00 0.00 N +ATOM 3505 H ASN A 228 -29.244 -42.124 40.007 1.00 0.00 H +ATOM 3506 HA ASN A 228 -30.579 -43.624 37.795 1.00 0.00 H +ATOM 3507 HB2 ASN A 228 -30.673 -44.406 40.729 1.00 0.00 H +ATOM 3508 HB3 ASN A 228 -31.289 -45.320 39.311 1.00 0.00 H +ATOM 3509 HD21 ASN A 228 -27.515 -45.830 40.621 1.00 0.00 H +ATOM 3510 HD22 ASN A 228 -28.713 -44.911 41.469 1.00 0.00 H +ATOM 3511 N TYR A 229 -31.882 -41.294 38.973 1.00 0.00 N +ATOM 3512 CA TYR A 229 -33.086 -40.502 39.202 1.00 0.00 C +ATOM 3513 C TYR A 229 -33.710 -40.156 37.853 1.00 0.00 C +ATOM 3514 O TYR A 229 -33.122 -39.406 37.067 1.00 0.00 O +ATOM 3515 CB TYR A 229 -32.783 -39.237 40.001 1.00 0.00 C +ATOM 3516 CG TYR A 229 -33.998 -38.360 40.115 1.00 0.00 C +ATOM 3517 CD1 TYR A 229 -35.048 -38.719 40.942 1.00 0.00 C +ATOM 3518 CD2 TYR A 229 -34.113 -37.195 39.371 1.00 0.00 C +ATOM 3519 CE1 TYR A 229 -36.173 -37.938 41.044 1.00 0.00 C +ATOM 3520 CE2 TYR A 229 -35.237 -36.403 39.465 1.00 0.00 C +ATOM 3521 CZ TYR A 229 -36.266 -36.779 40.304 1.00 0.00 C +ATOM 3522 OH TYR A 229 -37.397 -36.000 40.407 1.00 0.00 O +ATOM 3523 H TYR A 229 -31.052 -40.834 38.658 1.00 0.00 H +ATOM 3524 HA TYR A 229 -33.810 -41.104 39.772 1.00 0.00 H +ATOM 3525 HB2 TYR A 229 -32.450 -39.521 41.010 1.00 0.00 H +ATOM 3526 HB3 TYR A 229 -31.983 -38.677 39.495 1.00 0.00 H +ATOM 3527 HD1 TYR A 229 -34.981 -39.630 41.518 1.00 0.00 H +ATOM 3528 HD2 TYR A 229 -33.311 -36.904 38.709 1.00 0.00 H +ATOM 3529 HE1 TYR A 229 -36.980 -38.230 41.700 1.00 0.00 H +ATOM 3530 HE2 TYR A 229 -35.312 -35.494 38.886 1.00 0.00 H +ATOM 3531 HH TYR A 229 -37.148 -35.085 40.466 1.00 0.00 H +ATOM 3532 N ASP A 230 -34.911 -40.673 37.596 1.00 0.00 N +ATOM 3533 CA ASP A 230 -35.668 -41.451 38.577 1.00 0.00 C +ATOM 3534 C ASP A 230 -35.618 -42.943 38.268 1.00 0.00 C +ATOM 3535 O ASP A 230 -36.261 -43.752 38.941 1.00 0.00 O +ATOM 3536 CB ASP A 230 -37.118 -40.975 38.630 1.00 0.00 C +ATOM 3537 CG ASP A 230 -37.800 -41.024 37.276 1.00 0.00 C +ATOM 3538 OD1 ASP A 230 -37.092 -41.132 36.253 1.00 0.00 O +ATOM 3539 OD2 ASP A 230 -39.047 -40.945 37.238 1.00 0.00 O1- +ATOM 3540 H ASP A 230 -35.313 -40.523 36.693 1.00 0.00 H +ATOM 3541 HA ASP A 230 -35.224 -41.295 39.571 1.00 0.00 H +ATOM 3542 HB2 ASP A 230 -37.674 -41.618 39.328 1.00 0.00 H +ATOM 3543 HB3 ASP A 230 -37.135 -39.938 38.995 1.00 0.00 H +ATOM 3544 N LYS A 231 -34.852 -43.294 37.239 1.00 0.00 N +ATOM 3545 CA LYS A 231 -34.632 -44.686 36.876 1.00 0.00 C +ATOM 3546 C LYS A 231 -33.446 -44.752 35.925 1.00 0.00 C +ATOM 3547 O LYS A 231 -33.059 -43.753 35.313 1.00 0.00 O +ATOM 3548 CB LYS A 231 -35.879 -45.312 36.236 1.00 0.00 C +ATOM 3549 CG LYS A 231 -36.200 -44.782 34.847 1.00 0.00 C +ATOM 3550 CD LYS A 231 -37.503 -45.361 34.306 1.00 0.00 C +ATOM 3551 CE LYS A 231 -37.883 -44.727 32.970 1.00 0.00 C +ATOM 3552 NZ LYS A 231 -38.221 -43.279 33.092 1.00 0.00 N1+ +ATOM 3553 H LYS A 231 -34.413 -42.578 36.697 1.00 0.00 H +ATOM 3554 HA LYS A 231 -34.384 -45.262 37.780 1.00 0.00 H +ATOM 3555 HB2 LYS A 231 -35.721 -46.398 36.163 1.00 0.00 H +ATOM 3556 HB3 LYS A 231 -36.740 -45.110 36.890 1.00 0.00 H +ATOM 3557 HG2 LYS A 231 -36.291 -43.687 34.896 1.00 0.00 H +ATOM 3558 HG3 LYS A 231 -35.380 -45.052 34.165 1.00 0.00 H +ATOM 3559 HD2 LYS A 231 -37.382 -46.445 34.166 1.00 0.00 H +ATOM 3560 HD3 LYS A 231 -38.307 -45.173 35.033 1.00 0.00 H +ATOM 3561 HE2 LYS A 231 -37.035 -44.833 32.277 1.00 0.00 H +ATOM 3562 HE3 LYS A 231 -38.756 -45.259 32.564 1.00 0.00 H +ATOM 3563 HZ1 LYS A 231 -38.891 -43.032 32.392 1.00 0.00 H +ATOM 3564 HZ2 LYS A 231 -38.605 -43.102 33.998 1.00 0.00 H +ATOM 3565 HZ3 LYS A 231 -37.393 -42.732 32.968 1.00 0.00 H +ATOM 3566 N SER A 232 -32.861 -45.941 35.828 1.00 0.00 N +ATOM 3567 CA SER A 232 -31.796 -46.232 34.877 1.00 0.00 C +ATOM 3568 C SER A 232 -32.203 -47.470 34.094 1.00 0.00 C +ATOM 3569 O SER A 232 -32.444 -48.525 34.688 1.00 0.00 O +ATOM 3570 CB SER A 232 -30.458 -46.454 35.588 1.00 0.00 C +ATOM 3571 OG SER A 232 -29.871 -45.227 35.983 1.00 0.00 O +ATOM 3572 H SER A 232 -33.167 -46.672 36.438 1.00 0.00 H +ATOM 3573 HA SER A 232 -31.686 -45.391 34.177 1.00 0.00 H +ATOM 3574 HB2 SER A 232 -30.626 -47.074 36.481 1.00 0.00 H +ATOM 3575 HB3 SER A 232 -29.772 -46.975 34.904 1.00 0.00 H +ATOM 3576 HG SER A 232 -29.350 -45.362 36.766 1.00 0.00 H +ATOM 3577 N ILE A 233 -32.294 -47.342 32.769 1.00 0.00 N +ATOM 3578 CA ILE A 233 -32.775 -48.429 31.929 1.00 0.00 C +ATOM 3579 C ILE A 233 -31.815 -48.656 30.769 1.00 0.00 C +ATOM 3580 O ILE A 233 -30.983 -47.808 30.433 1.00 0.00 O +ATOM 3581 CB ILE A 233 -34.202 -48.171 31.392 1.00 0.00 C +ATOM 3582 CG1 ILE A 233 -34.175 -47.073 30.330 1.00 0.00 C +ATOM 3583 CG2 ILE A 233 -35.151 -47.805 32.526 1.00 0.00 C +ATOM 3584 CD1 ILE A 233 -35.464 -46.955 29.542 1.00 0.00 C +ATOM 3585 H ILE A 233 -32.025 -46.478 32.343 1.00 0.00 H +ATOM 3586 HA ILE A 233 -32.806 -49.351 32.528 1.00 0.00 H +ATOM 3587 HB ILE A 233 -34.563 -49.098 30.923 1.00 0.00 H +ATOM 3588 HG12 ILE A 233 -33.984 -46.112 30.829 1.00 0.00 H +ATOM 3589 HG13 ILE A 233 -33.357 -47.290 29.627 1.00 0.00 H +ATOM 3590 HG21 ILE A 233 -36.157 -47.627 32.119 1.00 0.00 H +ATOM 3591 HG22 ILE A 233 -35.190 -48.630 33.252 1.00 0.00 H +ATOM 3592 HG23 ILE A 233 -34.791 -46.894 33.026 1.00 0.00 H +ATOM 3593 HD11 ILE A 233 -35.369 -46.148 28.801 1.00 0.00 H +ATOM 3594 HD12 ILE A 233 -35.666 -47.905 29.026 1.00 0.00 H +ATOM 3595 HD13 ILE A 233 -36.293 -46.727 30.228 1.00 0.00 H +ATOM 3596 N VAL A 234 -31.954 -49.830 30.160 1.00 0.00 N +ATOM 3597 CA VAL A 234 -31.153 -50.265 29.022 1.00 0.00 C +ATOM 3598 C VAL A 234 -32.090 -50.352 27.824 1.00 0.00 C +ATOM 3599 O VAL A 234 -32.998 -51.191 27.800 1.00 0.00 O +ATOM 3600 CB VAL A 234 -30.474 -51.613 29.297 1.00 0.00 C +ATOM 3601 CG1 VAL A 234 -29.689 -52.076 28.084 1.00 0.00 C +ATOM 3602 CG2 VAL A 234 -29.585 -51.511 30.532 1.00 0.00 C +ATOM 3603 H VAL A 234 -32.654 -50.456 30.505 1.00 0.00 H +ATOM 3604 HA VAL A 234 -30.375 -49.517 28.809 1.00 0.00 H +ATOM 3605 HB VAL A 234 -31.260 -52.355 29.502 1.00 0.00 H +ATOM 3606 HG11 VAL A 234 -29.211 -53.042 28.303 1.00 0.00 H +ATOM 3607 HG12 VAL A 234 -30.370 -52.189 27.228 1.00 0.00 H +ATOM 3608 HG13 VAL A 234 -28.916 -51.332 27.842 1.00 0.00 H +ATOM 3609 HG21 VAL A 234 -29.104 -52.482 30.719 1.00 0.00 H +ATOM 3610 HG22 VAL A 234 -28.813 -50.745 30.366 1.00 0.00 H +ATOM 3611 HG23 VAL A 234 -30.197 -51.231 31.402 1.00 0.00 H +ATOM 3612 N ASP A 235 -31.876 -49.499 26.824 1.00 0.00 N +ATOM 3613 CA ASP A 235 -32.873 -49.258 25.780 1.00 0.00 C +ATOM 3614 C ASP A 235 -32.218 -49.281 24.401 1.00 0.00 C +ATOM 3615 O ASP A 235 -31.535 -48.328 24.017 1.00 0.00 O +ATOM 3616 CB ASP A 235 -33.578 -47.929 26.030 1.00 0.00 C +ATOM 3617 CG ASP A 235 -34.644 -47.623 24.995 1.00 0.00 C +ATOM 3618 OD1 ASP A 235 -35.096 -46.459 24.936 1.00 0.00 O +ATOM 3619 OD2 ASP A 235 -35.026 -48.541 24.240 1.00 0.00 O1- +ATOM 3620 H ASP A 235 -31.006 -49.007 26.785 1.00 0.00 H +ATOM 3621 HA ASP A 235 -33.627 -50.058 25.815 1.00 0.00 H +ATOM 3622 HB2 ASP A 235 -34.052 -47.964 27.022 1.00 0.00 H +ATOM 3623 HB3 ASP A 235 -32.828 -47.125 26.010 1.00 0.00 H +ATOM 3624 N SER A 236 -32.464 -50.352 23.641 1.00 0.00 N +ATOM 3625 CA SER A 236 -31.954 -50.429 22.278 1.00 0.00 C +ATOM 3626 C SER A 236 -32.717 -49.537 21.308 1.00 0.00 C +ATOM 3627 O SER A 236 -32.288 -49.396 20.158 1.00 0.00 O +ATOM 3628 CB SER A 236 -31.995 -51.871 21.772 1.00 0.00 C +ATOM 3629 OG SER A 236 -33.329 -52.298 21.564 1.00 0.00 O +ATOM 3630 H SER A 236 -33.004 -51.108 24.011 1.00 0.00 H +ATOM 3631 HA SER A 236 -30.903 -50.104 22.278 1.00 0.00 H +ATOM 3632 HB2 SER A 236 -31.445 -51.935 20.822 1.00 0.00 H +ATOM 3633 HB3 SER A 236 -31.519 -52.527 22.516 1.00 0.00 H +ATOM 3634 HG SER A 236 -33.597 -52.081 20.679 1.00 0.00 H +ATOM 3635 N GLY A 237 -33.833 -48.950 21.727 1.00 0.00 N +ATOM 3636 CA GLY A 237 -34.587 -48.022 20.912 1.00 0.00 C +ATOM 3637 C GLY A 237 -34.179 -46.575 21.065 1.00 0.00 C +ATOM 3638 O GLY A 237 -34.915 -45.680 20.635 1.00 0.00 O +ATOM 3639 H GLY A 237 -34.167 -49.159 22.646 1.00 0.00 H +ATOM 3640 HA2 GLY A 237 -34.455 -48.306 19.858 1.00 0.00 H +ATOM 3641 HA3 GLY A 237 -35.649 -48.110 21.185 1.00 0.00 H +ATOM 3642 N THR A 238 -33.029 -46.317 21.683 1.00 0.00 N +ATOM 3643 CA THR A 238 -32.495 -44.974 21.847 1.00 0.00 C +ATOM 3644 C THR A 238 -31.061 -44.973 21.354 1.00 0.00 C +ATOM 3645 O THR A 238 -30.304 -45.903 21.645 1.00 0.00 O +ATOM 3646 CB THR A 238 -32.551 -44.523 23.311 1.00 0.00 C +ATOM 3647 OG1 THR A 238 -33.917 -44.414 23.728 1.00 0.00 O +ATOM 3648 CG2 THR A 238 -31.863 -43.172 23.486 1.00 0.00 C +ATOM 3649 H THR A 238 -32.506 -47.085 22.053 1.00 0.00 H +ATOM 3650 HA THR A 238 -33.079 -44.267 21.240 1.00 0.00 H +ATOM 3651 HB THR A 238 -32.028 -45.268 23.928 1.00 0.00 H +ATOM 3652 HG1 THR A 238 -34.273 -45.282 23.879 1.00 0.00 H +ATOM 3653 HG21 THR A 238 -31.915 -42.868 24.542 1.00 0.00 H +ATOM 3654 HG22 THR A 238 -30.810 -43.255 23.179 1.00 0.00 H +ATOM 3655 HG23 THR A 238 -32.368 -42.420 22.863 1.00 0.00 H +ATOM 3656 N THR A 239 -30.694 -43.944 20.596 1.00 0.00 N +ATOM 3657 CA THR A 239 -29.318 -43.848 20.128 1.00 0.00 C +ATOM 3658 C THR A 239 -28.376 -43.490 21.269 1.00 0.00 C +ATOM 3659 O THR A 239 -27.389 -44.191 21.518 1.00 0.00 O +ATOM 3660 CB THR A 239 -29.210 -42.812 19.013 1.00 0.00 C +ATOM 3661 OG1 THR A 239 -29.927 -43.267 17.860 1.00 0.00 O +ATOM 3662 CG2 THR A 239 -27.754 -42.598 18.646 1.00 0.00 C +ATOM 3663 H THR A 239 -31.359 -43.240 20.348 1.00 0.00 H +ATOM 3664 HA THR A 239 -29.009 -44.822 19.721 1.00 0.00 H +ATOM 3665 HB THR A 239 -29.632 -41.861 19.370 1.00 0.00 H +ATOM 3666 HG1 THR A 239 -29.324 -43.375 17.134 1.00 0.00 H +ATOM 3667 HG21 THR A 239 -27.684 -41.850 17.842 1.00 0.00 H +ATOM 3668 HG22 THR A 239 -27.202 -42.241 19.528 1.00 0.00 H +ATOM 3669 HG23 THR A 239 -27.319 -43.548 18.302 1.00 0.00 H +ATOM 3670 N ASN A 240 -28.690 -42.424 21.994 1.00 0.00 N +ATOM 3671 CA ASN A 240 -27.738 -41.752 22.862 1.00 0.00 C +ATOM 3672 C ASN A 240 -27.616 -42.419 24.223 1.00 0.00 C +ATOM 3673 O ASN A 240 -28.499 -43.150 24.677 1.00 0.00 O +ATOM 3674 CB ASN A 240 -28.147 -40.295 23.077 1.00 0.00 C +ATOM 3675 CG ASN A 240 -27.966 -39.449 21.839 1.00 0.00 C +ATOM 3676 OD1 ASN A 240 -27.559 -39.939 20.788 1.00 0.00 O +ATOM 3677 ND2 ASN A 240 -28.274 -38.162 21.958 1.00 0.00 N +ATOM 3678 H ASN A 240 -29.623 -42.067 21.941 1.00 0.00 H +ATOM 3679 HA ASN A 240 -26.747 -41.762 22.385 1.00 0.00 H +ATOM 3680 HB2 ASN A 240 -29.207 -40.267 23.370 1.00 0.00 H +ATOM 3681 HB3 ASN A 240 -27.531 -39.872 23.885 1.00 0.00 H +ATOM 3682 HD21 ASN A 240 -28.178 -37.550 21.173 1.00 0.00 H +ATOM 3683 HD22 ASN A 240 -28.602 -37.805 22.833 1.00 0.00 H +ATOM 3684 N LEU A 241 -26.486 -42.145 24.872 1.00 0.00 N +ATOM 3685 CA LEU A 241 -26.392 -42.226 26.321 1.00 0.00 C +ATOM 3686 C LEU A 241 -27.049 -40.970 26.880 1.00 0.00 C +ATOM 3687 O LEU A 241 -26.539 -39.859 26.695 1.00 0.00 O +ATOM 3688 CB LEU A 241 -24.936 -42.340 26.766 1.00 0.00 C +ATOM 3689 CG LEU A 241 -24.706 -42.306 28.278 1.00 0.00 C +ATOM 3690 CD1 LEU A 241 -25.477 -43.426 28.954 1.00 0.00 C +ATOM 3691 CD2 LEU A 241 -23.220 -42.385 28.622 1.00 0.00 C +ATOM 3692 H LEU A 241 -25.678 -41.876 24.348 1.00 0.00 H +ATOM 3693 HA LEU A 241 -26.944 -43.107 26.682 1.00 0.00 H +ATOM 3694 HB2 LEU A 241 -24.538 -43.292 26.384 1.00 0.00 H +ATOM 3695 HB3 LEU A 241 -24.378 -41.504 26.319 1.00 0.00 H +ATOM 3696 HG LEU A 241 -25.092 -41.348 28.658 1.00 0.00 H +ATOM 3697 HD11 LEU A 241 -25.302 -43.389 30.039 1.00 0.00 H +ATOM 3698 HD12 LEU A 241 -26.552 -43.306 28.751 1.00 0.00 H +ATOM 3699 HD13 LEU A 241 -25.136 -44.395 28.561 1.00 0.00 H +ATOM 3700 HD21 LEU A 241 -23.094 -42.358 29.714 1.00 0.00 H +ATOM 3701 HD22 LEU A 241 -22.802 -43.323 28.228 1.00 0.00 H +ATOM 3702 HD23 LEU A 241 -22.694 -41.531 28.171 1.00 0.00 H +ATOM 3703 N ARG A 242 -28.194 -41.136 27.533 1.00 0.00 N +ATOM 3704 CA ARG A 242 -28.951 -40.026 28.093 1.00 0.00 C +ATOM 3705 C ARG A 242 -28.810 -40.034 29.608 1.00 0.00 C +ATOM 3706 O ARG A 242 -28.955 -41.078 30.251 1.00 0.00 O +ATOM 3707 CB ARG A 242 -30.427 -40.106 27.680 1.00 0.00 C +ATOM 3708 CG ARG A 242 -30.610 -40.196 26.167 1.00 0.00 C +ATOM 3709 CD ARG A 242 -32.070 -40.097 25.708 1.00 0.00 C +ATOM 3710 NE ARG A 242 -32.647 -38.768 25.901 1.00 0.00 N +ATOM 3711 CZ ARG A 242 -33.757 -38.532 26.594 1.00 0.00 C +ATOM 3712 NH1 ARG A 242 -34.419 -39.538 27.150 1.00 0.00 N +ATOM 3713 NH2 ARG A 242 -34.211 -37.292 26.723 1.00 0.00 N1+ +ATOM 3714 H ARG A 242 -28.551 -42.063 27.645 1.00 0.00 H +ATOM 3715 HA ARG A 242 -28.539 -39.079 27.712 1.00 0.00 H +ATOM 3716 HB2 ARG A 242 -30.874 -40.998 28.142 1.00 0.00 H +ATOM 3717 HB3 ARG A 242 -30.944 -39.206 28.044 1.00 0.00 H +ATOM 3718 HG2 ARG A 242 -30.045 -39.376 25.701 1.00 0.00 H +ATOM 3719 HG3 ARG A 242 -30.205 -41.160 25.826 1.00 0.00 H +ATOM 3720 HD2 ARG A 242 -32.117 -40.346 24.638 1.00 0.00 H +ATOM 3721 HD3 ARG A 242 -32.666 -40.823 26.280 1.00 0.00 H +ATOM 3722 HE ARG A 242 -32.178 -37.988 25.486 1.00 0.00 H +ATOM 3723 HH11 ARG A 242 -35.258 -39.361 27.665 1.00 0.00 H +ATOM 3724 HH12 ARG A 242 -34.079 -40.474 27.055 1.00 0.00 H +ATOM 3725 HH21 ARG A 242 -35.050 -37.118 27.238 1.00 0.00 H +ATOM 3726 HH22 ARG A 242 -33.714 -36.532 26.305 1.00 0.00 H +ATOM 3727 N LEU A 243 -28.520 -38.861 30.169 1.00 0.00 N +ATOM 3728 CA LEU A 243 -28.187 -38.716 31.574 1.00 0.00 C +ATOM 3729 C LEU A 243 -29.051 -37.641 32.221 1.00 0.00 C +ATOM 3730 O LEU A 243 -29.412 -36.652 31.569 1.00 0.00 O +ATOM 3731 CB LEU A 243 -26.705 -38.347 31.747 1.00 0.00 C +ATOM 3732 CG LEU A 243 -25.689 -39.305 31.135 1.00 0.00 C +ATOM 3733 CD1 LEU A 243 -24.314 -38.660 31.109 1.00 0.00 C +ATOM 3734 CD2 LEU A 243 -25.662 -40.596 31.929 1.00 0.00 C +ATOM 3735 H LEU A 243 -28.532 -38.042 29.595 1.00 0.00 H +ATOM 3736 HA LEU A 243 -28.369 -39.668 32.093 1.00 0.00 H +ATOM 3737 HB2 LEU A 243 -26.551 -37.359 31.288 1.00 0.00 H +ATOM 3738 HB3 LEU A 243 -26.500 -38.287 32.826 1.00 0.00 H +ATOM 3739 HG LEU A 243 -25.994 -39.530 30.102 1.00 0.00 H +ATOM 3740 HD11 LEU A 243 -23.589 -39.359 30.666 1.00 0.00 H +ATOM 3741 HD12 LEU A 243 -24.352 -37.740 30.507 1.00 0.00 H +ATOM 3742 HD13 LEU A 243 -24.005 -38.414 32.136 1.00 0.00 H +ATOM 3743 HD21 LEU A 243 -24.928 -41.285 31.485 1.00 0.00 H +ATOM 3744 HD22 LEU A 243 -25.379 -40.380 32.970 1.00 0.00 H +ATOM 3745 HD23 LEU A 243 -26.659 -41.060 31.909 1.00 0.00 H +ATOM 3746 N PRO A 244 -29.397 -37.811 33.496 1.00 0.00 N +ATOM 3747 CA PRO A 244 -30.120 -36.754 34.212 1.00 0.00 C +ATOM 3748 C PRO A 244 -29.375 -35.427 34.155 1.00 0.00 C +ATOM 3749 O PRO A 244 -28.158 -35.382 33.961 1.00 0.00 O +ATOM 3750 CB PRO A 244 -30.206 -37.295 35.641 1.00 0.00 C +ATOM 3751 CG PRO A 244 -30.195 -38.775 35.467 1.00 0.00 C +ATOM 3752 CD PRO A 244 -29.301 -39.050 34.287 1.00 0.00 C +ATOM 3753 HA PRO A 244 -31.133 -36.631 33.800 1.00 0.00 H +ATOM 3754 HB2 PRO A 244 -29.342 -36.965 36.237 1.00 0.00 H +ATOM 3755 HB3 PRO A 244 -31.135 -36.965 36.129 1.00 0.00 H +ATOM 3756 HG2 PRO A 244 -29.797 -39.263 36.369 1.00 0.00 H +ATOM 3757 HG3 PRO A 244 -31.212 -39.144 35.269 1.00 0.00 H +ATOM 3758 HD2 PRO A 244 -29.663 -39.915 33.711 1.00 0.00 H +ATOM 3759 HD3 PRO A 244 -28.266 -39.232 34.611 1.00 0.00 H +ATOM 3760 N LYS A 245 -30.132 -34.342 34.337 1.00 0.00 N +ATOM 3761 CA LYS A 245 -29.609 -32.998 34.108 1.00 0.00 C +ATOM 3762 C LYS A 245 -28.331 -32.740 34.899 1.00 0.00 C +ATOM 3763 O LYS A 245 -27.332 -32.262 34.349 1.00 0.00 O +ATOM 3764 CB LYS A 245 -30.685 -31.970 34.456 1.00 0.00 C +ATOM 3765 CG LYS A 245 -30.276 -30.522 34.248 1.00 0.00 C +ATOM 3766 CD LYS A 245 -31.456 -29.594 34.486 1.00 0.00 C +ATOM 3767 CE LYS A 245 -31.067 -28.138 34.308 1.00 0.00 C +ATOM 3768 NZ LYS A 245 -30.050 -27.706 35.305 1.00 0.00 N1+ +ATOM 3769 H LYS A 245 -31.079 -34.452 34.637 1.00 0.00 H +ATOM 3770 HA LYS A 245 -29.372 -32.889 33.039 1.00 0.00 H +ATOM 3771 HB2 LYS A 245 -31.565 -32.170 33.827 1.00 0.00 H +ATOM 3772 HB3 LYS A 245 -30.953 -32.101 35.515 1.00 0.00 H +ATOM 3773 HG2 LYS A 245 -29.469 -30.269 34.952 1.00 0.00 H +ATOM 3774 HG3 LYS A 245 -29.916 -30.392 33.217 1.00 0.00 H +ATOM 3775 HD2 LYS A 245 -32.254 -29.840 33.770 1.00 0.00 H +ATOM 3776 HD3 LYS A 245 -31.825 -29.742 35.512 1.00 0.00 H +ATOM 3777 HE2 LYS A 245 -30.654 -28.002 33.298 1.00 0.00 H +ATOM 3778 HE3 LYS A 245 -31.965 -27.514 34.424 1.00 0.00 H +ATOM 3779 HZ1 LYS A 245 -30.166 -26.732 35.500 1.00 0.00 H +ATOM 3780 HZ2 LYS A 245 -30.167 -28.230 36.148 1.00 0.00 H +ATOM 3781 HZ3 LYS A 245 -29.134 -27.865 34.937 1.00 0.00 H +ATOM 3782 N LYS A 246 -28.339 -33.063 36.191 1.00 0.00 N +ATOM 3783 CA LYS A 246 -27.165 -32.790 37.016 1.00 0.00 C +ATOM 3784 C LYS A 246 -25.998 -33.698 36.647 1.00 0.00 C +ATOM 3785 O LYS A 246 -24.833 -33.288 36.727 1.00 0.00 O +ATOM 3786 CB LYS A 246 -27.519 -32.937 38.495 1.00 0.00 C +ATOM 3787 CG LYS A 246 -28.575 -31.956 38.980 1.00 0.00 C +ATOM 3788 CD LYS A 246 -28.015 -30.547 39.126 1.00 0.00 C +ATOM 3789 CE LYS A 246 -29.039 -29.611 39.752 1.00 0.00 C +ATOM 3790 NZ LYS A 246 -28.512 -28.229 39.922 1.00 0.00 N1+ +ATOM 3791 H LYS A 246 -29.147 -33.492 36.595 1.00 0.00 H +ATOM 3792 HA LYS A 246 -26.850 -31.750 36.847 1.00 0.00 H +ATOM 3793 HB2 LYS A 246 -27.893 -33.958 38.661 1.00 0.00 H +ATOM 3794 HB3 LYS A 246 -26.605 -32.781 39.086 1.00 0.00 H +ATOM 3795 HG2 LYS A 246 -29.402 -31.937 38.255 1.00 0.00 H +ATOM 3796 HG3 LYS A 246 -28.951 -32.292 39.957 1.00 0.00 H +ATOM 3797 HD2 LYS A 246 -27.121 -30.581 39.766 1.00 0.00 H +ATOM 3798 HD3 LYS A 246 -27.740 -30.165 38.132 1.00 0.00 H +ATOM 3799 HE2 LYS A 246 -29.927 -29.574 39.104 1.00 0.00 H +ATOM 3800 HE3 LYS A 246 -29.322 -30.006 40.739 1.00 0.00 H +ATOM 3801 HZ1 LYS A 246 -29.265 -27.574 39.856 1.00 0.00 H +ATOM 3802 HZ2 LYS A 246 -28.076 -28.146 40.818 1.00 0.00 H +ATOM 3803 HZ3 LYS A 246 -27.840 -28.038 39.206 1.00 0.00 H +ATOM 3804 N VAL A 247 -26.286 -34.932 36.237 1.00 0.00 N +ATOM 3805 CA VAL A 247 -25.219 -35.845 35.841 1.00 0.00 C +ATOM 3806 C VAL A 247 -24.643 -35.431 34.496 1.00 0.00 C +ATOM 3807 O VAL A 247 -23.421 -35.411 34.305 1.00 0.00 O +ATOM 3808 CB VAL A 247 -25.732 -37.295 35.815 1.00 0.00 C +ATOM 3809 CG1 VAL A 247 -24.619 -38.249 35.414 1.00 0.00 C +ATOM 3810 CG2 VAL A 247 -26.300 -37.670 37.170 1.00 0.00 C +ATOM 3811 H VAL A 247 -27.239 -35.234 36.200 1.00 0.00 H +ATOM 3812 HA VAL A 247 -24.412 -35.789 36.586 1.00 0.00 H +ATOM 3813 HB VAL A 247 -26.536 -37.363 35.068 1.00 0.00 H +ATOM 3814 HG11 VAL A 247 -25.005 -39.279 35.401 1.00 0.00 H +ATOM 3815 HG12 VAL A 247 -24.251 -37.983 34.412 1.00 0.00 H +ATOM 3816 HG13 VAL A 247 -23.795 -38.175 36.139 1.00 0.00 H +ATOM 3817 HG21 VAL A 247 -26.664 -38.708 37.141 1.00 0.00 H +ATOM 3818 HG22 VAL A 247 -25.515 -37.578 37.935 1.00 0.00 H +ATOM 3819 HG23 VAL A 247 -27.133 -36.996 37.417 1.00 0.00 H +ATOM 3820 N PHE A 248 -25.515 -35.088 33.547 1.00 0.00 N +ATOM 3821 CA PHE A 248 -25.069 -34.673 32.222 1.00 0.00 C +ATOM 3822 C PHE A 248 -24.134 -33.470 32.299 1.00 0.00 C +ATOM 3823 O PHE A 248 -23.094 -33.430 31.632 1.00 0.00 O +ATOM 3824 CB PHE A 248 -26.286 -34.362 31.348 1.00 0.00 C +ATOM 3825 CG PHE A 248 -25.948 -33.663 30.066 1.00 0.00 C +ATOM 3826 CD1 PHE A 248 -25.440 -34.370 28.989 1.00 0.00 C +ATOM 3827 CD2 PHE A 248 -26.146 -32.298 29.931 1.00 0.00 C +ATOM 3828 CE1 PHE A 248 -25.130 -33.728 27.806 1.00 0.00 C +ATOM 3829 CE2 PHE A 248 -25.836 -31.653 28.752 1.00 0.00 C +ATOM 3830 CZ PHE A 248 -25.327 -32.369 27.688 1.00 0.00 C +ATOM 3831 H PHE A 248 -26.494 -35.116 33.748 1.00 0.00 H +ATOM 3832 HA PHE A 248 -24.519 -35.503 31.755 1.00 0.00 H +ATOM 3833 HB2 PHE A 248 -26.789 -35.309 31.104 1.00 0.00 H +ATOM 3834 HB3 PHE A 248 -26.971 -33.721 31.923 1.00 0.00 H +ATOM 3835 HD1 PHE A 248 -25.285 -35.435 29.075 1.00 0.00 H +ATOM 3836 HD2 PHE A 248 -26.548 -31.733 30.759 1.00 0.00 H +ATOM 3837 HE1 PHE A 248 -24.733 -34.291 26.974 1.00 0.00 H +ATOM 3838 HE2 PHE A 248 -25.992 -30.588 28.662 1.00 0.00 H +ATOM 3839 HZ PHE A 248 -25.083 -31.865 26.764 1.00 0.00 H +ATOM 3840 N GLU A 249 -24.498 -32.472 33.106 1.00 0.00 N +ATOM 3841 CA GLU A 249 -23.672 -31.274 33.218 1.00 0.00 C +ATOM 3842 C GLU A 249 -22.296 -31.596 33.787 1.00 0.00 C +ATOM 3843 O GLU A 249 -21.280 -31.106 33.283 1.00 0.00 O +ATOM 3844 CB GLU A 249 -24.382 -30.225 34.071 1.00 0.00 C +ATOM 3845 CG GLU A 249 -25.584 -29.587 33.386 1.00 0.00 C +ATOM 3846 CD GLU A 249 -26.346 -28.663 34.308 1.00 0.00 C +ATOM 3847 OE1 GLU A 249 -25.716 -28.083 35.216 1.00 0.00 O +ATOM 3848 OE2 GLU A 249 -27.575 -28.525 34.128 1.00 0.00 O1- +ATOM 3849 H GLU A 249 -25.342 -32.545 33.637 1.00 0.00 H +ATOM 3850 HA GLU A 249 -23.530 -30.850 32.213 1.00 0.00 H +ATOM 3851 HB2 GLU A 249 -24.726 -30.707 34.998 1.00 0.00 H +ATOM 3852 HB3 GLU A 249 -23.661 -29.431 34.316 1.00 0.00 H +ATOM 3853 HG2 GLU A 249 -25.232 -29.010 32.519 1.00 0.00 H +ATOM 3854 HG3 GLU A 249 -26.261 -30.384 33.045 1.00 0.00 H +ATOM 3855 N ALA A 250 -22.240 -32.425 34.830 1.00 0.00 N +ATOM 3856 CA ALA A 250 -20.949 -32.826 35.383 1.00 0.00 C +ATOM 3857 C ALA A 250 -20.136 -33.622 34.369 1.00 0.00 C +ATOM 3858 O ALA A 250 -18.920 -33.440 34.252 1.00 0.00 O +ATOM 3859 CB ALA A 250 -21.152 -33.632 36.665 1.00 0.00 C +ATOM 3860 H ALA A 250 -23.085 -32.773 35.235 1.00 0.00 H +ATOM 3861 HA ALA A 250 -20.378 -31.921 35.640 1.00 0.00 H +ATOM 3862 HB1 ALA A 250 -20.174 -33.928 37.071 1.00 0.00 H +ATOM 3863 HB2 ALA A 250 -21.685 -33.016 37.404 1.00 0.00 H +ATOM 3864 HB3 ALA A 250 -21.744 -34.532 36.442 1.00 0.00 H +ATOM 3865 N ALA A 251 -20.794 -34.510 33.622 1.00 0.00 N +ATOM 3866 CA ALA A 251 -20.072 -35.349 32.670 1.00 0.00 C +ATOM 3867 C ALA A 251 -19.475 -34.517 31.542 1.00 0.00 C +ATOM 3868 O ALA A 251 -18.333 -34.739 31.126 1.00 0.00 O +ATOM 3869 CB ALA A 251 -20.997 -36.432 32.112 1.00 0.00 C +ATOM 3870 H ALA A 251 -21.785 -34.601 33.714 1.00 0.00 H +ATOM 3871 HA ALA A 251 -19.246 -35.849 33.196 1.00 0.00 H +ATOM 3872 HB1 ALA A 251 -20.442 -37.056 31.397 1.00 0.00 H +ATOM 3873 HB2 ALA A 251 -21.366 -37.059 32.937 1.00 0.00 H +ATOM 3874 HB3 ALA A 251 -21.849 -35.959 31.601 1.00 0.00 H +ATOM 3875 N VAL A 252 -20.233 -33.543 31.044 1.00 0.00 N +ATOM 3876 CA VAL A 252 -19.765 -32.743 29.915 1.00 0.00 C +ATOM 3877 C VAL A 252 -18.571 -31.888 30.319 1.00 0.00 C +ATOM 3878 O VAL A 252 -17.624 -31.712 29.543 1.00 0.00 O +ATOM 3879 CB VAL A 252 -20.919 -31.892 29.353 1.00 0.00 C +ATOM 3880 CG1 VAL A 252 -20.390 -30.806 28.436 1.00 0.00 C +ATOM 3881 CG2 VAL A 252 -21.894 -32.782 28.611 1.00 0.00 C +ATOM 3882 H VAL A 252 -21.130 -33.357 31.446 1.00 0.00 H +ATOM 3883 HA VAL A 252 -19.435 -33.424 29.117 1.00 0.00 H +ATOM 3884 HB VAL A 252 -21.444 -31.418 30.196 1.00 0.00 H +ATOM 3885 HG11 VAL A 252 -21.230 -30.212 28.047 1.00 0.00 H +ATOM 3886 HG12 VAL A 252 -19.708 -30.152 28.998 1.00 0.00 H +ATOM 3887 HG13 VAL A 252 -19.848 -31.267 27.597 1.00 0.00 H +ATOM 3888 HG21 VAL A 252 -22.717 -32.172 28.211 1.00 0.00 H +ATOM 3889 HG22 VAL A 252 -21.374 -33.285 27.782 1.00 0.00 H +ATOM 3890 HG23 VAL A 252 -22.299 -33.537 29.301 1.00 0.00 H +ATOM 3891 N LYS A 253 -18.594 -31.344 31.535 1.00 0.00 N +ATOM 3892 CA LYS A 253 -17.464 -30.555 32.012 1.00 0.00 C +ATOM 3893 C LYS A 253 -16.198 -31.402 32.088 1.00 0.00 C +ATOM 3894 O LYS A 253 -15.107 -30.945 31.727 1.00 0.00 O +ATOM 3895 CB LYS A 253 -17.798 -29.942 33.374 1.00 0.00 C +ATOM 3896 CG LYS A 253 -16.699 -29.089 33.978 1.00 0.00 C +ATOM 3897 CD LYS A 253 -17.196 -28.388 35.233 1.00 0.00 C +ATOM 3898 CE LYS A 253 -16.048 -27.804 36.040 1.00 0.00 C +ATOM 3899 NZ LYS A 253 -16.526 -27.093 37.259 1.00 0.00 N1+ +ATOM 3900 H LYS A 253 -19.391 -31.478 32.124 1.00 0.00 H +ATOM 3901 HA LYS A 253 -17.281 -29.732 31.305 1.00 0.00 H +ATOM 3902 HB2 LYS A 253 -18.692 -29.313 33.255 1.00 0.00 H +ATOM 3903 HB3 LYS A 253 -18.017 -30.762 34.073 1.00 0.00 H +ATOM 3904 HG2 LYS A 253 -15.844 -29.731 34.237 1.00 0.00 H +ATOM 3905 HG3 LYS A 253 -16.382 -28.334 33.243 1.00 0.00 H +ATOM 3906 HD2 LYS A 253 -17.877 -27.575 34.942 1.00 0.00 H +ATOM 3907 HD3 LYS A 253 -17.737 -29.114 35.857 1.00 0.00 H +ATOM 3908 HE2 LYS A 253 -15.377 -28.621 36.346 1.00 0.00 H +ATOM 3909 HE3 LYS A 253 -15.496 -27.093 35.408 1.00 0.00 H +ATOM 3910 HZ1 LYS A 253 -15.742 -26.725 37.760 1.00 0.00 H +ATOM 3911 HZ2 LYS A 253 -17.132 -26.344 36.992 1.00 0.00 H +ATOM 3912 HZ3 LYS A 253 -17.025 -27.733 37.844 1.00 0.00 H +ATOM 3913 N SER A 254 -16.331 -32.651 32.540 1.00 0.00 N +ATOM 3914 CA SER A 254 -15.168 -33.526 32.657 1.00 0.00 C +ATOM 3915 C SER A 254 -14.656 -33.961 31.292 1.00 0.00 C +ATOM 3916 O SER A 254 -13.441 -34.066 31.084 1.00 0.00 O +ATOM 3917 CB SER A 254 -15.510 -34.744 33.513 1.00 0.00 C +ATOM 3918 OG SER A 254 -14.356 -35.529 33.755 1.00 0.00 O +ATOM 3919 H SER A 254 -17.235 -32.990 32.801 1.00 0.00 H +ATOM 3920 HA SER A 254 -14.361 -32.975 33.162 1.00 0.00 H +ATOM 3921 HB2 SER A 254 -15.923 -34.405 34.474 1.00 0.00 H +ATOM 3922 HB3 SER A 254 -16.258 -35.355 32.987 1.00 0.00 H +ATOM 3923 HG SER A 254 -13.715 -35.370 33.072 1.00 0.00 H +ATOM 3924 N ILE A 255 -15.565 -34.224 30.351 1.00 0.00 N +ATOM 3925 CA ILE A 255 -15.152 -34.606 29.002 1.00 0.00 C +ATOM 3926 C ILE A 255 -14.467 -33.441 28.301 1.00 0.00 C +ATOM 3927 O ILE A 255 -13.505 -33.630 27.546 1.00 0.00 O +ATOM 3928 CB ILE A 255 -16.358 -35.117 28.192 1.00 0.00 C +ATOM 3929 CG1 ILE A 255 -16.945 -36.364 28.845 1.00 0.00 C +ATOM 3930 CG2 ILE A 255 -15.953 -35.410 26.757 1.00 0.00 C +ATOM 3931 CD1 ILE A 255 -18.216 -36.845 28.196 1.00 0.00 C +ATOM 3932 H ILE A 255 -16.538 -34.159 30.571 1.00 0.00 H +ATOM 3933 HA ILE A 255 -14.426 -35.429 29.076 1.00 0.00 H +ATOM 3934 HB ILE A 255 -17.128 -34.331 28.185 1.00 0.00 H +ATOM 3935 HG12 ILE A 255 -16.199 -37.170 28.788 1.00 0.00 H +ATOM 3936 HG13 ILE A 255 -17.158 -36.136 29.900 1.00 0.00 H +ATOM 3937 HG21 ILE A 255 -16.827 -35.773 26.196 1.00 0.00 H +ATOM 3938 HG22 ILE A 255 -15.573 -34.490 26.288 1.00 0.00 H +ATOM 3939 HG23 ILE A 255 -15.166 -36.179 26.748 1.00 0.00 H +ATOM 3940 HD11 ILE A 255 -18.578 -37.743 28.718 1.00 0.00 H +ATOM 3941 HD12 ILE A 255 -18.979 -36.055 28.254 1.00 0.00 H +ATOM 3942 HD13 ILE A 255 -18.020 -37.089 27.142 1.00 0.00 H +ATOM 3943 N LYS A 256 -14.964 -32.222 28.515 1.00 0.00 N +ATOM 3944 CA LYS A 256 -14.297 -31.043 27.974 1.00 0.00 C +ATOM 3945 C LYS A 256 -12.874 -30.925 28.502 1.00 0.00 C +ATOM 3946 O LYS A 256 -11.931 -30.701 27.736 1.00 0.00 O +ATOM 3947 CB LYS A 256 -15.086 -29.783 28.324 1.00 0.00 C +ATOM 3948 CG LYS A 256 -16.357 -29.553 27.533 1.00 0.00 C +ATOM 3949 CD LYS A 256 -17.224 -28.552 28.279 1.00 0.00 C +ATOM 3950 CE LYS A 256 -18.164 -27.791 27.362 1.00 0.00 C +ATOM 3951 NZ LYS A 256 -19.106 -26.949 28.158 1.00 0.00 N1+ +ATOM 3952 H LYS A 256 -15.801 -32.115 29.051 1.00 0.00 H +ATOM 3953 HA LYS A 256 -14.252 -31.127 26.878 1.00 0.00 H +ATOM 3954 HB2 LYS A 256 -15.358 -29.841 29.388 1.00 0.00 H +ATOM 3955 HB3 LYS A 256 -14.427 -28.917 28.161 1.00 0.00 H +ATOM 3956 HG2 LYS A 256 -16.107 -29.156 26.538 1.00 0.00 H +ATOM 3957 HG3 LYS A 256 -16.901 -30.503 27.423 1.00 0.00 H +ATOM 3958 HD2 LYS A 256 -17.823 -29.094 29.026 1.00 0.00 H +ATOM 3959 HD3 LYS A 256 -16.569 -27.830 28.788 1.00 0.00 H +ATOM 3960 HE2 LYS A 256 -17.574 -27.143 26.696 1.00 0.00 H +ATOM 3961 HE3 LYS A 256 -18.741 -28.508 26.760 1.00 0.00 H +ATOM 3962 HZ1 LYS A 256 -19.357 -26.138 27.629 1.00 0.00 H +ATOM 3963 HZ2 LYS A 256 -19.928 -27.477 28.369 1.00 0.00 H +ATOM 3964 HZ3 LYS A 256 -18.663 -26.667 29.009 1.00 0.00 H +ATOM 3965 N ALA A 257 -12.703 -31.063 29.818 1.00 0.00 N +ATOM 3966 CA ALA A 257 -11.382 -30.883 30.412 1.00 0.00 C +ATOM 3967 C ALA A 257 -10.405 -31.950 29.934 1.00 0.00 C +ATOM 3968 O ALA A 257 -9.226 -31.659 29.696 1.00 0.00 O +ATOM 3969 CB ALA A 257 -11.486 -30.889 31.937 1.00 0.00 C +ATOM 3970 H ALA A 257 -13.484 -31.291 30.400 1.00 0.00 H +ATOM 3971 HA ALA A 257 -10.988 -29.903 30.105 1.00 0.00 H +ATOM 3972 HB1 ALA A 257 -10.486 -30.753 32.374 1.00 0.00 H +ATOM 3973 HB2 ALA A 257 -12.143 -30.069 32.263 1.00 0.00 H +ATOM 3974 HB3 ALA A 257 -11.905 -31.850 32.272 1.00 0.00 H +ATOM 3975 N ALA A 258 -10.874 -33.189 29.781 1.00 0.00 N +ATOM 3976 CA ALA A 258 -9.983 -34.260 29.346 1.00 0.00 C +ATOM 3977 C ALA A 258 -9.579 -34.102 27.886 1.00 0.00 C +ATOM 3978 O ALA A 258 -8.484 -34.523 27.499 1.00 0.00 O +ATOM 3979 CB ALA A 258 -10.644 -35.620 29.570 1.00 0.00 C +ATOM 3980 H ALA A 258 -11.837 -33.384 29.965 1.00 0.00 H +ATOM 3981 HA ALA A 258 -9.068 -34.226 29.955 1.00 0.00 H +ATOM 3982 HB1 ALA A 258 -9.963 -36.418 29.239 1.00 0.00 H +ATOM 3983 HB2 ALA A 258 -10.866 -35.748 30.640 1.00 0.00 H +ATOM 3984 HB3 ALA A 258 -11.579 -35.673 28.993 1.00 0.00 H +ATOM 3985 N SER A 259 -10.434 -33.494 27.067 1.00 0.00 N +ATOM 3986 CA SER A 259 -10.171 -33.315 25.645 1.00 0.00 C +ATOM 3987 C SER A 259 -9.694 -31.908 25.315 1.00 0.00 C +ATOM 3988 O SER A 259 -9.844 -31.459 24.174 1.00 0.00 O +ATOM 3989 CB SER A 259 -11.428 -33.649 24.839 1.00 0.00 C +ATOM 3990 OG SER A 259 -12.542 -32.928 25.337 1.00 0.00 O +ATOM 3991 H SER A 259 -11.292 -33.146 27.443 1.00 0.00 H +ATOM 3992 HA SER A 259 -9.382 -34.018 25.342 1.00 0.00 H +ATOM 3993 HB2 SER A 259 -11.264 -33.380 23.785 1.00 0.00 H +ATOM 3994 HB3 SER A 259 -11.631 -34.727 24.915 1.00 0.00 H +ATOM 3995 HG SER A 259 -12.807 -33.292 26.174 1.00 0.00 H +ATOM 3996 N SER A 260 -9.110 -31.209 26.289 1.00 0.00 N +ATOM 3997 CA SER A 260 -8.876 -29.778 26.145 1.00 0.00 C +ATOM 3998 C SER A 260 -7.729 -29.435 25.205 1.00 0.00 C +ATOM 3999 O SER A 260 -7.473 -28.246 24.991 1.00 0.00 O +ATOM 4000 CB SER A 260 -8.617 -29.151 27.515 1.00 0.00 C +ATOM 4001 OG SER A 260 -7.345 -29.534 28.008 1.00 0.00 O +ATOM 4002 H SER A 260 -8.829 -31.673 27.129 1.00 0.00 H +ATOM 4003 HA SER A 260 -9.788 -29.317 25.738 1.00 0.00 H +ATOM 4004 HB2 SER A 260 -8.654 -28.055 27.424 1.00 0.00 H +ATOM 4005 HB3 SER A 260 -9.393 -29.487 28.219 1.00 0.00 H +ATOM 4006 HG SER A 260 -7.429 -30.333 28.516 1.00 0.00 H +ATOM 4007 N THR A 261 -7.029 -30.423 24.646 1.00 0.00 N +ATOM 4008 CA THR A 261 -6.005 -30.098 23.659 1.00 0.00 C +ATOM 4009 C THR A 261 -6.610 -29.584 22.361 1.00 0.00 C +ATOM 4010 O THR A 261 -5.896 -28.981 21.553 1.00 0.00 O +ATOM 4011 CB THR A 261 -5.115 -31.309 23.374 1.00 0.00 C +ATOM 4012 OG1 THR A 261 -5.927 -32.425 22.996 1.00 0.00 O +ATOM 4013 CG2 THR A 261 -4.301 -31.661 24.609 1.00 0.00 C +ATOM 4014 H THR A 261 -7.204 -31.374 24.900 1.00 0.00 H +ATOM 4015 HA THR A 261 -5.365 -29.303 24.068 1.00 0.00 H +ATOM 4016 HB THR A 261 -4.425 -31.053 22.557 1.00 0.00 H +ATOM 4017 HG1 THR A 261 -6.194 -32.330 22.089 1.00 0.00 H +ATOM 4018 HG21 THR A 261 -3.665 -32.532 24.394 1.00 0.00 H +ATOM 4019 HG22 THR A 261 -3.668 -30.805 24.887 1.00 0.00 H +ATOM 4020 HG23 THR A 261 -4.981 -31.900 25.440 1.00 0.00 H +ATOM 4021 N GLU A 262 -7.904 -29.809 22.148 1.00 0.00 N +ATOM 4022 CA GLU A 262 -8.648 -29.210 21.051 1.00 0.00 C +ATOM 4023 C GLU A 262 -9.948 -28.637 21.595 1.00 0.00 C +ATOM 4024 O GLU A 262 -10.598 -29.250 22.447 1.00 0.00 O +ATOM 4025 CB GLU A 262 -8.939 -30.226 19.936 1.00 0.00 C +ATOM 4026 CG GLU A 262 -7.731 -30.587 19.082 1.00 0.00 C +ATOM 4027 CD GLU A 262 -8.071 -31.571 17.978 1.00 0.00 C +ATOM 4028 OE1 GLU A 262 -9.265 -31.691 17.628 1.00 0.00 O +ATOM 4029 OE2 GLU A 262 -7.144 -32.232 17.463 1.00 0.00 O1- +ATOM 4030 H GLU A 262 -8.389 -30.420 22.773 1.00 0.00 H +ATOM 4031 HA GLU A 262 -8.059 -28.387 20.620 1.00 0.00 H +ATOM 4032 HB2 GLU A 262 -9.319 -31.147 20.401 1.00 0.00 H +ATOM 4033 HB3 GLU A 262 -9.711 -29.802 19.278 1.00 0.00 H +ATOM 4034 HG2 GLU A 262 -7.335 -29.668 18.625 1.00 0.00 H +ATOM 4035 HG3 GLU A 262 -6.963 -31.035 19.729 1.00 0.00 H +ATOM 4036 N LYS A 263 -10.313 -27.452 21.116 1.00 0.00 N +ATOM 4037 CA LYS A 263 -11.532 -26.781 21.542 1.00 0.00 C +ATOM 4038 C LYS A 263 -12.588 -26.887 20.452 1.00 0.00 C +ATOM 4039 O LYS A 263 -12.279 -26.799 19.260 1.00 0.00 O +ATOM 4040 CB LYS A 263 -11.267 -25.311 21.874 1.00 0.00 C +ATOM 4041 CG LYS A 263 -10.335 -25.092 23.054 1.00 0.00 C +ATOM 4042 CD LYS A 263 -10.952 -25.574 24.356 1.00 0.00 C +ATOM 4043 CE LYS A 263 -10.044 -25.264 25.536 1.00 0.00 C +ATOM 4044 NZ LYS A 263 -10.566 -25.837 26.809 1.00 0.00 N1+ +ATOM 4045 H LYS A 263 -9.729 -27.005 20.438 1.00 0.00 H +ATOM 4046 HA LYS A 263 -11.918 -27.273 22.447 1.00 0.00 H +ATOM 4047 HB2 LYS A 263 -10.820 -24.834 20.989 1.00 0.00 H +ATOM 4048 HB3 LYS A 263 -12.230 -24.831 22.103 1.00 0.00 H +ATOM 4049 HG2 LYS A 263 -9.400 -25.644 22.877 1.00 0.00 H +ATOM 4050 HG3 LYS A 263 -10.116 -24.017 23.140 1.00 0.00 H +ATOM 4051 HD2 LYS A 263 -11.919 -25.071 24.504 1.00 0.00 H +ATOM 4052 HD3 LYS A 263 -11.109 -26.661 24.299 1.00 0.00 H +ATOM 4053 HE2 LYS A 263 -9.048 -25.687 25.338 1.00 0.00 H +ATOM 4054 HE3 LYS A 263 -9.964 -24.172 25.645 1.00 0.00 H +ATOM 4055 HZ1 LYS A 263 -9.886 -25.718 27.532 1.00 0.00 H +ATOM 4056 HZ2 LYS A 263 -11.410 -25.366 27.065 1.00 0.00 H +ATOM 4057 HZ3 LYS A 263 -10.753 -26.811 26.684 1.00 0.00 H +ATOM 4058 N PHE A 264 -13.834 -27.085 20.870 1.00 0.00 N +ATOM 4059 CA PHE A 264 -14.959 -27.242 19.966 1.00 0.00 C +ATOM 4060 C PHE A 264 -16.062 -26.258 20.330 1.00 0.00 C +ATOM 4061 O PHE A 264 -16.154 -25.825 21.485 1.00 0.00 O +ATOM 4062 CB PHE A 264 -15.512 -28.672 20.017 1.00 0.00 C +ATOM 4063 CG PHE A 264 -14.457 -29.734 19.911 1.00 0.00 C +ATOM 4064 CD1 PHE A 264 -13.803 -29.966 18.712 1.00 0.00 C +ATOM 4065 CD2 PHE A 264 -14.124 -30.506 21.011 1.00 0.00 C +ATOM 4066 CE1 PHE A 264 -12.831 -30.946 18.614 1.00 0.00 C +ATOM 4067 CE2 PHE A 264 -13.156 -31.488 20.919 1.00 0.00 C +ATOM 4068 CZ PHE A 264 -12.509 -31.708 19.719 1.00 0.00 C +ATOM 4069 H PHE A 264 -14.005 -27.128 21.854 1.00 0.00 H +ATOM 4070 HA PHE A 264 -14.632 -27.034 18.936 1.00 0.00 H +ATOM 4071 HB2 PHE A 264 -16.043 -28.805 20.971 1.00 0.00 H +ATOM 4072 HB3 PHE A 264 -16.218 -28.801 19.184 1.00 0.00 H +ATOM 4073 HD1 PHE A 264 -14.055 -29.375 17.844 1.00 0.00 H +ATOM 4074 HD2 PHE A 264 -14.627 -30.339 21.952 1.00 0.00 H +ATOM 4075 HE1 PHE A 264 -12.325 -31.114 17.675 1.00 0.00 H +ATOM 4076 HE2 PHE A 264 -12.906 -32.083 21.785 1.00 0.00 H +ATOM 4077 HZ PHE A 264 -11.752 -32.475 19.645 1.00 0.00 H +ATOM 4078 N PRO A 265 -16.908 -25.875 19.372 1.00 0.00 N +ATOM 4079 CA PRO A 265 -17.991 -24.939 19.687 1.00 0.00 C +ATOM 4080 C PRO A 265 -18.949 -25.530 20.708 1.00 0.00 C +ATOM 4081 O PRO A 265 -19.103 -26.748 20.814 1.00 0.00 O +ATOM 4082 CB PRO A 265 -18.680 -24.720 18.335 1.00 0.00 C +ATOM 4083 CG PRO A 265 -17.646 -25.085 17.321 1.00 0.00 C +ATOM 4084 CD PRO A 265 -16.869 -26.205 17.936 1.00 0.00 C +ATOM 4085 HA PRO A 265 -17.587 -23.986 20.058 1.00 0.00 H +ATOM 4086 HB2 PRO A 265 -19.563 -25.369 18.240 1.00 0.00 H +ATOM 4087 HB3 PRO A 265 -18.983 -23.669 18.219 1.00 0.00 H +ATOM 4088 HG2 PRO A 265 -18.123 -25.414 16.386 1.00 0.00 H +ATOM 4089 HG3 PRO A 265 -16.989 -24.227 17.115 1.00 0.00 H +ATOM 4090 HD2 PRO A 265 -17.348 -27.175 17.739 1.00 0.00 H +ATOM 4091 HD3 PRO A 265 -15.835 -26.223 17.561 1.00 0.00 H +ATOM 4092 N ASP A 266 -19.594 -24.643 21.469 1.00 0.00 N +ATOM 4093 CA ASP A 266 -20.538 -25.091 22.487 1.00 0.00 C +ATOM 4094 C ASP A 266 -21.666 -25.922 21.888 1.00 0.00 C +ATOM 4095 O ASP A 266 -22.222 -26.796 22.567 1.00 0.00 O +ATOM 4096 CB ASP A 266 -21.106 -23.887 23.235 1.00 0.00 C +ATOM 4097 CG ASP A 266 -20.081 -23.220 24.123 1.00 0.00 C +ATOM 4098 OD1 ASP A 266 -18.900 -23.147 23.720 1.00 0.00 O +ATOM 4099 OD2 ASP A 266 -20.458 -22.760 25.223 1.00 0.00 O1- +ATOM 4100 H ASP A 266 -19.429 -23.665 21.340 1.00 0.00 H +ATOM 4101 HA ASP A 266 -20.003 -25.719 23.214 1.00 0.00 H +ATOM 4102 HB2 ASP A 266 -21.466 -23.153 22.499 1.00 0.00 H +ATOM 4103 HB3 ASP A 266 -21.947 -24.224 23.859 1.00 0.00 H +ATOM 4104 N GLY A 267 -22.013 -25.668 20.623 1.00 0.00 N +ATOM 4105 CA GLY A 267 -23.085 -26.414 19.986 1.00 0.00 C +ATOM 4106 C GLY A 267 -22.739 -27.867 19.731 1.00 0.00 C +ATOM 4107 O GLY A 267 -23.633 -28.709 19.607 1.00 0.00 O +ATOM 4108 H GLY A 267 -21.531 -24.958 20.110 1.00 0.00 H +ATOM 4109 HA2 GLY A 267 -23.971 -26.376 20.637 1.00 0.00 H +ATOM 4110 HA3 GLY A 267 -23.317 -25.937 19.022 1.00 0.00 H +ATOM 4111 N PHE A 268 -21.446 -28.183 19.645 1.00 0.00 N +ATOM 4112 CA PHE A 268 -21.031 -29.576 19.529 1.00 0.00 C +ATOM 4113 C PHE A 268 -21.443 -30.365 20.764 1.00 0.00 C +ATOM 4114 O PHE A 268 -21.997 -31.465 20.661 1.00 0.00 O +ATOM 4115 CB PHE A 268 -19.519 -29.647 19.313 1.00 0.00 C +ATOM 4116 CG PHE A 268 -18.955 -31.035 19.408 1.00 0.00 C +ATOM 4117 CD1 PHE A 268 -19.159 -31.953 18.389 1.00 0.00 C +ATOM 4118 CD2 PHE A 268 -18.218 -31.423 20.514 1.00 0.00 C +ATOM 4119 CE1 PHE A 268 -18.639 -33.231 18.473 1.00 0.00 C +ATOM 4120 CE2 PHE A 268 -17.699 -32.701 20.605 1.00 0.00 C +ATOM 4121 CZ PHE A 268 -17.909 -33.605 19.584 1.00 0.00 C +ATOM 4122 H PHE A 268 -20.756 -27.459 19.660 1.00 0.00 H +ATOM 4123 HA PHE A 268 -21.523 -30.025 18.654 1.00 0.00 H +ATOM 4124 HB2 PHE A 268 -19.293 -29.249 18.313 1.00 0.00 H +ATOM 4125 HB3 PHE A 268 -19.031 -29.022 20.075 1.00 0.00 H +ATOM 4126 HD1 PHE A 268 -19.731 -31.666 17.519 1.00 0.00 H +ATOM 4127 HD2 PHE A 268 -18.047 -30.719 21.315 1.00 0.00 H +ATOM 4128 HE1 PHE A 268 -18.803 -33.936 17.671 1.00 0.00 H +ATOM 4129 HE2 PHE A 268 -17.129 -32.992 21.475 1.00 0.00 H +ATOM 4130 HZ PHE A 268 -17.503 -34.603 19.654 1.00 0.00 H +ATOM 4131 N TRP A 269 -21.194 -29.802 21.946 1.00 0.00 N +ATOM 4132 CA TRP A 269 -21.506 -30.500 23.185 1.00 0.00 C +ATOM 4133 C TRP A 269 -23.004 -30.601 23.439 1.00 0.00 C +ATOM 4134 O TRP A 269 -23.425 -31.423 24.259 1.00 0.00 O +ATOM 4135 CB TRP A 269 -20.812 -29.808 24.355 1.00 0.00 C +ATOM 4136 CG TRP A 269 -19.319 -29.798 24.218 1.00 0.00 C +ATOM 4137 CD1 TRP A 269 -18.527 -28.724 23.940 1.00 0.00 C +ATOM 4138 CD2 TRP A 269 -18.442 -30.923 24.342 1.00 0.00 C +ATOM 4139 NE1 TRP A 269 -17.207 -29.107 23.893 1.00 0.00 N +ATOM 4140 CE2 TRP A 269 -17.128 -30.453 24.136 1.00 0.00 C +ATOM 4141 CE3 TRP A 269 -18.638 -32.279 24.613 1.00 0.00 C +ATOM 4142 CZ2 TRP A 269 -16.019 -31.293 24.190 1.00 0.00 C +ATOM 4143 CZ3 TRP A 269 -17.537 -33.112 24.666 1.00 0.00 C +ATOM 4144 CH2 TRP A 269 -16.244 -32.616 24.456 1.00 0.00 C +ATOM 4145 H TRP A 269 -20.788 -28.889 21.982 1.00 0.00 H +ATOM 4146 HA TRP A 269 -21.108 -31.524 23.119 1.00 0.00 H +ATOM 4147 HB2 TRP A 269 -21.167 -28.768 24.410 1.00 0.00 H +ATOM 4148 HB3 TRP A 269 -21.077 -30.336 25.283 1.00 0.00 H +ATOM 4149 HD1 TRP A 269 -18.885 -27.718 23.780 1.00 0.00 H +ATOM 4150 HE1 TRP A 269 -16.432 -28.502 23.711 1.00 0.00 H +ATOM 4151 HE3 TRP A 269 -19.632 -32.669 24.778 1.00 0.00 H +ATOM 4152 HZ2 TRP A 269 -15.020 -30.915 24.028 1.00 0.00 H +ATOM 4153 HZ3 TRP A 269 -17.676 -34.163 24.873 1.00 0.00 H +ATOM 4154 HH2 TRP A 269 -15.405 -33.294 24.505 1.00 0.00 H +ATOM 4155 N LEU A 270 -23.816 -29.791 22.765 1.00 0.00 N +ATOM 4156 CA LEU A 270 -25.266 -29.913 22.849 1.00 0.00 C +ATOM 4157 C LEU A 270 -25.836 -30.853 21.793 1.00 0.00 C +ATOM 4158 O LEU A 270 -27.053 -31.072 21.765 1.00 0.00 O +ATOM 4159 CB LEU A 270 -25.925 -28.534 22.719 1.00 0.00 C +ATOM 4160 CG LEU A 270 -25.517 -27.469 23.738 1.00 0.00 C +ATOM 4161 CD1 LEU A 270 -26.125 -26.130 23.355 1.00 0.00 C +ATOM 4162 CD2 LEU A 270 -25.939 -27.868 25.145 1.00 0.00 C +ATOM 4163 H LEU A 270 -23.421 -29.079 22.185 1.00 0.00 H +ATOM 4164 HA LEU A 270 -25.529 -30.318 23.837 1.00 0.00 H +ATOM 4165 HB2 LEU A 270 -25.685 -28.145 21.719 1.00 0.00 H +ATOM 4166 HB3 LEU A 270 -27.012 -28.677 22.806 1.00 0.00 H +ATOM 4167 HG LEU A 270 -24.421 -27.372 23.718 1.00 0.00 H +ATOM 4168 HD11 LEU A 270 -25.828 -25.368 24.091 1.00 0.00 H +ATOM 4169 HD12 LEU A 270 -25.765 -25.836 22.358 1.00 0.00 H +ATOM 4170 HD13 LEU A 270 -27.222 -26.217 23.339 1.00 0.00 H +ATOM 4171 HD21 LEU A 270 -25.634 -27.086 25.856 1.00 0.00 H +ATOM 4172 HD22 LEU A 270 -27.032 -27.988 25.180 1.00 0.00 H +ATOM 4173 HD23 LEU A 270 -25.457 -28.819 25.417 1.00 0.00 H +ATOM 4174 N GLY A 271 -24.989 -31.414 20.931 1.00 0.00 N +ATOM 4175 CA GLY A 271 -25.454 -32.293 19.878 1.00 0.00 C +ATOM 4176 C GLY A 271 -26.040 -31.588 18.677 1.00 0.00 C +ATOM 4177 O GLY A 271 -26.759 -32.216 17.892 1.00 0.00 O +ATOM 4178 H GLY A 271 -24.011 -31.224 21.013 1.00 0.00 H +ATOM 4179 HA2 GLY A 271 -24.602 -32.899 19.537 1.00 0.00 H +ATOM 4180 HA3 GLY A 271 -26.228 -32.952 20.299 1.00 0.00 H +ATOM 4181 N GLU A 272 -25.747 -30.301 18.499 1.00 0.00 N +ATOM 4182 CA GLU A 272 -26.354 -29.513 17.435 1.00 0.00 C +ATOM 4183 C GLU A 272 -25.416 -29.237 16.267 1.00 0.00 C +ATOM 4184 O GLU A 272 -25.880 -28.757 15.229 1.00 0.00 O +ATOM 4185 CB GLU A 272 -26.872 -28.180 17.995 1.00 0.00 C +ATOM 4186 CG GLU A 272 -27.908 -28.333 19.102 1.00 0.00 C +ATOM 4187 CD GLU A 272 -28.350 -27.001 19.684 1.00 0.00 C +ATOM 4188 OE1 GLU A 272 -27.487 -26.124 19.899 1.00 0.00 O +ATOM 4189 OE2 GLU A 272 -29.564 -26.829 19.922 1.00 0.00 O1- +ATOM 4190 H GLU A 272 -25.091 -29.863 19.114 1.00 0.00 H +ATOM 4191 HA GLU A 272 -27.221 -30.066 17.043 1.00 0.00 H +ATOM 4192 HB2 GLU A 272 -26.016 -27.618 18.397 1.00 0.00 H +ATOM 4193 HB3 GLU A 272 -27.328 -27.612 17.170 1.00 0.00 H +ATOM 4194 HG2 GLU A 272 -28.789 -28.846 18.690 1.00 0.00 H +ATOM 4195 HG3 GLU A 272 -27.473 -28.942 19.908 1.00 0.00 H +ATOM 4196 N GLN A 273 -24.123 -29.531 16.399 1.00 0.00 N +ATOM 4197 CA GLN A 273 -23.164 -29.235 15.344 1.00 0.00 C +ATOM 4198 C GLN A 273 -22.158 -30.372 15.231 1.00 0.00 C +ATOM 4199 O GLN A 273 -21.862 -31.058 16.212 1.00 0.00 O +ATOM 4200 CB GLN A 273 -22.449 -27.899 15.606 1.00 0.00 C +ATOM 4201 CG GLN A 273 -21.667 -27.358 14.420 1.00 0.00 C +ATOM 4202 CD GLN A 273 -20.957 -26.058 14.735 1.00 0.00 C +ATOM 4203 OE1 GLN A 273 -19.791 -25.879 14.387 1.00 0.00 O +ATOM 4204 NE2 GLN A 273 -21.661 -25.139 15.387 1.00 0.00 N +ATOM 4205 H GLN A 273 -23.804 -29.965 17.242 1.00 0.00 H +ATOM 4206 HA GLN A 273 -23.698 -29.153 14.386 1.00 0.00 H +ATOM 4207 HB2 GLN A 273 -23.207 -27.153 15.886 1.00 0.00 H +ATOM 4208 HB3 GLN A 273 -21.749 -28.042 16.442 1.00 0.00 H +ATOM 4209 HG2 GLN A 273 -20.917 -28.106 14.122 1.00 0.00 H +ATOM 4210 HG3 GLN A 273 -22.365 -27.186 13.587 1.00 0.00 H +ATOM 4211 HE21 GLN A 273 -21.245 -24.258 15.614 1.00 0.00 H +ATOM 4212 HE22 GLN A 273 -22.607 -25.328 15.651 1.00 0.00 H +ATOM 4213 N LEU A 274 -21.643 -30.572 14.020 1.00 0.00 N +ATOM 4214 CA LEU A 274 -20.680 -31.630 13.748 1.00 0.00 C +ATOM 4215 C LEU A 274 -19.257 -31.089 13.799 1.00 0.00 C +ATOM 4216 O LEU A 274 -18.986 -29.977 13.338 1.00 0.00 O +ATOM 4217 CB LEU A 274 -20.933 -32.260 12.377 1.00 0.00 C +ATOM 4218 CG LEU A 274 -22.300 -32.867 12.060 1.00 0.00 C +ATOM 4219 CD1 LEU A 274 -22.293 -33.441 10.653 1.00 0.00 C +ATOM 4220 CD2 LEU A 274 -22.667 -33.936 13.073 1.00 0.00 C +ATOM 4221 H LEU A 274 -21.927 -29.975 13.270 1.00 0.00 H +ATOM 4222 HA LEU A 274 -20.779 -32.415 14.512 1.00 0.00 H +ATOM 4223 HB2 LEU A 274 -20.751 -31.476 11.628 1.00 0.00 H +ATOM 4224 HB3 LEU A 274 -20.192 -33.063 12.254 1.00 0.00 H +ATOM 4225 HG LEU A 274 -23.054 -32.067 12.107 1.00 0.00 H +ATOM 4226 HD11 LEU A 274 -23.278 -33.876 10.429 1.00 0.00 H +ATOM 4227 HD12 LEU A 274 -22.074 -32.640 9.932 1.00 0.00 H +ATOM 4228 HD13 LEU A 274 -21.522 -34.222 10.580 1.00 0.00 H +ATOM 4229 HD21 LEU A 274 -23.653 -34.356 12.823 1.00 0.00 H +ATOM 4230 HD22 LEU A 274 -21.912 -34.736 13.052 1.00 0.00 H +ATOM 4231 HD23 LEU A 274 -22.702 -33.491 14.078 1.00 0.00 H +ATOM 4232 N VAL A 275 -18.350 -31.886 14.364 1.00 0.00 N +ATOM 4233 CA VAL A 275 -16.917 -31.613 14.328 1.00 0.00 C +ATOM 4234 C VAL A 275 -16.273 -32.594 13.361 1.00 0.00 C +ATOM 4235 O VAL A 275 -16.620 -33.782 13.340 1.00 0.00 O +ATOM 4236 CB VAL A 275 -16.291 -31.710 15.733 1.00 0.00 C +ATOM 4237 CG1 VAL A 275 -14.776 -31.807 15.643 1.00 0.00 C +ATOM 4238 CG2 VAL A 275 -16.684 -30.501 16.564 1.00 0.00 C +ATOM 4239 H VAL A 275 -18.667 -32.710 14.835 1.00 0.00 H +ATOM 4240 HA VAL A 275 -16.752 -30.594 13.949 1.00 0.00 H +ATOM 4241 HB VAL A 275 -16.675 -32.617 16.224 1.00 0.00 H +ATOM 4242 HG11 VAL A 275 -14.352 -31.875 16.656 1.00 0.00 H +ATOM 4243 HG12 VAL A 275 -14.500 -32.703 15.069 1.00 0.00 H +ATOM 4244 HG13 VAL A 275 -14.381 -30.913 15.139 1.00 0.00 H +ATOM 4245 HG21 VAL A 275 -16.232 -30.580 17.564 1.00 0.00 H +ATOM 4246 HG22 VAL A 275 -16.325 -29.585 16.071 1.00 0.00 H +ATOM 4247 HG23 VAL A 275 -17.779 -30.462 16.659 1.00 0.00 H +ATOM 4248 N CYS A 276 -15.341 -32.100 12.546 1.00 0.00 N +ATOM 4249 CA CYS A 276 -14.764 -32.883 11.465 1.00 0.00 C +ATOM 4250 C CYS A 276 -13.246 -32.899 11.559 1.00 0.00 C +ATOM 4251 O CYS A 276 -12.630 -32.007 12.147 1.00 0.00 O +ATOM 4252 CB CYS A 276 -15.166 -32.332 10.091 1.00 0.00 C +ATOM 4253 SG CYS A 276 -16.943 -32.170 9.812 1.00 0.00 S +ATOM 4254 H CYS A 276 -15.028 -31.160 12.682 1.00 0.00 H +ATOM 4255 HA CYS A 276 -15.125 -33.919 11.537 1.00 0.00 H +ATOM 4256 HB2 CYS A 276 -14.712 -31.336 9.979 1.00 0.00 H +ATOM 4257 HB3 CYS A 276 -14.762 -33.008 9.323 1.00 0.00 H +ATOM 4258 N TRP A 277 -12.653 -33.929 10.960 1.00 0.00 N +ATOM 4259 CA TRP A 277 -11.213 -34.023 10.787 1.00 0.00 C +ATOM 4260 C TRP A 277 -10.926 -34.597 9.408 1.00 0.00 C +ATOM 4261 O TRP A 277 -11.799 -35.184 8.764 1.00 0.00 O +ATOM 4262 CB TRP A 277 -10.554 -34.900 11.863 1.00 0.00 C +ATOM 4263 CG TRP A 277 -10.569 -34.314 13.238 1.00 0.00 C +ATOM 4264 CD1 TRP A 277 -9.679 -33.420 13.758 1.00 0.00 C +ATOM 4265 CD2 TRP A 277 -11.512 -34.594 14.278 1.00 0.00 C +ATOM 4266 NE1 TRP A 277 -10.015 -33.119 15.056 1.00 0.00 N +ATOM 4267 CE2 TRP A 277 -11.138 -33.826 15.399 1.00 0.00 C +ATOM 4268 CE3 TRP A 277 -12.641 -35.415 14.369 1.00 0.00 C +ATOM 4269 CZ2 TRP A 277 -11.849 -33.858 16.597 1.00 0.00 C +ATOM 4270 CZ3 TRP A 277 -13.348 -35.445 15.558 1.00 0.00 C +ATOM 4271 CH2 TRP A 277 -12.949 -34.672 16.656 1.00 0.00 C +ATOM 4272 H TRP A 277 -13.225 -34.673 10.613 1.00 0.00 H +ATOM 4273 HA TRP A 277 -10.772 -33.017 10.844 1.00 0.00 H +ATOM 4274 HB2 TRP A 277 -11.086 -35.862 11.894 1.00 0.00 H +ATOM 4275 HB3 TRP A 277 -9.506 -35.069 11.573 1.00 0.00 H +ATOM 4276 HD1 TRP A 277 -8.833 -33.009 13.227 1.00 0.00 H +ATOM 4277 HE1 TRP A 277 -9.521 -32.487 15.653 1.00 0.00 H +ATOM 4278 HE3 TRP A 277 -12.955 -36.014 13.527 1.00 0.00 H +ATOM 4279 HZ2 TRP A 277 -11.545 -33.264 17.446 1.00 0.00 H +ATOM 4280 HZ3 TRP A 277 -14.222 -36.075 15.641 1.00 0.00 H +ATOM 4281 HH2 TRP A 277 -13.522 -34.719 17.570 1.00 0.00 H +ATOM 4282 N GLN A 278 -9.688 -34.408 8.958 1.00 0.00 N +ATOM 4283 CA GLN A 278 -9.247 -35.018 7.713 1.00 0.00 C +ATOM 4284 C GLN A 278 -9.405 -36.529 7.806 1.00 0.00 C +ATOM 4285 O GLN A 278 -9.101 -37.132 8.840 1.00 0.00 O +ATOM 4286 CB GLN A 278 -7.791 -34.641 7.429 1.00 0.00 C +ATOM 4287 CG GLN A 278 -7.308 -34.993 6.030 1.00 0.00 C +ATOM 4288 CD GLN A 278 -5.902 -34.493 5.759 1.00 0.00 C +ATOM 4289 OE1 GLN A 278 -5.106 -34.325 6.682 1.00 0.00 O +ATOM 4290 NE2 GLN A 278 -5.593 -34.243 4.491 1.00 0.00 N +ATOM 4291 H GLN A 278 -9.053 -33.840 9.482 1.00 0.00 H +ATOM 4292 HA GLN A 278 -9.870 -34.651 6.884 1.00 0.00 H +ATOM 4293 HB2 GLN A 278 -7.685 -33.555 7.566 1.00 0.00 H +ATOM 4294 HB3 GLN A 278 -7.153 -35.166 8.155 1.00 0.00 H +ATOM 4295 HG2 GLN A 278 -7.321 -36.087 5.917 1.00 0.00 H +ATOM 4296 HG3 GLN A 278 -7.991 -34.540 5.296 1.00 0.00 H +ATOM 4297 HE21 GLN A 278 -4.682 -33.904 4.256 1.00 0.00 H +ATOM 4298 HE22 GLN A 278 -6.271 -34.394 3.772 1.00 0.00 H +ATOM 4299 N ALA A 279 -9.909 -37.129 6.727 1.00 0.00 N +ATOM 4300 CA ALA A 279 -10.279 -38.540 6.733 1.00 0.00 C +ATOM 4301 C ALA A 279 -9.145 -39.410 7.249 1.00 0.00 C +ATOM 4302 O ALA A 279 -8.016 -39.338 6.758 1.00 0.00 O +ATOM 4303 CB ALA A 279 -10.681 -38.982 5.326 1.00 0.00 C +ATOM 4304 H ALA A 279 -10.037 -36.597 5.890 1.00 0.00 H +ATOM 4305 HA ALA A 279 -11.146 -38.679 7.395 1.00 0.00 H +ATOM 4306 HB1 ALA A 279 -10.958 -40.046 5.341 1.00 0.00 H +ATOM 4307 HB2 ALA A 279 -11.539 -38.384 4.985 1.00 0.00 H +ATOM 4308 HB3 ALA A 279 -9.835 -38.833 4.639 1.00 0.00 H +ATOM 4309 N GLY A 280 -9.454 -40.227 8.257 1.00 0.00 N +ATOM 4310 CA GLY A 280 -8.477 -41.104 8.865 1.00 0.00 C +ATOM 4311 C GLY A 280 -7.678 -40.497 9.997 1.00 0.00 C +ATOM 4312 O GLY A 280 -6.965 -41.233 10.692 1.00 0.00 O +ATOM 4313 H GLY A 280 -10.392 -40.234 8.604 1.00 0.00 H +ATOM 4314 HA2 GLY A 280 -9.008 -41.984 9.258 1.00 0.00 H +ATOM 4315 HA3 GLY A 280 -7.771 -41.421 8.083 1.00 0.00 H +ATOM 4316 N THR A 281 -7.775 -39.186 10.219 1.00 0.00 N +ATOM 4317 CA THR A 281 -6.955 -38.505 11.211 1.00 0.00 C +ATOM 4318 C THR A 281 -7.731 -38.148 12.472 1.00 0.00 C +ATOM 4319 O THR A 281 -7.301 -37.267 13.226 1.00 0.00 O +ATOM 4320 CB THR A 281 -6.330 -37.249 10.604 1.00 0.00 C +ATOM 4321 OG1 THR A 281 -7.334 -36.237 10.461 1.00 0.00 O +ATOM 4322 CG2 THR A 281 -5.749 -37.566 9.239 1.00 0.00 C +ATOM 4323 H THR A 281 -8.433 -38.653 9.687 1.00 0.00 H +ATOM 4324 HA THR A 281 -6.134 -39.176 11.504 1.00 0.00 H +ATOM 4325 HB THR A 281 -5.524 -36.896 11.265 1.00 0.00 H +ATOM 4326 HG1 THR A 281 -8.172 -36.645 10.276 1.00 0.00 H +ATOM 4327 HG21 THR A 281 -5.302 -36.657 8.810 1.00 0.00 H +ATOM 4328 HG22 THR A 281 -4.976 -38.342 9.341 1.00 0.00 H +ATOM 4329 HG23 THR A 281 -6.548 -37.929 8.576 1.00 0.00 H +ATOM 4330 N THR A 282 -8.856 -38.805 12.714 1.00 0.00 N +ATOM 4331 CA THR A 282 -9.646 -38.522 13.903 1.00 0.00 C +ATOM 4332 C THR A 282 -8.825 -38.826 15.152 1.00 0.00 C +ATOM 4333 O THR A 282 -8.344 -39.959 15.306 1.00 0.00 O +ATOM 4334 CB THR A 282 -10.930 -39.343 13.905 1.00 0.00 C +ATOM 4335 OG1 THR A 282 -11.648 -39.105 12.690 1.00 0.00 O +ATOM 4336 CG2 THR A 282 -11.801 -38.949 15.090 1.00 0.00 C +ATOM 4337 H THR A 282 -9.166 -39.505 12.071 1.00 0.00 H +ATOM 4338 HA THR A 282 -9.915 -37.456 13.916 1.00 0.00 H +ATOM 4339 HB THR A 282 -10.672 -40.409 13.991 1.00 0.00 H +ATOM 4340 HG1 THR A 282 -11.739 -38.169 12.554 1.00 0.00 H +ATOM 4341 HG21 THR A 282 -12.724 -39.547 15.082 1.00 0.00 H +ATOM 4342 HG22 THR A 282 -11.253 -39.134 16.025 1.00 0.00 H +ATOM 4343 HG23 THR A 282 -12.055 -37.881 15.019 1.00 0.00 H +ATOM 4344 N PRO A 283 -8.630 -37.858 16.058 1.00 0.00 N +ATOM 4345 CA PRO A 283 -7.725 -38.052 17.210 1.00 0.00 C +ATOM 4346 C PRO A 283 -8.442 -38.678 18.402 1.00 0.00 C +ATOM 4347 O PRO A 283 -8.730 -38.026 19.416 1.00 0.00 O +ATOM 4348 CB PRO A 283 -7.253 -36.623 17.489 1.00 0.00 C +ATOM 4349 CG PRO A 283 -8.461 -35.790 17.163 1.00 0.00 C +ATOM 4350 CD PRO A 283 -9.211 -36.505 16.048 1.00 0.00 C +ATOM 4351 HA PRO A 283 -6.866 -38.676 16.923 1.00 0.00 H +ATOM 4352 HB2 PRO A 283 -6.964 -36.502 18.544 1.00 0.00 H +ATOM 4353 HB3 PRO A 283 -6.404 -36.355 16.843 1.00 0.00 H +ATOM 4354 HG2 PRO A 283 -9.104 -35.695 18.050 1.00 0.00 H +ATOM 4355 HG3 PRO A 283 -8.150 -34.790 16.827 1.00 0.00 H +ATOM 4356 HD2 PRO A 283 -10.290 -36.539 16.258 1.00 0.00 H +ATOM 4357 HD3 PRO A 283 -9.041 -36.011 15.080 1.00 0.00 H +ATOM 4358 N TRP A 284 -8.745 -39.973 18.280 1.00 0.00 N +ATOM 4359 CA TRP A 284 -9.511 -40.661 19.314 1.00 0.00 C +ATOM 4360 C TRP A 284 -8.850 -40.543 20.680 1.00 0.00 C +ATOM 4361 O TRP A 284 -9.536 -40.462 21.705 1.00 0.00 O +ATOM 4362 CB TRP A 284 -9.684 -42.134 18.944 1.00 0.00 C +ATOM 4363 CG TRP A 284 -10.328 -42.377 17.612 1.00 0.00 C +ATOM 4364 CD1 TRP A 284 -9.722 -42.846 16.484 1.00 0.00 C +ATOM 4365 CD2 TRP A 284 -11.704 -42.167 17.270 1.00 0.00 C +ATOM 4366 NE1 TRP A 284 -10.634 -42.941 15.460 1.00 0.00 N +ATOM 4367 CE2 TRP A 284 -11.858 -42.527 15.916 1.00 0.00 C +ATOM 4368 CE3 TRP A 284 -12.820 -41.705 17.976 1.00 0.00 C +ATOM 4369 CZ2 TRP A 284 -13.083 -42.443 15.254 1.00 0.00 C +ATOM 4370 CZ3 TRP A 284 -14.040 -41.621 17.314 1.00 0.00 C +ATOM 4371 CH2 TRP A 284 -14.158 -41.987 15.968 1.00 0.00 C +ATOM 4372 H TRP A 284 -8.443 -40.477 17.471 1.00 0.00 H +ATOM 4373 HA TRP A 284 -10.511 -40.207 19.378 1.00 0.00 H +ATOM 4374 HB2 TRP A 284 -8.689 -42.603 18.934 1.00 0.00 H +ATOM 4375 HB3 TRP A 284 -10.306 -42.610 19.716 1.00 0.00 H +ATOM 4376 HD1 TRP A 284 -8.677 -43.105 16.406 1.00 0.00 H +ATOM 4377 HE1 TRP A 284 -10.436 -43.260 14.533 1.00 0.00 H +ATOM 4378 HE3 TRP A 284 -12.736 -41.419 19.014 1.00 0.00 H +ATOM 4379 HZ2 TRP A 284 -13.180 -42.727 14.217 1.00 0.00 H +ATOM 4380 HZ3 TRP A 284 -14.910 -41.268 17.847 1.00 0.00 H +ATOM 4381 HH2 TRP A 284 -15.119 -41.908 15.482 1.00 0.00 H +ATOM 4382 N ASN A 285 -7.515 -40.509 20.716 1.00 0.00 N +ATOM 4383 CA ASN A 285 -6.822 -40.647 21.992 1.00 0.00 C +ATOM 4384 C ASN A 285 -6.982 -39.414 22.877 1.00 0.00 C +ATOM 4385 O ASN A 285 -6.844 -39.513 24.100 1.00 0.00 O +ATOM 4386 CB ASN A 285 -5.342 -40.943 21.758 1.00 0.00 C +ATOM 4387 CG ASN A 285 -4.597 -39.760 21.180 1.00 0.00 C +ATOM 4388 OD1 ASN A 285 -4.529 -39.590 19.965 1.00 0.00 O +ATOM 4389 ND2 ASN A 285 -4.029 -38.935 22.051 1.00 0.00 N +ATOM 4390 H ASN A 285 -6.993 -40.389 19.872 1.00 0.00 H +ATOM 4391 HA ASN A 285 -7.252 -41.503 22.532 1.00 0.00 H +ATOM 4392 HB2 ASN A 285 -4.881 -41.215 22.719 1.00 0.00 H +ATOM 4393 HB3 ASN A 285 -5.258 -41.788 21.059 1.00 0.00 H +ATOM 4394 HD21 ASN A 285 -3.521 -38.138 21.726 1.00 0.00 H +ATOM 4395 HD22 ASN A 285 -4.109 -39.111 23.032 1.00 0.00 H +ATOM 4396 N ILE A 286 -7.286 -38.249 22.302 1.00 0.00 N +ATOM 4397 CA ILE A 286 -7.441 -37.073 23.150 1.00 0.00 C +ATOM 4398 C ILE A 286 -8.784 -37.069 23.863 1.00 0.00 C +ATOM 4399 O ILE A 286 -8.968 -36.311 24.821 1.00 0.00 O +ATOM 4400 CB ILE A 286 -7.245 -35.780 22.341 1.00 0.00 C +ATOM 4401 CG1 ILE A 286 -8.458 -35.521 21.450 1.00 0.00 C +ATOM 4402 CG2 ILE A 286 -5.977 -35.860 21.512 1.00 0.00 C +ATOM 4403 CD1 ILE A 286 -8.317 -34.282 20.615 1.00 0.00 C +ATOM 4404 H ILE A 286 -7.405 -38.184 21.311 1.00 0.00 H +ATOM 4405 HA ILE A 286 -6.657 -37.098 23.921 1.00 0.00 H +ATOM 4406 HB ILE A 286 -7.148 -34.943 23.048 1.00 0.00 H +ATOM 4407 HG12 ILE A 286 -8.591 -36.383 20.779 1.00 0.00 H +ATOM 4408 HG13 ILE A 286 -9.347 -35.414 22.090 1.00 0.00 H +ATOM 4409 HG21 ILE A 286 -5.852 -34.928 20.941 1.00 0.00 H +ATOM 4410 HG22 ILE A 286 -5.112 -36.000 22.177 1.00 0.00 H +ATOM 4411 HG23 ILE A 286 -6.047 -36.710 20.817 1.00 0.00 H +ATOM 4412 HD11 ILE A 286 -9.218 -34.151 19.998 1.00 0.00 H +ATOM 4413 HD12 ILE A 286 -8.192 -33.409 21.273 1.00 0.00 H +ATOM 4414 HD13 ILE A 286 -7.437 -34.378 19.962 1.00 0.00 H +ATOM 4415 N PHE A 287 -9.726 -37.907 23.432 1.00 0.00 N +ATOM 4416 CA PHE A 287 -11.023 -38.015 24.086 1.00 0.00 C +ATOM 4417 C PHE A 287 -10.989 -39.097 25.164 1.00 0.00 C +ATOM 4418 O PHE A 287 -10.338 -40.131 24.986 1.00 0.00 O +ATOM 4419 CB PHE A 287 -12.117 -38.341 23.070 1.00 0.00 C +ATOM 4420 CG PHE A 287 -12.460 -37.197 22.156 1.00 0.00 C +ATOM 4421 CD1 PHE A 287 -13.380 -36.235 22.543 1.00 0.00 C +ATOM 4422 CD2 PHE A 287 -11.871 -37.089 20.906 1.00 0.00 C +ATOM 4423 CE1 PHE A 287 -13.702 -35.183 21.703 1.00 0.00 C +ATOM 4424 CE2 PHE A 287 -12.189 -36.037 20.061 1.00 0.00 C +ATOM 4425 CZ PHE A 287 -13.105 -35.084 20.461 1.00 0.00 C +ATOM 4426 H PHE A 287 -9.537 -38.480 22.635 1.00 0.00 H +ATOM 4427 HA PHE A 287 -11.270 -37.056 24.565 1.00 0.00 H +ATOM 4428 HB2 PHE A 287 -11.777 -39.187 22.454 1.00 0.00 H +ATOM 4429 HB3 PHE A 287 -13.025 -38.630 23.619 1.00 0.00 H +ATOM 4430 HD1 PHE A 287 -13.851 -36.307 23.512 1.00 0.00 H +ATOM 4431 HD2 PHE A 287 -11.156 -37.833 20.587 1.00 0.00 H +ATOM 4432 HE1 PHE A 287 -14.420 -34.440 22.018 1.00 0.00 H +ATOM 4433 HE2 PHE A 287 -11.721 -35.963 19.091 1.00 0.00 H +ATOM 4434 HZ PHE A 287 -13.354 -34.263 19.805 1.00 0.00 H +ATOM 4435 N PRO A 288 -11.691 -38.885 26.271 1.00 0.00 N +ATOM 4436 CA PRO A 288 -11.640 -39.841 27.378 1.00 0.00 C +ATOM 4437 C PRO A 288 -12.572 -41.026 27.179 1.00 0.00 C +ATOM 4438 O PRO A 288 -13.496 -41.010 26.363 1.00 0.00 O +ATOM 4439 CB PRO A 288 -12.083 -38.998 28.576 1.00 0.00 C +ATOM 4440 CG PRO A 288 -13.026 -38.013 27.985 1.00 0.00 C +ATOM 4441 CD PRO A 288 -12.469 -37.682 26.619 1.00 0.00 C +ATOM 4442 HA PRO A 288 -10.611 -40.197 27.535 1.00 0.00 H +ATOM 4443 HB2 PRO A 288 -12.588 -39.621 29.329 1.00 0.00 H +ATOM 4444 HB3 PRO A 288 -11.223 -38.490 29.037 1.00 0.00 H +ATOM 4445 HG2 PRO A 288 -14.031 -38.451 27.894 1.00 0.00 H +ATOM 4446 HG3 PRO A 288 -13.076 -37.108 28.608 1.00 0.00 H +ATOM 4447 HD2 PRO A 288 -11.823 -36.793 26.662 1.00 0.00 H +ATOM 4448 HD3 PRO A 288 -13.278 -37.512 25.894 1.00 0.00 H +ATOM 4449 N VAL A 289 -12.298 -42.074 27.955 1.00 0.00 N +ATOM 4450 CA VAL A 289 -13.236 -43.177 28.083 1.00 0.00 C +ATOM 4451 C VAL A 289 -14.272 -42.838 29.148 1.00 0.00 C +ATOM 4452 O VAL A 289 -14.076 -41.962 29.995 1.00 0.00 O +ATOM 4453 CB VAL A 289 -12.513 -44.492 28.414 1.00 0.00 C +ATOM 4454 CG1 VAL A 289 -11.506 -44.828 27.337 1.00 0.00 C +ATOM 4455 CG2 VAL A 289 -11.841 -44.395 29.773 1.00 0.00 C +ATOM 4456 H VAL A 289 -11.434 -42.102 28.457 1.00 0.00 H +ATOM 4457 HA VAL A 289 -13.762 -43.312 27.126 1.00 0.00 H +ATOM 4458 HB VAL A 289 -13.263 -45.296 28.454 1.00 0.00 H +ATOM 4459 HG11 VAL A 289 -10.999 -45.771 27.590 1.00 0.00 H +ATOM 4460 HG12 VAL A 289 -12.024 -44.938 26.373 1.00 0.00 H +ATOM 4461 HG13 VAL A 289 -10.763 -44.020 27.265 1.00 0.00 H +ATOM 4462 HG21 VAL A 289 -11.328 -45.342 29.997 1.00 0.00 H +ATOM 4463 HG22 VAL A 289 -11.108 -43.575 29.762 1.00 0.00 H +ATOM 4464 HG23 VAL A 289 -12.600 -44.198 30.544 1.00 0.00 H +ATOM 4465 N ILE A 290 -15.394 -43.546 29.104 1.00 0.00 N +ATOM 4466 CA ILE A 290 -16.496 -43.326 30.030 1.00 0.00 C +ATOM 4467 C ILE A 290 -16.839 -44.657 30.674 1.00 0.00 C +ATOM 4468 O ILE A 290 -17.088 -45.648 29.975 1.00 0.00 O +ATOM 4469 CB ILE A 290 -17.722 -42.721 29.333 1.00 0.00 C +ATOM 4470 CG1 ILE A 290 -17.356 -41.377 28.722 1.00 0.00 C +ATOM 4471 CG2 ILE A 290 -18.865 -42.560 30.318 1.00 0.00 C +ATOM 4472 CD1 ILE A 290 -18.485 -40.719 28.036 1.00 0.00 C +ATOM 4473 H ILE A 290 -15.486 -44.258 28.408 1.00 0.00 H +ATOM 4474 HA ILE A 290 -16.172 -42.632 30.819 1.00 0.00 H +ATOM 4475 HB ILE A 290 -18.040 -43.401 28.529 1.00 0.00 H +ATOM 4476 HG12 ILE A 290 -17.001 -40.714 29.525 1.00 0.00 H +ATOM 4477 HG13 ILE A 290 -16.548 -41.534 27.992 1.00 0.00 H +ATOM 4478 HG21 ILE A 290 -19.735 -42.126 29.803 1.00 0.00 H +ATOM 4479 HG22 ILE A 290 -19.136 -43.544 30.729 1.00 0.00 H +ATOM 4480 HG23 ILE A 290 -18.553 -41.894 31.136 1.00 0.00 H +ATOM 4481 HD11 ILE A 290 -18.151 -39.757 27.620 1.00 0.00 H +ATOM 4482 HD12 ILE A 290 -18.846 -41.365 27.222 1.00 0.00 H +ATOM 4483 HD13 ILE A 290 -19.299 -40.545 28.755 1.00 0.00 H +ATOM 4484 N SER A 291 -16.863 -44.678 31.999 1.00 0.00 N +ATOM 4485 CA SER A 291 -17.105 -45.892 32.761 1.00 0.00 C +ATOM 4486 C SER A 291 -18.362 -45.730 33.601 1.00 0.00 C +ATOM 4487 O SER A 291 -18.522 -44.726 34.302 1.00 0.00 O +ATOM 4488 CB SER A 291 -15.905 -46.223 33.654 1.00 0.00 C +ATOM 4489 OG SER A 291 -14.761 -46.503 32.872 1.00 0.00 O +ATOM 4490 H SER A 291 -16.708 -43.824 32.495 1.00 0.00 H +ATOM 4491 HA SER A 291 -17.258 -46.731 32.067 1.00 0.00 H +ATOM 4492 HB2 SER A 291 -15.694 -45.364 34.308 1.00 0.00 H +ATOM 4493 HB3 SER A 291 -16.145 -47.102 34.270 1.00 0.00 H +ATOM 4494 HG SER A 291 -13.980 -46.248 33.349 1.00 0.00 H +ATOM 4495 N LEU A 292 -19.255 -46.710 33.507 1.00 0.00 N +ATOM 4496 CA LEU A 292 -20.383 -46.854 34.414 1.00 0.00 C +ATOM 4497 C LEU A 292 -20.166 -48.109 35.246 1.00 0.00 C +ATOM 4498 O LEU A 292 -19.916 -49.186 34.692 1.00 0.00 O +ATOM 4499 CB LEU A 292 -21.711 -46.957 33.656 1.00 0.00 C +ATOM 4500 CG LEU A 292 -22.094 -45.914 32.604 1.00 0.00 C +ATOM 4501 CD1 LEU A 292 -23.488 -46.208 32.057 1.00 0.00 C +ATOM 4502 CD2 LEU A 292 -22.024 -44.505 33.171 1.00 0.00 C +ATOM 4503 H LEU A 292 -19.146 -47.383 32.775 1.00 0.00 H +ATOM 4504 HA LEU A 292 -20.429 -45.986 35.088 1.00 0.00 H +ATOM 4505 HB2 LEU A 292 -21.706 -47.931 33.145 1.00 0.00 H +ATOM 4506 HB3 LEU A 292 -22.507 -46.945 34.415 1.00 0.00 H +ATOM 4507 HG LEU A 292 -21.377 -45.987 31.773 1.00 0.00 H +ATOM 4508 HD11 LEU A 292 -23.753 -45.453 31.302 1.00 0.00 H +ATOM 4509 HD12 LEU A 292 -23.498 -47.207 31.596 1.00 0.00 H +ATOM 4510 HD13 LEU A 292 -24.218 -46.177 32.879 1.00 0.00 H +ATOM 4511 HD21 LEU A 292 -22.304 -43.781 32.392 1.00 0.00 H +ATOM 4512 HD22 LEU A 292 -22.718 -44.417 34.020 1.00 0.00 H +ATOM 4513 HD23 LEU A 292 -20.999 -44.298 33.512 1.00 0.00 H +ATOM 4514 N TYR A 293 -20.245 -47.967 36.565 1.00 0.00 N +ATOM 4515 CA TYR A 293 -20.219 -49.102 37.478 1.00 0.00 C +ATOM 4516 C TYR A 293 -21.652 -49.555 37.731 1.00 0.00 C +ATOM 4517 O TYR A 293 -22.504 -48.744 38.110 1.00 0.00 O +ATOM 4518 CB TYR A 293 -19.546 -48.742 38.804 1.00 0.00 C +ATOM 4519 CG TYR A 293 -18.039 -48.573 38.745 1.00 0.00 C +ATOM 4520 CD1 TYR A 293 -17.466 -47.484 38.106 1.00 0.00 C +ATOM 4521 CD2 TYR A 293 -17.191 -49.490 39.356 1.00 0.00 C +ATOM 4522 CE1 TYR A 293 -16.088 -47.318 38.059 1.00 0.00 C +ATOM 4523 CE2 TYR A 293 -15.808 -49.334 39.317 1.00 0.00 C +ATOM 4524 CZ TYR A 293 -15.264 -48.245 38.666 1.00 0.00 C +ATOM 4525 OH TYR A 293 -13.898 -48.072 38.616 1.00 0.00 O +ATOM 4526 H TYR A 293 -20.325 -47.046 36.945 1.00 0.00 H +ATOM 4527 HA TYR A 293 -19.664 -49.932 37.017 1.00 0.00 H +ATOM 4528 HB2 TYR A 293 -19.980 -47.796 39.159 1.00 0.00 H +ATOM 4529 HB3 TYR A 293 -19.770 -49.541 39.526 1.00 0.00 H +ATOM 4530 HD1 TYR A 293 -18.104 -46.750 37.636 1.00 0.00 H +ATOM 4531 HD2 TYR A 293 -17.614 -50.340 39.871 1.00 0.00 H +ATOM 4532 HE1 TYR A 293 -15.662 -46.466 37.549 1.00 0.00 H +ATOM 4533 HE2 TYR A 293 -15.165 -50.060 39.793 1.00 0.00 H +ATOM 4534 HH TYR A 293 -13.696 -47.144 38.610 1.00 0.00 H +ATOM 4535 N LEU A 294 -21.916 -50.841 37.526 1.00 0.00 N +ATOM 4536 CA LEU A 294 -23.239 -51.405 37.744 1.00 0.00 C +ATOM 4537 C LEU A 294 -23.231 -52.320 38.961 1.00 0.00 C +ATOM 4538 O LEU A 294 -22.222 -52.962 39.269 1.00 0.00 O +ATOM 4539 CB LEU A 294 -23.719 -52.185 36.516 1.00 0.00 C +ATOM 4540 CG LEU A 294 -23.780 -51.380 35.218 1.00 0.00 C +ATOM 4541 CD1 LEU A 294 -23.997 -52.298 34.030 1.00 0.00 C +ATOM 4542 CD2 LEU A 294 -24.870 -50.321 35.293 1.00 0.00 C +ATOM 4543 H LEU A 294 -21.181 -51.441 37.211 1.00 0.00 H +ATOM 4544 HA LEU A 294 -23.952 -50.589 37.931 1.00 0.00 H +ATOM 4545 HB2 LEU A 294 -23.033 -53.031 36.360 1.00 0.00 H +ATOM 4546 HB3 LEU A 294 -24.729 -52.566 36.728 1.00 0.00 H +ATOM 4547 HG LEU A 294 -22.814 -50.870 35.087 1.00 0.00 H +ATOM 4548 HD11 LEU A 294 -24.038 -51.701 33.107 1.00 0.00 H +ATOM 4549 HD12 LEU A 294 -23.166 -53.016 33.965 1.00 0.00 H +ATOM 4550 HD13 LEU A 294 -24.944 -52.843 34.156 1.00 0.00 H +ATOM 4551 HD21 LEU A 294 -24.897 -49.754 34.351 1.00 0.00 H +ATOM 4552 HD22 LEU A 294 -25.843 -50.807 35.456 1.00 0.00 H +ATOM 4553 HD23 LEU A 294 -24.658 -49.636 36.127 1.00 0.00 H +ATOM 4554 N MET A 295 -24.372 -52.377 39.645 1.00 0.00 N +ATOM 4555 CA MET A 295 -24.508 -53.248 40.806 1.00 0.00 C +ATOM 4556 C MET A 295 -24.302 -54.705 40.404 1.00 0.00 C +ATOM 4557 O MET A 295 -24.785 -55.155 39.362 1.00 0.00 O +ATOM 4558 CB MET A 295 -25.886 -53.061 41.446 1.00 0.00 C +ATOM 4559 CG MET A 295 -26.148 -53.956 42.650 1.00 0.00 C +ATOM 4560 SD MET A 295 -27.869 -53.946 43.193 1.00 0.00 S +ATOM 4561 CE MET A 295 -27.919 -52.484 44.230 1.00 0.00 C +ATOM 4562 H MET A 295 -25.147 -51.814 39.358 1.00 0.00 H +ATOM 4563 HA MET A 295 -23.743 -52.982 41.550 1.00 0.00 H +ATOM 4564 HB2 MET A 295 -25.975 -52.014 41.770 1.00 0.00 H +ATOM 4565 HB3 MET A 295 -26.651 -53.276 40.685 1.00 0.00 H +ATOM 4566 HG2 MET A 295 -25.873 -54.987 42.384 1.00 0.00 H +ATOM 4567 HG3 MET A 295 -25.518 -53.612 43.483 1.00 0.00 H +ATOM 4568 HE1 MET A 295 -28.955 -52.295 44.548 1.00 0.00 H +ATOM 4569 HE2 MET A 295 -27.286 -52.641 45.116 1.00 0.00 H +ATOM 4570 HE3 MET A 295 -27.546 -51.619 43.662 1.00 0.00 H +ATOM 4571 N GLY A 296 -23.553 -55.441 41.228 1.00 0.00 N +ATOM 4572 CA GLY A 296 -23.248 -56.826 40.943 1.00 0.00 C +ATOM 4573 C GLY A 296 -24.296 -57.767 41.507 1.00 0.00 C +ATOM 4574 O GLY A 296 -25.298 -57.356 42.092 1.00 0.00 O +ATOM 4575 H GLY A 296 -23.194 -55.025 42.063 1.00 0.00 H +ATOM 4576 HA2 GLY A 296 -23.199 -56.962 39.853 1.00 0.00 H +ATOM 4577 HA3 GLY A 296 -22.273 -57.074 41.387 1.00 0.00 H +ATOM 4578 N GLU A 297 -24.049 -59.066 41.317 1.00 0.00 N +ATOM 4579 CA GLU A 297 -24.965 -60.071 41.846 1.00 0.00 C +ATOM 4580 C GLU A 297 -24.635 -60.440 43.288 1.00 0.00 C +ATOM 4581 O GLU A 297 -25.520 -60.877 44.032 1.00 0.00 O +ATOM 4582 CB GLU A 297 -24.956 -61.317 40.956 1.00 0.00 C +ATOM 4583 CG GLU A 297 -25.664 -61.122 39.618 1.00 0.00 C +ATOM 4584 CD GLU A 297 -26.433 -62.354 39.169 1.00 0.00 C +ATOM 4585 OE1 GLU A 297 -25.827 -63.237 38.527 1.00 0.00 O +ATOM 4586 OE2 GLU A 297 -27.644 -62.441 39.464 1.00 0.00 O1- +ATOM 4587 H GLU A 297 -23.235 -59.350 40.811 1.00 0.00 H +ATOM 4588 HA GLU A 297 -25.984 -59.658 41.831 1.00 0.00 H +ATOM 4589 HB2 GLU A 297 -23.910 -61.595 40.758 1.00 0.00 H +ATOM 4590 HB3 GLU A 297 -25.456 -62.134 41.497 1.00 0.00 H +ATOM 4591 HG2 GLU A 297 -26.370 -60.284 39.714 1.00 0.00 H +ATOM 4592 HG3 GLU A 297 -24.910 -60.881 38.854 1.00 0.00 H +ATOM 4593 N VAL A 298 -23.393 -60.252 43.700 1.00 0.00 N +ATOM 4594 CA VAL A 298 -22.952 -60.555 45.055 1.00 0.00 C +ATOM 4595 C VAL A 298 -23.043 -59.290 45.893 1.00 0.00 C +ATOM 4596 O VAL A 298 -22.949 -58.168 45.380 1.00 0.00 O +ATOM 4597 CB VAL A 298 -21.522 -61.143 45.029 1.00 0.00 C +ATOM 4598 CG1 VAL A 298 -20.898 -61.188 46.417 1.00 0.00 C +ATOM 4599 CG2 VAL A 298 -21.544 -62.534 44.422 1.00 0.00 C +ATOM 4600 H VAL A 298 -22.726 -59.885 43.052 1.00 0.00 H +ATOM 4601 HA VAL A 298 -23.622 -61.309 45.493 1.00 0.00 H +ATOM 4602 HB VAL A 298 -20.900 -60.497 44.392 1.00 0.00 H +ATOM 4603 HG11 VAL A 298 -19.885 -61.612 46.351 1.00 0.00 H +ATOM 4604 HG12 VAL A 298 -20.843 -60.169 46.827 1.00 0.00 H +ATOM 4605 HG13 VAL A 298 -21.515 -61.816 47.076 1.00 0.00 H +ATOM 4606 HG21 VAL A 298 -20.524 -62.944 44.407 1.00 0.00 H +ATOM 4607 HG22 VAL A 298 -22.193 -63.187 45.025 1.00 0.00 H +ATOM 4608 HG23 VAL A 298 -21.932 -62.479 43.394 1.00 0.00 H +ATOM 4609 N THR A 299 -23.265 -59.472 47.194 1.00 0.00 N +ATOM 4610 CA THR A 299 -23.288 -58.349 48.118 1.00 0.00 C +ATOM 4611 C THR A 299 -21.973 -57.582 48.063 1.00 0.00 C +ATOM 4612 O THR A 299 -20.889 -58.174 48.117 1.00 0.00 O +ATOM 4613 CB THR A 299 -23.544 -58.847 49.538 1.00 0.00 C +ATOM 4614 OG1 THR A 299 -24.572 -59.846 49.521 1.00 0.00 O +ATOM 4615 CG2 THR A 299 -23.981 -57.697 50.427 1.00 0.00 C +ATOM 4616 H THR A 299 -23.419 -60.397 47.541 1.00 0.00 H +ATOM 4617 HA THR A 299 -24.102 -57.664 47.839 1.00 0.00 H +ATOM 4618 HB THR A 299 -22.611 -59.272 49.938 1.00 0.00 H +ATOM 4619 HG1 THR A 299 -25.423 -59.425 49.500 1.00 0.00 H +ATOM 4620 HG21 THR A 299 -24.162 -58.069 51.446 1.00 0.00 H +ATOM 4621 HG22 THR A 299 -23.191 -56.932 50.451 1.00 0.00 H +ATOM 4622 HG23 THR A 299 -24.906 -57.256 50.028 1.00 0.00 H +ATOM 4623 N ASN A 300 -22.077 -56.256 47.946 1.00 0.00 N +ATOM 4624 CA ASN A 300 -20.936 -55.339 47.985 1.00 0.00 C +ATOM 4625 C ASN A 300 -19.976 -55.533 46.817 1.00 0.00 C +ATOM 4626 O ASN A 300 -18.795 -55.188 46.920 1.00 0.00 O +ATOM 4627 CB ASN A 300 -20.172 -55.451 49.312 1.00 0.00 C +ATOM 4628 CG ASN A 300 -21.016 -55.056 50.507 1.00 0.00 C +ATOM 4629 OD1 ASN A 300 -22.014 -54.347 50.375 1.00 0.00 O +ATOM 4630 ND2 ASN A 300 -20.610 -55.508 51.686 1.00 0.00 N +ATOM 4631 H ASN A 300 -22.990 -55.865 47.824 1.00 0.00 H +ATOM 4632 HA ASN A 300 -21.324 -54.312 47.921 1.00 0.00 H +ATOM 4633 HB2 ASN A 300 -19.843 -56.493 49.442 1.00 0.00 H +ATOM 4634 HB3 ASN A 300 -19.293 -54.792 49.269 1.00 0.00 H +ATOM 4635 HD21 ASN A 300 -21.121 -55.275 52.514 1.00 0.00 H +ATOM 4636 HD22 ASN A 300 -19.793 -56.082 51.747 1.00 0.00 H +ATOM 4637 N GLN A 301 -20.450 -56.084 45.707 1.00 0.00 N +ATOM 4638 CA GLN A 301 -19.644 -56.211 44.503 1.00 0.00 C +ATOM 4639 C GLN A 301 -20.287 -55.425 43.373 1.00 0.00 C +ATOM 4640 O GLN A 301 -21.512 -55.444 43.210 1.00 0.00 O +ATOM 4641 CB GLN A 301 -19.474 -57.676 44.081 1.00 0.00 C +ATOM 4642 CG GLN A 301 -18.304 -58.391 44.736 1.00 0.00 C +ATOM 4643 CD GLN A 301 -18.052 -59.756 44.122 1.00 0.00 C +ATOM 4644 OE1 GLN A 301 -18.475 -60.030 42.997 1.00 0.00 O +ATOM 4645 NE2 GLN A 301 -17.362 -60.619 44.858 1.00 0.00 N +ATOM 4646 H GLN A 301 -21.391 -56.423 45.698 1.00 0.00 H +ATOM 4647 HA GLN A 301 -18.645 -55.791 44.691 1.00 0.00 H +ATOM 4648 HB2 GLN A 301 -20.396 -58.217 44.341 1.00 0.00 H +ATOM 4649 HB3 GLN A 301 -19.327 -57.704 42.991 1.00 0.00 H +ATOM 4650 HG2 GLN A 301 -17.400 -57.776 44.616 1.00 0.00 H +ATOM 4651 HG3 GLN A 301 -18.521 -58.519 45.807 1.00 0.00 H +ATOM 4652 HE21 GLN A 301 -17.167 -61.534 44.504 1.00 0.00 H +ATOM 4653 HE22 GLN A 301 -17.038 -60.354 45.766 1.00 0.00 H +ATOM 4654 N SER A 302 -19.453 -54.725 42.606 1.00 0.00 N +ATOM 4655 CA SER A 302 -19.868 -54.093 41.366 1.00 0.00 C +ATOM 4656 C SER A 302 -18.875 -54.457 40.274 1.00 0.00 C +ATOM 4657 O SER A 302 -17.758 -54.909 40.541 1.00 0.00 O +ATOM 4658 CB SER A 302 -19.965 -52.563 41.501 1.00 0.00 C +ATOM 4659 OG SER A 302 -18.718 -52.000 41.875 1.00 0.00 O +ATOM 4660 H SER A 302 -18.501 -54.631 42.896 1.00 0.00 H +ATOM 4661 HA SER A 302 -20.858 -54.475 41.077 1.00 0.00 H +ATOM 4662 HB2 SER A 302 -20.276 -52.137 40.536 1.00 0.00 H +ATOM 4663 HB3 SER A 302 -20.714 -52.318 42.268 1.00 0.00 H +ATOM 4664 HG SER A 302 -18.438 -52.372 42.703 1.00 0.00 H +ATOM 4665 N PHE A 303 -19.308 -54.286 39.034 1.00 0.00 N +ATOM 4666 CA PHE A 303 -18.415 -54.327 37.891 1.00 0.00 C +ATOM 4667 C PHE A 303 -18.582 -53.016 37.140 1.00 0.00 C +ATOM 4668 O PHE A 303 -19.447 -52.202 37.465 1.00 0.00 O +ATOM 4669 CB PHE A 303 -18.692 -55.540 36.991 1.00 0.00 C +ATOM 4670 CG PHE A 303 -20.045 -55.522 36.342 1.00 0.00 C +ATOM 4671 CD1 PHE A 303 -21.165 -55.981 37.018 1.00 0.00 C +ATOM 4672 CD2 PHE A 303 -20.193 -55.060 35.047 1.00 0.00 C +ATOM 4673 CE1 PHE A 303 -22.410 -55.969 36.411 1.00 0.00 C +ATOM 4674 CE2 PHE A 303 -21.433 -55.048 34.435 1.00 0.00 C +ATOM 4675 CZ PHE A 303 -22.540 -55.501 35.118 1.00 0.00 C +ATOM 4676 H PHE A 303 -20.283 -54.123 38.880 1.00 0.00 H +ATOM 4677 HA PHE A 303 -17.376 -54.393 38.245 1.00 0.00 H +ATOM 4678 HB2 PHE A 303 -17.929 -55.565 36.199 1.00 0.00 H +ATOM 4679 HB3 PHE A 303 -18.614 -56.450 37.604 1.00 0.00 H +ATOM 4680 HD1 PHE A 303 -21.066 -56.352 38.028 1.00 0.00 H +ATOM 4681 HD2 PHE A 303 -19.329 -54.704 34.506 1.00 0.00 H +ATOM 4682 HE1 PHE A 303 -23.277 -56.325 36.947 1.00 0.00 H +ATOM 4683 HE2 PHE A 303 -21.533 -54.684 33.423 1.00 0.00 H +ATOM 4684 HZ PHE A 303 -23.510 -55.490 34.643 1.00 0.00 H +ATOM 4685 N ARG A 304 -17.735 -52.792 36.144 1.00 0.00 N +ATOM 4686 CA ARG A 304 -17.786 -51.558 35.378 1.00 0.00 C +ATOM 4687 C ARG A 304 -17.650 -51.862 33.897 1.00 0.00 C +ATOM 4688 O ARG A 304 -16.902 -52.754 33.490 1.00 0.00 O +ATOM 4689 CB ARG A 304 -16.697 -50.561 35.815 1.00 0.00 C +ATOM 4690 CG ARG A 304 -15.402 -50.650 35.031 1.00 0.00 C +ATOM 4691 CD ARG A 304 -14.325 -49.773 35.618 1.00 0.00 C +ATOM 4692 NE ARG A 304 -13.000 -50.173 35.158 1.00 0.00 N +ATOM 4693 CZ ARG A 304 -11.893 -50.024 35.878 1.00 0.00 C +ATOM 4694 NH1 ARG A 304 -11.959 -49.488 37.091 1.00 0.00 N +ATOM 4695 NH2 ARG A 304 -10.726 -50.422 35.392 1.00 0.00 N1+ +ATOM 4696 H ARG A 304 -17.050 -53.484 35.916 1.00 0.00 H +ATOM 4697 HA ARG A 304 -18.763 -51.079 35.539 1.00 0.00 H +ATOM 4698 HB2 ARG A 304 -17.098 -49.543 35.699 1.00 0.00 H +ATOM 4699 HB3 ARG A 304 -16.469 -50.746 36.875 1.00 0.00 H +ATOM 4700 HG2 ARG A 304 -15.054 -51.693 35.038 1.00 0.00 H +ATOM 4701 HG3 ARG A 304 -15.592 -50.334 33.995 1.00 0.00 H +ATOM 4702 HD2 ARG A 304 -14.509 -48.731 35.316 1.00 0.00 H +ATOM 4703 HD3 ARG A 304 -14.361 -49.849 36.715 1.00 0.00 H +ATOM 4704 HE ARG A 304 -12.920 -50.583 34.249 1.00 0.00 H +ATOM 4705 HH11 ARG A 304 -11.128 -49.385 37.638 1.00 0.00 H +ATOM 4706 HH12 ARG A 304 -12.840 -49.188 37.458 1.00 0.00 H +ATOM 4707 HH21 ARG A 304 -9.895 -50.319 35.939 1.00 0.00 H +ATOM 4708 HH22 ARG A 304 -10.676 -50.826 34.479 1.00 0.00 H +ATOM 4709 N ILE A 305 -18.405 -51.115 33.102 1.00 0.00 N +ATOM 4710 CA ILE A 305 -18.337 -51.169 31.651 1.00 0.00 C +ATOM 4711 C ILE A 305 -17.792 -49.835 31.167 1.00 0.00 C +ATOM 4712 O ILE A 305 -18.126 -48.778 31.715 1.00 0.00 O +ATOM 4713 CB ILE A 305 -19.713 -51.481 31.033 1.00 0.00 C +ATOM 4714 CG1 ILE A 305 -20.708 -50.368 31.352 1.00 0.00 C +ATOM 4715 CG2 ILE A 305 -20.237 -52.806 31.562 1.00 0.00 C +ATOM 4716 CD1 ILE A 305 -22.098 -50.636 30.839 1.00 0.00 C +ATOM 4717 H ILE A 305 -19.055 -50.482 33.523 1.00 0.00 H +ATOM 4718 HA ILE A 305 -17.637 -51.962 31.348 1.00 0.00 H +ATOM 4719 HB ILE A 305 -19.600 -51.553 29.941 1.00 0.00 H +ATOM 4720 HG12 ILE A 305 -20.758 -50.249 32.444 1.00 0.00 H +ATOM 4721 HG13 ILE A 305 -20.344 -49.435 30.897 1.00 0.00 H +ATOM 4722 HG21 ILE A 305 -21.219 -53.018 31.114 1.00 0.00 H +ATOM 4723 HG22 ILE A 305 -19.533 -53.610 31.299 1.00 0.00 H +ATOM 4724 HG23 ILE A 305 -20.338 -52.750 32.656 1.00 0.00 H +ATOM 4725 HD11 ILE A 305 -22.756 -49.796 31.105 1.00 0.00 H +ATOM 4726 HD12 ILE A 305 -22.070 -50.748 29.745 1.00 0.00 H +ATOM 4727 HD13 ILE A 305 -22.484 -51.561 31.292 1.00 0.00 H +ATOM 4728 N THR A 306 -16.940 -49.885 30.150 1.00 0.00 N +ATOM 4729 CA THR A 306 -16.202 -48.715 29.699 1.00 0.00 C +ATOM 4730 C THR A 306 -16.342 -48.592 28.193 1.00 0.00 C +ATOM 4731 O THR A 306 -16.020 -49.532 27.461 1.00 0.00 O +ATOM 4732 CB THR A 306 -14.726 -48.817 30.097 1.00 0.00 C +ATOM 4733 OG1 THR A 306 -14.616 -48.787 31.525 1.00 0.00 O +ATOM 4734 CG2 THR A 306 -13.929 -47.675 29.501 1.00 0.00 C +ATOM 4735 H THR A 306 -16.801 -50.756 29.678 1.00 0.00 H +ATOM 4736 HA THR A 306 -16.626 -47.813 30.165 1.00 0.00 H +ATOM 4737 HB THR A 306 -14.325 -49.766 29.712 1.00 0.00 H +ATOM 4738 HG1 THR A 306 -14.800 -47.909 31.839 1.00 0.00 H +ATOM 4739 HG21 THR A 306 -12.874 -47.768 29.799 1.00 0.00 H +ATOM 4740 HG22 THR A 306 -14.004 -47.709 28.404 1.00 0.00 H +ATOM 4741 HG23 THR A 306 -14.330 -46.718 29.867 1.00 0.00 H +ATOM 4742 N ILE A 307 -16.824 -47.443 27.734 1.00 0.00 N +ATOM 4743 CA ILE A 307 -16.959 -47.179 26.311 1.00 0.00 C +ATOM 4744 C ILE A 307 -15.936 -46.131 25.901 1.00 0.00 C +ATOM 4745 O ILE A 307 -15.350 -45.425 26.727 1.00 0.00 O +ATOM 4746 CB ILE A 307 -18.379 -46.725 25.925 1.00 0.00 C +ATOM 4747 CG1 ILE A 307 -18.714 -45.410 26.626 1.00 0.00 C +ATOM 4748 CG2 ILE A 307 -19.394 -47.803 26.262 1.00 0.00 C +ATOM 4749 CD1 ILE A 307 -19.881 -44.681 26.008 1.00 0.00 C +ATOM 4750 H ILE A 307 -17.102 -46.738 28.387 1.00 0.00 H +ATOM 4751 HA ILE A 307 -16.740 -48.103 25.756 1.00 0.00 H +ATOM 4752 HB ILE A 307 -18.401 -46.554 24.839 1.00 0.00 H +ATOM 4753 HG12 ILE A 307 -18.956 -45.627 27.677 1.00 0.00 H +ATOM 4754 HG13 ILE A 307 -17.831 -44.756 26.580 1.00 0.00 H +ATOM 4755 HG21 ILE A 307 -20.400 -47.461 25.979 1.00 0.00 H +ATOM 4756 HG22 ILE A 307 -19.150 -48.722 25.708 1.00 0.00 H +ATOM 4757 HG23 ILE A 307 -19.366 -48.008 27.342 1.00 0.00 H +ATOM 4758 HD11 ILE A 307 -20.065 -43.748 26.560 1.00 0.00 H +ATOM 4759 HD12 ILE A 307 -19.652 -44.446 24.958 1.00 0.00 H +ATOM 4760 HD13 ILE A 307 -20.777 -45.317 26.055 1.00 0.00 H +ATOM 4761 N LEU A 308 -15.738 -46.029 24.599 1.00 0.00 N +ATOM 4762 CA LEU A 308 -14.741 -45.186 23.970 1.00 0.00 C +ATOM 4763 C LEU A 308 -15.391 -44.043 23.209 1.00 0.00 C +ATOM 4764 O LEU A 308 -16.620 -44.029 22.997 1.00 0.00 O +ATOM 4765 CB LEU A 308 -13.880 -46.024 23.015 1.00 0.00 C +ATOM 4766 CG LEU A 308 -13.343 -47.364 23.522 1.00 0.00 C +ATOM 4767 CD1 LEU A 308 -12.500 -48.019 22.443 1.00 0.00 C +ATOM 4768 CD2 LEU A 308 -12.539 -47.191 24.800 1.00 0.00 C +ATOM 4769 H LEU A 308 -16.322 -46.577 24.000 1.00 0.00 H +ATOM 4770 HA LEU A 308 -14.084 -44.760 24.742 1.00 0.00 H +ATOM 4771 HB2 LEU A 308 -14.489 -46.232 22.123 1.00 0.00 H +ATOM 4772 HB3 LEU A 308 -13.012 -45.410 22.732 1.00 0.00 H +ATOM 4773 HG LEU A 308 -14.200 -48.019 23.738 1.00 0.00 H +ATOM 4774 HD11 LEU A 308 -12.116 -48.982 22.811 1.00 0.00 H +ATOM 4775 HD12 LEU A 308 -13.117 -48.188 21.548 1.00 0.00 H +ATOM 4776 HD13 LEU A 308 -11.656 -47.361 22.187 1.00 0.00 H +ATOM 4777 HD21 LEU A 308 -12.169 -48.171 25.136 1.00 0.00 H +ATOM 4778 HD22 LEU A 308 -11.686 -46.523 24.609 1.00 0.00 H +ATOM 4779 HD23 LEU A 308 -13.180 -46.754 25.580 1.00 0.00 H +ATOM 4780 N PRO A 309 -14.621 -43.040 22.779 1.00 0.00 N +ATOM 4781 CA PRO A 309 -15.173 -42.024 21.875 1.00 0.00 C +ATOM 4782 C PRO A 309 -15.669 -42.594 20.562 1.00 0.00 C +ATOM 4783 O PRO A 309 -16.489 -41.950 19.898 1.00 0.00 O +ATOM 4784 CB PRO A 309 -14.001 -41.055 21.659 1.00 0.00 C +ATOM 4785 CG PRO A 309 -12.784 -41.802 22.103 1.00 0.00 C +ATOM 4786 CD PRO A 309 -13.246 -42.701 23.199 1.00 0.00 C +ATOM 4787 HA PRO A 309 -15.995 -41.487 22.372 1.00 0.00 H +ATOM 4788 HB2 PRO A 309 -13.919 -40.781 20.597 1.00 0.00 H +ATOM 4789 HB3 PRO A 309 -14.137 -40.146 22.263 1.00 0.00 H +ATOM 4790 HG2 PRO A 309 -12.370 -42.391 21.271 1.00 0.00 H +ATOM 4791 HG3 PRO A 309 -12.019 -41.104 22.475 1.00 0.00 H +ATOM 4792 HD2 PRO A 309 -13.244 -42.180 24.168 1.00 0.00 H +ATOM 4793 HD3 PRO A 309 -12.618 -43.601 23.266 1.00 0.00 H +ATOM 4794 N GLN A 310 -15.198 -43.775 20.157 1.00 0.00 N +ATOM 4795 CA GLN A 310 -15.765 -44.421 18.981 1.00 0.00 C +ATOM 4796 C GLN A 310 -17.232 -44.776 19.181 1.00 0.00 C +ATOM 4797 O GLN A 310 -17.951 -44.976 18.196 1.00 0.00 O +ATOM 4798 CB GLN A 310 -14.962 -45.671 18.623 1.00 0.00 C +ATOM 4799 CG GLN A 310 -13.648 -45.379 17.925 1.00 0.00 C +ATOM 4800 CD GLN A 310 -12.472 -45.266 18.881 1.00 0.00 C +ATOM 4801 OE1 GLN A 310 -12.641 -45.023 20.075 1.00 0.00 O +ATOM 4802 NE2 GLN A 310 -11.270 -45.458 18.354 1.00 0.00 N +ATOM 4803 H GLN A 310 -14.456 -44.217 20.661 1.00 0.00 H +ATOM 4804 HA GLN A 310 -15.700 -43.725 18.131 1.00 0.00 H +ATOM 4805 HB2 GLN A 310 -14.746 -46.221 19.551 1.00 0.00 H +ATOM 4806 HB3 GLN A 310 -15.575 -46.298 17.959 1.00 0.00 H +ATOM 4807 HG2 GLN A 310 -13.443 -46.192 17.213 1.00 0.00 H +ATOM 4808 HG3 GLN A 310 -13.746 -44.429 17.379 1.00 0.00 H +ATOM 4809 HE21 GLN A 310 -10.456 -45.408 18.933 1.00 0.00 H +ATOM 4810 HE22 GLN A 310 -11.177 -45.653 17.378 1.00 0.00 H +ATOM 4811 N GLN A 311 -17.689 -44.854 20.431 1.00 0.00 N +ATOM 4812 CA GLN A 311 -19.089 -45.109 20.736 1.00 0.00 C +ATOM 4813 C GLN A 311 -19.902 -43.824 20.844 1.00 0.00 C +ATOM 4814 O GLN A 311 -21.020 -43.753 20.325 1.00 0.00 O +ATOM 4815 CB GLN A 311 -19.214 -45.913 22.035 1.00 0.00 C +ATOM 4816 CG GLN A 311 -19.086 -47.416 21.855 1.00 0.00 C +ATOM 4817 CD GLN A 311 -17.661 -47.855 21.591 1.00 0.00 C +ATOM 4818 OE1 GLN A 311 -16.768 -47.633 22.410 1.00 0.00 O +ATOM 4819 NE2 GLN A 311 -17.440 -48.482 20.443 1.00 0.00 N +ATOM 4820 H GLN A 311 -17.046 -44.733 21.187 1.00 0.00 H +ATOM 4821 HA GLN A 311 -19.522 -45.714 19.925 1.00 0.00 H +ATOM 4822 HB2 GLN A 311 -18.424 -45.580 22.724 1.00 0.00 H +ATOM 4823 HB3 GLN A 311 -20.199 -45.702 22.478 1.00 0.00 H +ATOM 4824 HG2 GLN A 311 -19.443 -47.911 22.770 1.00 0.00 H +ATOM 4825 HG3 GLN A 311 -19.712 -47.723 21.004 1.00 0.00 H +ATOM 4826 HE21 GLN A 311 -16.518 -48.795 20.214 1.00 0.00 H +ATOM 4827 HE22 GLN A 311 -18.195 -48.641 19.807 1.00 0.00 H +ATOM 4828 N TYR A 312 -19.373 -42.801 21.513 1.00 0.00 N +ATOM 4829 CA TYR A 312 -20.181 -41.611 21.750 1.00 0.00 C +ATOM 4830 C TYR A 312 -19.926 -40.487 20.753 1.00 0.00 C +ATOM 4831 O TYR A 312 -20.612 -39.462 20.818 1.00 0.00 O +ATOM 4832 CB TYR A 312 -20.008 -41.105 23.196 1.00 0.00 C +ATOM 4833 CG TYR A 312 -18.637 -40.618 23.633 1.00 0.00 C +ATOM 4834 CD1 TYR A 312 -18.187 -39.347 23.301 1.00 0.00 C +ATOM 4835 CD2 TYR A 312 -17.831 -41.399 24.453 1.00 0.00 C +ATOM 4836 CE1 TYR A 312 -16.948 -38.888 23.732 1.00 0.00 C +ATOM 4837 CE2 TYR A 312 -16.595 -40.948 24.889 1.00 0.00 C +ATOM 4838 CZ TYR A 312 -16.159 -39.693 24.525 1.00 0.00 C +ATOM 4839 OH TYR A 312 -14.932 -39.246 24.958 1.00 0.00 O +ATOM 4840 H TYR A 312 -18.432 -42.849 21.849 1.00 0.00 H +ATOM 4841 HA TYR A 312 -21.237 -41.899 21.642 1.00 0.00 H +ATOM 4842 HB2 TYR A 312 -20.709 -40.269 23.334 1.00 0.00 H +ATOM 4843 HB3 TYR A 312 -20.288 -41.932 23.865 1.00 0.00 H +ATOM 4844 HD1 TYR A 312 -18.810 -38.704 22.698 1.00 0.00 H +ATOM 4845 HD2 TYR A 312 -18.175 -42.377 24.757 1.00 0.00 H +ATOM 4846 HE1 TYR A 312 -16.605 -37.904 23.447 1.00 0.00 H +ATOM 4847 HE2 TYR A 312 -15.976 -41.578 25.511 1.00 0.00 H +ATOM 4848 HH TYR A 312 -14.497 -39.936 25.445 1.00 0.00 H +ATOM 4849 N LEU A 313 -18.976 -40.650 19.838 1.00 0.00 N +ATOM 4850 CA LEU A 313 -18.840 -39.769 18.683 1.00 0.00 C +ATOM 4851 C LEU A 313 -19.411 -40.523 17.489 1.00 0.00 C +ATOM 4852 O LEU A 313 -18.811 -41.493 17.016 1.00 0.00 O +ATOM 4853 CB LEU A 313 -17.385 -39.366 18.446 1.00 0.00 C +ATOM 4854 CG LEU A 313 -16.692 -38.558 19.544 1.00 0.00 C +ATOM 4855 CD1 LEU A 313 -15.243 -38.316 19.158 1.00 0.00 C +ATOM 4856 CD2 LEU A 313 -17.415 -37.242 19.792 1.00 0.00 C +ATOM 4857 H LEU A 313 -18.330 -41.405 19.945 1.00 0.00 H +ATOM 4858 HA LEU A 313 -19.436 -38.858 18.843 1.00 0.00 H +ATOM 4859 HB2 LEU A 313 -16.806 -40.290 18.301 1.00 0.00 H +ATOM 4860 HB3 LEU A 313 -17.355 -38.765 17.525 1.00 0.00 H +ATOM 4861 HG LEU A 313 -16.712 -39.147 20.473 1.00 0.00 H +ATOM 4862 HD11 LEU A 313 -14.745 -37.735 19.948 1.00 0.00 H +ATOM 4863 HD12 LEU A 313 -14.730 -39.281 19.035 1.00 0.00 H +ATOM 4864 HD13 LEU A 313 -15.204 -37.757 18.212 1.00 0.00 H +ATOM 4865 HD21 LEU A 313 -16.897 -36.682 20.584 1.00 0.00 H +ATOM 4866 HD22 LEU A 313 -17.421 -36.647 18.867 1.00 0.00 H +ATOM 4867 HD23 LEU A 313 -18.450 -37.445 20.104 1.00 0.00 H +ATOM 4868 N ARG A 314 -20.578 -40.093 17.015 1.00 0.00 N +ATOM 4869 CA ARG A 314 -21.230 -40.960 16.043 1.00 0.00 C +ATOM 4870 C ARG A 314 -21.058 -40.419 14.637 1.00 0.00 C +ATOM 4871 O ARG A 314 -21.301 -39.228 14.398 1.00 0.00 O +ATOM 4872 CB ARG A 314 -22.709 -41.123 16.377 1.00 0.00 C +ATOM 4873 CG ARG A 314 -22.912 -41.622 17.792 1.00 0.00 C +ATOM 4874 CD ARG A 314 -24.327 -42.080 18.043 1.00 0.00 C +ATOM 4875 NE ARG A 314 -24.696 -43.259 17.268 1.00 0.00 N +ATOM 4876 CZ ARG A 314 -24.293 -44.496 17.544 1.00 0.00 C +ATOM 4877 NH1 ARG A 314 -24.696 -45.503 16.783 1.00 0.00 N +ATOM 4878 NH2 ARG A 314 -23.479 -44.729 18.567 1.00 0.00 N1+ +ATOM 4879 H ARG A 314 -20.983 -39.227 17.309 1.00 0.00 H +ATOM 4880 HA ARG A 314 -20.762 -41.954 16.085 1.00 0.00 H +ATOM 4881 HB2 ARG A 314 -23.209 -40.149 16.267 1.00 0.00 H +ATOM 4882 HB3 ARG A 314 -23.155 -41.845 15.677 1.00 0.00 H +ATOM 4883 HG2 ARG A 314 -22.230 -42.467 17.970 1.00 0.00 H +ATOM 4884 HG3 ARG A 314 -22.675 -40.807 18.491 1.00 0.00 H +ATOM 4885 HD2 ARG A 314 -24.433 -42.317 19.112 1.00 0.00 H +ATOM 4886 HD3 ARG A 314 -25.012 -41.260 17.779 1.00 0.00 H +ATOM 4887 HE ARG A 314 -25.292 -43.129 16.476 1.00 0.00 H +ATOM 4888 HH11 ARG A 314 -24.387 -46.433 16.980 1.00 0.00 H +ATOM 4889 HH12 ARG A 314 -25.309 -45.333 16.011 1.00 0.00 H +ATOM 4890 HH21 ARG A 314 -23.173 -45.661 18.760 1.00 0.00 H +ATOM 4891 HH22 ARG A 314 -23.172 -43.972 19.144 1.00 0.00 H +ATOM 4892 N PRO A 315 -20.646 -41.273 13.701 1.00 0.00 N +ATOM 4893 CA PRO A 315 -20.279 -40.794 12.363 1.00 0.00 C +ATOM 4894 C PRO A 315 -21.489 -40.353 11.558 1.00 0.00 C +ATOM 4895 O PRO A 315 -22.557 -40.966 11.613 1.00 0.00 O +ATOM 4896 CB PRO A 315 -19.603 -42.012 11.724 1.00 0.00 C +ATOM 4897 CG PRO A 315 -20.184 -43.178 12.446 1.00 0.00 C +ATOM 4898 CD PRO A 315 -20.435 -42.722 13.851 1.00 0.00 C +ATOM 4899 HA PRO A 315 -19.558 -39.967 12.434 1.00 0.00 H +ATOM 4900 HB2 PRO A 315 -19.832 -42.066 10.649 1.00 0.00 H +ATOM 4901 HB3 PRO A 315 -18.513 -41.971 11.865 1.00 0.00 H +ATOM 4902 HG2 PRO A 315 -21.127 -43.488 11.972 1.00 0.00 H +ATOM 4903 HG3 PRO A 315 -19.477 -44.020 12.440 1.00 0.00 H +ATOM 4904 HD2 PRO A 315 -21.327 -43.209 14.271 1.00 0.00 H +ATOM 4905 HD3 PRO A 315 -19.567 -42.930 14.494 1.00 0.00 H +ATOM 4906 N VAL A 316 -21.253 -39.194 10.862 1.00 0.00 N +ATOM 4907 CA VAL A 316 -22.249 -38.582 10.044 1.00 0.00 C +ATOM 4908 C VAL A 316 -22.006 -38.575 8.560 1.00 0.00 C +ATOM 4909 O VAL A 316 -20.966 -38.153 8.049 1.00 0.00 O +ATOM 4910 CB VAL A 316 -23.413 -37.893 10.661 1.00 0.00 C +ATOM 4911 CG1 VAL A 316 -24.171 -36.862 9.897 1.00 0.00 C +ATOM 4912 CG2 VAL A 316 -23.841 -38.164 12.077 1.00 0.00 C +ATOM 4913 H VAL A 316 -20.353 -38.765 10.930 1.00 0.00 H +ATOM 4914 HA VAL A 316 -22.811 -39.525 9.974 1.00 0.00 H +ATOM 4915 HB VAL A 316 -24.088 -38.679 10.293 1.00 0.00 H +ATOM 4916 HG11 VAL A 316 -24.989 -36.472 10.520 1.00 0.00 H +ATOM 4917 HG12 VAL A 316 -23.495 -36.039 9.622 1.00 0.00 H +ATOM 4918 HG13 VAL A 316 -24.588 -37.315 8.985 1.00 0.00 H +ATOM 4919 HG21 VAL A 316 -24.719 -37.546 12.319 1.00 0.00 H +ATOM 4920 HG22 VAL A 316 -24.101 -39.227 12.184 1.00 0.00 H +ATOM 4921 HG23 VAL A 316 -23.018 -37.916 12.763 1.00 0.00 H +ATOM 4922 N GLU A 317 -23.062 -39.100 7.843 1.00 0.00 N +ATOM 4923 CA GLU A 317 -22.932 -39.196 6.428 1.00 0.00 C +ATOM 4924 C GLU A 317 -23.132 -37.980 5.577 1.00 0.00 C +ATOM 4925 O GLU A 317 -24.128 -37.256 5.652 1.00 0.00 O +ATOM 4926 CB GLU A 317 -22.627 -40.527 5.795 1.00 0.00 C +ATOM 4927 CG GLU A 317 -21.160 -40.916 5.922 1.00 0.00 C +ATOM 4928 CD GLU A 317 -20.819 -41.467 7.288 1.00 0.00 C +ATOM 4929 OE1 GLU A 317 -21.747 -42.016 7.961 1.00 0.00 O +ATOM 4930 OE2 GLU A 317 -19.608 -41.331 7.646 1.00 0.00 O1- +ATOM 4931 H GLU A 317 -23.895 -39.404 8.304 1.00 0.00 H +ATOM 4932 HA GLU A 317 -21.857 -38.976 6.503 1.00 0.00 H +ATOM 4933 HB2 GLU A 317 -23.239 -41.299 6.285 1.00 0.00 H +ATOM 4934 HB3 GLU A 317 -22.886 -40.477 4.727 1.00 0.00 H +ATOM 4935 HG2 GLU A 317 -20.932 -41.682 5.167 1.00 0.00 H +ATOM 4936 HG3 GLU A 317 -20.542 -40.024 5.738 1.00 0.00 H +ATOM 4937 N ASP A 318 -22.044 -37.737 4.770 1.00 0.00 N +ATOM 4938 CA ASP A 318 -21.888 -36.527 4.018 1.00 0.00 C +ATOM 4939 C ASP A 318 -21.344 -36.656 2.615 1.00 0.00 C +ATOM 4940 O ASP A 318 -20.162 -36.960 2.404 1.00 0.00 O +ATOM 4941 CB ASP A 318 -22.077 -35.234 4.740 1.00 0.00 C +ATOM 4942 CG ASP A 318 -22.097 -33.981 3.927 1.00 0.00 C +ATOM 4943 OD1 ASP A 318 -22.781 -33.966 2.866 1.00 0.00 O +ATOM 4944 OD2 ASP A 318 -21.449 -32.994 4.416 1.00 0.00 O1- +ATOM 4945 H ASP A 318 -21.331 -38.435 4.705 1.00 0.00 H +ATOM 4946 HA ASP A 318 -22.932 -36.545 3.670 1.00 0.00 H +ATOM 4947 HB2 ASP A 318 -23.036 -35.293 5.276 1.00 0.00 H +ATOM 4948 HB3 ASP A 318 -21.257 -35.141 5.467 1.00 0.00 H +ATOM 4949 N VAL A 319 -22.275 -36.459 1.616 1.00 0.00 N +ATOM 4950 CA VAL A 319 -21.854 -36.466 0.244 1.00 0.00 C +ATOM 4951 C VAL A 319 -20.913 -35.414 -0.284 1.00 0.00 C +ATOM 4952 O VAL A 319 -19.948 -35.660 -1.018 1.00 0.00 O +ATOM 4953 CB VAL A 319 -22.399 -37.500 -0.673 1.00 0.00 C +ATOM 4954 CG1 VAL A 319 -23.800 -37.385 -1.179 1.00 0.00 C +ATOM 4955 CG2 VAL A 319 -21.543 -38.642 -1.119 1.00 0.00 C +ATOM 4956 H VAL A 319 -23.237 -36.313 1.847 1.00 0.00 H +ATOM 4957 HA VAL A 319 -21.004 -37.118 0.495 1.00 0.00 H +ATOM 4958 HB VAL A 319 -22.750 -38.089 0.187 1.00 0.00 H +ATOM 4959 HG11 VAL A 319 -24.023 -38.236 -1.840 1.00 0.00 H +ATOM 4960 HG12 VAL A 319 -24.498 -37.389 -0.329 1.00 0.00 H +ATOM 4961 HG13 VAL A 319 -23.911 -36.446 -1.741 1.00 0.00 H +ATOM 4962 HG21 VAL A 319 -22.121 -39.295 -1.789 1.00 0.00 H +ATOM 4963 HG22 VAL A 319 -20.663 -38.255 -1.654 1.00 0.00 H +ATOM 4964 HG23 VAL A 319 -21.213 -39.218 -0.241 1.00 0.00 H +ATOM 4965 N ALA A 320 -21.233 -34.145 0.139 1.00 0.00 N +ATOM 4966 CA ALA A 320 -20.252 -33.110 0.058 1.00 0.00 C +ATOM 4967 C ALA A 320 -19.091 -33.046 1.024 1.00 0.00 C +ATOM 4968 O ALA A 320 -18.207 -32.185 1.005 1.00 0.00 O +ATOM 4969 CB ALA A 320 -20.438 -32.027 -0.969 1.00 0.00 C +ATOM 4970 H ALA A 320 -22.145 -33.948 0.500 1.00 0.00 H +ATOM 4971 HA ALA A 320 -19.655 -33.704 -0.649 1.00 0.00 H +ATOM 4972 HB1 ALA A 320 -19.604 -31.312 -0.904 1.00 0.00 H +ATOM 4973 HB2 ALA A 320 -20.459 -32.474 -1.973 1.00 0.00 H +ATOM 4974 HB3 ALA A 320 -21.386 -31.502 -0.781 1.00 0.00 H +ATOM 4975 N THR A 321 -19.136 -34.072 1.944 1.00 0.00 N +ATOM 4976 CA THR A 321 -18.129 -34.288 2.926 1.00 0.00 C +ATOM 4977 C THR A 321 -17.043 -35.313 2.770 1.00 0.00 C +ATOM 4978 O THR A 321 -16.398 -35.757 3.729 1.00 0.00 O +ATOM 4979 CB THR A 321 -18.241 -33.601 4.236 1.00 0.00 C +ATOM 4980 OG1 THR A 321 -18.673 -34.333 5.352 1.00 0.00 O +ATOM 4981 CG2 THR A 321 -17.941 -32.144 4.416 1.00 0.00 C +ATOM 4982 H THR A 321 -19.916 -34.697 1.919 1.00 0.00 H +ATOM 4983 HA THR A 321 -17.493 -33.490 2.514 1.00 0.00 H +ATOM 4984 HB THR A 321 -17.195 -33.852 4.466 1.00 0.00 H +ATOM 4985 HG1 THR A 321 -19.493 -33.972 5.670 1.00 0.00 H +ATOM 4986 HG21 THR A 321 -18.094 -31.865 5.469 1.00 0.00 H +ATOM 4987 HG22 THR A 321 -16.897 -31.947 4.132 1.00 0.00 H +ATOM 4988 HG23 THR A 321 -18.612 -31.550 3.779 1.00 0.00 H +ATOM 4989 N SER A 322 -16.782 -35.679 1.469 1.00 0.00 N +ATOM 4990 CA SER A 322 -16.033 -36.876 1.222 1.00 0.00 C +ATOM 4991 C SER A 322 -14.640 -37.154 1.720 1.00 0.00 C +ATOM 4992 O SER A 322 -14.229 -38.296 1.985 1.00 0.00 O +ATOM 4993 CB SER A 322 -16.569 -37.882 0.248 1.00 0.00 C +ATOM 4994 OG SER A 322 -17.957 -38.141 0.490 1.00 0.00 O +ATOM 4995 H SER A 322 -17.110 -35.119 0.708 1.00 0.00 H +ATOM 4996 HA SER A 322 -16.490 -37.394 2.077 1.00 0.00 H +ATOM 4997 HB2 SER A 322 -16.449 -37.492 -0.774 1.00 0.00 H +ATOM 4998 HB3 SER A 322 -16.004 -38.820 0.351 1.00 0.00 H +ATOM 4999 HG SER A 322 -18.276 -37.554 1.166 1.00 0.00 H +ATOM 5000 N GLN A 323 -13.836 -36.034 1.773 1.00 0.00 N +ATOM 5001 CA GLN A 323 -12.474 -36.158 2.276 1.00 0.00 C +ATOM 5002 C GLN A 323 -12.370 -35.936 3.781 1.00 0.00 C +ATOM 5003 O GLN A 323 -11.256 -35.954 4.316 1.00 0.00 O +ATOM 5004 CB GLN A 323 -11.549 -35.179 1.540 1.00 0.00 C +ATOM 5005 CG GLN A 323 -11.384 -35.474 0.053 1.00 0.00 C +ATOM 5006 CD GLN A 323 -10.315 -34.612 -0.599 1.00 0.00 C +ATOM 5007 OE1 GLN A 323 -10.013 -33.515 -0.129 1.00 0.00 O +ATOM 5008 NE2 GLN A 323 -9.732 -35.112 -1.683 1.00 0.00 N +ATOM 5009 H GLN A 323 -14.184 -35.147 1.469 1.00 0.00 H +ATOM 5010 HA GLN A 323 -12.115 -37.176 2.064 1.00 0.00 H +ATOM 5011 HB2 GLN A 323 -11.964 -34.166 1.647 1.00 0.00 H +ATOM 5012 HB3 GLN A 323 -10.556 -35.221 2.012 1.00 0.00 H +ATOM 5013 HG2 GLN A 323 -11.106 -36.531 -0.068 1.00 0.00 H +ATOM 5014 HG3 GLN A 323 -12.343 -35.287 -0.452 1.00 0.00 H +ATOM 5015 HE21 GLN A 323 -9.017 -34.592 -2.151 1.00 0.00 H +ATOM 5016 HE22 GLN A 323 -10.008 -36.008 -2.031 1.00 0.00 H +ATOM 5017 N ASP A 324 -13.490 -35.737 4.472 1.00 0.00 N +ATOM 5018 CA ASP A 324 -13.507 -35.497 5.909 1.00 0.00 C +ATOM 5019 C ASP A 324 -14.390 -36.520 6.611 1.00 0.00 C +ATOM 5020 O ASP A 324 -15.423 -36.936 6.075 1.00 0.00 O +ATOM 5021 CB ASP A 324 -14.015 -34.086 6.235 1.00 0.00 C +ATOM 5022 CG ASP A 324 -12.974 -33.016 5.989 1.00 0.00 C +ATOM 5023 OD1 ASP A 324 -11.818 -33.362 5.665 1.00 0.00 O +ATOM 5024 OD2 ASP A 324 -13.318 -31.822 6.126 1.00 0.00 O1- +ATOM 5025 H ASP A 324 -14.361 -35.753 3.981 1.00 0.00 H +ATOM 5026 HA ASP A 324 -12.485 -35.593 6.304 1.00 0.00 H +ATOM 5027 HB2 ASP A 324 -14.892 -33.874 5.606 1.00 0.00 H +ATOM 5028 HB3 ASP A 324 -14.308 -34.054 7.295 1.00 0.00 H +ATOM 5029 N ASP A 325 -13.974 -36.927 7.810 1.00 0.00 N +ATOM 5030 CA ASP A 325 -14.811 -37.702 8.718 1.00 0.00 C +ATOM 5031 C ASP A 325 -15.424 -36.738 9.728 1.00 0.00 C +ATOM 5032 O ASP A 325 -14.700 -35.974 10.375 1.00 0.00 O +ATOM 5033 CB ASP A 325 -14.007 -38.788 9.438 1.00 0.00 C +ATOM 5034 CG ASP A 325 -13.371 -39.785 8.487 1.00 0.00 C +ATOM 5035 OD1 ASP A 325 -13.873 -39.948 7.354 1.00 0.00 O +ATOM 5036 OD2 ASP A 325 -12.369 -40.416 8.885 1.00 0.00 O1- +ATOM 5037 H ASP A 325 -13.047 -36.690 8.100 1.00 0.00 H +ATOM 5038 HA ASP A 325 -15.622 -38.183 8.152 1.00 0.00 H +ATOM 5039 HB2 ASP A 325 -13.211 -38.305 10.023 1.00 0.00 H +ATOM 5040 HB3 ASP A 325 -14.681 -39.332 10.116 1.00 0.00 H +ATOM 5041 N CYS A 326 -16.748 -36.764 9.857 1.00 0.00 N +ATOM 5042 CA CYS A 326 -17.465 -35.865 10.752 1.00 0.00 C +ATOM 5043 C CYS A 326 -18.319 -36.667 11.724 1.00 0.00 C +ATOM 5044 O CYS A 326 -18.741 -37.786 11.422 1.00 0.00 O +ATOM 5045 CB CYS A 326 -18.347 -34.883 9.972 1.00 0.00 C +ATOM 5046 SG CYS A 326 -17.466 -33.841 8.779 1.00 0.00 S +ATOM 5047 H CYS A 326 -17.271 -37.425 9.319 1.00 0.00 H +ATOM 5048 HA CYS A 326 -16.737 -35.282 11.335 1.00 0.00 H +ATOM 5049 HB2 CYS A 326 -19.103 -35.465 9.424 1.00 0.00 H +ATOM 5050 HB3 CYS A 326 -18.847 -34.224 10.697 1.00 0.00 H +ATOM 5051 N TYR A 327 -18.578 -36.084 12.896 1.00 0.00 N +ATOM 5052 CA TYR A 327 -19.195 -36.815 13.995 1.00 0.00 C +ATOM 5053 C TYR A 327 -20.105 -35.911 14.814 1.00 0.00 C +ATOM 5054 O TYR A 327 -19.908 -34.695 14.879 1.00 0.00 O +ATOM 5055 CB TYR A 327 -18.139 -37.421 14.934 1.00 0.00 C +ATOM 5056 CG TYR A 327 -17.092 -38.265 14.247 1.00 0.00 C +ATOM 5057 CD1 TYR A 327 -15.951 -37.689 13.707 1.00 0.00 C +ATOM 5058 CD2 TYR A 327 -17.239 -39.640 14.151 1.00 0.00 C +ATOM 5059 CE1 TYR A 327 -14.992 -38.457 13.080 1.00 0.00 C +ATOM 5060 CE2 TYR A 327 -16.284 -40.417 13.528 1.00 0.00 C +ATOM 5061 CZ TYR A 327 -15.163 -39.822 12.995 1.00 0.00 C +ATOM 5062 OH TYR A 327 -14.210 -40.596 12.375 1.00 0.00 O +ATOM 5063 H TYR A 327 -18.343 -35.120 13.023 1.00 0.00 H +ATOM 5064 HA TYR A 327 -19.801 -37.637 13.586 1.00 0.00 H +ATOM 5065 HB2 TYR A 327 -17.628 -36.596 15.452 1.00 0.00 H +ATOM 5066 HB3 TYR A 327 -18.658 -38.052 15.671 1.00 0.00 H +ATOM 5067 HD1 TYR A 327 -15.811 -36.620 13.779 1.00 0.00 H +ATOM 5068 HD2 TYR A 327 -18.116 -40.111 14.571 1.00 0.00 H +ATOM 5069 HE1 TYR A 327 -14.113 -37.992 12.658 1.00 0.00 H +ATOM 5070 HE2 TYR A 327 -16.415 -41.487 13.459 1.00 0.00 H +ATOM 5071 HH TYR A 327 -13.385 -40.126 12.353 1.00 0.00 H +ATOM 5072 N LYS A 328 -21.088 -36.533 15.464 1.00 0.00 N +ATOM 5073 CA LYS A 328 -21.992 -35.866 16.390 1.00 0.00 C +ATOM 5074 C LYS A 328 -21.781 -36.409 17.798 1.00 0.00 C +ATOM 5075 O LYS A 328 -21.630 -37.620 17.992 1.00 0.00 O +ATOM 5076 CB LYS A 328 -23.457 -36.057 15.973 1.00 0.00 C +ATOM 5077 CG LYS A 328 -24.475 -35.712 17.067 1.00 0.00 C +ATOM 5078 CD LYS A 328 -25.915 -35.854 16.577 1.00 0.00 C +ATOM 5079 CE LYS A 328 -26.921 -35.642 17.707 1.00 0.00 C +ATOM 5080 NZ LYS A 328 -28.333 -35.634 17.223 1.00 0.00 N1+ +ATOM 5081 H LYS A 328 -21.212 -37.513 15.307 1.00 0.00 H +ATOM 5082 HA LYS A 328 -21.774 -34.788 16.397 1.00 0.00 H +ATOM 5083 HB2 LYS A 328 -23.654 -35.413 15.103 1.00 0.00 H +ATOM 5084 HB3 LYS A 328 -23.600 -37.110 15.689 1.00 0.00 H +ATOM 5085 HG2 LYS A 328 -24.322 -36.389 17.920 1.00 0.00 H +ATOM 5086 HG3 LYS A 328 -24.311 -34.673 17.389 1.00 0.00 H +ATOM 5087 HD2 LYS A 328 -26.099 -35.107 15.791 1.00 0.00 H +ATOM 5088 HD3 LYS A 328 -26.052 -36.864 16.163 1.00 0.00 H +ATOM 5089 HE2 LYS A 328 -26.804 -36.454 18.440 1.00 0.00 H +ATOM 5090 HE3 LYS A 328 -26.709 -34.677 18.191 1.00 0.00 H +ATOM 5091 HZ1 LYS A 328 -28.885 -35.045 17.813 1.00 0.00 H +ATOM 5092 HZ2 LYS A 328 -28.362 -35.287 16.286 1.00 0.00 H +ATOM 5093 HZ3 LYS A 328 -28.698 -36.565 17.245 1.00 0.00 H +ATOM 5094 N PHE A 329 -21.764 -35.505 18.775 1.00 0.00 N +ATOM 5095 CA PHE A 329 -21.671 -35.890 20.178 1.00 0.00 C +ATOM 5096 C PHE A 329 -23.002 -36.485 20.621 1.00 0.00 C +ATOM 5097 O PHE A 329 -24.050 -35.846 20.481 1.00 0.00 O +ATOM 5098 CB PHE A 329 -21.303 -34.667 21.016 1.00 0.00 C +ATOM 5099 CG PHE A 329 -21.070 -34.959 22.473 1.00 0.00 C +ATOM 5100 CD1 PHE A 329 -19.988 -35.724 22.881 1.00 0.00 C +ATOM 5101 CD2 PHE A 329 -21.910 -34.428 23.440 1.00 0.00 C +ATOM 5102 CE1 PHE A 329 -19.766 -35.978 24.220 1.00 0.00 C +ATOM 5103 CE2 PHE A 329 -21.690 -34.677 24.783 1.00 0.00 C +ATOM 5104 CZ PHE A 329 -20.615 -35.452 25.173 1.00 0.00 C +ATOM 5105 H PHE A 329 -21.816 -34.534 18.540 1.00 0.00 H +ATOM 5106 HA PHE A 329 -20.886 -36.651 20.301 1.00 0.00 H +ATOM 5107 HB2 PHE A 329 -20.383 -34.230 20.601 1.00 0.00 H +ATOM 5108 HB3 PHE A 329 -22.123 -33.937 20.938 1.00 0.00 H +ATOM 5109 HD1 PHE A 329 -19.310 -36.126 22.142 1.00 0.00 H +ATOM 5110 HD2 PHE A 329 -22.746 -33.813 23.141 1.00 0.00 H +ATOM 5111 HE1 PHE A 329 -18.928 -36.589 24.522 1.00 0.00 H +ATOM 5112 HE2 PHE A 329 -22.358 -34.266 25.526 1.00 0.00 H +ATOM 5113 HZ PHE A 329 -20.439 -35.646 26.221 1.00 0.00 H +ATOM 5114 N ALA A 330 -22.971 -37.710 21.146 1.00 0.00 N +ATOM 5115 CA ALA A 330 -24.194 -38.461 21.403 1.00 0.00 C +ATOM 5116 C ALA A 330 -24.388 -38.776 22.880 1.00 0.00 C +ATOM 5117 O ALA A 330 -24.906 -39.836 23.233 1.00 0.00 O +ATOM 5118 CB ALA A 330 -24.223 -39.743 20.577 1.00 0.00 C +ATOM 5119 H ALA A 330 -22.088 -38.124 21.369 1.00 0.00 H +ATOM 5120 HA ALA A 330 -25.048 -37.846 21.083 1.00 0.00 H +ATOM 5121 HB1 ALA A 330 -25.152 -40.293 20.786 1.00 0.00 H +ATOM 5122 HB2 ALA A 330 -24.179 -39.491 19.507 1.00 0.00 H +ATOM 5123 HB3 ALA A 330 -23.359 -40.369 20.842 1.00 0.00 H +ATOM 5124 N ILE A 331 -23.964 -37.872 23.754 1.00 0.00 N +ATOM 5125 CA ILE A 331 -24.372 -37.872 25.151 1.00 0.00 C +ATOM 5126 C ILE A 331 -25.264 -36.662 25.355 1.00 0.00 C +ATOM 5127 O ILE A 331 -24.907 -35.548 24.956 1.00 0.00 O +ATOM 5128 CB ILE A 331 -23.169 -37.837 26.105 1.00 0.00 C +ATOM 5129 CG1 ILE A 331 -22.209 -38.973 25.776 1.00 0.00 C +ATOM 5130 CG2 ILE A 331 -23.633 -37.956 27.547 1.00 0.00 C +ATOM 5131 CD1 ILE A 331 -20.999 -38.957 26.625 1.00 0.00 C +ATOM 5132 H ILE A 331 -23.337 -37.160 23.439 1.00 0.00 H +ATOM 5133 HA ILE A 331 -24.957 -38.779 25.362 1.00 0.00 H +ATOM 5134 HB ILE A 331 -22.644 -36.879 25.977 1.00 0.00 H +ATOM 5135 HG12 ILE A 331 -22.731 -39.930 25.924 1.00 0.00 H +ATOM 5136 HG13 ILE A 331 -21.902 -38.882 24.723 1.00 0.00 H +ATOM 5137 HG21 ILE A 331 -22.761 -37.929 28.217 1.00 0.00 H +ATOM 5138 HG22 ILE A 331 -24.305 -37.118 27.786 1.00 0.00 H +ATOM 5139 HG23 ILE A 331 -24.170 -38.907 27.682 1.00 0.00 H +ATOM 5140 HD11 ILE A 331 -20.344 -39.795 26.346 1.00 0.00 H +ATOM 5141 HD12 ILE A 331 -20.462 -38.008 26.480 1.00 0.00 H +ATOM 5142 HD13 ILE A 331 -21.291 -39.056 27.681 1.00 0.00 H +ATOM 5143 N SER A 332 -26.425 -36.876 25.964 1.00 0.00 N +ATOM 5144 CA SER A 332 -27.430 -35.828 26.015 1.00 0.00 C +ATOM 5145 C SER A 332 -28.208 -35.924 27.317 1.00 0.00 C +ATOM 5146 O SER A 332 -28.036 -36.854 28.107 1.00 0.00 O +ATOM 5147 CB SER A 332 -28.362 -35.908 24.804 1.00 0.00 C +ATOM 5148 OG SER A 332 -28.927 -37.202 24.694 1.00 0.00 O +ATOM 5149 H SER A 332 -26.608 -37.762 26.389 1.00 0.00 H +ATOM 5150 HA SER A 332 -26.927 -34.850 25.990 1.00 0.00 H +ATOM 5151 HB2 SER A 332 -29.169 -35.170 24.920 1.00 0.00 H +ATOM 5152 HB3 SER A 332 -27.789 -35.685 23.892 1.00 0.00 H +ATOM 5153 HG SER A 332 -28.550 -37.770 25.356 1.00 0.00 H +ATOM 5154 N GLN A 333 -29.073 -34.938 27.524 1.00 0.00 N +ATOM 5155 CA GLN A 333 -29.797 -34.783 28.774 1.00 0.00 C +ATOM 5156 C GLN A 333 -31.135 -35.504 28.712 1.00 0.00 C +ATOM 5157 O GLN A 333 -31.782 -35.558 27.662 1.00 0.00 O +ATOM 5158 CB GLN A 333 -30.013 -33.301 29.086 1.00 0.00 C +ATOM 5159 CG GLN A 333 -30.803 -33.043 30.362 1.00 0.00 C +ATOM 5160 CD GLN A 333 -30.930 -31.570 30.693 1.00 0.00 C +ATOM 5161 OE1 GLN A 333 -29.951 -30.824 30.655 1.00 0.00 O +ATOM 5162 NE2 GLN A 333 -32.145 -31.142 31.021 1.00 0.00 N +ATOM 5163 H GLN A 333 -29.232 -34.277 26.791 1.00 0.00 H +ATOM 5164 HA GLN A 333 -29.206 -35.223 29.591 1.00 0.00 H +ATOM 5165 HB2 GLN A 333 -29.028 -32.822 29.187 1.00 0.00 H +ATOM 5166 HB3 GLN A 333 -30.557 -32.847 28.245 1.00 0.00 H +ATOM 5167 HG2 GLN A 333 -31.812 -33.463 30.240 1.00 0.00 H +ATOM 5168 HG3 GLN A 333 -30.294 -33.547 31.197 1.00 0.00 H +ATOM 5169 HE21 GLN A 333 -32.291 -30.180 31.250 1.00 0.00 H +ATOM 5170 HE22 GLN A 333 -32.913 -31.782 31.039 1.00 0.00 H +ATOM 5171 N SER A 334 -31.540 -36.060 29.847 1.00 0.00 N +ATOM 5172 CA SER A 334 -32.808 -36.754 29.975 1.00 0.00 C +ATOM 5173 C SER A 334 -33.587 -36.189 31.152 1.00 0.00 C +ATOM 5174 O SER A 334 -33.004 -35.821 32.177 1.00 0.00 O +ATOM 5175 CB SER A 334 -32.594 -38.254 30.164 1.00 0.00 C +ATOM 5176 OG SER A 334 -33.756 -38.871 30.691 1.00 0.00 O +ATOM 5177 H SER A 334 -30.946 -35.998 30.649 1.00 0.00 H +ATOM 5178 HA SER A 334 -33.400 -36.602 29.061 1.00 0.00 H +ATOM 5179 HB2 SER A 334 -32.355 -38.709 29.191 1.00 0.00 H +ATOM 5180 HB3 SER A 334 -31.756 -38.413 30.859 1.00 0.00 H +ATOM 5181 HG SER A 334 -33.511 -39.656 31.166 1.00 0.00 H +ATOM 5182 N SER A 335 -34.906 -36.116 30.991 1.00 0.00 N +ATOM 5183 CA SER A 335 -35.807 -35.734 32.068 1.00 0.00 C +ATOM 5184 C SER A 335 -36.442 -36.936 32.749 1.00 0.00 C +ATOM 5185 O SER A 335 -37.212 -36.760 33.700 1.00 0.00 O +ATOM 5186 CB SER A 335 -36.903 -34.806 31.536 1.00 0.00 C +ATOM 5187 OG SER A 335 -36.345 -33.633 30.973 1.00 0.00 O +ATOM 5188 H SER A 335 -35.293 -36.332 30.095 1.00 0.00 H +ATOM 5189 HA SER A 335 -35.236 -35.179 32.826 1.00 0.00 H +ATOM 5190 HB2 SER A 335 -37.480 -35.336 30.764 1.00 0.00 H +ATOM 5191 HB3 SER A 335 -37.570 -34.525 32.364 1.00 0.00 H +ATOM 5192 HG SER A 335 -35.478 -33.825 30.636 1.00 0.00 H +ATOM 5193 N THR A 336 -36.131 -38.152 32.296 1.00 0.00 N +ATOM 5194 CA THR A 336 -36.784 -39.360 32.787 1.00 0.00 C +ATOM 5195 C THR A 336 -35.787 -40.390 33.300 1.00 0.00 C +ATOM 5196 O THR A 336 -36.106 -41.582 33.355 1.00 0.00 O +ATOM 5197 CB THR A 336 -37.649 -39.979 31.689 1.00 0.00 C +ATOM 5198 OG1 THR A 336 -36.838 -40.243 30.536 1.00 0.00 O +ATOM 5199 CG2 THR A 336 -38.783 -39.036 31.308 1.00 0.00 C +ATOM 5200 H THR A 336 -35.424 -38.238 31.594 1.00 0.00 H +ATOM 5201 HA THR A 336 -37.446 -39.087 33.622 1.00 0.00 H +ATOM 5202 HB THR A 336 -38.081 -40.917 32.067 1.00 0.00 H +ATOM 5203 HG1 THR A 336 -36.657 -39.428 30.082 1.00 0.00 H +ATOM 5204 HG21 THR A 336 -39.394 -39.497 30.518 1.00 0.00 H +ATOM 5205 HG22 THR A 336 -39.410 -38.842 32.190 1.00 0.00 H +ATOM 5206 HG23 THR A 336 -38.363 -38.088 30.941 1.00 0.00 H +ATOM 5207 N GLY A 337 -34.590 -39.963 33.667 1.00 0.00 N +ATOM 5208 CA GLY A 337 -33.584 -40.875 34.162 1.00 0.00 C +ATOM 5209 C GLY A 337 -32.565 -41.235 33.103 1.00 0.00 C +ATOM 5210 O GLY A 337 -32.578 -40.740 31.974 1.00 0.00 O +ATOM 5211 H GLY A 337 -34.377 -38.988 33.600 1.00 0.00 H +ATOM 5212 HA2 GLY A 337 -33.063 -40.402 35.008 1.00 0.00 H +ATOM 5213 HA3 GLY A 337 -34.079 -41.796 34.504 1.00 0.00 H +ATOM 5214 N THR A 338 -31.660 -42.127 33.491 1.00 0.00 N +ATOM 5215 CA THR A 338 -30.588 -42.550 32.601 1.00 0.00 C +ATOM 5216 C THR A 338 -31.104 -43.537 31.564 1.00 0.00 C +ATOM 5217 O THR A 338 -31.830 -44.479 31.888 1.00 0.00 O +ATOM 5218 CB THR A 338 -29.450 -43.197 33.391 1.00 0.00 C +ATOM 5219 OG1 THR A 338 -28.970 -42.281 34.382 1.00 0.00 O +ATOM 5220 CG2 THR A 338 -28.311 -43.577 32.460 1.00 0.00 C +ATOM 5221 H THR A 338 -31.716 -42.515 34.411 1.00 0.00 H +ATOM 5222 HA THR A 338 -30.187 -41.672 32.074 1.00 0.00 H +ATOM 5223 HB THR A 338 -29.831 -44.108 33.875 1.00 0.00 H +ATOM 5224 HG1 THR A 338 -28.950 -42.711 35.229 1.00 0.00 H +ATOM 5225 HG21 THR A 338 -27.501 -44.041 33.042 1.00 0.00 H +ATOM 5226 HG22 THR A 338 -28.676 -44.290 31.706 1.00 0.00 H +ATOM 5227 HG23 THR A 338 -27.932 -42.675 31.958 1.00 0.00 H +ATOM 5228 N VAL A 339 -30.711 -43.327 30.313 1.00 0.00 N +ATOM 5229 CA VAL A 339 -31.024 -44.239 29.222 1.00 0.00 C +ATOM 5230 C VAL A 339 -29.704 -44.727 28.643 1.00 0.00 C +ATOM 5231 O VAL A 339 -28.971 -43.953 28.015 1.00 0.00 O +ATOM 5232 CB VAL A 339 -31.883 -43.575 28.138 1.00 0.00 C +ATOM 5233 CG1 VAL A 339 -32.250 -44.592 27.070 1.00 0.00 C +ATOM 5234 CG2 VAL A 339 -33.131 -42.957 28.741 1.00 0.00 C +ATOM 5235 H VAL A 339 -30.177 -42.506 30.111 1.00 0.00 H +ATOM 5236 HA VAL A 339 -31.574 -45.106 29.617 1.00 0.00 H +ATOM 5237 HB VAL A 339 -31.290 -42.776 27.669 1.00 0.00 H +ATOM 5238 HG11 VAL A 339 -32.865 -44.107 26.298 1.00 0.00 H +ATOM 5239 HG12 VAL A 339 -31.332 -44.989 26.612 1.00 0.00 H +ATOM 5240 HG13 VAL A 339 -32.818 -45.416 27.528 1.00 0.00 H +ATOM 5241 HG21 VAL A 339 -33.729 -42.488 27.946 1.00 0.00 H +ATOM 5242 HG22 VAL A 339 -33.726 -43.740 29.234 1.00 0.00 H +ATOM 5243 HG23 VAL A 339 -32.842 -42.195 29.480 1.00 0.00 H +ATOM 5244 N MET A 340 -29.398 -46.004 28.854 1.00 0.00 N +ATOM 5245 CA MET A 340 -28.222 -46.623 28.246 1.00 0.00 C +ATOM 5246 C MET A 340 -28.628 -47.119 26.867 1.00 0.00 C +ATOM 5247 O MET A 340 -29.100 -48.244 26.697 1.00 0.00 O +ATOM 5248 CB MET A 340 -27.683 -47.748 29.122 1.00 0.00 C +ATOM 5249 CG MET A 340 -27.090 -47.264 30.438 1.00 0.00 C +ATOM 5250 SD MET A 340 -27.042 -48.509 31.743 1.00 0.00 S +ATOM 5251 CE MET A 340 -25.793 -49.633 31.130 1.00 0.00 C +ATOM 5252 H MET A 340 -29.988 -46.555 29.444 1.00 0.00 H +ATOM 5253 HA MET A 340 -27.433 -45.866 28.127 1.00 0.00 H +ATOM 5254 HB2 MET A 340 -28.508 -48.440 29.346 1.00 0.00 H +ATOM 5255 HB3 MET A 340 -26.900 -48.280 28.562 1.00 0.00 H +ATOM 5256 HG2 MET A 340 -26.060 -46.927 30.247 1.00 0.00 H +ATOM 5257 HG3 MET A 340 -27.693 -46.416 30.795 1.00 0.00 H +ATOM 5258 HE1 MET A 340 -25.537 -50.361 31.914 1.00 0.00 H +ATOM 5259 HE2 MET A 340 -26.180 -50.165 30.248 1.00 0.00 H +ATOM 5260 HE3 MET A 340 -24.894 -49.064 30.850 1.00 0.00 H +ATOM 5261 N GLY A 341 -28.455 -46.253 25.875 1.00 0.00 N +ATOM 5262 CA GLY A 341 -28.850 -46.536 24.513 1.00 0.00 C +ATOM 5263 C GLY A 341 -27.758 -47.198 23.706 1.00 0.00 C +ATOM 5264 O GLY A 341 -26.861 -47.855 24.240 1.00 0.00 O +ATOM 5265 H GLY A 341 -28.034 -45.369 26.078 1.00 0.00 H +ATOM 5266 HA2 GLY A 341 -29.724 -47.203 24.533 1.00 0.00 H +ATOM 5267 HA3 GLY A 341 -29.123 -45.589 24.023 1.00 0.00 H +ATOM 5268 N ALA A 342 -27.841 -47.015 22.386 1.00 0.00 N +ATOM 5269 CA ALA A 342 -26.877 -47.632 21.479 1.00 0.00 C +ATOM 5270 C ALA A 342 -25.453 -47.197 21.785 1.00 0.00 C +ATOM 5271 O ALA A 342 -24.509 -47.960 21.557 1.00 0.00 O +ATOM 5272 CB ALA A 342 -27.226 -47.297 20.025 1.00 0.00 C +ATOM 5273 H ALA A 342 -28.574 -46.447 22.013 1.00 0.00 H +ATOM 5274 HA ALA A 342 -26.931 -48.724 21.595 1.00 0.00 H +ATOM 5275 HB1 ALA A 342 -26.493 -47.768 19.354 1.00 0.00 H +ATOM 5276 HB2 ALA A 342 -28.232 -47.676 19.793 1.00 0.00 H +ATOM 5277 HB3 ALA A 342 -27.204 -46.206 19.885 1.00 0.00 H +ATOM 5278 N VAL A 343 -25.280 -45.977 22.298 1.00 0.00 N +ATOM 5279 CA VAL A 343 -23.945 -45.506 22.666 1.00 0.00 C +ATOM 5280 C VAL A 343 -23.295 -46.484 23.634 1.00 0.00 C +ATOM 5281 O VAL A 343 -22.124 -46.852 23.489 1.00 0.00 O +ATOM 5282 CB VAL A 343 -24.016 -44.082 23.250 1.00 0.00 C +ATOM 5283 CG1 VAL A 343 -22.684 -43.688 23.879 1.00 0.00 C +ATOM 5284 CG2 VAL A 343 -24.395 -43.099 22.164 1.00 0.00 C +ATOM 5285 H VAL A 343 -26.070 -45.379 22.432 1.00 0.00 H +ATOM 5286 HA VAL A 343 -23.324 -45.465 21.759 1.00 0.00 H +ATOM 5287 HB VAL A 343 -24.792 -44.064 24.030 1.00 0.00 H +ATOM 5288 HG11 VAL A 343 -22.758 -42.670 24.288 1.00 0.00 H +ATOM 5289 HG12 VAL A 343 -22.438 -44.391 24.688 1.00 0.00 H +ATOM 5290 HG13 VAL A 343 -21.894 -43.719 23.114 1.00 0.00 H +ATOM 5291 HG21 VAL A 343 -24.444 -42.085 22.587 1.00 0.00 H +ATOM 5292 HG22 VAL A 343 -23.640 -43.126 21.365 1.00 0.00 H +ATOM 5293 HG23 VAL A 343 -25.377 -43.371 21.750 1.00 0.00 H +ATOM 5294 N ILE A 344 -24.057 -46.938 24.627 1.00 0.00 N +ATOM 5295 CA ILE A 344 -23.551 -47.951 25.546 1.00 0.00 C +ATOM 5296 C ILE A 344 -23.514 -49.318 24.870 1.00 0.00 C +ATOM 5297 O ILE A 344 -22.469 -49.976 24.821 1.00 0.00 O +ATOM 5298 CB ILE A 344 -24.399 -47.985 26.829 1.00 0.00 C +ATOM 5299 CG1 ILE A 344 -24.201 -46.700 27.638 1.00 0.00 C +ATOM 5300 CG2 ILE A 344 -24.065 -49.221 27.652 1.00 0.00 C +ATOM 5301 CD1 ILE A 344 -22.783 -46.513 28.155 1.00 0.00 C +ATOM 5302 H ILE A 344 -24.984 -46.581 24.744 1.00 0.00 H +ATOM 5303 HA ILE A 344 -22.522 -47.687 25.831 1.00 0.00 H +ATOM 5304 HB ILE A 344 -25.457 -48.044 26.535 1.00 0.00 H +ATOM 5305 HG12 ILE A 344 -24.452 -45.843 26.995 1.00 0.00 H +ATOM 5306 HG13 ILE A 344 -24.884 -46.724 28.500 1.00 0.00 H +ATOM 5307 HG21 ILE A 344 -24.679 -49.231 28.565 1.00 0.00 H +ATOM 5308 HG22 ILE A 344 -24.275 -50.124 27.059 1.00 0.00 H +ATOM 5309 HG23 ILE A 344 -23.000 -49.202 27.926 1.00 0.00 H +ATOM 5310 HD11 ILE A 344 -22.720 -45.573 28.723 1.00 0.00 H +ATOM 5311 HD12 ILE A 344 -22.518 -47.356 28.810 1.00 0.00 H +ATOM 5312 HD13 ILE A 344 -22.085 -46.475 27.306 1.00 0.00 H +ATOM 5313 N MET A 345 -24.654 -49.759 24.328 1.00 0.00 N +ATOM 5314 CA MET A 345 -24.792 -51.144 23.882 1.00 0.00 C +ATOM 5315 C MET A 345 -23.824 -51.505 22.764 1.00 0.00 C +ATOM 5316 O MET A 345 -23.469 -52.679 22.613 1.00 0.00 O +ATOM 5317 CB MET A 345 -26.229 -51.411 23.429 1.00 0.00 C +ATOM 5318 CG MET A 345 -27.279 -51.231 24.516 1.00 0.00 C +ATOM 5319 SD MET A 345 -28.948 -51.713 24.026 1.00 0.00 S +ATOM 5320 CE MET A 345 -28.750 -53.468 23.724 1.00 0.00 C +ATOM 5321 H MET A 345 -25.425 -49.131 24.226 1.00 0.00 H +ATOM 5322 HA MET A 345 -24.582 -51.807 24.734 1.00 0.00 H +ATOM 5323 HB2 MET A 345 -26.464 -50.719 22.607 1.00 0.00 H +ATOM 5324 HB3 MET A 345 -26.287 -52.447 23.064 1.00 0.00 H +ATOM 5325 HG2 MET A 345 -26.985 -51.841 25.383 1.00 0.00 H +ATOM 5326 HG3 MET A 345 -27.298 -50.169 24.804 1.00 0.00 H +ATOM 5327 HE1 MET A 345 -29.644 -54.004 24.074 1.00 0.00 H +ATOM 5328 HE2 MET A 345 -28.617 -53.642 22.646 1.00 0.00 H +ATOM 5329 HE3 MET A 345 -27.866 -53.836 24.266 1.00 0.00 H +ATOM 5330 N GLU A 346 -23.389 -50.531 21.966 1.00 0.00 N +ATOM 5331 CA GLU A 346 -22.510 -50.871 20.853 1.00 0.00 C +ATOM 5332 C GLU A 346 -21.107 -51.252 21.303 1.00 0.00 C +ATOM 5333 O GLU A 346 -20.336 -51.792 20.499 1.00 0.00 O +ATOM 5334 CB GLU A 346 -22.450 -49.720 19.853 1.00 0.00 C +ATOM 5335 CG GLU A 346 -23.678 -49.650 18.968 1.00 0.00 C +ATOM 5336 CD GLU A 346 -23.690 -48.435 18.072 1.00 0.00 C +ATOM 5337 OE1 GLU A 346 -22.645 -47.760 17.970 1.00 0.00 O +ATOM 5338 OE2 GLU A 346 -24.751 -48.158 17.470 1.00 0.00 O1- +ATOM 5339 H GLU A 346 -23.661 -49.582 22.128 1.00 0.00 H +ATOM 5340 HA GLU A 346 -22.936 -51.740 20.330 1.00 0.00 H +ATOM 5341 HB2 GLU A 346 -22.362 -48.775 20.409 1.00 0.00 H +ATOM 5342 HB3 GLU A 346 -21.564 -49.854 19.215 1.00 0.00 H +ATOM 5343 HG2 GLU A 346 -23.708 -50.551 18.338 1.00 0.00 H +ATOM 5344 HG3 GLU A 346 -24.571 -49.622 19.609 1.00 0.00 H +ATOM 5345 N GLY A 347 -20.751 -50.984 22.560 1.00 0.00 N +ATOM 5346 CA GLY A 347 -19.487 -51.486 23.060 1.00 0.00 C +ATOM 5347 C GLY A 347 -19.529 -52.930 23.502 1.00 0.00 C +ATOM 5348 O GLY A 347 -18.471 -53.538 23.701 1.00 0.00 O +ATOM 5349 H GLY A 347 -21.350 -50.443 23.151 1.00 0.00 H +ATOM 5350 HA2 GLY A 347 -18.737 -51.391 22.261 1.00 0.00 H +ATOM 5351 HA3 GLY A 347 -19.186 -50.870 23.920 1.00 0.00 H +ATOM 5352 N PHE A 348 -20.726 -53.506 23.625 1.00 0.00 N +ATOM 5353 CA PHE A 348 -20.890 -54.724 24.399 1.00 0.00 C +ATOM 5354 C PHE A 348 -21.865 -55.698 23.755 1.00 0.00 C +ATOM 5355 O PHE A 348 -22.684 -55.345 22.903 1.00 0.00 O +ATOM 5356 CB PHE A 348 -21.361 -54.400 25.818 1.00 0.00 C +ATOM 5357 CG PHE A 348 -20.565 -53.313 26.470 1.00 0.00 C +ATOM 5358 CD1 PHE A 348 -19.274 -53.551 26.910 1.00 0.00 C +ATOM 5359 CD2 PHE A 348 -21.098 -52.046 26.623 1.00 0.00 C +ATOM 5360 CE1 PHE A 348 -18.535 -52.548 27.502 1.00 0.00 C +ATOM 5361 CE2 PHE A 348 -20.366 -51.040 27.216 1.00 0.00 C +ATOM 5362 CZ PHE A 348 -19.078 -51.291 27.654 1.00 0.00 C +ATOM 5363 H PHE A 348 -21.520 -53.094 23.179 1.00 0.00 H +ATOM 5364 HA PHE A 348 -19.914 -55.226 24.477 1.00 0.00 H +ATOM 5365 HB2 PHE A 348 -22.414 -54.084 25.773 1.00 0.00 H +ATOM 5366 HB3 PHE A 348 -21.277 -55.310 26.430 1.00 0.00 H +ATOM 5367 HD1 PHE A 348 -18.841 -54.533 26.788 1.00 0.00 H +ATOM 5368 HD2 PHE A 348 -22.100 -51.843 26.274 1.00 0.00 H +ATOM 5369 HE1 PHE A 348 -17.531 -52.748 27.846 1.00 0.00 H +ATOM 5370 HE2 PHE A 348 -20.797 -50.057 27.338 1.00 0.00 H +ATOM 5371 HZ PHE A 348 -18.500 -50.504 28.114 1.00 0.00 H +ATOM 5372 N TYR A 349 -21.724 -56.948 24.177 1.00 0.00 N +ATOM 5373 CA TYR A 349 -22.722 -57.989 23.997 1.00 0.00 C +ATOM 5374 C TYR A 349 -23.552 -58.015 25.274 1.00 0.00 C +ATOM 5375 O TYR A 349 -23.011 -58.240 26.363 1.00 0.00 O +ATOM 5376 CB TYR A 349 -22.040 -59.333 23.741 1.00 0.00 C +ATOM 5377 CG TYR A 349 -22.934 -60.500 23.377 1.00 0.00 C +ATOM 5378 CD1 TYR A 349 -24.065 -60.336 22.583 1.00 0.00 C +ATOM 5379 CD2 TYR A 349 -22.612 -61.783 23.797 1.00 0.00 C +ATOM 5380 CE1 TYR A 349 -24.860 -61.423 22.241 1.00 0.00 C +ATOM 5381 CE2 TYR A 349 -23.397 -62.869 23.462 1.00 0.00 C +ATOM 5382 CZ TYR A 349 -24.522 -62.685 22.686 1.00 0.00 C +ATOM 5383 OH TYR A 349 -25.297 -63.774 22.357 1.00 0.00 O +ATOM 5384 H TYR A 349 -20.876 -57.189 24.649 1.00 0.00 H +ATOM 5385 HA TYR A 349 -23.375 -57.747 23.145 1.00 0.00 H +ATOM 5386 HB2 TYR A 349 -21.327 -59.191 22.916 1.00 0.00 H +ATOM 5387 HB3 TYR A 349 -21.493 -59.607 24.655 1.00 0.00 H +ATOM 5388 HD1 TYR A 349 -24.328 -59.351 22.228 1.00 0.00 H +ATOM 5389 HD2 TYR A 349 -21.728 -61.936 24.399 1.00 0.00 H +ATOM 5390 HE1 TYR A 349 -25.739 -61.281 21.629 1.00 0.00 H +ATOM 5391 HE2 TYR A 349 -23.131 -63.857 23.806 1.00 0.00 H +ATOM 5392 HH TYR A 349 -26.181 -63.487 22.162 1.00 0.00 H +ATOM 5393 N VAL A 350 -24.847 -57.746 25.148 1.00 0.00 N +ATOM 5394 CA VAL A 350 -25.723 -57.528 26.294 1.00 0.00 C +ATOM 5395 C VAL A 350 -26.708 -58.686 26.372 1.00 0.00 C +ATOM 5396 O VAL A 350 -27.446 -58.950 25.414 1.00 0.00 O +ATOM 5397 CB VAL A 350 -26.452 -56.179 26.195 1.00 0.00 C +ATOM 5398 CG1 VAL A 350 -27.349 -55.968 27.405 1.00 0.00 C +ATOM 5399 CG2 VAL A 350 -25.448 -55.045 26.059 1.00 0.00 C +ATOM 5400 H VAL A 350 -25.238 -57.691 24.229 1.00 0.00 H +ATOM 5401 HA VAL A 350 -25.120 -57.525 27.214 1.00 0.00 H +ATOM 5402 HB VAL A 350 -27.083 -56.194 25.294 1.00 0.00 H +ATOM 5403 HG11 VAL A 350 -27.862 -54.999 27.317 1.00 0.00 H +ATOM 5404 HG12 VAL A 350 -28.095 -56.775 27.453 1.00 0.00 H +ATOM 5405 HG13 VAL A 350 -26.739 -55.978 28.320 1.00 0.00 H +ATOM 5406 HG21 VAL A 350 -25.984 -54.087 25.989 1.00 0.00 H +ATOM 5407 HG22 VAL A 350 -24.788 -55.033 26.939 1.00 0.00 H +ATOM 5408 HG23 VAL A 350 -24.846 -55.195 25.151 1.00 0.00 H +ATOM 5409 N VAL A 351 -26.736 -59.362 27.515 1.00 0.00 N +ATOM 5410 CA VAL A 351 -27.569 -60.542 27.713 1.00 0.00 C +ATOM 5411 C VAL A 351 -28.741 -60.154 28.602 1.00 0.00 C +ATOM 5412 O VAL A 351 -28.561 -59.828 29.779 1.00 0.00 O +ATOM 5413 CB VAL A 351 -26.769 -61.702 28.318 1.00 0.00 C +ATOM 5414 CG1 VAL A 351 -27.702 -62.857 28.659 1.00 0.00 C +ATOM 5415 CG2 VAL A 351 -25.693 -62.154 27.348 1.00 0.00 C +ATOM 5416 H VAL A 351 -26.162 -59.049 28.271 1.00 0.00 H +ATOM 5417 HA VAL A 351 -27.966 -60.870 26.741 1.00 0.00 H +ATOM 5418 HB VAL A 351 -26.288 -61.352 29.243 1.00 0.00 H +ATOM 5419 HG11 VAL A 351 -27.120 -63.684 29.092 1.00 0.00 H +ATOM 5420 HG12 VAL A 351 -28.454 -62.518 29.386 1.00 0.00 H +ATOM 5421 HG13 VAL A 351 -28.206 -63.203 27.745 1.00 0.00 H +ATOM 5422 HG21 VAL A 351 -25.125 -62.986 27.791 1.00 0.00 H +ATOM 5423 HG22 VAL A 351 -26.162 -62.488 26.411 1.00 0.00 H +ATOM 5424 HG23 VAL A 351 -25.012 -61.316 27.139 1.00 0.00 H +ATOM 5425 N PHE A 352 -29.945 -60.197 28.038 1.00 0.00 N +ATOM 5426 CA PHE A 352 -31.172 -59.950 28.792 1.00 0.00 C +ATOM 5427 C PHE A 352 -31.657 -61.295 29.315 1.00 0.00 C +ATOM 5428 O PHE A 352 -32.464 -61.981 28.689 1.00 0.00 O +ATOM 5429 CB PHE A 352 -32.212 -59.254 27.923 1.00 0.00 C +ATOM 5430 CG PHE A 352 -31.775 -57.906 27.425 1.00 0.00 C +ATOM 5431 CD1 PHE A 352 -30.929 -57.797 26.333 1.00 0.00 C +ATOM 5432 CD2 PHE A 352 -32.209 -56.748 28.047 1.00 0.00 C +ATOM 5433 CE1 PHE A 352 -30.525 -56.558 25.870 1.00 0.00 C +ATOM 5434 CE2 PHE A 352 -31.809 -55.504 27.585 1.00 0.00 C +ATOM 5435 CZ PHE A 352 -30.965 -55.412 26.496 1.00 0.00 C +ATOM 5436 H PHE A 352 -30.013 -60.406 27.062 1.00 0.00 H +ATOM 5437 HA PHE A 352 -30.946 -59.302 29.652 1.00 0.00 H +ATOM 5438 HB2 PHE A 352 -32.423 -59.894 27.054 1.00 0.00 H +ATOM 5439 HB3 PHE A 352 -33.130 -59.125 28.515 1.00 0.00 H +ATOM 5440 HD1 PHE A 352 -30.581 -58.691 25.837 1.00 0.00 H +ATOM 5441 HD2 PHE A 352 -32.866 -56.815 28.901 1.00 0.00 H +ATOM 5442 HE1 PHE A 352 -29.865 -56.489 25.018 1.00 0.00 H +ATOM 5443 HE2 PHE A 352 -32.157 -54.607 28.076 1.00 0.00 H +ATOM 5444 HZ PHE A 352 -30.650 -54.444 26.136 1.00 0.00 H +ATOM 5445 N ASP A 353 -31.138 -61.676 30.479 1.00 0.00 N +ATOM 5446 CA ASP A 353 -31.432 -62.966 31.101 1.00 0.00 C +ATOM 5447 C ASP A 353 -32.690 -62.799 31.941 1.00 0.00 C +ATOM 5448 O ASP A 353 -32.631 -62.543 33.143 1.00 0.00 O +ATOM 5449 CB ASP A 353 -30.244 -63.435 31.936 1.00 0.00 C +ATOM 5450 CG ASP A 353 -30.439 -64.819 32.527 1.00 0.00 C +ATOM 5451 OD1 ASP A 353 -31.548 -65.384 32.417 1.00 0.00 O +ATOM 5452 OD2 ASP A 353 -29.467 -65.350 33.105 1.00 0.00 O1- +ATOM 5453 H ASP A 353 -30.517 -61.050 30.951 1.00 0.00 H +ATOM 5454 HA ASP A 353 -31.628 -63.714 30.319 1.00 0.00 H +ATOM 5455 HB2 ASP A 353 -29.350 -63.451 31.295 1.00 0.00 H +ATOM 5456 HB3 ASP A 353 -30.092 -62.721 32.759 1.00 0.00 H +ATOM 5457 N ARG A 354 -33.849 -62.956 31.298 1.00 0.00 N +ATOM 5458 CA ARG A 354 -35.112 -62.781 32.007 1.00 0.00 C +ATOM 5459 C ARG A 354 -35.359 -63.903 33.011 1.00 0.00 C +ATOM 5460 O ARG A 354 -35.991 -63.681 34.050 1.00 0.00 O +ATOM 5461 CB ARG A 354 -36.263 -62.686 31.004 1.00 0.00 C +ATOM 5462 CG ARG A 354 -36.229 -61.437 30.138 1.00 0.00 C +ATOM 5463 CD ARG A 354 -36.859 -61.684 28.776 1.00 0.00 C +ATOM 5464 NE ARG A 354 -38.263 -62.084 28.853 1.00 0.00 N +ATOM 5465 CZ ARG A 354 -39.291 -61.245 28.759 1.00 0.00 C +ATOM 5466 NH1 ARG A 354 -39.083 -59.945 28.587 1.00 0.00 N +ATOM 5467 NH2 ARG A 354 -40.533 -61.706 28.834 1.00 0.00 N1+ +ATOM 5468 H ARG A 354 -33.852 -63.194 30.327 1.00 0.00 H +ATOM 5469 HA ARG A 354 -35.074 -61.833 32.564 1.00 0.00 H +ATOM 5470 HB2 ARG A 354 -36.220 -63.565 30.344 1.00 0.00 H +ATOM 5471 HB3 ARG A 354 -37.210 -62.693 31.563 1.00 0.00 H +ATOM 5472 HG2 ARG A 354 -36.782 -60.634 30.647 1.00 0.00 H +ATOM 5473 HG3 ARG A 354 -35.183 -61.129 29.997 1.00 0.00 H +ATOM 5474 HD2 ARG A 354 -36.792 -60.757 28.187 1.00 0.00 H +ATOM 5475 HD3 ARG A 354 -36.296 -62.482 28.270 1.00 0.00 H +ATOM 5476 HE ARG A 354 -38.464 -63.055 28.985 1.00 0.00 H +ATOM 5477 HH11 ARG A 354 -39.860 -59.320 28.515 1.00 0.00 H +ATOM 5478 HH12 ARG A 354 -38.149 -59.591 28.530 1.00 0.00 H +ATOM 5479 HH21 ARG A 354 -41.306 -61.075 28.761 1.00 0.00 H +ATOM 5480 HH22 ARG A 354 -40.696 -62.684 28.963 1.00 0.00 H +ATOM 5481 N ALA A 355 -34.872 -65.112 32.720 1.00 0.00 N +ATOM 5482 CA ALA A 355 -35.094 -66.236 33.626 1.00 0.00 C +ATOM 5483 C ALA A 355 -34.456 -65.992 34.989 1.00 0.00 C +ATOM 5484 O ALA A 355 -35.040 -66.327 36.026 1.00 0.00 O +ATOM 5485 CB ALA A 355 -34.555 -67.528 33.011 1.00 0.00 C +ATOM 5486 H ALA A 355 -34.352 -65.249 31.877 1.00 0.00 H +ATOM 5487 HA ALA A 355 -36.177 -66.360 33.776 1.00 0.00 H +ATOM 5488 HB1 ALA A 355 -34.729 -68.365 33.703 1.00 0.00 H +ATOM 5489 HB2 ALA A 355 -35.072 -67.725 32.061 1.00 0.00 H +ATOM 5490 HB3 ALA A 355 -33.476 -67.424 32.826 1.00 0.00 H +ATOM 5491 N ARG A 356 -33.262 -65.397 35.010 1.00 0.00 N +ATOM 5492 CA ARG A 356 -32.546 -65.133 36.253 1.00 0.00 C +ATOM 5493 C ARG A 356 -32.546 -63.651 36.624 1.00 0.00 C +ATOM 5494 O ARG A 356 -31.743 -63.228 37.463 1.00 0.00 O +ATOM 5495 CB ARG A 356 -31.116 -65.671 36.167 1.00 0.00 C +ATOM 5496 CG ARG A 356 -31.043 -67.189 36.022 1.00 0.00 C +ATOM 5497 CD ARG A 356 -29.611 -67.683 35.832 1.00 0.00 C +ATOM 5498 NE ARG A 356 -28.832 -67.683 37.070 1.00 0.00 N +ATOM 5499 CZ ARG A 356 -27.623 -68.225 37.187 1.00 0.00 C +ATOM 5500 NH1 ARG A 356 -27.054 -68.811 36.141 1.00 0.00 N +ATOM 5501 NH2 ARG A 356 -26.981 -68.187 38.347 1.00 0.00 N1+ +ATOM 5502 H ARG A 356 -32.844 -65.123 34.144 1.00 0.00 H +ATOM 5503 HA ARG A 356 -33.053 -65.675 37.065 1.00 0.00 H +ATOM 5504 HB2 ARG A 356 -30.624 -65.214 35.296 1.00 0.00 H +ATOM 5505 HB3 ARG A 356 -30.580 -65.383 37.083 1.00 0.00 H +ATOM 5506 HG2 ARG A 356 -31.460 -67.652 36.929 1.00 0.00 H +ATOM 5507 HG3 ARG A 356 -31.640 -67.490 35.149 1.00 0.00 H +ATOM 5508 HD2 ARG A 356 -29.645 -68.710 35.440 1.00 0.00 H +ATOM 5509 HD3 ARG A 356 -29.109 -67.029 35.103 1.00 0.00 H +ATOM 5510 HE ARG A 356 -29.233 -67.249 37.877 1.00 0.00 H +ATOM 5511 HH11 ARG A 356 -26.146 -69.221 36.230 1.00 0.00 H +ATOM 5512 HH12 ARG A 356 -27.533 -68.843 35.264 1.00 0.00 H +ATOM 5513 HH21 ARG A 356 -26.073 -68.599 38.430 1.00 0.00 H +ATOM 5514 HH22 ARG A 356 -27.405 -67.747 39.139 1.00 0.00 H +ATOM 5515 N LYS A 357 -33.436 -62.862 36.015 1.00 0.00 N +ATOM 5516 CA LYS A 357 -33.692 -61.473 36.410 1.00 0.00 C +ATOM 5517 C LYS A 357 -32.391 -60.677 36.536 1.00 0.00 C +ATOM 5518 O LYS A 357 -32.104 -60.050 37.560 1.00 0.00 O +ATOM 5519 CB LYS A 357 -34.501 -61.427 37.710 1.00 0.00 C +ATOM 5520 CG LYS A 357 -35.292 -60.138 37.944 1.00 0.00 C +ATOM 5521 CD LYS A 357 -36.098 -60.220 39.237 1.00 0.00 C +ATOM 5522 CE LYS A 357 -36.776 -58.897 39.570 1.00 0.00 C +ATOM 5523 NZ LYS A 357 -37.530 -58.949 40.858 1.00 0.00 N1+ +ATOM 5524 H LYS A 357 -33.956 -63.239 35.248 1.00 0.00 H +ATOM 5525 HA LYS A 357 -34.298 -60.996 35.625 1.00 0.00 H +ATOM 5526 HB2 LYS A 357 -35.214 -62.265 37.696 1.00 0.00 H +ATOM 5527 HB3 LYS A 357 -33.802 -61.554 38.550 1.00 0.00 H +ATOM 5528 HG2 LYS A 357 -34.591 -59.292 38.009 1.00 0.00 H +ATOM 5529 HG3 LYS A 357 -35.980 -59.980 37.100 1.00 0.00 H +ATOM 5530 HD2 LYS A 357 -36.869 -60.997 39.126 1.00 0.00 H +ATOM 5531 HD3 LYS A 357 -35.421 -60.490 40.061 1.00 0.00 H +ATOM 5532 HE2 LYS A 357 -36.006 -58.115 39.643 1.00 0.00 H +ATOM 5533 HE3 LYS A 357 -37.477 -58.646 38.761 1.00 0.00 H +ATOM 5534 HZ1 LYS A 357 -38.054 -58.105 40.972 1.00 0.00 H +ATOM 5535 HZ2 LYS A 357 -38.154 -59.730 40.847 1.00 0.00 H +ATOM 5536 HZ3 LYS A 357 -36.887 -59.047 41.617 1.00 0.00 H +ATOM 5537 N ARG A 358 -31.593 -60.719 35.473 1.00 0.00 N +ATOM 5538 CA ARG A 358 -30.295 -60.066 35.461 1.00 0.00 C +ATOM 5539 C ARG A 358 -29.951 -59.696 34.027 1.00 0.00 C +ATOM 5540 O ARG A 358 -30.515 -60.241 33.074 1.00 0.00 O +ATOM 5541 CB ARG A 358 -29.209 -60.969 36.061 1.00 0.00 C +ATOM 5542 CG ARG A 358 -29.041 -62.286 35.311 1.00 0.00 C +ATOM 5543 CD ARG A 358 -28.029 -63.221 35.968 1.00 0.00 C +ATOM 5544 NE ARG A 358 -27.720 -64.370 35.117 1.00 0.00 N +ATOM 5545 CZ ARG A 358 -26.749 -65.248 35.356 1.00 0.00 C +ATOM 5546 NH1 ARG A 358 -25.976 -65.117 36.427 1.00 0.00 N +ATOM 5547 NH2 ARG A 358 -26.546 -66.257 34.517 1.00 0.00 N1+ +ATOM 5548 H ARG A 358 -31.895 -61.214 34.658 1.00 0.00 H +ATOM 5549 HA ARG A 358 -30.345 -59.143 36.057 1.00 0.00 H +ATOM 5550 HB2 ARG A 358 -28.251 -60.428 36.036 1.00 0.00 H +ATOM 5551 HB3 ARG A 358 -29.477 -61.193 37.104 1.00 0.00 H +ATOM 5552 HG2 ARG A 358 -30.015 -62.795 35.273 1.00 0.00 H +ATOM 5553 HG3 ARG A 358 -28.702 -62.066 34.288 1.00 0.00 H +ATOM 5554 HD2 ARG A 358 -27.102 -62.662 36.162 1.00 0.00 H +ATOM 5555 HD3 ARG A 358 -28.445 -63.584 36.919 1.00 0.00 H +ATOM 5556 HE ARG A 358 -28.278 -64.505 34.298 1.00 0.00 H +ATOM 5557 HH11 ARG A 358 -25.244 -65.777 36.599 1.00 0.00 H +ATOM 5558 HH12 ARG A 358 -26.125 -64.359 37.062 1.00 0.00 H +ATOM 5559 HH21 ARG A 358 -25.813 -66.914 34.693 1.00 0.00 H +ATOM 5560 HH22 ARG A 358 -27.126 -66.360 33.709 1.00 0.00 H +ATOM 5561 N ILE A 359 -29.023 -58.754 33.882 1.00 0.00 N +ATOM 5562 CA ILE A 359 -28.551 -58.294 32.578 1.00 0.00 C +ATOM 5563 C ILE A 359 -27.033 -58.432 32.538 1.00 0.00 C +ATOM 5564 O ILE A 359 -26.330 -57.841 33.370 1.00 0.00 O +ATOM 5565 CB ILE A 359 -28.967 -56.844 32.288 1.00 0.00 C +ATOM 5566 CG1 ILE A 359 -30.481 -56.711 32.181 1.00 0.00 C +ATOM 5567 CG2 ILE A 359 -28.337 -56.367 31.001 1.00 0.00 C +ATOM 5568 CD1 ILE A 359 -30.922 -55.321 31.763 1.00 0.00 C +ATOM 5569 H ILE A 359 -28.630 -58.341 34.704 1.00 0.00 H +ATOM 5570 HA ILE A 359 -28.977 -58.936 31.793 1.00 0.00 H +ATOM 5571 HB ILE A 359 -28.615 -56.208 33.113 1.00 0.00 H +ATOM 5572 HG12 ILE A 359 -30.845 -57.434 31.436 1.00 0.00 H +ATOM 5573 HG13 ILE A 359 -30.924 -56.940 33.161 1.00 0.00 H +ATOM 5574 HG21 ILE A 359 -28.643 -55.329 30.805 1.00 0.00 H +ATOM 5575 HG22 ILE A 359 -27.242 -56.416 31.089 1.00 0.00 H +ATOM 5576 HG23 ILE A 359 -28.668 -57.009 30.171 1.00 0.00 H +ATOM 5577 HD11 ILE A 359 -32.020 -55.287 31.703 1.00 0.00 H +ATOM 5578 HD12 ILE A 359 -30.572 -54.587 32.504 1.00 0.00 H +ATOM 5579 HD13 ILE A 359 -30.494 -55.081 30.779 1.00 0.00 H +ATOM 5580 N GLY A 360 -26.530 -59.191 31.567 1.00 0.00 N +ATOM 5581 CA GLY A 360 -25.101 -59.423 31.442 1.00 0.00 C +ATOM 5582 C GLY A 360 -24.464 -58.515 30.406 1.00 0.00 C +ATOM 5583 O GLY A 360 -25.080 -58.162 29.403 1.00 0.00 O +ATOM 5584 H GLY A 360 -27.152 -59.612 30.907 1.00 0.00 H +ATOM 5585 HA2 GLY A 360 -24.625 -59.238 32.416 1.00 0.00 H +ATOM 5586 HA3 GLY A 360 -24.936 -60.470 31.146 1.00 0.00 H +ATOM 5587 N PHE A 361 -23.212 -58.147 30.669 1.00 0.00 N +ATOM 5588 CA PHE A 361 -22.409 -57.322 29.781 1.00 0.00 C +ATOM 5589 C PHE A 361 -21.072 -58.003 29.538 1.00 0.00 C +ATOM 5590 O PHE A 361 -20.482 -58.580 30.459 1.00 0.00 O +ATOM 5591 CB PHE A 361 -22.164 -55.928 30.373 1.00 0.00 C +ATOM 5592 CG PHE A 361 -23.402 -55.093 30.511 1.00 0.00 C +ATOM 5593 CD1 PHE A 361 -24.251 -55.262 31.590 1.00 0.00 C +ATOM 5594 CD2 PHE A 361 -23.706 -54.125 29.571 1.00 0.00 C +ATOM 5595 CE1 PHE A 361 -25.394 -54.488 31.718 1.00 0.00 C +ATOM 5596 CE2 PHE A 361 -24.837 -53.347 29.695 1.00 0.00 C +ATOM 5597 CZ PHE A 361 -25.684 -53.530 30.770 1.00 0.00 C +ATOM 5598 H PHE A 361 -22.800 -58.456 31.526 1.00 0.00 H +ATOM 5599 HA PHE A 361 -22.926 -57.206 28.817 1.00 0.00 H +ATOM 5600 HB2 PHE A 361 -21.717 -56.050 31.371 1.00 0.00 H +ATOM 5601 HB3 PHE A 361 -21.459 -55.394 29.719 1.00 0.00 H +ATOM 5602 HD1 PHE A 361 -24.021 -56.004 32.340 1.00 0.00 H +ATOM 5603 HD2 PHE A 361 -23.048 -53.977 28.728 1.00 0.00 H +ATOM 5604 HE1 PHE A 361 -26.056 -54.635 32.559 1.00 0.00 H +ATOM 5605 HE2 PHE A 361 -25.060 -52.595 28.952 1.00 0.00 H +ATOM 5606 HZ PHE A 361 -26.572 -52.924 30.868 1.00 0.00 H +ATOM 5607 N ALA A 362 -20.598 -57.932 28.299 1.00 0.00 N +ATOM 5608 CA ALA A 362 -19.283 -58.440 27.951 1.00 0.00 C +ATOM 5609 C ALA A 362 -18.767 -57.629 26.776 1.00 0.00 C +ATOM 5610 O ALA A 362 -19.542 -57.050 26.009 1.00 0.00 O +ATOM 5611 CB ALA A 362 -19.316 -59.935 27.617 1.00 0.00 C +ATOM 5612 H ALA A 362 -21.163 -57.517 27.586 1.00 0.00 H +ATOM 5613 HA ALA A 362 -18.600 -58.292 28.801 1.00 0.00 H +ATOM 5614 HB1 ALA A 362 -18.302 -60.276 27.360 1.00 0.00 H +ATOM 5615 HB2 ALA A 362 -19.682 -60.497 28.489 1.00 0.00 H +ATOM 5616 HB3 ALA A 362 -19.987 -60.105 26.763 1.00 0.00 H +ATOM 5617 N VAL A 363 -17.442 -57.572 26.661 1.00 0.00 N +ATOM 5618 CA VAL A 363 -16.819 -56.848 25.560 1.00 0.00 C +ATOM 5619 C VAL A 363 -17.290 -57.438 24.247 1.00 0.00 C +ATOM 5620 O VAL A 363 -17.080 -58.632 23.977 1.00 0.00 O +ATOM 5621 CB VAL A 363 -15.294 -56.902 25.667 1.00 0.00 C +ATOM 5622 CG1 VAL A 363 -14.672 -56.313 24.412 1.00 0.00 C +ATOM 5623 CG2 VAL A 363 -14.826 -56.165 26.909 1.00 0.00 C +ATOM 5624 H VAL A 363 -16.867 -58.031 27.338 1.00 0.00 H +ATOM 5625 HA VAL A 363 -17.130 -55.794 25.599 1.00 0.00 H +ATOM 5626 HB VAL A 363 -14.988 -57.956 25.748 1.00 0.00 H +ATOM 5627 HG11 VAL A 363 -13.576 -56.353 24.492 1.00 0.00 H +ATOM 5628 HG12 VAL A 363 -14.997 -56.892 23.535 1.00 0.00 H +ATOM 5629 HG13 VAL A 363 -14.993 -55.267 24.300 1.00 0.00 H +ATOM 5630 HG21 VAL A 363 -13.729 -56.212 26.972 1.00 0.00 H +ATOM 5631 HG22 VAL A 363 -15.145 -55.114 26.853 1.00 0.00 H +ATOM 5632 HG23 VAL A 363 -15.265 -56.635 27.801 1.00 0.00 H +ATOM 5633 N SER A 364 -17.934 -56.623 23.418 1.00 0.00 N +ATOM 5634 CA SER A 364 -18.432 -57.096 22.136 1.00 0.00 C +ATOM 5635 C SER A 364 -17.281 -57.419 21.196 1.00 0.00 C +ATOM 5636 O SER A 364 -16.324 -56.654 21.079 1.00 0.00 O +ATOM 5637 CB SER A 364 -19.338 -56.051 21.491 1.00 0.00 C +ATOM 5638 OG SER A 364 -19.576 -56.354 20.127 1.00 0.00 O +ATOM 5639 H SER A 364 -18.079 -55.669 23.680 1.00 0.00 H +ATOM 5640 HA SER A 364 -19.019 -58.013 22.292 1.00 0.00 H +ATOM 5641 HB2 SER A 364 -20.298 -56.028 22.027 1.00 0.00 H +ATOM 5642 HB3 SER A 364 -18.856 -55.065 21.561 1.00 0.00 H +ATOM 5643 HG SER A 364 -20.487 -56.184 19.919 1.00 0.00 H +ATOM 5644 N ALA A 365 -17.391 -58.548 20.502 1.00 0.00 N +ATOM 5645 CA ALA A 365 -16.382 -58.929 19.522 1.00 0.00 C +ATOM 5646 C ALA A 365 -16.424 -58.101 18.242 1.00 0.00 C +ATOM 5647 O ALA A 365 -15.526 -58.251 17.409 1.00 0.00 O +ATOM 5648 CB ALA A 365 -16.521 -60.408 19.154 1.00 0.00 C +ATOM 5649 H ALA A 365 -18.178 -59.145 20.655 1.00 0.00 H +ATOM 5650 HA ALA A 365 -15.389 -58.792 19.974 1.00 0.00 H +ATOM 5651 HB1 ALA A 365 -15.752 -60.676 18.414 1.00 0.00 H +ATOM 5652 HB2 ALA A 365 -16.392 -61.024 20.056 1.00 0.00 H +ATOM 5653 HB3 ALA A 365 -17.519 -60.587 18.727 1.00 0.00 H +ATOM 5654 N CYS A 366 -17.435 -57.256 18.040 1.00 0.00 N +ATOM 5655 CA CYS A 366 -17.518 -56.467 16.817 1.00 0.00 C +ATOM 5656 C CYS A 366 -17.422 -54.969 17.059 1.00 0.00 C +ATOM 5657 O CYS A 366 -17.631 -54.194 16.120 1.00 0.00 O +ATOM 5658 CB CYS A 366 -18.817 -56.772 16.054 1.00 0.00 C +ATOM 5659 SG CYS A 366 -20.315 -56.096 16.805 1.00 0.00 S +ATOM 5660 H CYS A 366 -18.146 -57.162 18.737 1.00 0.00 H +ATOM 5661 HA CYS A 366 -16.680 -56.752 16.164 1.00 0.00 H +ATOM 5662 HB2 CYS A 366 -18.724 -56.355 15.040 1.00 0.00 H +ATOM 5663 HB3 CYS A 366 -18.929 -57.865 15.992 1.00 0.00 H +ATOM 5664 N HIS A 367 -17.095 -54.540 18.274 1.00 0.00 N +ATOM 5665 CA HIS A 367 -17.195 -53.126 18.599 1.00 0.00 C +ATOM 5666 C HIS A 367 -16.067 -52.332 17.948 1.00 0.00 C +ATOM 5667 O HIS A 367 -14.954 -52.827 17.739 1.00 0.00 O +ATOM 5668 CB HIS A 367 -17.192 -52.914 20.114 1.00 0.00 C +ATOM 5669 CG HIS A 367 -15.835 -52.990 20.740 1.00 0.00 C +ATOM 5670 ND1 HIS A 367 -15.286 -54.170 21.192 1.00 0.00 N +ATOM 5671 CD2 HIS A 367 -14.922 -52.026 21.006 1.00 0.00 C +ATOM 5672 CE1 HIS A 367 -14.091 -53.932 21.702 1.00 0.00 C +ATOM 5673 NE2 HIS A 367 -13.847 -52.638 21.602 1.00 0.00 N +ATOM 5674 H HIS A 367 -16.780 -55.190 18.965 1.00 0.00 H +ATOM 5675 HA HIS A 367 -18.148 -52.740 18.208 1.00 0.00 H +ATOM 5676 HB2 HIS A 367 -17.614 -51.920 20.324 1.00 0.00 H +ATOM 5677 HB3 HIS A 367 -17.827 -53.687 20.572 1.00 0.00 H +ATOM 5678 HD1 HIS A 367 -15.724 -55.068 21.142 1.00 0.00 H +ATOM 5679 HD2 HIS A 367 -15.021 -50.972 20.790 1.00 0.00 H +ATOM 5680 HE1 HIS A 367 -13.427 -54.670 22.128 1.00 0.00 H +ATOM 5681 N VAL A 368 -16.382 -51.082 17.613 1.00 0.00 N +ATOM 5682 CA VAL A 368 -15.436 -50.203 16.938 1.00 0.00 C +ATOM 5683 C VAL A 368 -14.413 -49.693 17.941 1.00 0.00 C +ATOM 5684 O VAL A 368 -14.770 -49.183 19.012 1.00 0.00 O +ATOM 5685 CB VAL A 368 -16.178 -49.039 16.263 1.00 0.00 C +ATOM 5686 CG1 VAL A 368 -15.200 -48.117 15.559 1.00 0.00 C +ATOM 5687 CG2 VAL A 368 -17.233 -49.568 15.297 1.00 0.00 C +ATOM 5688 H VAL A 368 -17.295 -50.736 17.831 1.00 0.00 H +ATOM 5689 HA VAL A 368 -14.906 -50.771 16.159 1.00 0.00 H +ATOM 5690 HB VAL A 368 -16.691 -48.463 17.047 1.00 0.00 H +ATOM 5691 HG11 VAL A 368 -15.750 -47.292 15.083 1.00 0.00 H +ATOM 5692 HG12 VAL A 368 -14.489 -47.708 16.292 1.00 0.00 H +ATOM 5693 HG13 VAL A 368 -14.651 -48.682 14.791 1.00 0.00 H +ATOM 5694 HG21 VAL A 368 -17.754 -48.723 14.823 1.00 0.00 H +ATOM 5695 HG22 VAL A 368 -16.747 -50.180 14.523 1.00 0.00 H +ATOM 5696 HG23 VAL A 368 -17.959 -50.183 15.848 1.00 0.00 H +ATOM 5697 N HIS A 369 -13.133 -49.823 17.604 1.00 0.00 N +ATOM 5698 CA HIS A 369 -12.070 -49.264 18.427 1.00 0.00 C +ATOM 5699 C HIS A 369 -10.825 -49.066 17.571 1.00 0.00 C +ATOM 5700 O HIS A 369 -10.832 -49.304 16.360 1.00 0.00 O +ATOM 5701 CB HIS A 369 -11.785 -50.157 19.637 1.00 0.00 C +ATOM 5702 CG HIS A 369 -11.311 -51.530 19.278 1.00 0.00 C +ATOM 5703 ND1 HIS A 369 -12.172 -52.549 18.932 1.00 0.00 N +ATOM 5704 CD2 HIS A 369 -10.064 -52.055 19.221 1.00 0.00 C +ATOM 5705 CE1 HIS A 369 -11.476 -53.642 18.673 1.00 0.00 C +ATOM 5706 NE2 HIS A 369 -10.195 -53.370 18.845 1.00 0.00 N +ATOM 5707 H HIS A 369 -12.895 -50.316 16.767 1.00 0.00 H +ATOM 5708 HA HIS A 369 -12.388 -48.279 18.799 1.00 0.00 H +ATOM 5709 HB2 HIS A 369 -11.011 -49.673 20.250 1.00 0.00 H +ATOM 5710 HB3 HIS A 369 -12.711 -50.252 20.223 1.00 0.00 H +ATOM 5711 HD1 HIS A 369 -13.168 -52.473 18.884 1.00 0.00 H +ATOM 5712 HD2 HIS A 369 -9.140 -51.537 19.432 1.00 0.00 H +ATOM 5713 HE1 HIS A 369 -11.885 -54.595 18.372 1.00 0.00 H +ATOM 5714 N ASP A 370 -9.750 -48.619 18.213 1.00 0.00 N +ATOM 5715 CA ASP A 370 -8.477 -48.372 17.555 1.00 0.00 C +ATOM 5716 C ASP A 370 -7.415 -49.324 18.100 1.00 0.00 C +ATOM 5717 O ASP A 370 -7.681 -50.176 18.955 1.00 0.00 O +ATOM 5718 CB ASP A 370 -8.055 -46.908 17.717 1.00 0.00 C +ATOM 5719 CG ASP A 370 -7.966 -46.476 19.169 1.00 0.00 C +ATOM 5720 OD1 ASP A 370 -7.057 -46.948 19.886 1.00 0.00 O +ATOM 5721 OD2 ASP A 370 -8.800 -45.646 19.593 1.00 0.00 O1- +ATOM 5722 H ASP A 370 -9.820 -48.444 19.195 1.00 0.00 H +ATOM 5723 HA ASP A 370 -8.590 -48.572 16.479 1.00 0.00 H +ATOM 5724 HB2 ASP A 370 -7.068 -46.773 17.250 1.00 0.00 H +ATOM 5725 HB3 ASP A 370 -8.792 -46.272 17.206 1.00 0.00 H +ATOM 5726 N GLU A 371 -6.190 -49.165 17.594 1.00 0.00 N +ATOM 5727 CA GLU A 371 -5.135 -50.121 17.910 1.00 0.00 C +ATOM 5728 C GLU A 371 -4.759 -50.100 19.384 1.00 0.00 C +ATOM 5729 O GLU A 371 -4.238 -51.093 19.900 1.00 0.00 O +ATOM 5730 CB GLU A 371 -3.903 -49.847 17.049 1.00 0.00 C +ATOM 5731 CG GLU A 371 -4.059 -50.240 15.588 1.00 0.00 C +ATOM 5732 CD GLU A 371 -2.774 -50.082 14.802 1.00 0.00 C +ATOM 5733 OE1 GLU A 371 -1.887 -49.327 15.251 1.00 0.00 O +ATOM 5734 OE2 GLU A 371 -2.649 -50.719 13.735 1.00 0.00 O1- +ATOM 5735 H GLU A 371 -5.995 -48.388 16.996 1.00 0.00 H +ATOM 5736 HA GLU A 371 -5.494 -51.132 17.669 1.00 0.00 H +ATOM 5737 HB2 GLU A 371 -3.684 -48.770 17.094 1.00 0.00 H +ATOM 5738 HB3 GLU A 371 -3.057 -50.411 17.469 1.00 0.00 H +ATOM 5739 HG2 GLU A 371 -4.375 -51.292 15.538 1.00 0.00 H +ATOM 5740 HG3 GLU A 371 -4.831 -49.603 15.132 1.00 0.00 H +ATOM 5741 N PHE A 372 -5.037 -49.001 20.082 1.00 0.00 N +ATOM 5742 CA PHE A 372 -4.506 -48.800 21.420 1.00 0.00 C +ATOM 5743 C PHE A 372 -5.549 -48.805 22.526 1.00 0.00 C +ATOM 5744 O PHE A 372 -5.176 -48.959 23.691 1.00 0.00 O +ATOM 5745 CB PHE A 372 -3.712 -47.488 21.473 1.00 0.00 C +ATOM 5746 CG PHE A 372 -2.592 -47.430 20.471 1.00 0.00 C +ATOM 5747 CD1 PHE A 372 -1.464 -48.220 20.631 1.00 0.00 C +ATOM 5748 CD2 PHE A 372 -2.672 -46.600 19.363 1.00 0.00 C +ATOM 5749 CE1 PHE A 372 -0.435 -48.181 19.712 1.00 0.00 C +ATOM 5750 CE2 PHE A 372 -1.642 -46.557 18.440 1.00 0.00 C +ATOM 5751 CZ PHE A 372 -0.522 -47.349 18.617 1.00 0.00 C +ATOM 5752 H PHE A 372 -5.623 -48.299 19.677 1.00 0.00 H +ATOM 5753 HA PHE A 372 -3.799 -49.615 21.633 1.00 0.00 H +ATOM 5754 HB2 PHE A 372 -4.402 -46.654 21.275 1.00 0.00 H +ATOM 5755 HB3 PHE A 372 -3.285 -47.379 22.481 1.00 0.00 H +ATOM 5756 HD1 PHE A 372 -1.390 -48.875 21.486 1.00 0.00 H +ATOM 5757 HD2 PHE A 372 -3.546 -45.982 19.219 1.00 0.00 H +ATOM 5758 HE1 PHE A 372 0.438 -48.802 19.851 1.00 0.00 H +ATOM 5759 HE2 PHE A 372 -1.713 -45.905 17.582 1.00 0.00 H +ATOM 5760 HZ PHE A 372 0.283 -47.315 17.898 1.00 0.00 H +ATOM 5761 N ARG A 373 -6.829 -48.652 22.207 1.00 0.00 N +ATOM 5762 CA ARG A 373 -7.872 -48.720 23.217 1.00 0.00 C +ATOM 5763 C ARG A 373 -8.991 -49.640 22.755 1.00 0.00 C +ATOM 5764 O ARG A 373 -9.295 -49.727 21.563 1.00 0.00 O +ATOM 5765 CB ARG A 373 -8.442 -47.331 23.544 1.00 0.00 C +ATOM 5766 CG ARG A 373 -7.768 -46.670 24.722 1.00 0.00 C +ATOM 5767 CD ARG A 373 -8.592 -45.532 25.282 1.00 0.00 C +ATOM 5768 NE ARG A 373 -9.055 -44.614 24.248 1.00 0.00 N +ATOM 5769 CZ ARG A 373 -9.205 -43.308 24.434 1.00 0.00 C +ATOM 5770 NH1 ARG A 373 -8.914 -42.771 25.612 1.00 0.00 N +ATOM 5771 NH2 ARG A 373 -9.634 -42.538 23.443 1.00 0.00 N1+ +ATOM 5772 H ARG A 373 -7.080 -48.487 21.253 1.00 0.00 H +ATOM 5773 HA ARG A 373 -7.447 -49.137 24.142 1.00 0.00 H +ATOM 5774 HB2 ARG A 373 -8.316 -46.686 22.662 1.00 0.00 H +ATOM 5775 HB3 ARG A 373 -9.513 -47.438 23.770 1.00 0.00 H +ATOM 5776 HG2 ARG A 373 -7.617 -47.421 25.512 1.00 0.00 H +ATOM 5777 HG3 ARG A 373 -6.793 -46.277 24.398 1.00 0.00 H +ATOM 5778 HD2 ARG A 373 -9.468 -45.952 25.798 1.00 0.00 H +ATOM 5779 HD3 ARG A 373 -7.976 -44.972 26.001 1.00 0.00 H +ATOM 5780 HE ARG A 373 -9.272 -44.990 23.347 1.00 0.00 H +ATOM 5781 HH11 ARG A 373 -9.018 -41.786 25.752 1.00 0.00 H +ATOM 5782 HH12 ARG A 373 -8.590 -43.351 26.360 1.00 0.00 H +ATOM 5783 HH21 ARG A 373 -9.738 -41.553 23.584 1.00 0.00 H +ATOM 5784 HH22 ARG A 373 -9.854 -42.942 22.555 1.00 0.00 H +ATOM 5785 N THR A 374 -9.581 -50.353 23.711 1.00 0.00 N +ATOM 5786 CA THR A 374 -10.766 -51.162 23.470 1.00 0.00 C +ATOM 5787 C THR A 374 -11.800 -50.856 24.542 1.00 0.00 C +ATOM 5788 O THR A 374 -11.491 -50.273 25.584 1.00 0.00 O +ATOM 5789 CB THR A 374 -10.468 -52.675 23.478 1.00 0.00 C +ATOM 5790 OG1 THR A 374 -9.971 -53.067 24.765 1.00 0.00 O +ATOM 5791 CG2 THR A 374 -9.457 -53.045 22.406 1.00 0.00 C +ATOM 5792 H THR A 374 -9.194 -50.332 24.633 1.00 0.00 H +ATOM 5793 HA THR A 374 -11.192 -50.901 22.490 1.00 0.00 H +ATOM 5794 HB THR A 374 -11.406 -53.209 23.268 1.00 0.00 H +ATOM 5795 HG1 THR A 374 -10.693 -53.360 25.308 1.00 0.00 H +ATOM 5796 HG21 THR A 374 -9.267 -54.128 22.438 1.00 0.00 H +ATOM 5797 HG22 THR A 374 -9.855 -52.772 21.417 1.00 0.00 H +ATOM 5798 HG23 THR A 374 -8.517 -52.502 22.586 1.00 0.00 H +ATOM 5799 N ALA A 375 -13.041 -51.248 24.272 1.00 0.00 N +ATOM 5800 CA ALA A 375 -14.063 -51.210 25.302 1.00 0.00 C +ATOM 5801 C ALA A 375 -13.778 -52.321 26.307 1.00 0.00 C +ATOM 5802 O ALA A 375 -13.078 -53.290 26.012 1.00 0.00 O +ATOM 5803 CB ALA A 375 -15.459 -51.364 24.694 1.00 0.00 C +ATOM 5804 H ALA A 375 -13.272 -51.571 23.354 1.00 0.00 H +ATOM 5805 HA ALA A 375 -14.018 -50.245 25.827 1.00 0.00 H +ATOM 5806 HB1 ALA A 375 -16.213 -51.332 25.494 1.00 0.00 H +ATOM 5807 HB2 ALA A 375 -15.642 -50.544 23.984 1.00 0.00 H +ATOM 5808 HB3 ALA A 375 -15.525 -52.327 24.167 1.00 0.00 H +ATOM 5809 N ALA A 376 -14.307 -52.173 27.516 1.00 0.00 N +ATOM 5810 CA ALA A 376 -13.915 -53.080 28.583 1.00 0.00 C +ATOM 5811 C ALA A 376 -15.068 -53.352 29.533 1.00 0.00 C +ATOM 5812 O ALA A 376 -15.944 -52.512 29.743 1.00 0.00 O +ATOM 5813 CB ALA A 376 -12.724 -52.532 29.387 1.00 0.00 C +ATOM 5814 H ALA A 376 -14.969 -51.444 27.691 1.00 0.00 H +ATOM 5815 HA ALA A 376 -13.607 -54.038 28.139 1.00 0.00 H +ATOM 5816 HB1 ALA A 376 -12.459 -53.244 30.182 1.00 0.00 H +ATOM 5817 HB2 ALA A 376 -11.863 -52.393 28.717 1.00 0.00 H +ATOM 5818 HB3 ALA A 376 -12.999 -51.567 29.837 1.00 0.00 H +ATOM 5819 N VAL A 377 -15.044 -54.553 30.100 1.00 0.00 N +ATOM 5820 CA VAL A 377 -15.902 -54.952 31.206 1.00 0.00 C +ATOM 5821 C VAL A 377 -15.003 -55.615 32.240 1.00 0.00 C +ATOM 5822 O VAL A 377 -14.378 -56.642 31.952 1.00 0.00 O +ATOM 5823 CB VAL A 377 -17.015 -55.911 30.759 1.00 0.00 C +ATOM 5824 CG1 VAL A 377 -17.917 -56.245 31.924 1.00 0.00 C +ATOM 5825 CG2 VAL A 377 -17.814 -55.300 29.618 1.00 0.00 C +ATOM 5826 H VAL A 377 -14.395 -55.226 29.744 1.00 0.00 H +ATOM 5827 HA VAL A 377 -16.365 -54.062 31.657 1.00 0.00 H +ATOM 5828 HB VAL A 377 -16.547 -56.839 30.399 1.00 0.00 H +ATOM 5829 HG11 VAL A 377 -18.709 -56.932 31.590 1.00 0.00 H +ATOM 5830 HG12 VAL A 377 -17.327 -56.725 32.719 1.00 0.00 H +ATOM 5831 HG13 VAL A 377 -18.372 -55.322 32.312 1.00 0.00 H +ATOM 5832 HG21 VAL A 377 -18.606 -55.998 29.309 1.00 0.00 H +ATOM 5833 HG22 VAL A 377 -18.268 -54.355 29.953 1.00 0.00 H +ATOM 5834 HG23 VAL A 377 -17.146 -55.104 28.766 1.00 0.00 H +ATOM 5835 N GLU A 378 -14.916 -55.019 33.427 1.00 0.00 N +ATOM 5836 CA GLU A 378 -13.992 -55.482 34.452 1.00 0.00 C +ATOM 5837 C GLU A 378 -14.684 -55.536 35.805 1.00 0.00 C +ATOM 5838 O GLU A 378 -15.617 -54.776 36.080 1.00 0.00 O +ATOM 5839 CB GLU A 378 -12.753 -54.575 34.544 1.00 0.00 C +ATOM 5840 CG GLU A 378 -11.966 -54.443 33.246 1.00 0.00 C +ATOM 5841 CD GLU A 378 -11.194 -55.701 32.887 1.00 0.00 C +ATOM 5842 OE1 GLU A 378 -10.993 -56.559 33.772 1.00 0.00 O +ATOM 5843 OE2 GLU A 378 -10.782 -55.828 31.715 1.00 0.00 O1- +ATOM 5844 H GLU A 378 -15.502 -54.232 33.620 1.00 0.00 H +ATOM 5845 HA GLU A 378 -13.653 -56.497 34.198 1.00 0.00 H +ATOM 5846 HB2 GLU A 378 -13.084 -53.571 34.848 1.00 0.00 H +ATOM 5847 HB3 GLU A 378 -12.082 -54.988 35.312 1.00 0.00 H +ATOM 5848 HG2 GLU A 378 -12.670 -54.220 32.431 1.00 0.00 H +ATOM 5849 HG3 GLU A 378 -11.252 -53.613 33.352 1.00 0.00 H +ATOM 5850 N GLY A 379 -14.205 -56.442 36.652 1.00 0.00 N +ATOM 5851 CA GLY A 379 -14.724 -56.601 37.991 1.00 0.00 C +ATOM 5852 C GLY A 379 -13.958 -57.661 38.758 1.00 0.00 C +ATOM 5853 O GLY A 379 -13.096 -58.353 38.205 1.00 0.00 O +ATOM 5854 H GLY A 379 -13.458 -57.035 36.351 1.00 0.00 H +ATOM 5855 HA2 GLY A 379 -14.640 -55.642 38.524 1.00 0.00 H +ATOM 5856 HA3 GLY A 379 -15.782 -56.896 37.932 1.00 0.00 H +ATOM 5857 N PRO A 380 -14.259 -57.824 40.056 1.00 0.00 N +ATOM 5858 CA PRO A 380 -15.234 -57.087 40.860 1.00 0.00 C +ATOM 5859 C PRO A 380 -14.636 -55.878 41.569 1.00 0.00 C +ATOM 5860 O PRO A 380 -13.416 -55.716 41.612 1.00 0.00 O +ATOM 5861 CB PRO A 380 -15.682 -58.131 41.879 1.00 0.00 C +ATOM 5862 CG PRO A 380 -14.430 -58.916 42.128 1.00 0.00 C +ATOM 5863 CD PRO A 380 -13.697 -58.962 40.806 1.00 0.00 C +ATOM 5864 HA PRO A 380 -16.090 -56.774 40.244 1.00 0.00 H +ATOM 5865 HB2 PRO A 380 -16.040 -57.654 42.803 1.00 0.00 H +ATOM 5866 HB3 PRO A 380 -16.475 -58.772 41.466 1.00 0.00 H +ATOM 5867 HG2 PRO A 380 -13.812 -58.419 42.890 1.00 0.00 H +ATOM 5868 HG3 PRO A 380 -14.677 -59.934 42.463 1.00 0.00 H +ATOM 5869 HD2 PRO A 380 -12.614 -58.838 40.953 1.00 0.00 H +ATOM 5870 HD3 PRO A 380 -13.890 -59.910 40.283 1.00 0.00 H +ATOM 5871 N PHE A 381 -15.500 -55.037 42.130 1.00 0.00 N +ATOM 5872 CA PHE A 381 -15.075 -53.884 42.908 1.00 0.00 C +ATOM 5873 C PHE A 381 -15.898 -53.807 44.182 1.00 0.00 C +ATOM 5874 O PHE A 381 -17.117 -54.000 44.157 1.00 0.00 O +ATOM 5875 CB PHE A 381 -15.228 -52.581 42.119 1.00 0.00 C +ATOM 5876 CG PHE A 381 -14.502 -52.576 40.805 1.00 0.00 C +ATOM 5877 CD1 PHE A 381 -13.140 -52.316 40.745 1.00 0.00 C +ATOM 5878 CD2 PHE A 381 -15.184 -52.823 39.628 1.00 0.00 C +ATOM 5879 CE1 PHE A 381 -12.476 -52.309 39.532 1.00 0.00 C +ATOM 5880 CE2 PHE A 381 -14.527 -52.816 38.413 1.00 0.00 C +ATOM 5881 CZ PHE A 381 -13.173 -52.559 38.363 1.00 0.00 C +ATOM 5882 H PHE A 381 -16.479 -55.204 42.013 1.00 0.00 H +ATOM 5883 HA PHE A 381 -14.016 -54.000 43.183 1.00 0.00 H +ATOM 5884 HB2 PHE A 381 -16.298 -52.418 41.924 1.00 0.00 H +ATOM 5885 HB3 PHE A 381 -14.838 -51.756 42.733 1.00 0.00 H +ATOM 5886 HD1 PHE A 381 -12.593 -52.117 41.655 1.00 0.00 H +ATOM 5887 HD2 PHE A 381 -16.245 -53.024 39.659 1.00 0.00 H +ATOM 5888 HE1 PHE A 381 -11.415 -52.109 39.497 1.00 0.00 H +ATOM 5889 HE2 PHE A 381 -15.074 -53.012 37.502 1.00 0.00 H +ATOM 5890 HZ PHE A 381 -12.658 -52.553 37.414 1.00 0.00 H +ATOM 5891 N VAL A 382 -15.225 -53.525 45.294 1.00 0.00 N +ATOM 5892 CA VAL A 382 -15.911 -53.355 46.567 1.00 0.00 C +ATOM 5893 C VAL A 382 -16.670 -52.036 46.556 1.00 0.00 C +ATOM 5894 O VAL A 382 -16.073 -50.961 46.681 1.00 0.00 O +ATOM 5895 CB VAL A 382 -14.924 -53.415 47.747 1.00 0.00 C +ATOM 5896 CG1 VAL A 382 -15.620 -53.005 49.038 1.00 0.00 C +ATOM 5897 CG2 VAL A 382 -14.344 -54.812 47.877 1.00 0.00 C +ATOM 5898 H VAL A 382 -14.231 -53.428 45.255 1.00 0.00 H +ATOM 5899 HA VAL A 382 -16.640 -54.169 46.693 1.00 0.00 H +ATOM 5900 HB VAL A 382 -14.104 -52.709 47.550 1.00 0.00 H +ATOM 5901 HG11 VAL A 382 -14.904 -53.052 49.872 1.00 0.00 H +ATOM 5902 HG12 VAL A 382 -16.000 -51.978 48.939 1.00 0.00 H +ATOM 5903 HG13 VAL A 382 -16.459 -53.689 49.236 1.00 0.00 H +ATOM 5904 HG21 VAL A 382 -13.641 -54.841 48.723 1.00 0.00 H +ATOM 5905 HG22 VAL A 382 -15.157 -55.532 48.052 1.00 0.00 H +ATOM 5906 HG23 VAL A 382 -13.813 -55.076 46.951 1.00 0.00 H +ATOM 5907 N THR A 383 -17.987 -52.109 46.391 1.00 0.00 N +ATOM 5908 CA THR A 383 -18.854 -50.942 46.457 1.00 0.00 C +ATOM 5909 C THR A 383 -19.892 -51.189 47.539 1.00 0.00 C +ATOM 5910 O THR A 383 -20.718 -52.100 47.414 1.00 0.00 O +ATOM 5911 CB THR A 383 -19.533 -50.669 45.113 1.00 0.00 C +ATOM 5912 OG1 THR A 383 -18.562 -50.729 44.061 1.00 0.00 O +ATOM 5913 CG2 THR A 383 -20.170 -49.289 45.119 1.00 0.00 C +ATOM 5914 H THR A 383 -18.399 -53.003 46.214 1.00 0.00 H +ATOM 5915 HA THR A 383 -18.260 -50.058 46.734 1.00 0.00 H +ATOM 5916 HB THR A 383 -20.316 -51.424 44.950 1.00 0.00 H +ATOM 5917 HG1 THR A 383 -17.717 -50.450 44.393 1.00 0.00 H +ATOM 5918 HG21 THR A 383 -20.654 -49.104 44.149 1.00 0.00 H +ATOM 5919 HG22 THR A 383 -20.922 -49.236 45.920 1.00 0.00 H +ATOM 5920 HG23 THR A 383 -19.395 -48.528 45.292 1.00 0.00 H +ATOM 5921 N LEU A 384 -19.845 -50.387 48.595 1.00 0.00 N +ATOM 5922 CA LEU A 384 -20.760 -50.547 49.713 1.00 0.00 C +ATOM 5923 C LEU A 384 -21.992 -49.669 49.527 1.00 0.00 C +ATOM 5924 O LEU A 384 -21.977 -48.681 48.788 1.00 0.00 O +ATOM 5925 CB LEU A 384 -20.064 -50.202 51.030 1.00 0.00 C +ATOM 5926 CG LEU A 384 -18.655 -50.773 51.200 1.00 0.00 C +ATOM 5927 CD1 LEU A 384 -18.067 -50.385 52.551 1.00 0.00 C +ATOM 5928 CD2 LEU A 384 -18.656 -52.283 51.022 1.00 0.00 C +ATOM 5929 H LEU A 384 -19.164 -49.655 48.622 1.00 0.00 H +ATOM 5930 HA LEU A 384 -21.089 -51.596 49.763 1.00 0.00 H +ATOM 5931 HB2 LEU A 384 -19.996 -49.106 51.101 1.00 0.00 H +ATOM 5932 HB3 LEU A 384 -20.686 -50.585 51.852 1.00 0.00 H +ATOM 5933 HG LEU A 384 -18.018 -50.339 50.416 1.00 0.00 H +ATOM 5934 HD11 LEU A 384 -17.056 -50.808 52.646 1.00 0.00 H +ATOM 5935 HD12 LEU A 384 -18.015 -49.289 52.627 1.00 0.00 H +ATOM 5936 HD13 LEU A 384 -18.706 -50.778 53.355 1.00 0.00 H +ATOM 5937 HD21 LEU A 384 -17.633 -52.668 51.149 1.00 0.00 H +ATOM 5938 HD22 LEU A 384 -19.316 -52.740 51.774 1.00 0.00 H +ATOM 5939 HD23 LEU A 384 -19.020 -52.533 50.014 1.00 0.00 H +ATOM 5940 N ASP A 385 -23.067 -50.048 50.216 1.00 0.00 N +ATOM 5941 CA ASP A 385 -24.318 -49.289 50.224 1.00 0.00 C +ATOM 5942 C ASP A 385 -24.830 -49.058 48.805 1.00 0.00 C +ATOM 5943 O ASP A 385 -25.123 -47.932 48.396 1.00 0.00 O +ATOM 5944 CB ASP A 385 -24.146 -47.963 50.967 1.00 0.00 C +ATOM 5945 CG ASP A 385 -23.660 -48.153 52.390 1.00 0.00 C +ATOM 5946 OD1 ASP A 385 -24.281 -48.945 53.131 1.00 0.00 O +ATOM 5947 OD2 ASP A 385 -22.648 -47.523 52.764 1.00 0.00 O1- +ATOM 5948 H ASP A 385 -23.017 -50.890 50.753 1.00 0.00 H +ATOM 5949 HA ASP A 385 -25.078 -49.876 50.762 1.00 0.00 H +ATOM 5950 HB2 ASP A 385 -23.414 -47.347 50.424 1.00 0.00 H +ATOM 5951 HB3 ASP A 385 -25.116 -47.444 50.992 1.00 0.00 H +ATOM 5952 N MET A 386 -24.932 -50.150 48.045 1.00 0.00 N +ATOM 5953 CA MET A 386 -25.382 -50.049 46.663 1.00 0.00 C +ATOM 5954 C MET A 386 -26.896 -49.956 46.553 1.00 0.00 C +ATOM 5955 O MET A 386 -27.404 -49.355 45.600 1.00 0.00 O +ATOM 5956 CB MET A 386 -24.876 -51.240 45.850 1.00 0.00 C +ATOM 5957 CG MET A 386 -23.410 -51.167 45.469 1.00 0.00 C +ATOM 5958 SD MET A 386 -22.924 -52.542 44.408 1.00 0.00 S +ATOM 5959 CE MET A 386 -23.325 -53.938 45.456 1.00 0.00 C +ATOM 5960 H MET A 386 -24.698 -51.044 48.427 1.00 0.00 H +ATOM 5961 HA MET A 386 -24.956 -49.136 46.221 1.00 0.00 H +ATOM 5962 HB2 MET A 386 -25.031 -52.152 46.445 1.00 0.00 H +ATOM 5963 HB3 MET A 386 -25.468 -51.300 44.925 1.00 0.00 H +ATOM 5964 HG2 MET A 386 -23.228 -50.223 44.934 1.00 0.00 H +ATOM 5965 HG3 MET A 386 -22.802 -51.193 46.386 1.00 0.00 H +ATOM 5966 HE1 MET A 386 -22.771 -54.825 45.114 1.00 0.00 H +ATOM 5967 HE2 MET A 386 -23.045 -53.709 46.495 1.00 0.00 H +ATOM 5968 HE3 MET A 386 -24.405 -54.138 45.403 1.00 0.00 H +ATOM 5969 N GLU A 387 -27.632 -50.546 47.498 1.00 0.00 N +ATOM 5970 CA GLU A 387 -29.082 -50.381 47.493 1.00 0.00 C +ATOM 5971 C GLU A 387 -29.476 -48.928 47.714 1.00 0.00 C +ATOM 5972 O GLU A 387 -30.558 -48.506 47.288 1.00 0.00 O +ATOM 5973 CB GLU A 387 -29.727 -51.278 48.551 1.00 0.00 C +ATOM 5974 CG GLU A 387 -29.909 -52.727 48.123 1.00 0.00 C +ATOM 5975 CD GLU A 387 -28.598 -53.482 48.018 1.00 0.00 C +ATOM 5976 OE1 GLU A 387 -27.727 -53.297 48.895 1.00 0.00 O +ATOM 5977 OE2 GLU A 387 -28.437 -54.261 47.055 1.00 0.00 O1- +ATOM 5978 H GLU A 387 -27.192 -51.098 48.206 1.00 0.00 H +ATOM 5979 HA GLU A 387 -29.468 -50.690 46.510 1.00 0.00 H +ATOM 5980 HB2 GLU A 387 -29.092 -51.262 49.449 1.00 0.00 H +ATOM 5981 HB3 GLU A 387 -30.717 -50.865 48.795 1.00 0.00 H +ATOM 5982 HG2 GLU A 387 -30.547 -53.234 48.862 1.00 0.00 H +ATOM 5983 HG3 GLU A 387 -30.403 -52.743 47.140 1.00 0.00 H +ATOM 5984 N ASP A 388 -28.614 -48.147 48.369 1.00 0.00 N +ATOM 5985 CA ASP A 388 -28.905 -46.730 48.562 1.00 0.00 C +ATOM 5986 C ASP A 388 -28.968 -45.982 47.240 1.00 0.00 C +ATOM 5987 O ASP A 388 -29.650 -44.958 47.142 1.00 0.00 O +ATOM 5988 CB ASP A 388 -27.856 -46.086 49.467 1.00 0.00 C +ATOM 5989 CG ASP A 388 -27.969 -46.542 50.900 1.00 0.00 C +ATOM 5990 OD1 ASP A 388 -28.852 -47.377 51.186 1.00 0.00 O +ATOM 5991 OD2 ASP A 388 -27.175 -46.065 51.742 1.00 0.00 O1- +ATOM 5992 H ASP A 388 -27.765 -48.535 48.729 1.00 0.00 H +ATOM 5993 HA ASP A 388 -29.883 -46.633 49.055 1.00 0.00 H +ATOM 5994 HB2 ASP A 388 -26.856 -46.350 49.091 1.00 0.00 H +ATOM 5995 HB3 ASP A 388 -27.984 -44.994 49.433 1.00 0.00 H +ATOM 5996 N CYS A 389 -28.266 -46.472 46.217 1.00 0.00 N +ATOM 5997 CA CYS A 389 -28.214 -45.767 44.942 1.00 0.00 C +ATOM 5998 C CYS A 389 -29.539 -45.806 44.198 1.00 0.00 C +ATOM 5999 O CYS A 389 -29.714 -45.066 43.225 1.00 0.00 O +ATOM 6000 CB CYS A 389 -27.108 -46.352 44.065 1.00 0.00 C +ATOM 6001 SG CYS A 389 -25.468 -46.264 44.825 1.00 0.00 S +ATOM 6002 H CYS A 389 -27.770 -47.333 46.326 1.00 0.00 H +ATOM 6003 HA CYS A 389 -27.967 -44.713 45.134 1.00 0.00 H +ATOM 6004 HB2 CYS A 389 -27.343 -47.408 43.864 1.00 0.00 H +ATOM 6005 HB3 CYS A 389 -27.083 -45.795 43.117 1.00 0.00 H +ATOM 6006 N GLY A 390 -30.471 -46.653 44.628 1.00 0.00 N +ATOM 6007 CA GLY A 390 -31.746 -46.753 43.940 1.00 0.00 C +ATOM 6008 C GLY A 390 -32.705 -45.665 44.398 1.00 0.00 C +ATOM 6009 O GLY A 390 -32.873 -45.430 45.597 1.00 0.00 O +ATOM 6010 H GLY A 390 -30.292 -47.224 45.429 1.00 0.00 H +ATOM 6011 HA2 GLY A 390 -31.579 -46.651 42.858 1.00 0.00 H +ATOM 6012 HA3 GLY A 390 -32.192 -47.736 44.151 1.00 0.00 H +ATOM 6013 N TYR A 391 -33.357 -45.016 43.437 1.00 0.00 N +ATOM 6014 CA TYR A 391 -34.386 -44.035 43.766 1.00 0.00 C +ATOM 6015 C TYR A 391 -35.623 -44.792 44.224 1.00 0.00 C +ATOM 6016 O TYR A 391 -36.384 -45.320 43.412 1.00 0.00 O +ATOM 6017 CB TYR A 391 -34.687 -43.141 42.571 1.00 0.00 C +ATOM 6018 CG TYR A 391 -35.716 -42.086 42.890 1.00 0.00 C +ATOM 6019 CD1 TYR A 391 -35.429 -41.056 43.775 1.00 0.00 C +ATOM 6020 CD2 TYR A 391 -36.974 -42.122 42.315 1.00 0.00 C +ATOM 6021 CE1 TYR A 391 -36.367 -40.090 44.075 1.00 0.00 C +ATOM 6022 CE2 TYR A 391 -37.920 -41.161 42.606 1.00 0.00 C +ATOM 6023 CZ TYR A 391 -37.611 -40.146 43.488 1.00 0.00 C +ATOM 6024 OH TYR A 391 -38.544 -39.184 43.786 1.00 0.00 O +ATOM 6025 H TYR A 391 -33.140 -45.202 42.479 1.00 0.00 H +ATOM 6026 HA TYR A 391 -34.035 -43.403 44.596 1.00 0.00 H +ATOM 6027 HB2 TYR A 391 -33.757 -42.645 42.258 1.00 0.00 H +ATOM 6028 HB3 TYR A 391 -35.064 -43.766 41.748 1.00 0.00 H +ATOM 6029 HD1 TYR A 391 -34.454 -41.010 44.237 1.00 0.00 H +ATOM 6030 HD2 TYR A 391 -37.220 -42.917 41.626 1.00 0.00 H +ATOM 6031 HE1 TYR A 391 -36.127 -39.295 44.766 1.00 0.00 H +ATOM 6032 HE2 TYR A 391 -38.896 -41.203 42.146 1.00 0.00 H +ATOM 6033 HH TYR A 391 -38.415 -38.883 44.678 1.00 0.00 H +ATOM 6034 N ASN A 392 -35.826 -44.841 45.533 1.00 0.00 N +ATOM 6035 CA ASN A 392 -36.824 -45.713 46.134 1.00 0.00 C +ATOM 6036 C ASN A 392 -38.248 -45.275 45.812 1.00 0.00 C +ATOM 6037 O ASN A 392 -38.868 -45.804 44.877 1.00 0.00 O +ATOM 6038 CB ASN A 392 -36.601 -45.773 47.645 1.00 0.00 C +ATOM 6039 CG ASN A 392 -35.171 -46.142 47.997 1.00 0.00 C +ATOM 6040 OD1 ASN A 392 -34.762 -47.294 47.847 1.00 0.00 O +ATOM 6041 ND2 ASN A 392 -34.400 -45.162 48.454 1.00 0.00 N +ATOM 6042 OXT ASN A 392 -38.841 -44.300 46.553 1.00 0.00 O1- +ATOM 6043 H ASN A 392 -35.274 -44.258 46.129 1.00 0.00 H +ATOM 6044 HA ASN A 392 -36.688 -46.729 45.736 1.00 0.00 H +ATOM 6045 HB2 ASN A 392 -36.829 -44.787 48.077 1.00 0.00 H +ATOM 6046 HB3 ASN A 392 -37.278 -46.527 48.074 1.00 0.00 H +ATOM 6047 HD21 ASN A 392 -33.446 -45.346 48.692 1.00 0.00 H +ATOM 6048 HD22 ASN A 392 -34.773 -44.240 48.560 1.00 0.00 H +TER 6049 ASN A 392 +CONECT 1 3 2 7 +CONECT 2 1 +CONECT 3 1 4 5 6 +CONECT 4 3 +CONECT 5 3 +CONECT 6 3 +CONECT 7 1 +CONECT 2474 5659 +CONECT 3434 6001 +CONECT 4253 5046 +CONECT 5046 4253 +CONECT 5659 2474 +CONECT 6001 3434 +END diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb new file mode 100644 index 0000000..1e916c2 --- /dev/null +++ b/mixed_networks/mixed_networks.ipynb @@ -0,0 +1,2892 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Mixed Protocol Networks\n", + "\n", + "## Introduction\n", + "\n", + "**openfe** provides a number of protocols to estimate free energy differences, each with their own areas of applicability and limitations, suited to answering different types of scientific questions. While traditionally these protocols have been used in isolation, it is possible, thanks to the interchangeable nature of **openfe**, to combine them into a single network of transformations to leverage the strengths of each protocol.\n", + "\n", + "This notebook demonstrates one way to create a mixed protocol network, combining two relative binding free energy approaches: the [Relative Hybrid Topology Protocol](https://docs.openfree.energy/en/latest/guide/protocols/relativehybridtopology.html#userguide-relative-hybrid-topology-protocol) and the [Separated Topologies Protocol](https://docs.openfree.energy/en/latest/guide/protocols/septop.html) to compute absolute binding affinities. A complete list of the available protocols along with their strengths and limitations can be found in the [protocol overview](https://docs.openfree.energy/en/latest/guide/protocols/index.html).\n", + "\n", + "## Overview\n", + "\n", + "In this tutorial, we will demonstrate how to create a mixed protocol network to compute absolute binding free energies for `three` distinct ligand scaffold series bound to $\\beta$-secretase 1 (BACE1) taken from [Baumann et al.](https://doi.org/10.1021/acs.jctc.3c00282). The dataset contains a total of 18 ligands with 6 ligands in each of the three unique scaffold series grouped as `biaryl`, `spirocyclic` and `amide`, examples of each are shown below:\n", + "\n", + "\n", + "\n", + "While each subset of ligands forms a congeneric series suitable for the hybrid topology approach, connections between the series are more challenging due to the large structural differences between the scaffolds. To overcome this, we will use the separated topologies approach to connect the three series together. Here we present one possible way to construct a mixed protocol with minimal manual intervention which can be broken down into the following steps:\n", + "\n", + "- Plan a single LOMAP style hybrid topology binding free energy network with the Kartograf atom mapper and default lomap scorer\n", + "- Identify edges within this network between the three distinct series that are not suitable for the hybrid topology approach\n", + "- Build the transformations for the network, assigning the appropriate protocol to each transformation\n", + "- Execute and analyze the results to estimate the absolute binding free energies for each ligand\n", + "\n", + "## Load and classify the ligands\n", + "\n", + "First we start by loading the ligands from the provided SDF file and creating a mapping between the ligand name and respective series. The ligands are then converted to `SmallMoleculeComponents` for use within **openfe**.\n", + "\n", + "
Note: Ligands should have partial charges assigned before planning the network, here we use pre-charged ligands but charges can be generated via the CLI or API.
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" + } + } + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T12:29:30.806011Z", + "start_time": "2026-10-28T12:29:30.421333Z" + } + }, + "cell_type": "code", + "source": [ + "from rdkit import Chem\n", + "from gufe import SmallMoleculeComponent\n", + "\n", + "ligands_file = \"inputs/ligands.sdf\"\n", + "\n", + "supplier = Chem.SDMolSupplier(ligands_file, removeHs=False)\n", + "ligands = [SmallMoleculeComponent.from_rdkit(mol) for mol in supplier if mol is not None]\n", + "\n", + "# Define a mapping between the ligand names and their respective series\n", + "ligand_series_mapping = {\n", + " \"lig_02\": \"biaryl\",\n", + " \"lig_03\": \"biaryl\",\n", + " \"lig_04\": \"biaryl\",\n", + " \"lig_05\": \"biaryl\",\n", + " \"lig_06\": \"biaryl\",\n", + " \"lig_07\": \"biaryl\",\n", + " \"lig_11\": \"spirocyclic\",\n", + " \"lig_13\": \"spirocyclic\",\n", + " \"lig_16\": \"spirocyclic\",\n", + " \"lig_1a\": \"spirocyclic\",\n", + " \"lig_32\": \"spirocyclic\",\n", + " \"lig_36\": \"spirocyclic\",\n", + " \"lig_41\": \"amide\",\n", + " \"lig_45\": \"amide\",\n", + " \"lig_67\": \"amide\",\n", + " \"lig_69\": \"amide\",\n", + " \"lig_74\": \"amide\",\n", + " \"lig_81\": \"amide\",\n", + "}" + ], + "id": "c2ec360a98734f5d", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-bac489de520eadaa7126ee6641163303 (name: 'lig_41')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-3a85837a992c824bb4e0ef1eb4f03b21 (name: 'lig_03')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-0b73c8a6df07a0beed455c479519b97f (name: 'lig_07')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-d8bb71cb61021ff28fa31ae90db5e9af (name: 'lig_1a')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e4c4a874b03c3a3cc36867807030f34f (name: 'lig_45')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-2ff86e4d2ac0278821209e3a10b83248 (name: 'lig_16')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-a98f0ca0ad081239d749efbe258e0bf3 (name: 'lig_11')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-02f2fb7b039d551f4d79f052220924b1 (name: 'lig_36')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-183ce45f91ea63c1b1a5c2ca71683a2e (name: 'lig_02')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-17032cd50d866ce3f805801db1cac89a (name: 'lig_74')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-4931369e87328f26f82811c9afc8e55c (name: 'lig_67')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-24d9e0a297a1559b9e36e980b8b321ba (name: 'lig_69')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e7b8bc753a9243d52039be2ad197a3b1 (name: 'lig_04')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-11a9e1c36f7dbaf234e69ef0d0a99b84 (name: 'lig_81')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-5119445cbe99f49d31eb73b97fc409a4 (name: 'lig_13')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-b900f42ff3161e6e4b663a908d99d382 (name: 'lig_06')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-6a2222de00a67b9cddf4402ae67abc46 (name: 'lig_05')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-73e7964450ae778448e6e7bb6ee9c24e (name: 'lig_32')\n" + ] + } + ], + "execution_count": 71 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Creating the `LigandNetwork`\n", + "\n", + "Next we will create a single `LigandNetwork` containing all 18 ligands. This network will be used to identify edges between the three series that are not suitable for the hybrid topology approach, and will be assigned the separated topologies protocol later. In order to create a `LigandNetwork`, we need to define three components:\n", + "\n", + "* **Atom Mapper**: Proposes potential atom mappings (descriptions of the alchemical change) for pairs of ligands. We will use the `KartografAtomMapper`.\n", + "* **Scorer**: Given an atom mapping, provides an estimate of the quality of that mapping (higher scores are better). We will use `default_lomap_scorer`.\n", + "* **Network Planner**: Creates the actual `LigandNetwork`; different network planners provide different strategies. We will create a `LOMAP` redundant network with the `generate_lomap_network` method.\n" + ], + "id": "ab9b576aad42da72" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T10:40:16.881828Z", + "start_time": "2026-10-28T10:40:10.501269Z" + } + }, + "cell_type": "code", + "source": [ + "import openfe\n", + "\n", + "# Build the mapper using the default settings\n", + "mapper = openfe.KartografAtomMapper()\n", + "# Use the default LOMAP scorer to score the proposed atom mappings\n", + "scorer = openfe.lomap_scorers.default_lomap_score\n", + "# Use the LOMAP network planner to generate a redundant network\n", + "network_planner = openfe.ligand_network_planning.generate_lomap_network\n", + "\n", + "# Build the network using the configured mapper, scorer, and network planner\n", + "network = network_planner(\n", + " ligands=ligands,\n", + " mappers=[mapper],\n", + " scorer=scorer\n", + ")" + ], + "id": "1df183ab22a81c3", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + 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"INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", + "INFO:kartograf.atom_mapper:#################################\n", + "INFO:kartograf.atom_mapper:Masking Atoms\n", + "INFO:kartograf.atom_mapper:Build Distance Matrix\n", + "INFO:kartograf.atom_mapper:Calculate Mapping\n", + "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", + "INFO:kartograf.atom_mapper:Filtering bond breaks\n", + "INFO:root:Trying to remove edge 7-17 with similarity 0.670320\n", + "INFO:root:Checking edge deletion on distance-to-actives 0 vs 0\n", + "INFO:root:Removed edge 7-17\n", + "INFO:root:Trying to remove edge 5-14 with similarity 0.670320\n", + "INFO:root:Checking edge deletion on distance-to-actives 0 vs 0\n", + "INFO:root:Removed edge 5-14\n", + "INFO:root:Trying to remove edge 6-7 with similarity 0.704688\n", + "INFO:root:Rejecting edge deletion on cycle covering\n", + "INFO:root:Trying to remove edge 5-17 with similarity 0.778801\n", + "INFO:root:Rejecting edge deletion on cycle covering\n", + "INFO:root:Trying to remove edge 14-17 with similarity 0.778801\n", + "INFO:root:Checking edge deletion on distance-to-actives 0 vs 0\n", + "INFO:root:Removed edge 14-17\n", + "INFO:root:Trying to remove edge 7-14 with similarity 0.818731\n", + "INFO:root:Rejecting edge deletion on cycle covering\n", + "INFO:root:Trying to remove edge 5-6 with similarity 0.818731\n", + "INFO:root:Rejecting edge deletion on cycle covering\n", + "INFO:root:Trying to remove edge 6-14 with similarity 0.818731\n", + "INFO:root:Rejecting edge deletion on cycle covering\n", + "INFO:root:Trying to remove edge 6-17 with similarity 0.951229\n", + "INFO:root:Rejecting edge deletion on cycle covering\n" + ] + } + ], + "execution_count": 29 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Visualize the `LigandNetwork`\n", + "\n", + "Now we can visualize the `LigandNetwork` while highlighting the three distinct series using colours to match the examples shown above. The `biaryl` series is shown in yellow, the `spirocyclic` series in cyan, and the `amide` series in magenta. Edges between the series are shown in red while edges within the series are shown in black." + ], + "id": "aac35319eaa125a7" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T10:56:23.165359Z", + "start_time": "2026-10-28T10:56:23.078480Z" + } + }, + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "series_to_color = {\n", + " \"biaryl\": \"yellow\",\n", + " \"spirocyclic\": \"cyan\",\n", + " \"amide\": \"magenta\"\n", + "}\n", + "\n", + "network_graph = nx.Graph()\n", + "for edge in network.edges:\n", + " network_graph.add_edge(edge.componentA.name, edge.componentB.name)\n", + "pos = nx.spring_layout(network_graph, seed=42)\n", + "node_colors = [series_to_color[ligand_series_mapping[node]] for node in network_graph.nodes()]\n", + "edge_colors = [\"black\" if ligand_series_mapping[edge[0]] == ligand_series_mapping[edge[1]] else \"red\" for edge in network_graph.edges()]\n", + "plt.figure(figsize=(10, 8))\n", + "nx.draw(network_graph, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors, node_size=900, font_size=10, font_color='black', font_weight='bold')\n", + "plt.axis(\"off\")\n", + "plt.show()" + ], + "id": "e4cba9083f76e449", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 48 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Inspecting the atom mappings\n", + "\n", + "Now we can inspect and compare the atom mappings and associated scores for edges between the series with those within each series. First lets inspect an edge between the `biaryl` and `spirocyclic` series involving ligands `lig_03` and `lig_11`. The atom mapping is shown below along with the associated score. The score is relatively low due to the large number of alchemical atoms required, indicating that this edge may be difficult to converge with the hybrid topology approach.\n", + "\n", + "
Reminder: Shared core (mapped) atoms are shown in black and thoses unique to each end state are shown in red. Mapped atoms whoes element change during the simulation are shown in blue.
" + ], + "id": "feadf6d35aa1a518" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T11:00:35.073751Z", + "start_time": "2026-10-28T11:00:34.934732Z" + } + }, + "cell_type": "code", + "source": [ + "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_03\", \"lig_11\"}][0]\n", + "edge" + ], + "id": "738e5bff854de03f", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 51 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T11:00:57.151097Z", + "start_time": "2026-10-28T11:00:57.102644Z" + } + }, + "cell_type": "code", + "source": [ + "# Higher score is better\n", + "edge.annotations" + ], + "id": "782559866e5eb3f5", + "outputs": [ + { + "data": { + "text/plain": [ + "{'score': 0.008208499862389881}" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 52 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T11:05:51.518159Z", + "start_time": "2026-10-28T11:05:51.459260Z" + } + }, + "cell_type": "code", + "source": [ + "# Now find an edge within the spirocyclic series\n", + "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_11\", \"lig_32\"}][0]\n", + "edge\n" + ], + "id": "11976268a7459cc3", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 53 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T11:05:59.860621Z", + "start_time": "2026-10-28T11:05:59.835572Z" + } + }, + "cell_type": "code", + "source": [ + "# Higher score is better\n", + "edge.annotations" + ], + "id": "e9df66ff253b9e4b", + "outputs": [ + { + "data": { + "text/plain": [ + "{'score': 0.951229424500714}" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 54 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Configure the `Protocols`\n", + "\n", + "Before building the transformations for the network we need to configure the protocol settings for both the hybrid topology and separated topologies protocols. By default, the force fields used by these protocols match however changes to one protocol should be reflected in the other. We will also be using the `_adaptive_settings` method to automatically adjust the settings based on the systems involved in the transformation, this is particularly useful for the hybrid topology protocol which adjusts the solvent padding based on the presence of a receptor in the system." + ], + "id": "ab9906252acf1ca" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T11:28:28.334763Z", + "start_time": "2026-10-28T11:28:28.299630Z" + } + }, + "cell_type": "code", + "source": [ + "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n", + "from openfe.protocols.openmm_septop import SepTopProtocol\n", + "\n", + "# Configure the small molecule force field to be used by both protocols and the number of repeats per execution of the protocol\n", + "small_molecule_forcefield = \"openff-2.3.0\"\n", + "default_hybrid_settings = RelativeHybridTopologyProtocol.default_settings()\n", + "default_hybrid_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", + "default_hybrid_settings.protocol_repeats = 1\n", + "\n", + "default_septop_settings = SepTopProtocol.default_settings()\n", + "default_septop_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", + "default_septop_settings.protocol_repeats = 1" + ], + "id": "43fc256c3e0ff239", + "outputs": [], + "execution_count": 65 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Building the `Transformations`\n", + "\n", + "Now we can build the transformations for this network, assigning the appropriate protocol for each transformation. Here we will use the hybrid topology protocol for edges within each series and the separated topologies protocol for edges between the series.\n", + "\n", + "Each protocol has different requirements for the `ChemicalSystem` compositions used to represent the end states of the transformation. This is due to the hybrid topology protocol requiring the `complex` and `solvent` legs be constructed manually as separate `Transformation` objects while the separated topologies protocol runs both the `complex` and `solvent` legs within a single `Transformation` object. The code below demonstrates how to build the transformations for both protocols while ensuring the correct `ChemicalSystem` objects are used for each protocol. For more information on the requirements for each protocol see the [examples documentation](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html#system-composition-examples)." + ], + "id": "2c1e9779b733eb13" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T11:52:55.933717Z", + "start_time": "2026-10-28T11:52:55.331525Z" + } + }, + "cell_type": "code", + "source": [ + "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocolSettings\n", + "from openfe.protocols.openmm_septop import SepTopSettings\n", + "\n", + "# load the receptor from the provided PDB file and create the solvent component\n", + "protein = openfe.ProteinComponent.from_pdb_file(\"inputs/protein.pdb\")\n", + "solvent = openfe.SolventComponent()\n", + "\n", + "\n", + "# define helper functions to build the transformations for each protocol\n", + "def build_hybrid_topology_transformation(edge: openfe.LigandAtomMapping, default_settings: RelativeHybridTopologyProtocolSettings, protein_comp: openfe.ProteinComponent, solvent_comp: openfe.SolventComponent) -> list[openfe.Transformation]:\n", + " \"\"\"Build the complex and solvent leg transformations for a given ligand atom mapping using the hybrid topology protocol.\"\"\"\n", + " transformations = []\n", + " for leg in [\"solvent\", \"complex\"]:\n", + " state_a_dict = {\"ligand\": edge.componentA, \"solvent\": solvent_comp}\n", + " state_b_dict = {\"ligand\": edge.componentB, \"solvent\": solvent_comp}\n", + " if leg == \"complex\":\n", + " state_a_dict[\"protein\"] = protein_comp\n", + " state_b_dict[\"protein\"] = protein_comp\n", + "\n", + " state_a = openfe.ChemicalSystem(state_a_dict, name=f\"{edge.componentA.name}_{leg}\")\n", + " state_b = openfe.ChemicalSystem(state_b_dict, name=f\"{edge.componentB.name}_{leg}\")\n", + "\n", + " # adjust the settings based on the systems involved in the transformation\n", + " ts_settings = RelativeHybridTopologyProtocol._adaptive_settings(\n", + " stateA=state_a,\n", + " stateB=state_b,\n", + " mapping=edge,\n", + " initial_settings=default_settings,\n", + " )\n", + "\n", + " # create the transformation for this leg\n", + " transformation = openfe.Transformation(\n", + " stateA=state_a,\n", + " stateB=state_b,\n", + " mapping=edge,\n", + " protocol=RelativeHybridTopologyProtocol(ts_settings),\n", + " name=f\"{leg}_{edge.componentA.name}_{edge.componentB.name}\"\n", + " )\n", + " transformations.append(transformation)\n", + " return transformations\n", + "\n", + "\n", + "def build_septop_transformation(edge: openfe.LigandAtomMapping, default_settings: SepTopSettings, protein_comp: openfe.ProteinComponent, solvent_comp: openfe.SolventComponent) -> list[openfe.Transformation]:\n", + " \"\"\"Build the transformation for a given ligand atom mapping using the separated topologies protocol.\"\"\"\n", + " state_a_dict = {\"ligand\": edge.componentA, \"protein\": protein_comp, \"solvent\": solvent_comp}\n", + " state_b_dict = {\"ligand\": edge.componentB, \"protein\": protein_comp, \"solvent\": solvent_comp}\n", + "\n", + " state_a = openfe.ChemicalSystem(state_a_dict, name=f\"{edge.componentA.name}_septop\")\n", + " state_b = openfe.ChemicalSystem(state_b_dict, name=f\"{edge.componentB.name}_septop\")\n", + "\n", + " # adjust the settings based on the systems involved in the transformation\n", + " ts_settings = SepTopProtocol._adaptive_settings(\n", + " stateA=state_a,\n", + " stateB=state_b,\n", + " initial_settings=default_settings,\n", + " )\n", + "\n", + " # create the transformation for this edge\n", + " transformation = openfe.Transformation(\n", + " stateA=state_a,\n", + " stateB=state_b,\n", + " mapping=None,\n", + " protocol=SepTopProtocol(ts_settings),\n", + " name=f\"{edge.componentA.name}_{edge.componentB.name}\"\n", + " )\n", + " return [transformation, ]\n" + ], + "id": "4b08f0258bbdd052", + "outputs": [], + "execution_count": 67 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We can now build the transformations required to execute the edges in the planned network. The code below iterates through the edges in the `LigandNetwork` and builds the appropriate transformations for each edge based on whether the edge is within a series or between series. The transformations can then be writen to disk which can be executed using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow).", + "id": "c3b0890f5793275c" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T12:27:09.467138Z", + "start_time": "2026-10-28T12:26:57.477396Z" + } + }, + "cell_type": "code", + "source": [ + "import pathlib\n", + "\n", + "all_transformations = []\n", + "for edge in network.edges:\n", + " if ligand_series_mapping[edge.componentA.name] == ligand_series_mapping[edge.componentB.name]:\n", + " # build the hybrid topology transformations for edges within a series\n", + " transformations = build_hybrid_topology_transformation(edge, default_hybrid_settings, protein, solvent)\n", + " else:\n", + " # build the separated topologies transformation for edges between series\n", + " transformations = build_septop_transformation(edge, default_septop_settings, protein, solvent)\n", + "\n", + " # add the transformations to the network\n", + " all_transformations.extend(transformations)\n", + "\n", + "alchemical_network = openfe.AlchemicalNetwork(all_transformations)\n", + "\n", + "# first we create the directory\n", + "transformation_dir = pathlib.Path(\"transformations\")\n", + "transformation_dir.mkdir(exist_ok=True)\n", + "\n", + "# then we write out each transformation\n", + "for transformation in alchemical_network.edges:\n", + " transformation.to_json(transformation_dir / f\"{transformation.name}.json\")" + ], + "id": "fcc7a807d6edc1a8", + "outputs": [], + "execution_count": 69 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We can also check how many transformations were created for each protocol. The hybrid topology protocol should have 44 transformations (22 edges * 2 legs) while the separated topologies protocol should have 6 transformations (6 connecting edges).", + "id": "8aa9a3a8ad2ce7c5" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T12:57:19.849140Z", + "start_time": "2026-10-28T12:57:19.817053Z" + } + }, + "cell_type": "code", + "source": [ + "edges_per_protocol = {\n", + " RelativeHybridTopologyProtocol: 0,\n", + " SepTopProtocol: 0\n", + "}\n", + "for transformation in alchemical_network.edges:\n", + " edges_per_protocol[transformation.protocol.__class__] += 1\n", + "\n", + "edges_per_protocol" + ], + "id": "d4f5c88c40f92a9", + "outputs": [ + { + "data": { + "text/plain": [ + "{openfe.protocols.openmm_rfe.hybridtop_protocols.RelativeHybridTopologyProtocol: 44,\n", + " openfe.protocols.openmm_septop.equil_septop_method.SepTopProtocol: 6}" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 77 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "27d647049e330837" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 7e39962f3de52c569d797980cce3ed9e16381a7c Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Fri, 30 Oct 2026 11:36:06 +0000 Subject: [PATCH 2/9] fix ligands order, clean up notebook and add results analysis --- mixed_networks/inputs/ligands.sdf | 2144 --------------------------- mixed_networks/mixed_networks.ipynb | 1036 +++++++++++-- 2 files changed, 951 insertions(+), 2229 deletions(-) delete mode 100644 mixed_networks/inputs/ligands.sdf diff --git a/mixed_networks/inputs/ligands.sdf b/mixed_networks/inputs/ligands.sdf deleted file mode 100644 index 3be0502..0000000 --- a/mixed_networks/inputs/ligands.sdf +++ /dev/null @@ -1,2144 +0,0 @@ - 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2 26 1 0 - 3 27 1 0 - 4 28 1 0 - 5 29 1 0 - 6 30 1 0 - 7 31 1 0 - 8 32 1 0 - 15 33 1 0 - 15 34 1 0 - 17 35 1 0 - 17 36 1 0 - 17 37 1 0 - 18 38 1 0 - 18 39 1 0 - 18 40 1 0 - 19 41 1 0 - 19 42 1 0 - 19 43 1 0 - 21 44 1 0 - 22 45 1 0 - 22 46 1 0 -M CHG 1 21 1 -M END -> (2) -6.5700000000000003 - -> (2) -10.1021/ml3001165 - -> (2) -lig_03 - -> (2) --0.11095263136793738 -0.09334935320784217 -0.099566058053270637 -0.13318472428490286 -0.115234249068991 -0.19174704773594503 -0.091113538278833686 -0.14451132042576437 --0.019377135469213776 0.0028317786915146785 -0.14109196586777334 0.13953528480361338 0.70398049728701939 0.48898395078013773 -0.17956253214050893 0.11954519824813241 -0.11578555478026038 -0.03879441620539064 0.11556380169223184 -0.40000084860493307 -0.37596312983204488 -0.49046490414311056 -0.50077176911999355 -0.32162410838772421 0.15000080244372721 0.13377190308402415 -0.14620045499633189 0.14072715418170328 0.1212532438499772 0.145523465440973 0.13773419635127421 0.1512595482332551 0.12534954325984354 0.12534954325984354 0.064915507302983941 -0.064915507302983941 0.064915507302983941 0.080984628961785973 0.080984628961785973 0.080984628961785973 0.049717157350286194 0.049717157350286194 0.049717157350286194 0.3120375491840684 -0.31950332896540995 0.31950332896540995 - -> (2) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 42 45 0 0 0 0 0 0 0 0999 V2000 - -40.1853 -45.2236 17.1561 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8417 -45.1130 16.8105 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.5502 -45.5374 18.4642 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2297 -45.6216 19.0844 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.9620 -44.0919 16.6242 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.4608 -46.1044 17.8512 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.6802 -44.9041 16.7249 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.0778 -43.7889 23.6837 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1753 -43.3854 22.9550 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8361 -45.3033 17.7689 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4116 -45.1659 17.4132 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5815 -45.7580 19.4543 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.6123 -43.9694 16.2856 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.1130 -45.9688 17.5090 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8508 -45.5605 22.8009 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0693 -46.9005 22.3437 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9150 -44.7397 21.7307 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9669 -46.0516 20.9058 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.3726 -46.6701 21.0588 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8800 -45.1459 23.5836 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6705 -44.5382 22.4340 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0464 -47.1015 21.4574 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2773 -47.8522 22.8168 N 0 0 0 0 0 0 0 0 0 0 0 0 - -34.0980 -42.6468 15.3130 Cl 0 0 0 0 0 0 0 0 0 0 0 0 - -32.9756 -47.1360 18.0609 Cl 0 0 0 0 0 0 0 0 0 0 0 0 - -40.9522 -45.0593 16.4048 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.5911 -44.8754 15.7789 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.6107 -45.5921 18.6898 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4584 -45.7453 19.8419 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.6678 -43.3394 16.2764 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.7737 -46.9540 18.4559 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.6311 -44.8044 16.4592 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4222 -43.1717 24.2795 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6360 -42.4214 22.7956 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.0970 -43.8560 21.1068 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6803 -44.7464 22.5168 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.5396 -46.9939 22.0935 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4909 -47.5555 20.4233 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.1663 -45.9576 20.8102 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1630 -48.0587 21.1347 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.5485 -47.6376 23.4895 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3883 -48.8136 22.5159 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 3 1 0 - 2 10 1 0 - 3 12 2 0 - 4 10 2 0 - 4 12 1 0 - 5 11 2 0 - 5 13 1 0 - 6 11 1 0 - 6 14 2 0 - 7 13 2 0 - 7 14 1 0 - 8 9 2 0 - 8 20 1 0 - 9 21 1 0 - 10 11 1 0 - 12 18 1 0 - 13 24 1 0 - 14 25 1 0 - 15 16 1 0 - 15 20 2 0 - 15 21 1 0 - 16 22 2 0 - 16 23 1 0 - 17 18 1 0 - 17 21 1 0 - 18 19 1 6 - 18 22 1 0 - 1 26 1 0 - 2 27 1 0 - 3 28 1 0 - 4 29 1 0 - 5 30 1 0 - 6 31 1 0 - 7 32 1 0 - 8 33 1 0 - 9 34 1 0 - 17 35 1 0 - 17 36 1 0 - 19 37 1 0 - 19 38 1 0 - 19 39 1 0 - 22 40 1 0 - 23 41 1 0 - 23 42 1 0 -M CHG 1 22 1 -M END -> (3) -5.6699999999999999 - -> (3) -Preparation of 5,6-dihydro-imidazo[1,2-a]pyrazin-8-ylamine derivatives as beta-secretase inhibitors By: Trabanco-Suarez, Andres Avelino; Delgado-Jimenez, Francisca; Vega Ramiro, Juan Antonio; Tresadern, Gary John; Gijsen, Henricus Jacobus Maria; Oehlrich, DanielWorld Intellectual Property Organization, WO2012085038 A1 2012-06-28 - -> (3) -lig_07 - -> (3) --0.11149073090581667 -0.084030222857282275 -0.11456604745416414 -0.089445848727510088 -0.10307528522043001 -0.10307528522043001 -0.11684875633744966 0.35011908713550793 --0.19180805679588092 -0.022775248402640932 -0.0097861818614460166 -0.14117531176833881 0.043696935510351545 0.043696935510351545 0.18151846829624402 0.42853355709285962 0.02483856234522093 -0.10009227249593962 -0.11977779563693773 -0.56343757804660566 -0.14623522456912769 -0.34253310855655444 -0.492649194562719 -0.081031289661214465 -0.081031289661214465 0.15900236669750439 -0.14974271062584149 0.12628163698883282 0.14332244101734387 0.15987374190063702 0.15987374190063702 0.17050101164551007 0.068826201593592054 0.20014170113773572 0.097198846971704847 -0.097198846971704847 0.070553909277632124 0.070553909277632124 0.070553909277632124 0.32157388631077038 0.33853888813228833 0.33853888813228833 - -> (3) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 37 39 0 0 0 0 0 0 0 0999 V2000 - -40.5651 -45.2426 17.4631 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3154 -45.0579 16.8765 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2913 -45.1190 19.0612 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5457 -45.2917 19.6705 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6797 -45.3754 18.8510 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1775 -45.0023 17.6751 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8910 -44.2680 21.9028 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9598 -46.2749 22.5630 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1306 -45.3615 21.6253 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6726 -45.3897 21.1676 C 0 0 1 0 0 0 0 0 0 0 0 0 - -42.0733 -46.0923 20.6549 C 0 0 0 0 0 0 0 0 0 0 0 0 - -43.5241 -45.8148 21.0820 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.8679 -47.6141 20.6266 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.9837 -44.1619 23.5762 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8967 -44.9116 22.7214 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9712 -46.6068 21.6589 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1621 -47.2051 23.1796 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0708 -43.0645 21.8078 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9525 -45.5714 19.3199 O 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4780 -44.7615 16.8844 Br 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4485 -45.2942 16.8316 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2459 -44.9641 15.7961 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3967 -45.0704 19.6788 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.2246 -45.7700 22.6398 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4540 -44.3120 21.6836 H 0 0 0 0 0 0 0 0 0 0 0 0 - -44.2014 -46.3253 20.4048 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.7102 -44.7461 21.0470 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.6763 -46.1787 22.0932 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.5649 -48.0590 19.9227 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.0442 -48.0201 21.6182 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8501 -47.8349 20.3190 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4346 -44.0331 24.5712 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.0359 -44.7121 23.6716 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.7914 -43.1749 23.1303 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1736 -47.5686 21.3995 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4630 -46.8823 23.8068 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3148 -48.1647 22.9746 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 5 1 0 - 2 6 1 0 - 3 4 1 0 - 3 6 2 0 - 4 5 2 0 - 4 10 1 0 - 5 19 1 0 - 6 20 1 0 - 7 10 1 0 - 7 15 1 0 - 7 18 2 0 - 8 15 1 0 - 8 16 2 0 - 8 17 1 0 - 10 9 1 6 - 9 11 1 0 - 10 16 1 0 - 11 12 1 0 - 11 13 1 0 - 11 19 1 0 - 14 15 1 0 - 1 21 1 0 - 2 22 1 0 - 3 23 1 0 - 9 24 1 0 - 9 25 1 0 - 12 26 1 0 - 12 27 1 0 - 12 28 1 0 - 13 29 1 0 - 13 30 1 0 - 13 31 1 0 - 14 32 1 0 - 14 33 1 0 - 14 34 1 0 - 16 35 1 0 - 17 36 1 0 - 17 37 1 0 -M CHG 1 16 1 -M END -> (4) -36830 - -> (4) -10.1021/jm4002154 - -> (4) -lig_1a - -> (4) --0.13820772819422386 -0.040676945971476065 -0.060662372188793647 -0.21887303345106743 0.20543394036389687 -0.039562082069145667 0.70342094249821996 0.48382276840306615 -0.10818807237051628 -0.069949148071778794 0.18115969545937874 -0.10976989023588798 -0.10976989023588798 0.05468243159152366 -0.42325972967051173 -0.40329446844957972 -0.47505244069002772 -0.4595672433843484 --0.31774922661684657 -0.046943338738905417 0.16996263511277535 0.15574612267114021 0.14501204140282967 0.090639015508664628 0.090639015508664628 0.059724261833203805 0.059724261833203805 -0.059724261833203805 0.059724261833203805 0.059724261833203805 0.059724261833203805 0.083447223378194352 0.083447223378194352 0.083447223378194352 0.33727068252660131 0.32757539100743627 -0.32757539100743627 - -> (4) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 43 45 0 0 0 0 0 0 0 0999 V2000 - -38.2195 -44.8055 17.0642 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5671 -44.6062 17.3433 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9274 -45.8168 19.2363 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.8326 -44.7993 16.0919 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2882 -45.6183 19.5460 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3800 -45.3986 18.0108 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.0843 -44.9987 18.5703 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.8186 -45.3799 16.8575 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.9738 -46.2686 17.3935 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.0155 -44.5986 21.6655 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1273 -44.2021 22.5865 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1011 -46.3206 22.6634 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.2295 -45.0628 16.7765 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.8536 -45.9118 20.9393 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.1694 -46.7041 20.8953 C 0 0 0 0 0 0 0 0 0 0 0 0 - -30.8639 -47.0676 17.9426 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0931 -42.8220 23.1470 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.2567 -46.3121 17.6985 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9118 -46.7694 21.7052 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2460 -47.0957 23.3110 N 0 0 0 0 0 0 0 0 0 0 0 0 - -35.9846 -45.5803 17.8219 N 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6415 -44.3664 15.8554 O 0 0 0 0 0 0 0 0 0 0 0 0 - -31.6442 -45.3616 16.4260 O 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1109 -45.0294 23.0224 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3720 -44.7195 18.8185 F 0 0 0 0 0 0 0 0 0 0 0 0 - -29.6731 -46.7122 17.3861 F 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8091 -42.4454 23.4058 F 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8708 -44.4795 16.0927 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.2080 -44.1230 16.6125 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2604 -46.2602 19.9725 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.8113 -44.0353 15.3295 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8114 -43.9270 21.3525 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4770 -47.0147 21.9006 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.0530 -47.6162 20.2969 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9996 -46.1334 20.4680 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.0203 -48.1300 17.7385 H 0 0 0 0 0 0 0 0 0 0 0 0 - -30.7824 -46.9210 19.0222 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6519 -42.7678 24.0836 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5018 -42.0916 22.4413 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8603 -47.7628 21.4962 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1824 -48.0855 23.1030 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.6478 -46.7015 24.0276 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.4985 -46.0857 18.5602 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 6 1 0 - 2 7 1 0 - 3 5 1 0 - 3 6 2 0 - 4 8 2 0 - 4 23 1 0 - 5 7 2 0 - 5 14 1 0 - 6 21 1 0 - 7 25 1 0 - 8 13 1 0 - 8 18 1 0 - 9 16 1 0 - 9 18 2 0 - 9 23 1 0 - 10 11 2 0 - 10 14 1 0 - 11 17 1 0 - 11 24 1 0 - 12 19 2 0 - 12 20 1 0 - 12 24 1 0 - 13 21 1 0 - 13 22 2 0 - 14 15 1 6 - 14 19 1 0 - 16 26 1 0 - 17 27 1 0 - 1 28 1 0 - 2 29 1 0 - 3 30 1 0 - 4 31 1 0 - 10 32 1 0 - 15 33 1 0 - 15 34 1 0 - 15 35 1 0 - 16 36 1 0 - 16 37 1 0 - 17 38 1 0 - 17 39 1 0 - 19 40 1 0 - 20 41 1 0 - 20 42 1 0 - 21 43 1 0 -M CHG 1 19 1 -M END -> (5) -7.3200000000000003 - -> (5) -10.1021/acs.jmedchem.8b00002 - -> (5) -lig_45 - -> (5) --0.087260056200415589 -0.14167382186928459 -0.12972720331230828 0.19615760081729225 -0.15710418290177056 0.070069965061753295 0.12604163223227791 0.19554052883109382 0.44368195169886876 --0.14182311660328575 0.016103264031022094 0.59940808647593791 0.62714141243418986 0.14903534584960274 -0.11198412990847299 0.160907011565774 0.22355997198542885 -0.58872956401386922 --0.35517317420521449 -0.45545062786617946 -0.47437319523373317 -0.54670650130787557 -0.35839337474384975 -0.30825007325688075 -0.13155010706463524 -0.21000866955795952 -0.20399660235920616 -0.17300879591426185 0.16538039976081184 0.14189585679492286 0.070186909374802611 0.19319244855365089 0.068921219644158385 0.068921219644158385 0.068921219644158385 0.076822687027066253 -0.076822687027066253 0.090902697381585143 0.090902697381585143 0.32266023391207982 0.33194493883570958 0.33194493883570958 0.32212867968997289 - -> (5) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 51 54 0 0 0 0 0 0 0 0999 V2000 - -39.2331 -44.9942 16.9142 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.6629 -44.0057 15.9801 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6745 -45.2767 17.7813 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.4927 -45.1974 17.4728 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.3988 -43.8111 15.4237 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.4103 -45.0822 17.2249 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2526 -45.0777 19.1181 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8008 -44.7385 17.1590 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1120 -44.9405 17.7363 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5175 -45.2692 19.6995 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6341 -45.3506 18.8562 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.2725 -44.3493 16.0460 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9165 -44.2717 21.9586 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9817 -46.2800 22.6094 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1407 -45.3796 21.6207 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6736 -45.3894 21.1922 C 0 0 1 0 0 0 0 0 0 0 0 0 - -42.0574 -46.1044 20.6214 C 0 0 0 0 0 0 0 0 0 0 0 0 - -43.5188 -45.8448 21.0229 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.8387 -47.6238 20.5761 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.0441 -44.1736 23.6714 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.9306 -44.7375 16.1904 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9334 -44.9186 22.7880 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9719 -46.6074 21.6806 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1886 -47.2122 23.2290 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1045 -43.0685 21.8767 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9143 -45.5637 19.2965 O 0 0 0 0 0 0 0 0 0 0 0 0 - -33.0393 -44.1598 15.5026 O 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1422 -44.8846 15.8363 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.5314 -43.5799 15.4833 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.7654 -45.8504 18.7006 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3629 -45.2474 16.8232 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.2978 -43.2395 14.5051 H 0 0 0 0 0 0 0 0 0 0 0 0 - -33.5364 -45.5040 17.7139 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3697 -45.0309 19.7533 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.2514 -45.8039 22.6270 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4742 -44.3338 21.6881 H 0 0 0 0 0 0 0 0 0 0 0 0 - -44.1779 -46.3513 20.3251 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.7131 -44.7774 20.9990 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.6880 -46.2242 22.0256 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.5176 -48.0646 19.8521 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.0313 -48.0454 21.5581 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8130 -47.8321 20.2860 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.5160 -44.0625 24.6589 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.0938 -44.7172 23.7783 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8513 -43.1788 23.2434 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.0079 -44.5111 15.6507 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.0680 -45.8195 16.2609 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.8756 -44.3130 17.1957 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1608 -47.5673 21.4038 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.5050 -46.8926 23.8746 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3291 -48.1702 23.0077 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 4 2 0 - 1 9 1 0 - 2 5 2 0 - 2 8 1 0 - 3 6 1 0 - 3 8 2 0 - 4 11 1 0 - 5 12 1 0 - 6 12 2 0 - 7 9 2 0 - 7 10 1 0 - 8 9 1 0 - 10 11 2 0 - 10 16 1 0 - 11 26 1 0 - 12 27 1 0 - 13 16 1 0 - 13 22 1 0 - 13 25 2 0 - 14 22 1 0 - 14 23 2 0 - 14 24 1 0 - 16 15 1 6 - 15 17 1 0 - 16 23 1 0 - 17 18 1 0 - 17 19 1 0 - 17 26 1 0 - 20 22 1 0 - 21 27 1 0 - 1 28 1 0 - 2 29 1 0 - 3 30 1 0 - 4 31 1 0 - 5 32 1 0 - 6 33 1 0 - 7 34 1 0 - 15 35 1 0 - 15 36 1 0 - 18 37 1 0 - 18 38 1 0 - 18 39 1 0 - 19 40 1 0 - 19 41 1 0 - 19 42 1 0 - 20 43 1 0 - 20 44 1 0 - 20 45 1 0 - 21 46 1 0 - 21 47 1 0 - 21 48 1 0 - 23 49 1 0 - 24 50 1 0 - 24 51 1 0 -M CHG 1 23 1 -M END -> (6) -7197 - -> (6) -10.1021/jm4002154 - -> (6) -lig_16 - -> (6) --0.061248306258052006 -0.080218471659749158 -0.080218471659749158 -0.13346163190755189 -0.17982292927655519 -0.17982292927655519 -0.064574204458325513 -0.038458734823792591 --0.04675184197577776 -0.22834187008294404 0.18581574402895629 0.14233108602610289 0.69921965144720732 0.48190469287481963 -0.11176379777345002 0.06467235826101958 0.18079579614248931 --0.11135203398618043 -0.11135203398618043 0.053100407050520763 0.12008615575876891 -0.42525693036469758 -0.40682350851449312 -0.47663497187051118 -0.46057098366174043 -0.32295244194421113 --0.31841673709306062 0.14153266690817534 0.13638503097143828 0.13638503097143828 0.16524196408835112 0.14913123093691527 0.14913123093691527 0.12187793098536193 0.088959641681582324 -0.088959641681582324 0.058142222391039715 0.058142222391039715 0.058142222391039715 0.058142222391039715 0.058142222391039715 0.058142222391039715 0.081865109430224292 0.081865109430224292 -0.081865109430224292 0.050097279177576885 0.050097279177576885 0.050097279177576885 0.3357853963240689 0.32599333666411101 0.32599333666411101 - -> (6) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 47 50 0 0 0 0 0 0 0 0999 V2000 - -34.2682 -44.3482 16.0497 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.4070 -45.0810 17.2283 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.3941 -43.8099 15.4264 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6717 -45.2757 17.7837 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.6587 -44.0046 15.9817 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2297 -44.9930 16.9141 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.4897 -45.1962 17.4719 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2505 -45.0769 19.1185 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.7975 -44.7375 17.1604 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1091 -44.9395 17.7369 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5158 -45.2685 19.6992 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6319 -45.3496 18.8552 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9160 -44.2713 21.9588 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9818 -46.2798 22.6098 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1401 -45.3790 21.6194 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6728 -45.3888 21.1918 C 0 0 1 0 0 0 0 0 0 0 0 0 - -42.0563 -46.1036 20.6195 C 0 0 0 0 0 0 0 0 0 0 0 0 - -43.5180 -45.8439 21.0202 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.8376 -47.6230 20.5740 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.0447 -44.1736 23.6727 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9335 -44.9184 22.7887 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9715 -46.6070 21.6804 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1892 -47.2122 23.2297 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1039 -43.0681 21.8770 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9123 -45.5627 19.2947 O 0 0 0 0 0 0 0 0 0 0 0 0 - -33.2833 -44.1952 15.6184 H 0 0 0 0 0 0 0 0 0 0 0 0 - -33.5304 -45.4987 17.7146 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.2866 -43.2380 14.5094 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.7633 -45.8492 18.7031 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.5271 -43.5785 15.4850 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1382 -44.8833 15.8362 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3595 -45.2460 16.8218 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3680 -45.0303 19.7543 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.2514 -45.8035 22.6256 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4736 -44.3332 21.6868 H 0 0 0 0 0 0 0 0 0 0 0 0 - -44.1766 -46.3502 20.3219 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.7121 -44.7765 20.9963 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.6877 -46.2235 22.0227 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.5161 -48.0636 19.8496 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.0309 -48.0447 21.5559 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8119 -47.8313 20.2846 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.5171 -44.0627 24.6599 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.0945 -44.7174 23.7801 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8515 -43.1788 23.2450 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1602 -47.5668 21.4033 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.5059 -46.8927 23.8758 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3295 -48.1701 23.0082 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 3 1 0 - 2 4 1 0 - 3 5 2 0 - 4 9 2 0 - 5 9 1 0 - 6 7 2 0 - 6 10 1 0 - 7 12 1 0 - 8 10 2 0 - 8 11 1 0 - 9 10 1 0 - 11 12 2 0 - 11 16 1 0 - 12 25 1 0 - 13 16 1 0 - 13 21 1 0 - 13 24 2 0 - 14 21 1 0 - 14 22 2 0 - 14 23 1 0 - 16 15 1 6 - 15 17 1 0 - 16 22 1 0 - 17 18 1 0 - 17 19 1 0 - 17 25 1 0 - 20 21 1 0 - 1 26 1 0 - 2 27 1 0 - 3 28 1 0 - 4 29 1 0 - 5 30 1 0 - 6 31 1 0 - 7 32 1 0 - 8 33 1 0 - 15 34 1 0 - 15 35 1 0 - 18 36 1 0 - 18 37 1 0 - 18 38 1 0 - 19 39 1 0 - 19 40 1 0 - 19 41 1 0 - 20 42 1 0 - 20 43 1 0 - 20 44 1 0 - 22 45 1 0 - 23 46 1 0 - 23 47 1 0 -M CHG 1 22 1 -M END -> (7) -455 - -> (7) -10.1021/jm4002154 - -> (7) -lig_11 - -> (7) --0.11581871753677409 -0.12502552664026301 -0.12502552664026301 -0.11280629878982584 -0.11280629878982584 -0.057765875962820457 -0.13289047872766535 -0.061805901137438224 --0.010692187604751992 -0.054761276600208686 -0.2277707169030575 0.18638685250535925 0.69979083442941625 0.48247584605470617 -0.11119265949472468 0.06524346673742254 0.18136687481656988 --0.11078088080629389 -0.11078088080629389 0.05367151552692373 -0.4246856281731991 -0.40625223612531702 -0.47606369948133509 -0.45999974107488673 -0.3223812589620022 0.13656213784471471 -0.13919532203927953 0.13919532203927953 0.1384579082752796 0.1384579082752796 0.14210382008806188 0.16581311726823766 0.12244906181350668 0.089530794861468865 0.089530794861468865 -0.058713338318023278 0.058713338318023278 0.058713338318023278 0.058713338318023278 0.058713338318023278 0.058713338318023278 0.082436247708949639 0.082436247708949639 0.082436247708949639 -0.33635651970163305 0.32656446004167516 0.32656446004167516 - -> (7) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 51 54 0 0 0 0 0 0 0 0999 V2000 - -33.3279 -45.7707 17.6348 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.3998 -46.3455 18.1337 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3623 -44.9664 16.9291 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6159 -45.1795 17.5073 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.7353 -45.3707 17.6195 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.3573 -45.1090 19.1034 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.3685 -44.1917 16.0671 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.6394 -43.8034 16.0925 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.4709 -45.1099 17.0961 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2034 -44.9303 17.7156 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8686 -44.7049 17.1328 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6121 -45.3134 19.7042 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.7399 -45.3646 18.8857 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9218 -44.3592 21.9394 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9699 -46.3859 22.4991 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1845 -45.4214 21.6644 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.7320 -45.4554 21.1980 C 0 0 1 0 0 0 0 0 0 0 0 0 - -42.1446 -46.1034 20.6742 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.2930 -47.0584 18.7654 C 0 0 0 0 0 0 0 0 0 0 0 0 - -43.5870 -45.7897 21.1020 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9812 -47.6288 20.6169 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8845 -44.2944 23.4477 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.4271 -43.5344 15.5474 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8820 -45.0264 22.6768 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0361 -46.6906 21.6487 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1488 -47.3352 23.0523 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1025 -43.1534 21.8874 O 0 0 0 0 0 0 0 0 0 0 0 0 - -42.0120 -45.5655 19.3470 O 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3070 -44.8405 15.8501 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4959 -45.2072 16.8697 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.8328 -46.1007 18.4221 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4775 -45.0666 19.7444 H 0 0 0 0 0 0 0 0 0 0 0 0 - -33.4127 -43.9428 15.6166 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4503 -43.2283 15.6535 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.2792 -45.8642 22.6644 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4899 -44.3700 21.7632 H 0 0 0 0 0 0 0 0 0 0 0 0 - -30.3321 -46.6732 18.4135 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.3281 -46.9448 19.8531 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.3393 -48.1268 18.5347 H 0 0 0 0 0 0 0 0 0 0 0 0 - -44.3063 -46.2155 20.3930 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.7647 -44.7078 21.1026 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.8078 -46.1798 22.1009 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.7546 -48.0752 19.9804 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.0559 -48.0778 21.6126 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.0285 -47.9254 20.1692 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3876 -43.7131 24.2246 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.1744 -44.9792 23.9152 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.3408 -43.6226 22.7771 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2740 -47.6464 21.3970 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.3793 -47.0878 23.6680 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2915 -48.3238 22.8598 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 3 0 - 1 9 1 0 - 2 19 1 0 - 3 4 2 0 - 3 10 1 0 - 4 13 1 0 - 5 9 2 0 - 5 11 1 0 - 6 10 2 0 - 6 12 1 0 - 7 9 1 0 - 7 23 2 0 - 8 11 2 0 - 8 23 1 0 - 10 11 1 0 - 12 13 2 0 - 12 17 1 0 - 13 28 1 0 - 14 17 1 0 - 14 24 1 0 - 14 27 2 0 - 15 24 1 0 - 15 25 2 0 - 15 26 1 0 - 17 16 1 6 - 16 18 1 0 - 17 25 1 0 - 18 20 1 0 - 18 21 1 0 - 18 28 1 0 - 22 24 1 0 - 3 29 1 0 - 4 30 1 0 - 5 31 1 0 - 6 32 1 0 - 7 33 1 0 - 8 34 1 0 - 16 35 1 0 - 16 36 1 0 - 19 37 1 0 - 19 38 1 0 - 19 39 1 0 - 20 40 1 0 - 20 41 1 0 - 20 42 1 0 - 21 43 1 0 - 21 44 1 0 - 21 45 1 0 - 22 46 1 0 - 22 47 1 0 - 22 48 1 0 - 25 49 1 0 - 26 50 1 0 - 26 51 1 0 -M CHG 1 25 1 -M END -> (8) -10 - -> (8) -10.1021/jm4002154 - -> (8) -lig_36 - -> (8) --0.13850266464492855 -0.066937573034973705 -0.061648100395413005 -0.13026923664352474 -0.061775441993685329 -0.062775201876373851 0.42394673101165714 0.42326002589919987 --0.084543086369247997 -0.040969364125938976 -0.13781673439285336 -0.22371888883850155 0.18623707465389194 0.69934909812667789 0.48203410975196781 -0.11163438834688243 0.064685307662276662 -0.18092519811847629 -0.016817115028114879 -0.11122261710903224 -0.11122261710903224 0.053229809026507771 -0.66107738741180477 -0.42512748368522701 -0.40669406183502255 -0.47650552519104061 --0.46044156678459225 -0.32282302506706295 0.14221990875461521 0.16603895298698368 0.14979634455898228 0.12452649347045842 0.037855699877528584 0.03115901372888509 0.089089058558730519 -0.089089058558730519 0.061768844942836201 0.061768844942836201 0.061768844942836201 0.058271631817607319 0.058271631817607319 0.058271631817607319 0.058271631817607319 0.058271631817607319 -0.058271631817607319 0.081994526307372487 0.081994526307372487 0.081994526307372487 0.33591478339889469 0.32612275354125919 0.32612275354125919 - -> (8) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 37 39 0 0 0 0 0 0 0 0999 V2000 - -38.0090 -45.6315 19.1393 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9692 -45.1638 17.2217 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.2004 -46.0152 17.8190 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.7492 -44.0607 16.6787 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.6155 -44.8304 16.6899 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.6132 -45.2875 17.8281 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.2008 -45.1247 17.4552 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3683 -45.7328 19.5198 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6278 -45.0833 16.8841 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.3241 -45.4655 18.5293 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8478 -46.5876 22.5205 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9995 -44.7608 21.7798 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.7192 -44.0071 22.0925 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.7803 -46.0487 20.9633 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.0746 -46.8925 21.0561 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.9031 -45.8991 17.4539 N 0 0 0 0 0 0 0 0 0 0 0 0 - -34.4728 -43.8844 16.2757 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.7506 -46.9116 21.5963 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.0324 -47.5182 23.0029 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.3177 -44.8022 15.6093 F 0 0 0 0 0 0 0 0 0 0 0 0 - -41.6313 -45.4746 18.8190 F 0 0 0 0 0 0 0 0 0 0 0 0 - -37.6579 -44.9875 23.1728 S 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2332 -45.7867 19.8872 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.7339 -44.9813 16.4745 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.4136 -46.8925 18.4228 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4263 -43.2813 16.3387 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.5860 -44.7195 16.3733 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.5093 -44.9964 22.7248 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6767 -44.0894 21.2351 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1722 -43.7361 21.1858 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9602 -43.0733 22.6095 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.2025 -47.2948 22.0687 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.0496 -47.7469 20.3704 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9727 -46.3045 20.8405 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.7379 -47.8855 21.3012 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.0923 -48.4785 22.6827 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.3461 -47.2767 23.7080 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 6 2 0 - 1 8 1 0 - 2 9 2 0 - 2 10 1 0 - 3 7 2 0 - 3 16 1 0 - 4 7 1 0 - 4 17 2 0 - 5 16 2 0 - 5 17 1 0 - 6 7 1 0 - 6 9 1 0 - 8 10 2 0 - 8 14 1 0 - 9 20 1 0 - 10 21 1 0 - 11 18 2 0 - 11 19 1 0 - 11 22 1 0 - 12 13 1 0 - 12 14 1 0 - 13 22 1 0 - 14 15 1 6 - 14 18 1 0 - 1 23 1 0 - 2 24 1 0 - 3 25 1 0 - 4 26 1 0 - 5 27 1 0 - 12 28 1 0 - 12 29 1 0 - 13 30 1 0 - 13 31 1 0 - 15 32 1 0 - 15 33 1 0 - 15 34 1 0 - 18 35 1 0 - 19 36 1 0 - 19 37 1 0 -M CHG 1 18 1 -M END -> (9) -6.6200000000000001 - -> (9) -10.1016/j.bmcl.2014.03.025 - -> (9) -lig_02 - -> (9) --0.072870730145557505 -0.19038902102289973 0.46754503370942296 0.46754503370942296 0.64574939133347686 -0.07450568555174647 -0.27020946024237452 -0.17516365526495753 0.20065447808922948 -0.16122843445958318 0.39908155919732274 -0.08173767356453715 -0.061795967354162318 0.10437273354949178 -0.11638261256991206 -0.68505269168196503 -0.68505269168196503 -0.35671508191405116 --0.5002598750430185 -0.1192969369727212 -0.13869638620196162 -0.013459826542718991 0.15052077295006933 0.18499207617463292 0.039472394899742025 0.039472394899742025 0.06163931013764562 -0.096674838380233663 0.096674838380233663 0.11710427077235402 0.11710427077235402 0.070771605984584707 0.070771605984584707 0.070771605984584707 0.32039994122208776 0.32952085258187475 -0.32952085258187475 - -> (9) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 45 48 0 0 0 0 0 0 0 0999 V2000 - -29.8986 -46.6441 17.3497 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.5877 -45.2101 16.2984 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2805 -44.7195 17.1426 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6232 -44.5082 17.4349 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.9166 -44.8258 16.1613 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9997 -45.7627 19.3052 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.2706 -46.8151 17.9247 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3803 -43.9268 23.4564 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.2524 -46.2242 17.1916 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3509 -45.5492 19.6316 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4535 -45.3375 18.0800 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.1397 -44.9086 18.6638 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1905 -44.8571 22.8522 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.8633 -45.4777 16.9414 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1441 -46.2798 22.6333 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.2927 -45.1037 16.8335 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.3902 -44.7179 21.8007 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9335 -45.9673 20.9954 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.1570 -46.8828 20.7721 C 0 0 0 0 0 0 0 0 0 0 0 0 - -28.7980 -46.9875 17.4797 N 0 0 0 0 0 0 0 0 0 0 0 0 - -33.5705 -46.4623 17.8230 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9661 -42.6859 23.3732 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1313 -42.8186 22.7429 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9895 -46.8083 21.7942 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2452 -44.1120 22.4346 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2378 -47.0744 23.2955 N 0 0 0 0 0 0 0 0 0 0 0 0 - -36.0706 -45.5588 17.8863 N 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6499 -44.4284 15.8757 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4333 -44.6519 18.9038 F 0 0 0 0 0 0 0 0 0 0 0 0 - -30.8193 -44.7246 15.7151 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8785 -44.4054 16.1907 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.2656 -44.0332 16.7082 H 0 0 0 0 0 0 0 0 0 0 0 0 - -33.2035 -44.0462 15.4711 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3624 -46.2679 20.0159 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.0093 -47.6023 18.6164 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4301 -44.1376 23.9244 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8587 -43.9929 21.1189 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1169 -45.0217 22.5686 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.5740 -47.1821 21.7450 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9213 -46.3394 20.1972 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8473 -47.7791 20.2147 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2261 -48.0612 23.1338 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.5905 -46.6654 23.9387 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6036 -46.1021 18.5839 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0075 -47.8018 21.6815 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 9 1 0 - 1 20 3 0 - 2 5 2 0 - 2 9 1 0 - 3 4 2 0 - 3 11 1 0 - 4 12 1 0 - 5 14 1 0 - 6 10 1 0 - 6 11 2 0 - 7 9 2 0 - 7 21 1 0 - 8 13 2 0 - 8 22 1 0 - 10 12 2 0 - 10 18 1 0 - 11 27 1 0 - 12 29 1 0 - 13 15 1 0 - 13 25 1 0 - 14 16 1 0 - 14 21 2 0 - 15 24 2 0 - 15 26 1 0 - 16 27 1 0 - 16 28 2 0 - 17 18 1 0 - 17 25 1 0 - 18 19 1 6 - 18 24 1 0 - 22 23 2 0 - 23 25 1 0 - 2 30 1 0 - 3 31 1 0 - 4 32 1 0 - 5 33 1 0 - 6 34 1 0 - 7 35 1 0 - 8 36 1 0 - 17 37 1 0 - 17 38 1 0 - 19 39 1 0 - 19 40 1 0 - 19 41 1 0 - 26 42 1 0 - 26 43 1 0 - 27 44 1 0 - 24 45 1 0 -M CHG 1 24 1 -M END -> (10) -8.6999999999999993 - -> (10) -10.1021/acsmedchemlett.9b00181 - -> (10) -lig_74 - -> (10) -0.2549954429268837 -0.050060562789440155 -0.080950327217578888 -0.12626560777425766 -0.21898747235536575 -0.13156857341527939 0.4520784392952919 0.41493693739175797 -0.11120452731847763 --0.17819885164499283 0.087892308831214905 0.12866855412721634 -0.45763634890317917 0.38663468509912491 0.55841029435396194 0.59936786442995071 0.03011678159236908 0.11066007614135742 --0.12321998924016953 -0.34208088368177414 -0.61834501475095749 -0.32156222313642502 -0.082448773086071014 -0.36007802933454514 0.11049909144639969 -0.50678526610136032 -0.46614303439855576 --0.54503261297941208 -0.13834203034639359 0.16433421522378922 0.17836042493581772 0.17095840722322464 0.16962481290102005 0.13475305587053299 0.052146084606647491 0.082492776215076447 -0.11251828819513321 0.11251828819513321 0.073805756866931915 0.073805756866931915 0.073805756866931915 0.33747995644807816 0.33747995644807816 0.32890001684427261 0.32166609913110733 - -> (10) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 48 50 0 0 0 0 0 0 0 0999 V2000 - -38.1986 -44.7279 17.0306 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5465 -44.5151 17.3023 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9581 -45.7840 19.1932 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.7541 -44.9904 16.2592 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.3064 -46.9624 18.0197 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3098 -45.5627 19.4988 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3934 -45.3552 17.9797 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.0830 -44.9185 18.5225 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.7860 -45.5696 16.9685 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.2723 -46.3866 17.3159 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1808 -46.3253 22.6421 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.1891 -45.1309 16.8037 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.1407 -44.6357 21.6789 C 0 0 1 0 0 0 0 0 0 0 0 0 - -38.8211 -44.1050 22.2297 C 0 0 1 0 0 0 0 0 0 0 0 0 - -39.8943 -45.9153 20.8681 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.1730 -46.7582 20.7373 C 0 0 0 0 0 0 0 0 0 0 0 0 - -28.9721 -46.2585 16.8315 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0243 -42.8457 23.0550 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.5779 -46.5628 17.8595 N 0 0 0 0 0 0 0 0 0 0 0 0 - -31.4764 -45.3906 16.4240 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9439 -46.7526 21.6370 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3490 -47.1276 23.2894 N 0 0 0 0 0 0 0 0 0 0 0 0 - -36.0123 -45.5842 17.8199 N 0 0 0 0 0 0 0 0 0 0 0 0 - -35.4868 -44.4226 15.8509 O 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2113 -45.0641 23.0848 O 0 0 0 0 0 0 0 0 0 0 0 0 - -30.0107 -46.8851 17.5725 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3731 -44.6411 18.7585 F 0 0 0 0 0 0 0 0 0 0 0 0 - -40.9542 -44.9286 22.7335 F 0 0 0 0 0 0 0 0 0 0 0 0 - -37.7991 -42.3847 23.4334 F 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8179 -44.3943 16.0727 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.1765 -44.0186 16.5706 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3060 -46.2595 19.9234 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.9054 -44.1858 15.5493 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.1499 -47.7612 18.7340 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6523 -43.8688 21.0873 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1129 -43.8742 21.4259 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4954 -47.1343 21.7155 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.0033 -47.6329 20.0982 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.0081 -46.1916 20.3159 H 0 0 0 0 0 0 0 0 0 0 0 0 - -28.0331 -46.7385 17.1213 H 0 0 0 0 0 0 0 0 0 0 0 0 - -28.8996 -45.1937 17.0765 H 0 0 0 0 0 0 0 0 0 0 0 0 - -29.1109 -46.4050 15.7552 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5953 -43.0404 23.9671 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5111 -42.0561 22.4760 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8503 -47.7351 21.3924 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2607 -48.1041 23.0329 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.7838 -46.7634 24.0480 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.5706 -46.1117 18.5706 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 7 1 0 - 2 8 1 0 - 3 6 1 0 - 3 7 2 0 - 4 9 2 0 - 4 20 1 0 - 5 10 1 0 - 5 19 2 0 - 6 8 2 0 - 6 15 1 0 - 7 23 1 0 - 8 27 1 0 - 9 12 1 0 - 9 19 1 0 - 10 20 2 0 - 10 26 1 0 - 11 21 2 0 - 11 22 1 0 - 11 25 1 0 - 12 23 1 0 - 12 24 2 0 - 13 14 1 0 - 13 15 1 0 - 13 28 1 0 - 14 18 1 0 - 14 25 1 0 - 15 16 1 6 - 15 21 1 0 - 17 26 1 0 - 18 29 1 0 - 1 30 1 0 - 2 31 1 0 - 3 32 1 0 - 4 33 1 0 - 5 34 1 0 - 13 35 1 6 - 14 36 1 6 - 16 37 1 0 - 16 38 1 0 - 16 39 1 0 - 17 40 1 0 - 17 41 1 0 - 17 42 1 0 - 18 43 1 0 - 18 44 1 0 - 21 45 1 0 - 22 46 1 0 - 22 47 1 0 - 23 48 1 0 -M CHG 1 21 1 -M END -> (11) -7.75 - -> (11) -10.1021/acs.jmedchem.8b00011 - -> (11) -lig_67 - -> (11) --0.0844747672478358 -0.12990779678026834 -0.14380706350008646 0.36762114365895587 0.26167233784993488 -0.15721849600474039 0.075968873997529343 0.11367544283469518 0.18071730931599936 -0.5486413141091665 0.60171227653821313 0.60250954826672876 0.16242713232835135 0.07007947315772374 0.11288466056187947 -0.11897932986418407 0.12722194691499075 0.17052826782067618 --0.5567438701788584 -0.67584706346193946 -0.37775556246439618 -0.45844502250353497 -0.46879250804583233 -0.55007929603258765 -0.36094630757967633 -0.41171366969744366 -0.13765848676363626 --0.19025469819704691 -0.19677982727686563 0.17779098947842917 0.16748820741971335 0.13728921612103781 0.046750922997792564 0.055396956702073417 0.092539136608441666 0.11167379220326741 -0.070667405923207596 0.070667405923207596 0.070667405923207596 0.053226386507352196 0.053226386507352196 0.053226386507352196 0.0728104089697202 0.0728104089697202 0.32418565948804218 -0.33332266410191852 0.33332266410191852 0.32668163379033405 - -> (11) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 52 55 0 0 0 0 0 0 0 0999 V2000 - -38.3102 -44.6858 17.0132 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6543 -44.4823 17.3124 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0379 -45.7876 19.1493 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.8431 -44.9774 16.2381 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.4539 -46.9534 18.0133 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3828 -45.5739 19.4848 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4926 -45.3299 17.9379 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.1715 -44.9090 18.5352 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.8936 -45.5432 16.9290 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.4018 -46.3907 17.3283 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1702 -46.5120 22.5561 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.2912 -45.0907 16.7523 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4340 -42.3260 23.4313 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.3750 -42.8627 24.4674 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6819 -44.2430 22.2290 C 0 0 1 0 0 0 0 0 0 0 0 0 - -40.0585 -44.6755 21.7274 C 0 0 1 0 0 0 0 0 0 0 0 0 - -39.9257 -45.9426 20.8679 C 0 0 1 0 0 0 0 0 0 0 0 0 - -38.7415 -42.9502 23.0047 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.2530 -46.7111 20.7766 C 0 0 0 0 0 0 0 0 0 0 0 0 - -29.0941 -46.2999 16.8983 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.7140 -46.5385 17.8251 N 0 0 0 0 0 0 0 0 0 0 0 0 - -31.5744 -45.3943 16.4293 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9901 -46.8592 21.5646 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3730 -47.3811 23.1597 N 0 0 0 0 0 0 0 0 0 0 0 0 - -36.1143 -45.5493 17.7646 N 0 0 0 0 0 0 0 0 0 0 0 0 - -35.5814 -44.3763 15.8025 O 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1050 -45.2651 23.0312 O 0 0 0 0 0 0 0 0 0 0 0 0 - -30.1553 -46.9065 17.6136 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4551 -44.6275 18.7998 F 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8386 -44.9492 22.8114 F 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6981 -42.0334 22.5719 F 0 0 0 0 0 0 0 0 0 0 0 0 - -27.9596 -46.9310 17.3033 F 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9402 -44.3297 16.0591 H 0 0 0 0 0 0 0 0 0 0 0 0 - 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1 2 2 0 - 1 7 1 0 - 2 8 1 0 - 3 6 1 0 - 3 7 2 0 - 4 9 2 0 - 4 22 1 0 - 5 10 1 0 - 5 21 2 0 - 6 8 2 0 - 6 17 1 0 - 7 25 1 0 - 8 29 1 0 - 9 12 1 0 - 9 21 1 0 - 10 22 2 0 - 10 28 1 0 - 11 23 2 0 - 11 24 1 0 - 11 27 1 0 - 12 25 1 0 - 12 26 2 0 - 13 14 1 0 - 13 18 1 0 - 14 18 1 0 - 15 16 1 0 - 15 18 1 0 - 15 27 1 0 - 16 17 1 0 - 16 30 1 0 - 17 19 1 6 - 17 23 1 0 - 18 31 1 0 - 20 28 1 0 - 20 32 1 0 - 1 33 1 0 - 2 34 1 0 - 3 35 1 0 - 4 36 1 0 - 5 37 1 0 - 13 38 1 0 - 13 39 1 0 - 14 40 1 0 - 14 41 1 0 - 15 42 1 6 - 16 43 1 6 - 19 44 1 0 - 19 45 1 0 - 19 46 1 0 - 20 47 1 0 - 20 48 1 0 - 23 49 1 0 - 24 50 1 0 - 24 51 1 0 - 25 52 1 0 -M CHG 1 23 1 -M END -> (12) -7.6900000000000004 - -> (12) -10.1021/acs.jmedchem.8b00011 - -> (12) -lig_69 - -> (12) --0.085379213667832882 -0.13166710791679528 -0.14420142827125695 0.35973313393501133 0.27621301951316685 -0.1596650188932052 0.073954432629621952 0.11408489885238501 0.18683436455634925 -0.55730730237869119 0.60191208066848612 0.60160493793395853 -0.12301386950107721 -0.12301386950107721 0.12135027291683051 0.17113420309928748 0.10949467065242621 0.072003766321218937 --0.11963594762178567 0.42146307172683567 -0.56043982563110495 -0.67649561224075461 -0.38141471204849392 -0.46135097799392849 -0.46969720778556973 -0.55098378715606833 -0.36055710969062954 --0.45552370009514004 -0.13860549090000299 -0.1935625231036773 -0.1464332198867431 -0.22832492051216272 0.17688655795959327 0.16658371629623267 0.13652221801189277 0.048253904454983197 -0.051519404523647748 0.097207702123201817 0.097207702123201817 0.097207702123201817 0.097207702123201817 0.1262260818710694 0.089753701709784001 0.069280534409559696 0.069280534409559696 -0.069280534409559696 0.069074019216574162 0.069074019216574162 0.32261672558692783 0.33195769729522556 0.33195769729522556 0.32577723207382053 - -> (12) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 45 48 0 0 0 0 0 0 0 0999 V2000 - -40.0554 -44.9369 16.8653 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6879 -44.9360 16.5904 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.5143 -45.3373 18.1202 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.8835 -44.2396 20.7233 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2382 -45.7361 18.8253 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.5043 -46.2378 17.9050 C 0 0 0 0 0 0 0 0 0 0 0 0 - -42.1548 -43.4338 21.8456 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.8128 -44.4326 16.3875 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.6967 -45.2709 16.6992 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.7793 -45.3356 17.5704 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.3607 -45.3345 17.2854 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6057 -45.7369 19.1002 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.1453 -46.2120 17.6119 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8581 -45.0688 21.1270 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6541 -46.1701 22.5163 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6206 -45.3486 23.2769 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.0989 -46.1665 20.4463 C 0 0 1 0 0 0 0 0 0 0 0 0 - -40.9476 -47.4293 20.3138 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.9070 -46.9824 17.8402 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.4982 -44.3773 16.0773 N 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3734 -43.7244 22.8891 N 0 0 0 0 0 0 0 0 0 0 0 0 - -40.5995 -44.7181 22.4193 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8949 -46.5220 21.2540 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.5592 -46.5307 23.1735 N 0 0 0 0 0 0 0 0 0 0 0 0 - -33.2865 -47.0805 18.2005 O 0 0 0 0 0 0 0 0 0 0 0 0 - -40.7628 -44.6260 16.1019 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.3457 -44.6209 15.6073 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.5816 -45.3348 18.3259 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.3653 -44.2230 19.7566 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.5348 -46.0493 19.5941 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.8873 -46.9646 18.6179 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.8913 -42.6498 21.9472 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4274 -43.7013 15.8702 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.6463 -45.2054 16.4332 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.1715 -45.9692 23.9916 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1024 -44.5489 23.8183 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3046 -47.7389 21.2947 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.3510 -48.2313 19.8820 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.8025 -47.2336 19.6685 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.3329 -47.7359 18.3850 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.5369 -45.9826 18.0808 H 0 0 0 0 0 0 0 0 0 0 0 0 - -31.8068 -47.1573 16.7664 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1812 -47.0979 20.8139 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4206 -46.2456 24.1379 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8486 -47.0954 22.7189 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 3 1 0 - 2 10 1 0 - 3 12 2 0 - 4 7 1 0 - 4 14 2 0 - 5 10 2 0 - 5 12 1 0 - 6 11 2 0 - 6 13 1 0 - 7 21 2 0 - 8 11 1 0 - 8 20 2 0 - 9 13 2 0 - 9 20 1 0 - 10 11 1 0 - 12 17 1 0 - 13 25 1 0 - 14 17 1 0 - 14 22 1 0 - 15 16 1 0 - 15 23 2 0 - 15 24 1 0 - 16 22 1 0 - 17 18 1 6 - 17 23 1 0 - 19 25 1 0 - 21 22 1 0 - 1 26 1 0 - 2 27 1 0 - 3 28 1 0 - 4 29 1 0 - 5 30 1 0 - 6 31 1 0 - 7 32 1 0 - 8 33 1 0 - 9 34 1 0 - 16 35 1 0 - 16 36 1 0 - 18 37 1 0 - 18 38 1 0 - 18 39 1 0 - 19 40 1 0 - 19 41 1 0 - 19 42 1 0 - 23 43 1 0 - 24 44 1 0 - 24 45 1 0 -M CHG 1 23 1 -M END -> (13) -6.96 - -> (13) -4,7-Dihydropyrazolo[1,5-a]pyrazin-6-yl-amine derivatives as β-secretase inhibitors and their preparation and use for the treatment of diseases by: Trabanco-Suarez, Andres Avelino; Tresadern, Gary John; Delgado-Jimenez, FranciscaWorld Intellectual Property Organization, WO2012038438 A1 2012-03-29 | Language: English, Database: CAplus - -> (13) -lig_04 - -> (13) --0.124494650753008 -0.091208541186319464 -0.11175242939756977 -0.23582320788668262 -0.094258689673410528 -0.11437618115709888 0.32905220112038985 0.39774414023591415 0.34187062701417342 --0.030848064811693297 -0.10873065272139179 -0.12642227986620533 0.027456906272305383 -0.11651957908438312 0.42457561811639205 -0.036344447525011168 0.20839559158517254 -0.11157278844879734 -0.11551877727939022 -0.64172936359213462 -0.46444407978819474 0.071965588836206323 -0.34991756954954728 -0.4803237406329976 -0.3089738277511464 0.15547200164033306 0.14776419719888104 -0.13993245979977978 0.17295087000562084 0.14908873161507977 0.15848930796815289 0.059860384671224488 0.033469023762477769 0.037259268222583665 0.11362042894793881 0.11362042894793881 -0.071949577538503534 0.071949577538503534 0.071949577538503534 0.052643558589948548 0.052643558589948548 0.052643558589948548 0.32153784355355636 0.32715814432336227 0.32715814432336227 - -> (13) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 45 48 0 0 0 0 0 0 0 0999 V2000 - -29.8991 -46.6444 17.3498 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.5880 -45.2092 16.2997 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2803 -44.7175 17.1436 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6235 -44.5074 17.4340 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.9168 -44.8244 16.1631 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0005 -45.7575 19.3081 C 0 0 0 0 0 0 0 0 0 0 0 0 - -32.2711 -46.8151 17.9250 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0485 -42.8007 22.7748 C 0 0 0 0 0 0 0 0 0 0 0 0 - -31.2529 -46.2240 17.1922 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3521 -45.5442 19.6329 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4536 -45.3336 18.0825 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.1408 -44.9069 18.6628 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.8636 -45.4764 16.9430 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2345 -44.8187 22.8526 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1576 -46.2394 22.6464 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.2928 -45.1019 16.8354 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.4180 -44.7168 21.7933 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9371 -45.9630 20.9952 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.1466 -46.8959 20.7665 C 0 0 0 0 0 0 0 0 0 0 0 0 - -28.7986 -46.9881 17.4796 N 0 0 0 0 0 0 0 0 0 0 0 0 - -33.5710 -46.4617 17.8239 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8875 -42.7445 23.3970 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3729 -44.0340 23.4583 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9871 -46.7880 21.8050 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2924 -44.0810 22.4313 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2387 -47.0061 23.3224 N 0 0 0 0 0 0 0 0 0 0 0 0 - -36.0706 -45.5553 17.8892 N 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6500 -44.4278 15.8767 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4358 -44.6547 18.8997 F 0 0 0 0 0 0 0 0 0 0 0 0 - -30.8196 -44.7235 15.7166 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8775 -44.4039 16.1919 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.2657 -44.0341 16.7060 H 0 0 0 0 0 0 0 0 0 0 0 0 - -33.2036 -44.0443 15.4734 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3635 -46.2614 20.0200 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.0099 -47.6030 18.6160 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6965 -41.9604 22.5737 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8975 -44.0052 21.1051 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1423 -45.0309 22.5592 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.5669 -47.1969 21.7374 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9138 -46.3651 20.1837 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.8206 -47.7901 20.2151 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2030 -47.9938 23.1702 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.6059 -46.5762 23.9664 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6035 -46.0974 18.5878 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9890 -47.7823 21.6980 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 9 1 0 - 1 20 3 0 - 2 5 2 0 - 2 9 1 0 - 3 4 2 0 - 3 11 1 0 - 4 12 1 0 - 5 13 1 0 - 6 10 1 0 - 6 11 2 0 - 7 9 2 0 - 7 21 1 0 - 8 22 2 0 - 8 25 1 0 - 10 12 2 0 - 10 18 1 0 - 11 27 1 0 - 12 29 1 0 - 13 16 1 0 - 13 21 2 0 - 14 15 1 0 - 14 23 2 0 - 14 25 1 0 - 15 24 2 0 - 15 26 1 0 - 16 27 1 0 - 16 28 2 0 - 17 18 1 0 - 17 25 1 0 - 18 19 1 6 - 18 24 1 0 - 22 23 1 0 - 2 30 1 0 - 3 31 1 0 - 4 32 1 0 - 5 33 1 0 - 6 34 1 0 - 7 35 1 0 - 8 36 1 0 - 17 37 1 0 - 17 38 1 0 - 19 39 1 0 - 19 40 1 0 - 19 41 1 0 - 26 42 1 0 - 26 43 1 0 - 27 44 1 0 - 24 45 1 0 -M CHG 1 24 1 -M END -> (14) -7.9699999999999998 - -> (14) -10.1021/acsmedchemlett.9b00181 - -> (14) -lig_81 - -> (14) -0.25441446387105515 -0.050641471064753002 -0.081531276471085018 -0.12690365827745861 -0.21956845141119427 -0.13147458947367138 0.45149757944875291 0.4225717731648021 -0.11178548402256436 --0.17814358092016644 0.087242695026927525 0.13002919637494617 0.38605382525258591 0.057832094364696079 0.45120280467801621 0.59878682576947739 0.032063393377595478 0.11407166206174427 --0.12015539920992321 -0.34266186273760268 -0.61892623222536514 -0.29494390404886672 -0.1768299690551228 -0.3389093629188008 -0.25636286056703994 -0.50001377380556533 -0.46672413266367385 --0.54561359203524062 -0.13789773500627941 0.1637532361679607 0.17777949058347278 0.17037744306855732 0.16904381894403034 0.13453936063581043 0.051565135353141361 0.088660652273231083 -0.10155790530973011 0.10155790530973011 0.072522858613067204 0.072522858613067204 0.072522858613067204 0.34167494856648972 0.34167494856648972 0.32831909739308884 0.32524850451283982 - -> (14) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 48 51 0 0 0 0 0 0 0 0999 V2000 - -33.2652 -45.8563 17.7205 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.3048 -43.5774 15.6028 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.2044 -44.2521 16.1255 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.5843 -43.8109 16.1158 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2916 -44.9925 16.9342 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.5536 -45.1949 17.4992 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6691 -45.4106 17.6625 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.3103 -45.1263 19.1185 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.3860 -45.1685 17.1591 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.7949 -44.7298 17.1593 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1393 -44.9556 17.7322 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5741 -45.3226 19.7058 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6942 -45.3723 18.8764 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9089 -44.3599 21.9429 C 0 0 0 0 0 0 0 0 0 0 0 0 - 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-> (15) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 42 45 0 0 0 0 0 0 0 0999 V2000 - -40.1582 -45.2247 17.3301 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8476 -45.0908 16.8719 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.4148 -45.5005 18.6747 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0695 -43.4695 21.8948 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0475 -45.5261 19.0957 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.9935 -43.9743 16.5045 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.3738 -45.9720 17.7035 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.6773 -44.6924 16.5416 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.1683 -43.0249 22.8811 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.7656 -45.2353 17.7493 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.3726 -45.0662 17.3035 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.3631 -45.6557 19.5850 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.6591 -43.7948 16.1324 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.0436 -45.7796 17.3281 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9489 -44.8434 21.9051 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0031 -47.4752 22.3314 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.6222 -46.4306 23.3018 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5657 -45.9324 21.0767 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.0298 -46.1329 21.5004 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.5357 -44.0289 23.4917 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0401 -45.1146 22.8842 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8828 -47.2312 21.3602 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4123 -48.6490 22.5098 N 0 0 0 0 0 0 0 0 0 0 0 0 - -34.2217 -42.4379 15.1689 Cl 0 0 0 0 0 0 0 0 0 0 0 0 - -32.8446 -46.8946 17.8559 Cl 0 0 0 0 0 0 0 0 0 0 0 0 - -40.9850 -45.1068 16.6352 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6846 -44.8741 15.8185 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4516 -45.5836 18.9854 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.7000 -42.8763 21.2486 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2189 -45.6312 19.7964 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.7385 -43.2462 16.1884 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6344 -46.8326 18.3176 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.6397 -44.5444 16.2548 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9431 -42.0122 23.1803 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0888 -46.6234 24.2737 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.5336 -46.4012 23.4256 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1022 -46.3779 22.5665 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4949 -46.9610 20.9521 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.6316 -45.2318 21.3323 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0948 -48.0176 20.7508 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.7472 -48.7827 23.2641 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.6101 -49.4276 21.8925 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 3 1 0 - 2 10 1 0 - 3 12 2 0 - 4 9 1 0 - 4 15 2 0 - 5 10 2 0 - 5 12 1 0 - 6 11 2 0 - 6 13 1 0 - 7 11 1 0 - 7 14 2 0 - 8 13 2 0 - 8 14 1 0 - 9 20 2 0 - 10 11 1 0 - 12 18 1 0 - 13 24 1 0 - 14 25 1 0 - 15 18 1 0 - 15 21 1 0 - 16 17 1 0 - 16 22 2 0 - 16 23 1 0 - 17 21 1 0 - 18 19 1 6 - 18 22 1 0 - 20 21 1 0 - 1 26 1 0 - 2 27 1 0 - 3 28 1 0 - 4 29 1 0 - 5 30 1 0 - 6 31 1 0 - 7 32 1 0 - 8 33 1 0 - 9 34 1 0 - 17 35 1 0 - 17 36 1 0 - 19 37 1 0 - 19 38 1 0 - 19 39 1 0 - 22 40 1 0 - 23 41 1 0 - 23 42 1 0 -M CHG 1 22 1 -M END -> (16) -6.8300000000000001 - -> (16) -4,7-Dihydropyrazolo[1,5-a]pyrazin-6-yl-amine derivatives as β-secretase inhibitors and their preparation and use for the treatment of diseases by: Trabanco-Suarez, Andres Avelino; Tresadern, Gary John; Delgado-Jimenez, Francisca^?World Intellectual Property Organization, WO2012038438 A1 2012-03-29 | Language: English, Database: CAplus - -> (16) -lig_06 - -> (16) --0.1235415122161309 -0.087366916860143348 -0.11141990932325523 -0.23489524486164251 -0.0918347096691529 -0.10341146650413673 -0.10341146650413673 -0.11812717529634635 0.3299799555291732 --0.032306057090560593 -0.014534120137492815 -0.12638892295459905 0.042418509100874267 0.042418509100874267 -0.11559166076282661 0.42550355133910972 -0.035416510577003159 0.2093922191609939 --0.11064486267666022 -0.46351593794922036 0.072893440102537468 -0.34898954691986245 -0.47939556899170083 -0.082309865082303688 -0.082309865082303688 0.15575215158363184 0.14894379613300165 -0.1408604079236587 0.17387884793182215 0.14980167088409266 0.15859524843593439 0.15859524843593439 0.16922251818080744 0.060788280641039215 0.1145483994235595 0.1145483994235595 -0.072877480958898858 0.072877480958898858 0.072877480958898858 0.32246568736930686 0.32808601794143516 0.32808601794143516 - -> (16) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 39 41 0 0 0 0 0 0 0 0999 V2000 - -40.2181 -45.1963 17.2016 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8778 -45.0934 16.8387 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.5708 -45.4986 18.5168 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2434 -45.5906 19.1072 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.9933 -44.0766 16.6239 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.4865 -46.0919 17.8440 C 0 0 0 0 0 0 0 0 0 0 0 0 - -33.7112 -44.8910 16.7092 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8618 -45.2818 17.7857 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4396 -45.1507 17.4150 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5924 -45.7182 19.4967 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.6452 -43.9548 16.2770 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.1401 -45.9569 17.4940 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0378 -46.6230 22.4156 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9699 -45.2314 22.9120 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9744 -44.7401 21.7887 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.9644 -46.0257 20.9503 C 0 0 1 0 0 0 0 0 0 0 0 0 - -41.3198 -46.7392 21.1008 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9636 -46.9617 21.5216 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1520 -47.4731 22.9162 N 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6346 -44.3200 22.0359 O 0 0 0 0 0 0 0 0 0 0 0 0 - -34.1345 -42.6300 15.3050 Cl 0 0 0 0 0 0 0 0 0 0 0 0 - -32.9985 -47.1237 18.0388 Cl 0 0 0 0 0 0 0 0 0 0 0 0 - -40.9924 -45.0365 16.4568 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.6378 -44.8633 15.8029 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.6286 -45.5509 18.7554 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.4620 -45.7157 19.8544 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.7003 -43.3221 16.2825 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.7962 -46.9421 18.4497 H 0 0 0 0 0 0 0 0 0 0 0 0 - -32.6633 -44.7912 16.4383 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.4137 -45.1594 23.9116 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.9279 -44.9028 22.9859 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.4939 -43.9282 21.2673 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.4816 -44.8828 22.7514 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4710 -47.0713 22.1351 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3715 -47.6323 20.4672 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.1610 -46.0864 20.8446 H 0 0 0 0 0 0 0 0 0 0 0 0 - -38.9629 -47.9314 21.2161 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.4668 -47.1726 23.6007 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1483 -48.4436 22.6228 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 3 1 0 - 2 8 1 0 - 3 10 2 0 - 4 8 2 0 - 4 10 1 0 - 5 9 2 0 - 5 11 1 0 - 6 9 1 0 - 6 12 2 0 - 7 11 2 0 - 7 12 1 0 - 8 9 1 0 - 10 16 1 0 - 11 21 1 0 - 12 22 1 0 - 13 14 1 0 - 13 18 2 0 - 13 19 1 0 - 14 20 1 0 - 15 16 1 0 - 15 20 1 0 - 16 17 1 6 - 16 18 1 0 - 1 23 1 0 - 2 24 1 0 - 3 25 1 0 - 4 26 1 0 - 5 27 1 0 - 6 28 1 0 - 7 29 1 0 - 14 30 1 0 - 14 31 1 0 - 15 32 1 0 - 15 33 1 0 - 17 34 1 0 - 17 35 1 0 - 17 36 1 0 - 18 37 1 0 - 19 38 1 0 - 19 39 1 0 -M CHG 1 18 1 -M END -> (17) -5.9500000000000002 - -> (17) -Preparation of 5,6-dihydro-2H-[1,4]oxazin-3-yl-amine derivatives useful as therapeutic inhibitors of β-secretase (BACE) By: Trabanco-Suarez, Andres Avelino; Rombouts, Frederik Jan Rita; Tresadern, Gary John; Van Gool, Michiel Luc Maria; Macdonald, Gregor James; Martinez Lamenca, Carolina; Gijsen, Henricus Jacobus MariaWorld Intellectual Property Organization, WO2011154431 A1 2011-12-15 | Language: English, Database: CAplus - -> (17) -lig_05 - -> (17) --0.1198843090962141 -0.088976715428706929 -0.11930216543185405 -0.098138545377132222 -0.10311335764634304 -0.10311335764634304 -0.11984210900771312 -0.035555117500898167 --0.015162017865058703 -0.12257529489504985 0.040703560488346294 0.040703560488346294 0.43447094716322726 0.056391089486005977 0.11399736561072178 0.082091118471744731 -0.11152655325638942 --0.3628870888780325 -0.49364000998246366 -0.35690582237946683 -0.084025007409927174 -0.084025007409927174 0.15620615638983557 0.14917644418967077 0.1318028972317011 0.14367965914500067 -0.15688022531760046 0.15688022531760046 0.16750748016131231 0.11251507260096379 0.11251507260096379 0.090272720234516338 0.090272720234516338 0.069570924418094829 0.069570924418094829 -0.069570924418094829 0.32382458009016818 0.32503440536749667 0.32503440536749667 - -> (17) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ - - RDKit 3D - - 46 49 0 0 0 0 0 0 0 0999 V2000 - -34.3135 -45.2077 17.1876 C 0 0 0 0 0 0 0 0 0 0 0 0 - -34.1973 -44.2951 16.1500 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.1764 -45.0011 16.9541 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.4542 -45.1821 17.4918 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.5656 -45.4789 17.7323 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.2381 -45.1951 19.1528 C 0 0 0 0 0 0 0 0 0 0 0 0 - -35.3543 -43.6914 15.6932 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.0382 -45.0149 17.7732 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.5193 -45.3544 19.7145 C 0 0 0 0 0 0 0 0 0 0 0 0 - -40.6249 -45.3728 18.8641 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.6860 -44.8194 17.2216 C 0 0 0 0 0 0 0 0 0 0 0 0 - -38.8939 -44.3714 21.9499 C 0 0 0 0 0 0 0 0 0 0 0 0 - -37.9558 -46.3933 22.5507 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.1544 -45.4253 21.6300 C 0 0 0 0 0 0 0 0 0 0 0 0 - -39.6895 -45.4757 21.2056 C 0 0 1 0 0 0 0 0 0 0 0 0 - -42.0923 -46.0925 20.6093 C 0 0 0 0 0 0 0 0 0 0 0 0 - -43.5439 -45.7704 21.0003 C 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9410 -47.6194 20.5506 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.8680 -44.2891 23.4685 C 0 0 0 0 0 0 0 0 0 0 0 0 - -36.5864 -43.9319 16.1983 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.8638 -45.0312 22.7065 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0139 -46.7075 21.6935 N 0 0 0 0 0 0 0 0 0 0 0 0 - -37.1447 -47.3366 23.1280 N 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0749 -43.1663 21.8840 O 0 0 0 0 0 0 0 0 0 0 0 0 - -41.9143 -45.5454 19.2901 O 0 0 0 0 0 0 0 0 0 0 0 0 - -33.4324 -45.7079 17.5781 H 0 0 0 0 0 0 0 0 0 0 0 0 - -33.2326 -44.0649 15.7136 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.0846 -44.8594 15.8793 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.3151 -45.1774 16.8279 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.6446 -46.1977 18.5425 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3767 -45.1829 19.8192 H 0 0 0 0 0 0 0 0 0 0 0 0 - -35.3216 -42.9701 14.8835 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.2830 -45.8704 22.6252 H 0 0 0 0 0 0 0 0 0 0 0 0 - -41.4515 -44.3711 21.7241 H 0 0 0 0 0 0 0 0 0 0 0 0 - -44.2483 -46.1902 20.2729 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.7158 -44.6876 21.0018 H 0 0 0 0 0 0 0 0 0 0 0 0 - -43.7926 -46.1628 21.9919 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.6909 -48.0552 19.8797 H 0 0 0 0 0 0 0 0 0 0 0 0 - -42.0619 -48.0718 21.5404 H 0 0 0 0 0 0 0 0 0 0 0 0 - -40.9725 -47.9242 20.1444 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.3730 -43.6990 24.2373 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.1567 -44.9674 23.9434 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.3255 -43.6246 22.7896 H 0 0 0 0 0 0 0 0 0 0 0 0 - -39.2564 -47.6658 21.4559 H 0 0 0 0 0 0 0 0 0 0 0 0 - -36.3797 -47.0817 23.7462 H 0 0 0 0 0 0 0 0 0 0 0 0 - -37.2893 -48.3276 22.9503 H 0 0 0 0 0 0 0 0 0 0 0 0 - 1 2 2 0 - 1 5 1 0 - 2 7 1 0 - 3 4 2 0 - 3 8 1 0 - 4 10 1 0 - 5 11 2 0 - 6 8 2 0 - 6 9 1 0 - 7 20 2 0 - 8 11 1 0 - 9 10 2 0 - 9 15 1 0 - 10 25 1 0 - 11 20 1 0 - 12 15 1 0 - 12 21 1 0 - 12 24 2 0 - 13 21 1 0 - 13 22 2 0 - 13 23 1 0 - 15 14 1 6 - 14 16 1 0 - 15 22 1 0 - 16 17 1 0 - 16 18 1 0 - 16 25 1 0 - 19 21 1 0 - 1 26 1 0 - 2 27 1 0 - 3 28 1 0 - 4 29 1 0 - 5 30 1 0 - 6 31 1 0 - 7 32 1 0 - 14 33 1 0 - 14 34 1 0 - 17 35 1 0 - 17 36 1 0 - 17 37 1 0 - 18 38 1 0 - 18 39 1 0 - 18 40 1 0 - 19 41 1 0 - 19 42 1 0 - 19 43 1 0 - 22 44 1 0 - 23 45 1 0 - 23 46 1 0 -M CHG 1 22 1 -M END -> (18) -2363 - -> (18) -10.1021/jm4002154 - -> (18) -lig_32 - -> (18) --0.094456587351210736 -0.24900171569670024 -0.047220897293933063 -0.13793898037756266 -0.17958119920576396 -0.05356376654589954 0.41618340798532183 -0.1650711934932548 -0.22787222675169291 -0.19354924150621114 0.43721666523133929 0.70055627413903887 0.48263132163201983 -0.11058447173918071 0.067200224482170917 0.18152233549272237 -0.11062539032781901 -0.11062539032781901 -0.053826991104237408 -0.65043092182958906 -0.42453009299124067 -0.40579355172003095 -0.47590816429937666 -0.45984420589292829 -0.32098704985464399 0.14327594228898702 0.15019799359952626 -0.14514892884408651 0.16679718681489644 0.15555485674058614 0.13223166354810414 0.027830237158290718 0.089686285339943744 0.089686285339943744 0.058868813895336956 0.058868813895336956 -0.058868813895336956 0.058868813895336956 0.058868813895336956 0.058868813895336956 0.082591738187424518 0.082591738187424518 0.082591738187424518 0.33651199527894671 0.32671993561898882 -0.32671993561898882 - -> (18) -{"openfe_version": "1.8.0", "openff_toolkit_version": "0.18.0", "rdkit_version": "2025.09.4", "charge_method": "nagl_off", "nagl_version": "0.5.4", "nagl_model": "openff-gnn-am1bcc-1.0.0.pt"} - -$$$$ diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb index 1e916c2..9039b1f 100644 --- a/mixed_networks/mixed_networks.ipynb +++ b/mixed_networks/mixed_networks.ipynb @@ -10,26 +10,26 @@ "\n", "**openfe** provides a number of protocols to estimate free energy differences, each with their own areas of applicability and limitations, suited to answering different types of scientific questions. While traditionally these protocols have been used in isolation, it is possible, thanks to the interchangeable nature of **openfe**, to combine them into a single network of transformations to leverage the strengths of each protocol.\n", "\n", - "This notebook demonstrates one way to create a mixed protocol network, combining two relative binding free energy approaches: the [Relative Hybrid Topology Protocol](https://docs.openfree.energy/en/latest/guide/protocols/relativehybridtopology.html#userguide-relative-hybrid-topology-protocol) and the [Separated Topologies Protocol](https://docs.openfree.energy/en/latest/guide/protocols/septop.html) to compute absolute binding affinities. A complete list of the available protocols along with their strengths and limitations can be found in the [protocol overview](https://docs.openfree.energy/en/latest/guide/protocols/index.html).\n", + "This notebook demonstrates one way to create a mixed protocol network, combining two relative binding free energy approaches: the [Relative Hybrid Topology Protocol](https://docs.openfree.energy/en/latest/guide/protocols/relativehybridtopology.html#userguide-relative-hybrid-topology-protocol) and the [Separated Topologies Protocol](https://docs.openfree.energy/en/latest/guide/protocols/septop.html) to compute absolute binding affinities of a set of small molecules. A complete list of the available protocols along with their strengths and limitations can be found in the [protocol overview](https://docs.openfree.energy/en/latest/guide/protocols/index.html).\n", "\n", "## Overview\n", "\n", - "In this tutorial, we will demonstrate how to create a mixed protocol network to compute absolute binding free energies for `three` distinct ligand scaffold series bound to $\\beta$-secretase 1 (BACE1) taken from [Baumann et al.](https://doi.org/10.1021/acs.jctc.3c00282). The dataset contains a total of 18 ligands with 6 ligands in each of the three unique scaffold series grouped as `biaryl`, `spirocyclic` and `amide`, examples of each are shown below:\n", + "In this tutorial, we will demonstrate how to create a mixed protocol network to estimate absolute binding free energies for `three` distinct ligand scaffold series bound to $\\beta$-secretase 1 (BACE1) taken from [Baumann et al.](https://doi.org/10.1021/acs.jctc.3c00282). The dataset contains a total of 18 ligands with 6 ligands in each of the three unique scaffold series grouped as `biaryl`, `spirocyclic` and `amide`, examples of each are shown below:\n", "\n", "\n", "\n", "While each subset of ligands forms a congeneric series suitable for the hybrid topology approach, connections between the series are more challenging due to the large structural differences between the scaffolds. To overcome this, we will use the separated topologies approach to connect the three series together. Here we present one possible way to construct a mixed protocol with minimal manual intervention which can be broken down into the following steps:\n", "\n", - "- Plan a single LOMAP style hybrid topology binding free energy network with the Kartograf atom mapper and default lomap scorer\n", + "- Plan a single LOMAP-style hybrid topology binding free energy network with the Kartograf atom mapper and default LOMAP scorer\n", "- Identify edges within this network between the three distinct series that are not suitable for the hybrid topology approach\n", "- Build the transformations for the network, assigning the appropriate protocol to each transformation\n", - "- Execute and analyze the results to estimate the absolute binding free energies for each ligand\n", + "- Analyze the results to estimate the absolute binding free energies for each ligand\n", "\n", - "## Load and classify the ligands\n", + "# Load and classify the ligands\n", "\n", - "First we start by loading the ligands from the provided SDF file and creating a mapping between the ligand name and respective series. The ligands are then converted to `SmallMoleculeComponents` for use within **openfe**.\n", + "First, load the ligands from the provided SDF file and map each ligand name to its series. Convert the ligands to `SmallMoleculeComponents` for use with **openfe**.\n", "\n", - "
Note: Ligands should have partial charges assigned before planning the network, here we use pre-charged ligands but charges can be generated via the CLI or API.
" + "
Note: Ligands should have partial charges assigned before planning the network. Here, we use pre-charged ligands, but charges can also be generated via the CLI or API.
" ], "id": "a0078e37a2ae4ea9", "attachments": { @@ -41,8 +41,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T12:29:30.806011Z", - "start_time": "2026-10-28T12:29:30.421333Z" + "end_time": "2026-10-28T16:18:02.731710Z", + "start_time": "2026-10-28T16:18:02.456708Z" } }, "cell_type": "code", @@ -83,34 +83,34 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-bac489de520eadaa7126ee6641163303 (name: 'lig_41')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-183ce45f91ea63c1b1a5c2ca71683a2e (name: 'lig_02')\n", "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-3a85837a992c824bb4e0ef1eb4f03b21 (name: 'lig_03')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e7b8bc753a9243d52039be2ad197a3b1 (name: 'lig_04')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-6a2222de00a67b9cddf4402ae67abc46 (name: 'lig_05')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-b900f42ff3161e6e4b663a908d99d382 (name: 'lig_06')\n", "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-0b73c8a6df07a0beed455c479519b97f (name: 'lig_07')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-d8bb71cb61021ff28fa31ae90db5e9af (name: 'lig_1a')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e4c4a874b03c3a3cc36867807030f34f (name: 'lig_45')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-2ff86e4d2ac0278821209e3a10b83248 (name: 'lig_16')\n", "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-a98f0ca0ad081239d749efbe258e0bf3 (name: 'lig_11')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-5119445cbe99f49d31eb73b97fc409a4 (name: 'lig_13')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-2ff86e4d2ac0278821209e3a10b83248 (name: 'lig_16')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-d8bb71cb61021ff28fa31ae90db5e9af (name: 'lig_1a')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-73e7964450ae778448e6e7bb6ee9c24e (name: 'lig_32')\n", "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-02f2fb7b039d551f4d79f052220924b1 (name: 'lig_36')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-183ce45f91ea63c1b1a5c2ca71683a2e (name: 'lig_02')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-17032cd50d866ce3f805801db1cac89a (name: 'lig_74')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-bac489de520eadaa7126ee6641163303 (name: 'lig_41')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e4c4a874b03c3a3cc36867807030f34f (name: 'lig_45')\n", "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-4931369e87328f26f82811c9afc8e55c (name: 'lig_67')\n", "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-24d9e0a297a1559b9e36e980b8b321ba (name: 'lig_69')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e7b8bc753a9243d52039be2ad197a3b1 (name: 'lig_04')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-11a9e1c36f7dbaf234e69ef0d0a99b84 (name: 'lig_81')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-5119445cbe99f49d31eb73b97fc409a4 (name: 'lig_13')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-b900f42ff3161e6e4b663a908d99d382 (name: 'lig_06')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-6a2222de00a67b9cddf4402ae67abc46 (name: 'lig_05')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-73e7964450ae778448e6e7bb6ee9c24e (name: 'lig_32')\n" + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-17032cd50d866ce3f805801db1cac89a (name: 'lig_74')\n", + "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-11a9e1c36f7dbaf234e69ef0d0a99b84 (name: 'lig_81')\n" ] } ], - "execution_count": 71 + "execution_count": 132 }, { "metadata": {}, "cell_type": "markdown", "source": [ - "## Creating the `LigandNetwork`\n", + "# Creating the `LigandNetwork`\n", "\n", "Next we will create a single `LigandNetwork` containing all 18 ligands. This network will be used to identify edges between the three series that are not suitable for the hybrid topology approach, and will be assigned the separated topologies protocol later. In order to create a `LigandNetwork`, we need to define three components:\n", "\n", @@ -123,8 +123,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T10:40:16.881828Z", - "start_time": "2026-10-28T10:40:10.501269Z" + "end_time": "2026-10-28T16:18:11.911974Z", + "start_time": "2026-10-28T16:18:03.841324Z" } }, "cell_type": "code", @@ -2446,31 +2446,31 @@ "INFO:kartograf.atom_mapper:Calculate Mapping\n", "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", "INFO:kartograf.atom_mapper:Filtering bond breaks\n", - "INFO:root:Trying to remove edge 7-17 with similarity 0.670320\n", + "INFO:root:Trying to remove edge 10-11 with similarity 0.670320\n", "INFO:root:Checking edge deletion on distance-to-actives 0 vs 0\n", - "INFO:root:Removed edge 7-17\n", - "INFO:root:Trying to remove edge 5-14 with similarity 0.670320\n", + "INFO:root:Removed edge 10-11\n", + "INFO:root:Trying to remove edge 7-8 with similarity 0.670320\n", "INFO:root:Checking edge deletion on distance-to-actives 0 vs 0\n", - "INFO:root:Removed edge 5-14\n", - "INFO:root:Trying to remove edge 6-7 with similarity 0.704688\n", - "INFO:root:Rejecting edge deletion on cycle covering\n", - "INFO:root:Trying to remove edge 5-17 with similarity 0.778801\n", + "INFO:root:Removed edge 7-8\n", + "INFO:root:Trying to remove edge 6-11 with similarity 0.704688\n", "INFO:root:Rejecting edge deletion on cycle covering\n", - "INFO:root:Trying to remove edge 14-17 with similarity 0.778801\n", + "INFO:root:Trying to remove edge 7-10 with similarity 0.778801\n", "INFO:root:Checking edge deletion on distance-to-actives 0 vs 0\n", - "INFO:root:Removed edge 14-17\n", - "INFO:root:Trying to remove edge 7-14 with similarity 0.818731\n", + "INFO:root:Removed edge 7-10\n", + "INFO:root:Trying to remove edge 8-10 with similarity 0.778801\n", "INFO:root:Rejecting edge deletion on cycle covering\n", - "INFO:root:Trying to remove edge 5-6 with similarity 0.818731\n", + "INFO:root:Trying to remove edge 7-11 with similarity 0.818731\n", "INFO:root:Rejecting edge deletion on cycle covering\n", - "INFO:root:Trying to remove edge 6-14 with similarity 0.818731\n", + "INFO:root:Trying to remove edge 6-7 with similarity 0.818731\n", "INFO:root:Rejecting edge deletion on cycle covering\n", - "INFO:root:Trying to remove edge 6-17 with similarity 0.951229\n", + "INFO:root:Trying to remove edge 6-8 with similarity 0.818731\n", + "INFO:root:Rejecting edge deletion on cycle covering\n", + "INFO:root:Trying to remove edge 6-10 with similarity 0.951229\n", "INFO:root:Rejecting edge deletion on cycle covering\n" ] } ], - "execution_count": 29 + "execution_count": 133 }, { "metadata": {}, @@ -2485,8 +2485,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T10:56:23.165359Z", - "start_time": "2026-10-28T10:56:23.078480Z" + "end_time": "2026-10-28T16:18:12.026699Z", + "start_time": "2026-10-28T16:18:11.918704Z" } }, "cell_type": "code", @@ -2504,7 +2504,7 @@ "network_graph = nx.Graph()\n", "for edge in network.edges:\n", " network_graph.add_edge(edge.componentA.name, edge.componentB.name)\n", - "pos = nx.spring_layout(network_graph, seed=42)\n", + "pos = nx.spring_layout(network_graph, seed=1)\n", "node_colors = [series_to_color[ligand_series_mapping[node]] for node in network_graph.nodes()]\n", "edge_colors = [\"black\" if ligand_series_mapping[edge[0]] == ligand_series_mapping[edge[1]] else \"red\" for edge in network_graph.edges()]\n", "plt.figure(figsize=(10, 8))\n", @@ -2519,13 +2519,13 @@ "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 48 + "execution_count": 134 }, { "metadata": {}, @@ -2533,17 +2533,18 @@ "source": [ "## Inspecting the atom mappings\n", "\n", - "Now we can inspect and compare the atom mappings and associated scores for edges between the series with those within each series. First lets inspect an edge between the `biaryl` and `spirocyclic` series involving ligands `lig_03` and `lig_11`. The atom mapping is shown below along with the associated score. The score is relatively low due to the large number of alchemical atoms required, indicating that this edge may be difficult to converge with the hybrid topology approach.\n", "\n", - "
Reminder: Shared core (mapped) atoms are shown in black and thoses unique to each end state are shown in red. Mapped atoms whoes element change during the simulation are shown in blue.
" + "Now we can compare atom mappings and scores for edges between and within the series. First, let's inspect an edge between the `biaryl` and `spirocyclic` series, connecting ligands `lig_03` and `lig_11`. The mapping and its score are shown below. The low score reflects the large number of alchemical atoms and suggests that calculations for this edge may be challenging to converge with the hybrid topology approach.\n", + "\n", + "
Reminder: Shared core (mapped) atoms are shown in black, and atoms unique to each end state are shown in red. Mapped atoms whose elements change during the simulation are shown in blue.
" ], "id": "feadf6d35aa1a518" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T11:00:35.073751Z", - "start_time": "2026-10-28T11:00:34.934732Z" + "end_time": "2026-10-30T11:30:59.149154Z", + "start_time": "2026-10-30T11:30:58.832652Z" } }, "cell_type": "code", @@ -2564,13 +2565,13 @@ "output_type": "display_data" } ], - "execution_count": 51 + "execution_count": 178 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T11:00:57.151097Z", - "start_time": "2026-10-28T11:00:57.102644Z" + "end_time": "2026-10-30T11:30:59.970338Z", + "start_time": "2026-10-30T11:30:59.925648Z" } }, "cell_type": "code", @@ -2586,18 +2587,18 @@ "{'score': 0.008208499862389881}" ] }, - "execution_count": 52, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 52 + "execution_count": 179 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T11:05:51.518159Z", - "start_time": "2026-10-28T11:05:51.459260Z" + "end_time": "2026-10-30T11:31:00.966094Z", + "start_time": "2026-10-30T11:31:00.915591Z" } }, "cell_type": "code", @@ -2619,13 +2620,13 @@ "output_type": "display_data" } ], - "execution_count": 53 + "execution_count": 180 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T11:05:59.860621Z", - "start_time": "2026-10-28T11:05:59.835572Z" + "end_time": "2026-10-30T11:31:01.947766Z", + "start_time": "2026-10-30T11:31:01.920788Z" } }, "cell_type": "code", @@ -2641,28 +2642,28 @@ "{'score': 0.951229424500714}" ] }, - "execution_count": 54, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 54 + "execution_count": 181 }, { "metadata": {}, "cell_type": "markdown", "source": [ - "## Configure the `Protocols`\n", + "# Configure the `Protocols`\n", "\n", - "Before building the transformations for the network we need to configure the protocol settings for both the hybrid topology and separated topologies protocols. By default, the force fields used by these protocols match however changes to one protocol should be reflected in the other. We will also be using the `_adaptive_settings` method to automatically adjust the settings based on the systems involved in the transformation, this is particularly useful for the hybrid topology protocol which adjusts the solvent padding based on the presence of a receptor in the system." + "Before building the network transformations, configure the settings for both the hybrid topology and separated topologies protocols. By default, the protocols use the same small molecule force field, if you want to use a different force field this setting should be changed in both protocols as shown. We also use `_adaptive_settings` to adjust settings for each system. This is particularly useful for the hybrid topology protocol, which adjusts solvent padding based on whether a receptor is present." ], "id": "ab9906252acf1ca" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T11:28:28.334763Z", - "start_time": "2026-10-28T11:28:28.299630Z" + "end_time": "2026-10-28T16:18:12.220434Z", + "start_time": "2026-10-28T16:18:12.203607Z" } }, "cell_type": "code", @@ -2682,25 +2683,25 @@ ], "id": "43fc256c3e0ff239", "outputs": [], - "execution_count": 65 + "execution_count": 139 }, { "metadata": {}, "cell_type": "markdown", "source": [ - "## Building the `Transformations`\n", + "# Building the `Transformation`\n", "\n", "Now we can build the transformations for this network, assigning the appropriate protocol for each transformation. Here we will use the hybrid topology protocol for edges within each series and the separated topologies protocol for edges between the series.\n", "\n", - "Each protocol has different requirements for the `ChemicalSystem` compositions used to represent the end states of the transformation. This is due to the hybrid topology protocol requiring the `complex` and `solvent` legs be constructed manually as separate `Transformation` objects while the separated topologies protocol runs both the `complex` and `solvent` legs within a single `Transformation` object. The code below demonstrates how to build the transformations for both protocols while ensuring the correct `ChemicalSystem` objects are used for each protocol. For more information on the requirements for each protocol see the [examples documentation](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html#system-composition-examples)." + "Each protocol has different requirements for the `ChemicalSystem` compositions that represent the transformation's end states. The hybrid topology protocol requires you to construct the `complex` and `solvent` legs as separate `Transformation` objects, while the separated topologies protocol runs both legs within a single `Transformation`. The code below shows how to build transformations for both protocols with the appropriate `ChemicalSystem` objects. For more information about each protocol's requirements, see the [examples documentation](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html#system-composition-examples)." ], "id": "2c1e9779b733eb13" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T11:52:55.933717Z", - "start_time": "2026-10-28T11:52:55.331525Z" + "end_time": "2026-10-30T11:32:10.898883Z", + "start_time": "2026-10-30T11:32:10.323090Z" } }, "cell_type": "code", @@ -2714,7 +2715,12 @@ "\n", "\n", "# define helper functions to build the transformations for each protocol\n", - "def build_hybrid_topology_transformation(edge: openfe.LigandAtomMapping, default_settings: RelativeHybridTopologyProtocolSettings, protein_comp: openfe.ProteinComponent, solvent_comp: openfe.SolventComponent) -> list[openfe.Transformation]:\n", + "def build_hybrid_topology_transformation(\n", + " edge: openfe.LigandAtomMapping,\n", + " default_settings: RelativeHybridTopologyProtocolSettings,\n", + " protein_comp: openfe.ProteinComponent,\n", + " solvent_comp: openfe.SolventComponent\n", + ") -> list[openfe.Transformation]:\n", " \"\"\"Build the complex and solvent leg transformations for a given ligand atom mapping using the hybrid topology protocol.\"\"\"\n", " transformations = []\n", " for leg in [\"solvent\", \"complex\"]:\n", @@ -2747,7 +2753,12 @@ " return transformations\n", "\n", "\n", - "def build_septop_transformation(edge: openfe.LigandAtomMapping, default_settings: SepTopSettings, protein_comp: openfe.ProteinComponent, solvent_comp: openfe.SolventComponent) -> list[openfe.Transformation]:\n", + "def build_septop_transformation(\n", + " edge: openfe.LigandAtomMapping,\n", + " default_settings: SepTopSettings,\n", + " protein_comp: openfe.ProteinComponent,\n", + " solvent_comp: openfe.SolventComponent\n", + ") -> list[openfe.Transformation]:\n", " \"\"\"Build the transformation for a given ligand atom mapping using the separated topologies protocol.\"\"\"\n", " state_a_dict = {\"ligand\": edge.componentA, \"protein\": protein_comp, \"solvent\": solvent_comp}\n", " state_b_dict = {\"ligand\": edge.componentB, \"protein\": protein_comp, \"solvent\": solvent_comp}\n", @@ -2774,19 +2785,19 @@ ], "id": "4b08f0258bbdd052", "outputs": [], - "execution_count": 67 + "execution_count": 182 }, { "metadata": {}, "cell_type": "markdown", - "source": "We can now build the transformations required to execute the edges in the planned network. The code below iterates through the edges in the `LigandNetwork` and builds the appropriate transformations for each edge based on whether the edge is within a series or between series. The transformations can then be writen to disk which can be executed using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow).", + "source": "We can now build the transformations required to execute the planned network's edges. The code below iterates through the `LigandNetwork` and builds transformations according to whether each edge is within or between series. We then write the transformations to disk where they can then be executed using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow).", "id": "c3b0890f5793275c" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T12:27:09.467138Z", - "start_time": "2026-10-28T12:26:57.477396Z" + "end_time": "2026-10-28T16:18:28.893208Z", + "start_time": "2026-10-28T16:18:21.554588Z" } }, "cell_type": "code", @@ -2817,19 +2828,19 @@ ], "id": "fcc7a807d6edc1a8", "outputs": [], - "execution_count": 69 + "execution_count": 141 }, { "metadata": {}, "cell_type": "markdown", - "source": "We can also check how many transformations were created for each protocol. The hybrid topology protocol should have 44 transformations (22 edges * 2 legs) while the separated topologies protocol should have 6 transformations (6 connecting edges).", + "source": "We can also check how many transformations were created for each protocol. The hybrid topology protocol should have 46 transformations (23 edges * 2 legs), while the separated topologies protocol should have 6 transformations (6 connecting edges).", "id": "8aa9a3a8ad2ce7c5" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T12:57:19.849140Z", - "start_time": "2026-10-28T12:57:19.817053Z" + "end_time": "2026-10-30T11:32:55.471534Z", + "start_time": "2026-10-30T11:32:55.433358Z" } }, "cell_type": "code", @@ -2848,24 +2859,879 @@ { "data": { "text/plain": [ - "{openfe.protocols.openmm_rfe.hybridtop_protocols.RelativeHybridTopologyProtocol: 44,\n", + "{openfe.protocols.openmm_rfe.hybridtop_protocols.RelativeHybridTopologyProtocol: 46,\n", " openfe.protocols.openmm_septop.equil_septop_method.SepTopProtocol: 6}" ] }, - "execution_count": 77, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 77 + "execution_count": 183 }, { "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Analyzing a mixed protocol network\n", + "\n", + "After executing each transformation we can analyze the results from a mixed-protocol network using the API. First load results from both protocols and extract the $\\Delta\\Delta$G estimates for each leg, then combine them to estimate the absolute binding free energies for each ligand. We begin by defining helper functions to load the results and group them by the ligands and protocols involved in each transformation.\n", + "\n", + "
Warning: Gathering the results of a mixed-protocol network is not currently possible via the CLI.
" + ], + "id": "ab7ecee9702fad84" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-30T11:33:54.755101Z", + "start_time": "2026-10-30T11:33:54.719436Z" + } + }, "cell_type": "code", + "source": [ + "from collections import defaultdict\n", + "\n", + "import numpy as np\n", + "from openff.units import unit\n", + "\n", + "def classify_result(fpath: pathlib.Path) -> dict:\n", + " \"\"\"Load a JSON file containing a quickrun result and return a dict containing the relevant information for classifying the result.\n", + "\n", + " Parameters\n", + " ----------\n", + " fpath : os.PathLike | str\n", + " The path to a gufe-serialized JSON.\n", + "\n", + "\n", + " Returns\n", + " -------\n", + " dict\n", + " A dict containing data from the results JSON.\n", + "\n", + " \"\"\"\n", + " with open(fpath, \"r\") as f:\n", + " result_data = json.load(f)\n", + "\n", + " def to_kcal(q: dict) -> float:\n", + " return unit.Quantity(q[\"magnitude\"], q[\"unit\"]).to(\"kilocalorie_per_mole\").m\n", + "\n", + " # grab all the units\n", + " units = [u for u in result_data[\"unit_results\"].values() if u.get(\"outputs\")]\n", + " is_septop = any(\"SepTop\" in u[\"source_key\"] for u in units)\n", + " # get the phase and ligands involved from the file name\n", + " edge_name = fpath.stem\n", + " if \"solvent\" in fpath.name:\n", + " phase = \"solvent\"\n", + " edge_name = edge_name.removeprefix(\"solvent_\")\n", + " elif \"complex\" in fpath.name:\n", + " phase = \"complex\"\n", + " edge_name = edge_name.removeprefix(\"complex_\")\n", + " else:\n", + " # this is the septop result\n", + " phase = None\n", + " lig_a = edge_name[:6]\n", + " lig_b = edge_name[7:]\n", + " classified = {\"lig_a\": lig_a, \"lig_b\": lig_b, \"phase\": phase}\n", + "\n", + " if not is_septop:\n", + " # the top level estimate is the DDG (or the leg DG) of the hybrid topology protocol, in kcal/mol\n", + " classified.update(\n", + " protocol=\"RelativeHybridTopologyProtocol\",\n", + " estimate=to_kcal(result_data[\"estimate\"]),\n", + " uncertainty=to_kcal(result_data[\"uncertainty\"]),\n", + " )\n", + " return classified\n", + "\n", + " # For SepTop, combine the individual legs following the approach used by SepTopProtocolResult.\n", + " complex_dg = solvent_dg = complex_error = solvent_error = None\n", + " complex_corr = solvent_corr = 0.0\n", + " for u in units:\n", + " outputs = u[\"outputs\"]\n", + " if \"unit_estimate\" in outputs:\n", + " dg, error = to_kcal(outputs[\"unit_estimate\"]), to_kcal(outputs[\"unit_estimate_error\"])\n", + " if outputs[\"simtype\"] == \"complex\":\n", + " complex_dg, complex_error = dg, error\n", + " else:\n", + " solvent_dg, solvent_error = dg, error\n", + " if \"standard_state_correction_A\" in outputs:\n", + " complex_corr = to_kcal(outputs[\"standard_state_correction_A\"]) + to_kcal(outputs[\"standard_state_correction_B\"])\n", + " elif \"standard_state_correction\" in outputs:\n", + " solvent_corr = to_kcal(outputs[\"standard_state_correction\"])\n", + " if complex_dg is None or solvent_dg is None:\n", + " raise ValueError(f\"{fpath}: missing a complex or solvent estimate, this is likely a failed simulation.\")\n", + "\n", + " classified.update(\n", + " protocol=\"SepTopProtocol\",\n", + " complex_dg=complex_dg + complex_corr,\n", + " solvent_dg=solvent_dg + solvent_corr,\n", + " complex_error=complex_error,\n", + " solvent_error=solvent_error,\n", + " )\n", + " return classified\n", + "\n", + "\n", + "def septop_ddg(repeats: list[dict]) -> tuple[float, float]:\n", + " \"\"\"Combine the septop results of an edge into a DDG (kcal/mol) and uncertainty, matching ``SepTopProtocolResult``.\n", + " \"\"\"\n", + " complex_dgs = np.array([r[\"complex_dg\"] for r in repeats])\n", + " solvent_dgs = np.array([r[\"solvent_dg\"] for r in repeats])\n", + " ddg = complex_dgs.mean() - solvent_dgs.mean()\n", + " if len(repeats) > 1:\n", + " error = np.sqrt(np.std(complex_dgs, ddof=1) ** 2 + np.std(solvent_dgs, ddof=1) ** 2)\n", + " else:\n", + " error = np.sqrt(repeats[0][\"complex_error\"] ** 2 + repeats[0][\"solvent_error\"] ** 2)\n", + " return float(ddg), float(error)\n", + "\n", + "\n", + "def hybrid_topology_ddg(complex_repeats: list[dict], solvent_repeats: list[dict]) -> tuple[float, float]:\n", + " \"\"\"Combine the hybrid topology complex and solvent leg results of an edge into a DDG (kcal/mol) and uncertainty.\n", + " \"\"\"\n", + " complex_dgs = np.array([r[\"estimate\"] for r in complex_repeats])\n", + " solvent_dgs = np.array([r[\"estimate\"] for r in solvent_repeats])\n", + " ddg = complex_dgs.mean() - solvent_dgs.mean()\n", + "\n", + " def sample_std(values):\n", + " # a single repeat has no defined sample std\n", + " return np.std(values, ddof=1) if len(values) > 1 else 0.0\n", + "\n", + " error = np.sqrt(sample_std(complex_dgs) ** 2 + sample_std(solvent_dgs) ** 2)\n", + " return float(ddg), float(error)" + ], + "id": "27d647049e330837", "outputs": [], - "execution_count": null, - "source": "", - "id": "27d647049e330837" + "execution_count": 184 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Downloading the example results\n", + "\n", + "First, let's download example results for the transformations created above.\n", + "\n", + "## Gathering the raw $\\Delta\\Delta$G results\n", + "\n", + "Now we can classify the results by protocol and collect the $\\Delta\\Delta$G estimates in kcal/mol in a DataFrame. We also combine repeated runs of each transformation into a single estimate and uncertainty for each edge and protocol." + ], + "id": "20736e7c898ee06a" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-28T17:38:44.786590Z", + "start_time": "2026-10-28T17:38:39.810431Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "all_results = pathlib.Path(\"/Users/joshua/Documents/Projects/openfe/free_energy_2026/mixed_networks/bace1_baumann/results_download/mixed_results\")\n", + "\n", + "# Containers for the results of each protocol, grouped by the ligands involved in the transformation\n", + "septop_results = defaultdict(list)\n", + "hybrid_topology_results = {\"complex\": defaultdict(list), \"solvent\": defaultdict(list)}\n", + "\n", + "for file in all_results.rglob(\"*.json\"):\n", + " result_data = classify_result(file)\n", + " if result_data[\"protocol\"] == \"SepTopProtocol\":\n", + " septop_results[(result_data[\"lig_a\"], result_data[\"lig_b\"])].append(result_data)\n", + " elif result_data[\"protocol\"] == \"RelativeHybridTopologyProtocol\":\n", + " hybrid_topology_results[result_data[\"phase\"]][(result_data[\"lig_a\"], result_data[\"lig_b\"])].append(result_data)\n", + "\n", + "# Create a dataframe to hold the results for each edge and protocol\n", + "results_df = []\n", + "for edge, repeats in septop_results.items():\n", + " ddg, error = septop_ddg(repeats)\n", + " results_df.append({\n", + " \"lig_a\": edge[0],\n", + " \"lig_b\": edge[1],\n", + " \"protocol\": \"SepTopProtocol\",\n", + " \"ddg (kcal/mol)\": ddg,\n", + " \"error (kcal/mol)\": error\n", + " })\n", + "\n", + "for edge, repeats in hybrid_topology_results[\"complex\"].items():\n", + " solvent_repeats = hybrid_topology_results[\"solvent\"][edge]\n", + " ddg, error = hybrid_topology_ddg(repeats, solvent_repeats)\n", + " results_df.append({\n", + " \"lig_a\": edge[0],\n", + " \"lig_b\": edge[1],\n", + " \"protocol\": \"RelativeHybridTopologyProtocol\",\n", + " \"ddg (kcal/mol)\": ddg,\n", + " \"error (kcal/mol)\": error\n", + " })\n", + "\n", + "results_df = pd.DataFrame(results_df)\n", + "results_df" + ], + "id": "804fec03b92d85f", + "outputs": [ + { + "data": { + "text/plain": [ + " lig_a lig_b protocol ddg (kcal/mol) \\\n", + "0 lig_02 lig_41 SepTopProtocol -2.922078 \n", + "1 lig_03 lig_32 SepTopProtocol 0.293982 \n", + "2 lig_02 lig_67 SepTopProtocol -4.698609 \n", + "3 lig_05 lig_45 SepTopProtocol -3.587460 \n", + "4 lig_02 lig_45 SepTopProtocol -1.938648 \n", + "5 lig_03 lig_11 SepTopProtocol 1.266813 \n", + "6 lig_11 lig_32 RelativeHybridTopologyProtocol 1.513028 \n", + "7 lig_04 lig_05 RelativeHybridTopologyProtocol 0.366913 \n", + "8 lig_06 lig_07 RelativeHybridTopologyProtocol 3.081323 \n", + "9 lig_41 lig_67 RelativeHybridTopologyProtocol -0.571720 \n", + "10 lig_02 lig_03 RelativeHybridTopologyProtocol -3.089208 \n", + "11 lig_11 lig_13 RelativeHybridTopologyProtocol -0.544669 \n", + "12 lig_67 lig_69 RelativeHybridTopologyProtocol -0.646350 \n", + "13 lig_13 lig_36 RelativeHybridTopologyProtocol -2.818366 \n", + "14 lig_13 lig_1a RelativeHybridTopologyProtocol 2.736797 \n", + "15 lig_05 lig_07 RelativeHybridTopologyProtocol 0.718161 \n", + "16 lig_02 lig_04 RelativeHybridTopologyProtocol 0.502572 \n", + "17 lig_02 lig_05 RelativeHybridTopologyProtocol -0.937120 \n", + "18 lig_05 lig_06 RelativeHybridTopologyProtocol -0.194521 \n", + "19 lig_74 lig_81 RelativeHybridTopologyProtocol 2.351567 \n", + "20 lig_11 lig_36 RelativeHybridTopologyProtocol -4.127683 \n", + "21 lig_41 lig_81 RelativeHybridTopologyProtocol -0.809334 \n", + "22 lig_11 lig_1a RelativeHybridTopologyProtocol 1.757897 \n", + "23 lig_41 lig_74 RelativeHybridTopologyProtocol -3.055768 \n", + "24 lig_11 lig_16 RelativeHybridTopologyProtocol 0.623395 \n", + "25 lig_02 lig_07 RelativeHybridTopologyProtocol -0.109589 \n", + "26 lig_03 lig_05 RelativeHybridTopologyProtocol 1.946267 \n", + "27 lig_41 lig_69 RelativeHybridTopologyProtocol -2.824896 \n", + "28 lig_16 lig_32 RelativeHybridTopologyProtocol 0.423409 \n", + "\n", + " error (kcal/mol) \n", + "0 1.322566 \n", + "1 1.265479 \n", + "2 0.435410 \n", + "3 1.891973 \n", + "4 2.541345 \n", + "5 0.559891 \n", + "6 0.213193 \n", + "7 3.247126 \n", + "8 4.612958 \n", + "9 0.227340 \n", + "10 1.556545 \n", + "11 0.372752 \n", + "12 0.593911 \n", + "13 0.153809 \n", + "14 0.317653 \n", + "15 0.284047 \n", + "16 1.895183 \n", + "17 0.608549 \n", + "18 4.321454 \n", + "19 0.281969 \n", + "20 0.386216 \n", + "21 0.112824 \n", + "22 0.251879 \n", + "23 0.200733 \n", + "24 0.398826 \n", + "25 0.381654 \n", + "26 0.826207 \n", + "27 0.860073 \n", + "28 0.581679 " + ], + "text/html": [ + "
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lig_alig_bprotocolddg (kcal/mol)error (kcal/mol)
0lig_02lig_41SepTopProtocol-2.9220781.322566
1lig_03lig_32SepTopProtocol0.2939821.265479
2lig_02lig_67SepTopProtocol-4.6986090.435410
3lig_05lig_45SepTopProtocol-3.5874601.891973
4lig_02lig_45SepTopProtocol-1.9386482.541345
5lig_03lig_11SepTopProtocol1.2668130.559891
6lig_11lig_32RelativeHybridTopologyProtocol1.5130280.213193
7lig_04lig_05RelativeHybridTopologyProtocol0.3669133.247126
8lig_06lig_07RelativeHybridTopologyProtocol3.0813234.612958
9lig_41lig_67RelativeHybridTopologyProtocol-0.5717200.227340
10lig_02lig_03RelativeHybridTopologyProtocol-3.0892081.556545
11lig_11lig_13RelativeHybridTopologyProtocol-0.5446690.372752
12lig_67lig_69RelativeHybridTopologyProtocol-0.6463500.593911
13lig_13lig_36RelativeHybridTopologyProtocol-2.8183660.153809
14lig_13lig_1aRelativeHybridTopologyProtocol2.7367970.317653
15lig_05lig_07RelativeHybridTopologyProtocol0.7181610.284047
16lig_02lig_04RelativeHybridTopologyProtocol0.5025721.895183
17lig_02lig_05RelativeHybridTopologyProtocol-0.9371200.608549
18lig_05lig_06RelativeHybridTopologyProtocol-0.1945214.321454
19lig_74lig_81RelativeHybridTopologyProtocol2.3515670.281969
20lig_11lig_36RelativeHybridTopologyProtocol-4.1276830.386216
21lig_41lig_81RelativeHybridTopologyProtocol-0.8093340.112824
22lig_11lig_1aRelativeHybridTopologyProtocol1.7578970.251879
23lig_41lig_74RelativeHybridTopologyProtocol-3.0557680.200733
24lig_11lig_16RelativeHybridTopologyProtocol0.6233950.398826
25lig_02lig_07RelativeHybridTopologyProtocol-0.1095890.381654
26lig_03lig_05RelativeHybridTopologyProtocol1.9462670.826207
27lig_41lig_69RelativeHybridTopologyProtocol-2.8248960.860073
28lig_16lig_32RelativeHybridTopologyProtocol0.4234090.581679
\n", + "
" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 161 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Obtain the MLE-derived absolute binding affinities\n", + "\n", + "We can then combine the $\\Delta\\Delta$G results from the two protocols to estimate the absolute binding free energies for each ligand on a single scale using the maximum-likelihood method as implemented in cinnabar.\n", + "\n", + "For more information on the MLE method or cinnabar, see the [documentation](https://cinnabar.readthedocs.io/en/stable/)." + ], + "id": "ee2b35ae85d82020" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-30T10:32:11.101280Z", + "start_time": "2026-10-30T10:32:10.360299Z" + } + }, + "cell_type": "code", + "source": [ + "from cinnabar import FEMap\n", + "\n", + "\n", + "fe_map = FEMap()\n", + "\n", + "# Add each of the relative estimates to the map, converting the values and uncertainties to kcal/mol\n", + "for _, row in results_df.iterrows():\n", + " fe_map.add_relative_calculation(\n", + " labelA=row[\"lig_a\"],\n", + " labelB=row[\"lig_b\"],\n", + " value=row[\"ddg (kcal/mol)\"] * unit.kilocalorie_per_mole,\n", + " uncertainty=row[\"error (kcal/mol)\"] * unit.kilocalorie_per_mole,\n", + " )\n", + "# Run the MLE method\n", + "fe_map.generate_absolute_values()\n", + "\n", + "# Get the absolute binding free energies for each ligand\n", + "abs_df = fe_map.get_absolute_dataframe()\n", + "abs_df" + ], + "id": "58d1c1b26eead798", + "outputs": [ + { + "data": { + "text/plain": [ + " label DG (kcal/mol) uncertainty (kcal/mol) source computational\n", + "0 lig_02 2.658459 0.452070 MLE True\n", + "1 lig_03 -0.189898 0.549595 MLE True\n", + "2 lig_04 2.728132 1.600468 MLE True\n", + "3 lig_05 1.824226 0.454703 MLE True\n", + "4 lig_06 0.618460 3.006083 MLE True\n", + "5 lig_07 2.547510 0.468182 MLE True\n", + "6 lig_11 0.684888 0.625128 MLE True\n", + "7 lig_13 -0.230382 0.655837 MLE True\n", + "8 lig_16 1.428234 0.697404 MLE True\n", + "9 lig_1a 2.467348 0.654343 MLE True\n", + "10 lig_32 2.106799 0.645500 MLE True\n", + "11 lig_36 -3.102689 0.660826 MLE True\n", + "12 lig_41 -1.259775 0.553071 MLE True\n", + "13 lig_45 -0.877777 1.490958 MLE True\n", + "14 lig_67 -1.932184 0.531346 MLE True\n", + "15 lig_69 -3.064832 0.701226 MLE True\n", + "16 lig_74 -4.347508 0.574791 MLE True\n", + "17 lig_81 -2.059012 0.561859 MLE True" + ], + "text/html": [ + "
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labelDG (kcal/mol)uncertainty (kcal/mol)sourcecomputational
0lig_022.6584590.452070MLETrue
1lig_03-0.1898980.549595MLETrue
2lig_042.7281321.600468MLETrue
3lig_051.8242260.454703MLETrue
4lig_060.6184603.006083MLETrue
5lig_072.5475100.468182MLETrue
6lig_110.6848880.625128MLETrue
7lig_13-0.2303820.655837MLETrue
8lig_161.4282340.697404MLETrue
9lig_1a2.4673480.654343MLETrue
10lig_322.1067990.645500MLETrue
11lig_36-3.1026890.660826MLETrue
12lig_41-1.2597750.553071MLETrue
13lig_45-0.8777771.490958MLETrue
14lig_67-1.9321840.531346MLETrue
15lig_69-3.0648320.701226MLETrue
16lig_74-4.3475080.574791MLETrue
17lig_81-2.0590120.561859MLETrue
\n", + "
" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 167 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We can then use `cinnabar` to visualize the mixed-network results and compare the estimates with the experimental values.", + "id": "10cd16eb838cc64c" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-10-30T11:10:01.251639Z", + "start_time": "2026-10-30T11:09:59.615343Z" + } + }, + "cell_type": "code", + "source": [ + "import json\n", + "from gufe.tokenization import JSON_HANDLER\n", + "from cinnabar import plotting\n", + "import requests\n", + "\n", + "# Load the experimental values from the provided JSON URL\n", + "experimental_data_url = \"https://raw.githubusercontent.com/OpenFreeEnergy/openfe-benchmarks/refs/heads/main/openfe_benchmarks/data/benchmark_systems/scaffold_hopping/bace_baumann/experimental_binding_data.json\"\n", + "\n", + "response = requests.get(experimental_data_url, timeout=30)\n", + "response.raise_for_status()\n", + "experimental_data = json.loads(response.text, cls=JSON_HANDLER.decoder)\n", + "\n", + "# Add the data to FEMap for plotting\n", + "for ligand_name, exp_data in experimental_data.items():\n", + " fe_map.add_experimental_measurement(\n", + " label=ligand_name,\n", + " value=exp_data[\"dg\"],\n", + " uncertainty=0.0 * unit.kilocalorie_per_mole,\n", + " )\n", + "\n", + "# Get the shift value to align the MLE estimates with the experimental values\n", + "exp_data = fe_map.get_absolute_dataframe()\n", + "exp_data = exp_data[exp_data[\"computational\"] == False][\"DG (kcal/mol)\"].values\n", + "\n", + "# Plot the results\n", + "plotting.plot_DGs(\n", + " fe_map,\n", + " source=\"MLE\",\n", + " target_name=\"BACE1\",\n", + " title=\"Mixed Protocol Network\",\n", + " figsize=5,\n", + " shift=exp_data.mean(),\n", + ")" + ], + "id": "4401ca21e5bd73c9", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 177 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Recap\n", + "\n", + "- **openfe** provides a number of free energy protocols, any number of which can be combined into a single alchemical campaign.\n", + "- The protocols should be configured to use the same force field and settings should be adjusted to ensure consistency between protocols.\n", + "- Each protocol has different requirements for the `ChemicalSystem` compositions used to represent transformation end states; take care to use the correct systems for each protocol.\n", + "- `Transformations` containing any **openfe** protocol can be written to disk for execution using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow).\n", + "- Analysis of a mixed-protocol network is not currently possible via the CLI; however, results can be gathered and combined using the API, as demonstrated above.\n", + "\n" + ], + "id": "e6e00203a0fb2af9" } ], "metadata": { From d1d2c42a5aa67b9398ff72cc2924d7f4a4808cba Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Fri, 30 Oct 2026 11:45:42 +0000 Subject: [PATCH 3/9] change to svg figures --- mixed_networks/mixed_networks.ipynb | 2459 +-------------------------- 1 file changed, 83 insertions(+), 2376 deletions(-) diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb index 9039b1f..431aa20 100644 --- a/mixed_networks/mixed_networks.ipynb +++ b/mixed_networks/mixed_networks.ipynb @@ -41,8 +41,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T16:18:02.731710Z", - "start_time": "2026-10-28T16:18:02.456708Z" + "end_time": "2026-10-30T11:40:58.537893Z", + "start_time": "2026-10-30T11:40:56.725310Z" } }, "cell_type": "code", @@ -78,33 +78,8 @@ "}" ], "id": "c2ec360a98734f5d", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-183ce45f91ea63c1b1a5c2ca71683a2e (name: 'lig_02')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-3a85837a992c824bb4e0ef1eb4f03b21 (name: 'lig_03')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e7b8bc753a9243d52039be2ad197a3b1 (name: 'lig_04')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-6a2222de00a67b9cddf4402ae67abc46 (name: 'lig_05')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-b900f42ff3161e6e4b663a908d99d382 (name: 'lig_06')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-0b73c8a6df07a0beed455c479519b97f (name: 'lig_07')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-a98f0ca0ad081239d749efbe258e0bf3 (name: 'lig_11')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-5119445cbe99f49d31eb73b97fc409a4 (name: 'lig_13')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-2ff86e4d2ac0278821209e3a10b83248 (name: 'lig_16')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-d8bb71cb61021ff28fa31ae90db5e9af (name: 'lig_1a')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-73e7964450ae778448e6e7bb6ee9c24e (name: 'lig_32')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-02f2fb7b039d551f4d79f052220924b1 (name: 'lig_36')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-bac489de520eadaa7126ee6641163303 (name: 'lig_41')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-e4c4a874b03c3a3cc36867807030f34f (name: 'lig_45')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-4931369e87328f26f82811c9afc8e55c (name: 'lig_67')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-24d9e0a297a1559b9e36e980b8b321ba (name: 'lig_69')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-17032cd50d866ce3f805801db1cac89a (name: 'lig_74')\n", - "INFO:gufekey.gufe.components.smallmoleculecomponent.SmallMoleculeComponent:Partial charges are present for SmallMoleculeComponent-11a9e1c36f7dbaf234e69ef0d0a99b84 (name: 'lig_81')\n" - ] - } - ], - "execution_count": 132 + "outputs": [], + "execution_count": 1 }, { "metadata": {}, @@ -123,8 +98,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T16:18:11.911974Z", - "start_time": "2026-10-28T16:18:03.841324Z" + "end_time": "2026-10-30T11:41:11.646049Z", + "start_time": "2026-10-30T11:40:58.540451Z" } }, "cell_type": "code", @@ -151,2301 +126,6 @@ "name": "stderr", "output_type": "stream", "text": [ - 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"INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:Filtering bond breaks\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:Filtering bond breaks\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:Filtering bond breaks\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:Filtering bond breaks\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:Filtering bond breaks\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:Filtering bond breaks\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Heavy Atoms \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n", - "INFO:kartograf.atom_mapper:#################################\n", - "INFO:kartograf.atom_mapper:Masking Atoms\n", - "INFO:kartograf.atom_mapper:Build Distance Matrix\n", - "INFO:kartograf.atom_mapper:Calculate Mapping\n", - "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n", - "INFO:kartograf.atom_mapper:Filtering bond breaks\n", "INFO:root:Trying to remove edge 10-11 with similarity 0.670320\n", "INFO:root:Checking edge deletion on distance-to-actives 0 vs 0\n", "INFO:root:Removed edge 10-11\n", @@ -2470,7 +150,7 @@ ] } ], - "execution_count": 133 + "execution_count": 2 }, { "metadata": {}, @@ -2485,8 +165,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T16:18:12.026699Z", - "start_time": "2026-10-28T16:18:11.918704Z" + "end_time": "2026-10-30T11:41:11.793622Z", + "start_time": "2026-10-30T11:41:11.648682Z" } }, "cell_type": "code", @@ -2519,13 +199,13 @@ "text/plain": [ "
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Gjb3sZRrTuMAF3ud9ACKJZDObqUMdYojJPoEfEAX0dHlochma2RcRERGRPDkcDqZPn05oaCiVKlUiKiqKO+64o9Ql+gCDBw/GZrMxf/58q0MRcY3tuf+YVxm/AweTmUwtalGDGoxnPM/wjPO4wljFKgBa0IK97GUPe2hBC4BcSf0HfEACCUxlau4T+AGbi/MFSkkp2RcRERGRS5w6dYrw8HAeeeQR7r//ftatW0eLFi2sDqvYateuTdeuXVXKL77j7OUPeY/3eJmXOc1pqlKVn/iJ6Uwv0mWu4zoAdrObZjSjOc3ZzW6u5Vpe5VXncVdyJf74X3qC9MLFKq6nZF9EREREclm+fDnt27dnzZo1zJ49m3feeYcKFVy3BtgqQ4cOZeHChaSlpVkdikjJJV/+kGlMA6Ab3YgmmgMc4CquKtJlJjOZp3kagH3sYy97sWGjDW1oQIPCnSSxSJcUF1GyLyIiIiIApKenM2XKFPr27UvTpk3ZunUrI0aMsDoslxk6dCjx8fGsWbPG6lBESi6PSfScEkjgEIcAGM1oylGOKlRhGMOKdJnv+I43eINmNOMgB4khhmY043u+ZxzjCneSckW6pLiIkn0RERERIS4ujr59+/Lyyy8zdepUlixZQnBwsNVhuVTHjh2pW7euSvnFN1Qs/KE51+c7cBTpMk/yJGmkMYQhNKIRDWnIYAYDsJjFhbk4VC7SJcVFlOyLiIiIlHERERG0a9eOgwcPsnz5ciZPnoy//2WmDUshPz8/hgwZomRffEPjgp8OJNBZZj+HOaSTzkUuMo+iff/HEw/AFraQQQZ27GzJ3EuvClUuf4IAIKRIlxQXUbIvIiIiUkYlJyczfvx4Ro0axfXXX8+WLVvo2dO398caOnQoO3bsIDY21upQRErmarhcQ/1JTAJgBStoQhMa05g44op0mRu4wXmOxpkfK1gBwB3c4TzuSZ6kKU15kiedjw1iEE3TmjL9ZNGaAoprKNkXERERKYN27dpFaGgon376Ke+//z7/+9//CAoKsjostxswYAD+/v6a3ZfSzwaUL/iQ8YznOZ4jiCDiiWc0o7mbuwGoWMh1AB/xEa/wCtdyLecyP9rRjulM5zVecx53nOPsZz8nOOF8LJZY9rOfM4FnivzlScnZHA5H0RZtiIiIiEip5XA4+Pzzz3n44Ydp1KgR33//PW3btrU6LI/q3bs3gYGBzJ492+pQRIolIyODNWvWkP5YOj039qRcPh3w4oknhRTqUAeAZJLpQhd2sIMwwljLWvcHWxk4j6aZLRBgdQAiIiIi4hnx8fHcd999/PDDD/z973/n7bffpkqVQqy59TFDhw7lhRdeIDk5mYoVi9DlTMRCiYmJLFq0iIiICH755RdOnTrFqJqj6EOffF8TTTRhhNGVrtSgBlFEcYQj+OPPC7wAmE79Rzma5+tnMpP61C9+0AHAcJToW0Qz+yIiIiJlwPr167nllls4ffo0H3/8MTfffLPVIVlmx44dtGnThvnz5zNo0CCrwxHJ14kTJ5gzZw4REREsWrSI5ORkrrnmGsLDwwkPD6drl674tfKDvZBXk/3DHGYc49jMZs5xjupUJ5RQnuZpemL6c4QQQgwxeV4/mmhCStpdbznQq2SnkOJRsi8iIiLiw+x2O2+88QbPPvssnTp14j//+Q+NG1+mjbePczgcNGrUiNGjR/POO+9YHY5ILrt37yYiIoKIiAjWrjVl9j169HAm+M2aNcv9gveBh8gz2beUDWgO7OKyjQTFPZTsi4iIiPioY8eOcccdd7Bw4UKefPJJXnrpJcqVy3ttb1nzj3/8g8WLF7N3716rQ5Eyzm63s27dOmeCv3v3bipVqsTAgQMJDw9n+PDh1K5dO/8TJAD1gCRPRVxINuBd4EGrAym7lOyLiIiI+KCFCxcyduxYbDYb33zzDQMGDLA6JK8yZ84cRo4cyZ49ey6dKRVxs6SkJBYvXkxERARz5szhxIkT1K5dmxEjRhAeHk7//v2pXLly4U/4JjDRbeEWnT9ma8BtQAWLYynDlOyLiIiI+JDU1FSee+45Xn/9dQYOHMjXX39N3bp1rQ7L6yQmJhIUFMQ///lPJkyYYHU4UgacOnWKuXPnMmvWLBYuXMjFixdp1qyZszy/W7du+Pv7F+/kGUB3YBOQ7sKgi8sPWAd0sTqQsk3JvoiIiIiPOHDgAGPGjGHTpk28+uqrPP744/j5qQ12fgYNGoTD4WDhwoVWhyI+av/+/c7y/FWrVuFwOAgNDXUm+C1btsRmc9GC9t1AWyDVNacrNhvwFPCqxXGItt4TERER8QXff/899913H7Vq1WL16tV07drV6pC83rBhw5g0aRIXLlygatWqVocjPsBut7Nx40Zngr9z504qVKhA//79+fDDDxkxYgT16tVzz8VbAO8A/3DP6QvFHzPgMMXCGMRJM/siIiIipVhiYiIPP/wwn3/+OWPGjOHDDz8kMDDQ6rBKhX379tGsWTMiIiIYOXKk1eFIKZWSksLSpUuJiIhg9uzZHD16lKCgIIYPH054eDgDBw707GDSC8BUz13OKQBoAqwGallwfbmEkn0RERGRUmrr1q3ccsstxMbG8t5773HnnXe6riS4jGjevDl9+vTho48+sjoUKUXOnj3L3LlziYiIYP78+Vy4cIEmTZo4y/N79OhBQIBFRdQO4EU8m/D7YyoLlmB2BhCvoDJ+ERERkVLG4XDw/vvvM3HiRFq0aEFUVBQtW7a0OqxSaejQofz3v//F4XBooEQKdPDgQWd5/ooVK8jIyKBz58489dRThIeHc+2113rH95ANU0ZfB5gA2HF/074+wI9ATTdfR4pEM/siIiIipciZM2e46667iIiIYPz48bz++utUrFjR6rBKrUWLFjFw4EC2bdtGmzZtrA5HvIjD4WDTpk3OBH/btm2UK1eOvn37Eh4ezsiRIwkODrY6zILtBm4H1rvh3AGYbfXeAe7CDDKIV9HMvoiIiEgpsXLlSm699VYuXrzIrFmzCA8PtzqkUq9Xr15UrlyZefPmKdkXUlNTWbZsmXP9/aFDh6hRowZDhw7l2WefZfDgwaWrJ0YLYA3wNjAZSM58vCTTvQGYSoF+wCdAgxKcS9xKM/siIiIiXi4jI4OXX36ZF198kZ49e/Ltt9/SoIHeYbtKeHg4Z8+eZcWKFVaHIhaIj48nMjKSiIgI5s2bR0JCAg0bNnSuv+/VqxflypWzOsySOw98i5mJ30120l4IjgAHjnQHGeUyKHdPOdPxv7W7AhVXUbIvIiIiUhAHsB/YApzBzIz5A5WAxkBHoLr7Ln/o0CFuu+02Vq5cyfPPP89zzz2Hv7+/+y5YBn300Uc8+OCDnDx5kpo1tei4LIiLi2P27NlERESwbNky0tLS6NChgzPBb9eunXesv3cHB7ASmIsp74/CDATkxQ9THRAGb/32FktrLeWX5b94Jk4pMZXxi4iIiPxZNPA5sALYBFzI8Zwf5s1yzumSRkAYMBz4K+CiJfSzZ89m3LhxVKpUiV9//ZVevXq55sSSy9ChQ8nIyGDRokXcdNNNVocjbuBwONi2bZtz/f2mTZsICAigd+/evPXWW4wcOZKGDRtaHaZn2IBemTcwv8uiM29JmNn+SkAQZva+kjms2ifViLw/kpMnT1K7dm0PBy3FoZl9EREREYAMYD7wHrAAk9RnFOH1/pnH1wDuy7w1Ll4oycnJPPHEE7z77ruMHDmSzz//nCuuuKJ4J5NCadu2LR06dOCrr76yOhRxkbS0NFauXOlcf3/w4EGqVavG0KFDCQ8PZ8iQIdSoUcPqMEuNkydPUq9ePT788EPuueceq8ORQlCyLyIiIhIFjAV2kZ20l4Q/Zrurh4FXgCqFf+nu3bu5+eab2bVrF2+++SYPPvig75YTe5GnnnqKzz//nGPHjuHn52d1OFJM58+fZ/78+c7192fPniU4ONhZnt+7d2/Kly9vdZilVr9+/fD392fhwoVWhyKFoN9kIiIiUnalYDpUdwX2ZD5W0kQ/6xwO4F3gWsz62MtwOBx88cUXdOzYkeTkZH777TfGjx+vRN9Dhg4dysmTJ4mKirI6FCmiI0eO8NFHHzFkyBBq1arFTTfdxLZt23jwwQfZsGEDcXFxvP/++wwcOFCJfgn99a9/ZenSpZw+fdrqUKQQlOyLiIhI2RQDdMDMvNtxTZL/Z3bgEHA98Cz5bneVkJDAbbfdxl133cUtt9xCVFQU7du3d0NAkp/u3btTvXp15s2bZ3UochkOh4OdO3fy6quvEhoaSnBwMA8++CDJycn885//5MCBA2zbto2XXnqJzp07a8DMhUaPHo3dbiciIsLqUKQQVMYvIiIiZc8fQB/gFIXeesol7gY+wpT5Z9qwYQNjxozhxIkTfPTRR4wZM8aDAUlON998M9HR0axfv97qUORP0tPTWbNmjbPB3v79+6lSpQqDBw8mPDycYcOGERQUZHWYZcL1119PlSpVNDBWCmhmX0RERMqWA5gu1CfxbKIPpsP/PwAH2O123njjDbp3705QUBCbN29Wom+xoUOHsmHDBo4fP251KAIkJiYyc+ZM7rzzTurVq8f111/Pd999R79+/Zg7dy6nTp3i559/ZuzYsUr0PejGG29k8eLFnD171upQ5DI0sy8iIiJlRzzQFjiC5xP9HC5MvMCNO25k/vz5TJo0iZdffllrib3A8ePHqVevHl999RW333671eGUScePH2fOnDlERESwePFikpOTadWqlbPBXpcuXdRA0WJHjhwhODhYPyelgJJ9ERERKTvuAr7GPevziyCDDIbUHMLj/3mcQYMGWRuM5NK1a1eaNGnC999/b3UoZcYff/zhLM9ft24dNpuN7t27M2rUKEaOHEmzZs2sDlH+pGfPngQFBTF79myrQ5ECBFgdgIiIiIhHRAJfWB1EtnlB8wjorbdi3mbo0KG88847pKenExCgfx93yMjI4LfffiMiIoJZs2axZ88eKlWqxMCBA/nss88YPnw4tWvXtjpMKcBf//pXnnzySRISEggMDLQ6HMmHZvZFRETE98UDLTDr9O0Wx5LFBjyN2Q1AvMb69esJDQ1lxYoVXHfddVaH4zOSkpJYvHgxERERzJkzhxMnTlC7dm1GjBhBeHg4/fv3p3LlylaHKYUUGxtLo0aN+Pe//82tt95qdTiSDyX7IiIi4vv+D7P1nbck+lkCgKNALasDkSx2u5169epx991389prr1kdTql26tQpfvnlFyIiIli4cCEXL16kefPmzvX3YWFh+Pv7X/5E4pXCwsK48sor+d///md1KJIPJfsiIiLi2zKARsBhqwPJgx9mIGKS1YFITnfccQdbtmxh69atVodS6uzbt8+5/n716tU4HA7CwsKcCX7Lli2tDlFc5I033mDy5MmcPHmSqlWrWh2O5EHJvoiIiPi2X4ARVgdRgAbAQbQhshf54YcfuOWWW4iNjaVBgwZWh+PV7HY7GzZscCb4v//+OxUqVGDAgAGEh4czfPhw6tWrZ3WY4gbR0dHOZpY333yz1eFIHpTsi4iIiG8bAizC8g78BZqHiVO8wtmzZ6lduzZfvvolt11zG0QBm4ATQBLgD1QGmgCdgM5Ae6CKRQF7WHJyMkuXLnWuvz969ChBQUEMHz6cUaNGMXDgQKpUKSN/GWVc586dady4MT/99JPVoUge1GJUREREfFZISAgxMTFMYQq96U0f+gAQTTQhhFgbXJZymMEIJfve4QTU/Kwmh/0PU/fJuuaxAMxg0Z+nyH4D/o3pBeEH9AAeAkZh/l19yJkzZ5g3bx4RERHMnz+fCxcu0KRJE2655RbCw8Pp0aOHdi8og/7617/y0ksvkRiXSJU/qpiBsY3AXszAWDpQCbgCMzCWNTjWHFUzeYB+IkVERMR3pWXfDSSQUEIBqEAFl14mkURe4zV+5EdiiKEylbmGa3ibt+lKV77kS8YxLt8Yf134K73p7dKYpIg2A68DPwF2qGOvk/1cej6vyfm4HVgDrARqA/8AJgA1XR+qpxw8eNBZnr9ixQoyMjLo0qULTz31FOHh4Vx77bXYbDarwxSrJMOd5e6kz8U+VGmYWcnhjxkUy6sZ6jqyfyfXAP4O3A9c7fZIyywl+yIiIuK7UrLvdqQj61jn8kskk0wf+rCBDfjhRzOaUZ7y7GAHe9hDV7pSm9rOgYYsscRylKMA1NtXL3t2WDwrBXgR0yjRD2cCb6MYSWzWUpGTmC0VZwCfA8NLHKVHOBwONm3a5Ezwt23bRvny5enbty/vvfceI0aMIDg42OowxWpngGnAR1DvXD1qUzv7uYKWS+UYfOUc8C/gTWAA8BRkFl6JCynZFxEREd+Vmn13GcsuKeN34OB5nmcGM0gnndu4jUACeQ2z5ZrjkrrtS73N22xgA/Wpz6/8SgtaAJBBBimZow3DMj9yaktbjnKUAQygZUpL2AOoUblnbQRuw/zd5zcbWVwZwGlMc8ixwDt45Sx/amoqy5YtIyIigtmzZ3Po0CFq1KjBsGHDeO655xg0aBCBgYFWhyneIgK4G5OsZyb2/hRz+8SsgYElwMLM874F6NvNZZTsi4iIiO/Kr/w603u8x8u8DEAwwfzETySSWKRL/MAPADShCWMZy0520pCGPMADPMiDeb5mPvPZznYAJmXtu3cYJfue9B9MEg6XrsV3lazBg++A5cAyoLGbrlUE8fHxzvX3kZGRJCQk0KhRI/7yl78QHh7OddddR7lyPtZ0QErmDKYfxXeYChhXD4wBfIlpVvolMNCF5y/DlOyLiIiI77pMEjeNaQB0oxvLWU4qqXSiE7vZXehLZB27mtXUohZ1qcsf/MHDPEwKKUxk4iWveZ3XATO7P4AB5sGkQl9SSuoz4B7cl+T/WQZwBAjDrOlv7qHr5hAXF+csz1+2bBnp6el06NCBxx57jPDwcNq1a6f195K3g0Bv4FDmn12Z6OeUARwHBgFvA4+46TpliJJ9ERERKZMSSOBQ5rvX0YymXObHMIYVKdlPzywfCCKIfewjkEAGMpDFLOY93rsk2d/CFpayFMgxqw/uewMtuf2AZxP9LOmY2dE+mEZlDdx7OYfDwbZt25wJ/qZNmwgICKB3797861//YuTIkTRs2NC9QUjptxe4DrMkxRPbl2b9HpwAJALPeOCaPkzJvoiIiPiuQk5U5mzGVph1+jkFE8xBDtKc5lSnOgCd6cxiFhNLLHbs+OXovJc1q9+ABtzMzc7Hj547yhWpV1C+fPkiXV+KYBtmjb5V0oETmHX8G3H5O/G0tDRWrlzpTPBjYmIIDAxkyJAhTJw4kSFDhlCjRg3XXlR81yHM4NRpLrskyi2eBaoCD1twbR+hZF9ERER8VwF9owIJpAENiCOOOcxhAhNIJZV5zCvSJfrTn0/5lD3sIYEEqlGNKKIAuJqrcyX6scTyIz8C8AiPUC7HZuxD7hjCtju3UbduXRo2bEiDBg2ct5x/rlevHn5+attfZGnA7ZgZfU/P6ueUjhl0mIZLZi3Pnz/P/PnziYiIYO7cuZw7d47g4GDCw8MJDw+nd+/eGkCSossA/oIpq7ci0c/yCNAe6GVhDKWYkn0RERHxXZfJcSYxiYd5mBWsoAlNSCGFC1wo0iWe4Rl+5mfOcIamNKUa1TjAAQCe5/lcx77N26STTnWqcw/3OB93BDh4I+INYo/FEhsbS1xcHHFxccyfP5/Y2FguXrzoPDYgIICrrroq38GABg0aEBQUpPXXf/ZPTJJtZaKfxQFMAUYCrYv+8iNHjjB79mwiIiJYunQpqamptG3bloceeojw8HA6duyof38pmX8BG6wOAjNgOxb4HahicSylkJJ9ERER8V2XSfbHM54TnOADPiCeeMYwhvKU513epSIVC3WJxjRmFat4iqdYwQpSSKE73ZnMZAYz2HlcPPF8yqcA3Mu9BObYX8rW2kb/of3zPL/D4eDs2bPOAYCcgwFxcXGsWbOGw4cPk5aWvYl15cqVCxwMaNCgAVWrVi3U1+cTdgMv4B2Jfk53YMr5L5OXOxwOdu7c6SzP37BhA/7+/vTq1Ytp06YxcuRIGjf2gjb/4hv+wHvWymdglhM8g9m+UorE5nA4vO3XnoiIiIhrJGHWfObT/C6eeFJIoQ51AEgmmS50YQc7CCOMtax1f4wBmP2lPyz+Kex2O8ePH89zMCDrz8eOHSPn276aNWvmOxjQsGFDgoODfaf8ezzwEdaWI+dnBaYB2p+kp6ezevVqZ4J/4MABqlatyuDBgwkPD2fo0KEEBQV5PFwpA3oA6/G+n5ffgK5WB1G6KNkXERER39YViCLPhH8LWwgjjK50pQY1iCKKIxzBH3/mMY+BDGQ0oznK0TxPPZOZ1Kd+yWP8N3BryU9TkNTUVI4cOZLvYEBcXBxnzpxxHm+z2ahbt26+gwENGjSgbt26+PsX0BjBG5wH6gEXL3egBQKAG4DvzR8TExNZsGCBc/396dOnqVevHiNHjmTUqFH06dOHihULV3EiUiwb8M6EOgD4K/AfqwMpXZTsi4iIiG/7EhiX91OHOcw4xrGZzZzjHNWpTiihPM3T9KQnACGEEENMnq+PJpoQQkoWXw3gKBRy1YBbJSYm5hoIyKtSIDEx0Xl8QEAAwcHBBTYUtLx/wIfAA3hfCX8mh7+D7/75Hd8v+55FixaRkpJCq1atnA32unTpooaM4jnjgG/xvll9MAn/IaCu1YGUHkr2RURExLclYWZ2E6wOJA/+wOOY5nGlwJ/7B+Q1GHDo0KEC+wfkVSng1v4BHYEteG2yn0EGT9me4reevzkT/KZNm1odlpRFp4ErgVSrA8mHH/AS3tNPoBRQsi8iIiK+byLwNqbZkzexAfuAJlYH4jo5+wfk10OgoP4BeQ0IXHXVVcXqHxDSKISY2BimMIXe9KYPfQAXVWS4iN1mJ21oGhV+qWB1KFLWfYDpb+HN2WEj4KDVQZQe6sYvIiIivu8RzBvZJAeXbX3uKf7ALfhUog/g5+dH/fr1qV+/Pl275r34N6t/QF6DAWvXruXHH3/M1T8AoF69egU2FMyzf0B2gQGBBBJKKAAVcE9iPYlJvMEbAIQSyjrWOZ/7iI/4hm/YxCaSSAJgF7to6WhJhc1K9MV6IU+EEOPw7sExYjAVCFdYHUjpoGRfREREfF8D4Ilj8EI9qyMx/DBr9cvoVlLly5cnJCSEkJCQfI9JTEzk0KFDeVYGzJ8/P9/+ATkHA5Ljk53Pd6RjruTb1ZaylDd5M9/nI4lkM5upQ51Le0AcQQmMWC8l+667BsdsBQy23sEdfMmXuR47z3na054DHABgBjO4P+p+GOiykHyakn0RERHxbXY7vP8+/N8TUOlXSA2FDItn9+3AZyi5K0CVKlVo0aIFLVq0yPN5h8PBuXPn8t1dYO3ataRfzO4ytoxll8xUOnDwPM8zgxmkk85t3EYggbzGa+YahaxnPsMZbud2mtCE6lRnE5suOeYDPqAudfmGbxiXV8fIrUDfQl1OxPUSydWUz12DY1kDCFmSSGIb2wDy3NlkPOOdiT5gCrOiULJfSEr2RURExHcdOgTjxsHixTB+PDzYDjrbTNO+PLbi8wg/zDZ74RZd30fYbDZq1qxJzZo1adeuXZ7HhFQJ4fTF0/me4z3e42VeBiCYYH7iJxJJzPf4/NzLvRznOKtYxZM8mecxV3JlwSeJL/JlRVxnZ+4/umtw7M8DCG/wBpOYRAAB3M/9uZ77kR/5mq+5iZv4kR+zn9hajK+vjFKyLyIiIr7H4YDvvoMHH4SqVWHBAhiYORUUCfTHzGJ5OuH3B3oAn3j4umXVZXKPaUwDoBvdWM5yUkmlE53Yze5CX+IzPuO//JeXefmSWcuiePjeh5nz2BxsNht+fn7YbLZcNz2mx9z5WKXdlS77PeqqwbEsaaTxTuZappu4iUY0cj4XRxz3cR+d6MTLvJyd7DvQwFgRKNkXERER33L6NPzjH/DTTzBmjCnhr1kz+/nrgDnASEwDN08l/H5AKPALUNFD1yzrClitkUAChzgEwGhGUy7zYxjDCp3sxxHHBCbQi148zdMlCrXbdd2o2rIqDocDh8OB3W533rf6sYyMDK+JxYrHyoJhDLvsMa4YHMvpe753/gxOZKLzcTt2xjKWNNL4ju8oR7ncL7xYrMuVSUr2RURExHdERsLdd0NyMnz/Pdx8c97HDQQWA8MwbxzT8z7MpQYDPwGVPXAtMQrZmiFn07DCrtMH2M9+LnCB3/iNQAIBnJ32N7CBqlRlLWtpQ5vLnmvMXWMYM3xMoa8tnvPnwQBvG4xwxWNB64P4y7N/yffvwBWDY3+W1dCyH/3oQAfn4+/wDstZzqd8SnOac/DPe+0VfRfOMkvJvoiIiJR+iYkwcSJ8+CEMGgSffQbBwQW/pifwB3APMBeTGLp6Ei8g8/YWcB9mdl88p4B3uoEE0oAGxBHHHOYwgQmkkso85hX5MimZHznZsZNIIhlkFO4kVxf5suIhWSXvPq0ICXRxB8dyWsQitmYuvp/EpFzPZT3+SOZHzmtMYAJfR33NGtYU67pljf7LERERkdJt3Tpo3x6++sqU7EdGXj7Rz1IfU9L/DVAN170zytruvQewC/iHC88thXeZHcOykowVrKAJTWhMY+KIK/Tpe9Mbx58+rud6wHQdd+CgPe0BeJInaUrTXA38BjGIpjRlesB0aF60L03EpZoW/HTW4BjAHOaQTjoXuViswTGA13kdgDa0YRCD8jwmMfPjYo66/RRSuBigOv7C0n87IiIiUjqlpsJzz0GPHnDFFbBlCzzwABR1Bs4G3AbsA14GZ9N0/3xfkT//zPP1xwwiLAVCinEecY3LzFaOZzzP8RxBBBFPPKMZzd3cDUBFFzdWOM5x9rOfE5xwPhZLLPvZz5l6Z4r3/SbiKldy2cywpINjWbaxjUUsAnKv1c/yJV/mGkCLJtr53AxmsGX6liJfs6xSGb+IiIiUPr//DrfdBtu3wwsvwFNPQUAJ39bUBp4GngDmAR8Bq8ju/BxAdql/1nhCBqbBnw1Thj0KuB+VZHuDpCQOfvAu3NgbUqoBl5YcJ5DAQzzES7wEQDLJdKELgHNGvqiWsSzPx7/M/LhEAHBDsS4l4loVILPlRJ7GM54TnOADPiCeeMYwhvKU513eLdLg2Bu8AZiO/mMoRp+KTkV/SVllc5SVFpMiIsXhwDTuSsXMEJUr+HARcTO7Hd55B55+Gpo0gW++gU5ufOfnAGKBKGAzcBbzZjgAqISZte8EdACqui8MKaSzZ2HuXJg1C+bPN70cavwH4m8Ex6VT51vYQhhhdKUrNahBFFEc4Qj++DOPeQxkIKMZzVGO5nm5mcykPvVLHvcSoG/JTyNSIlMx1U35tJmIJ54UUqhDHSB7cGwHOwgjjLWsdX+MVYAEVJ9eSEr2RUSy2IGNwAbMG/t1wF5yd+kuB7QAwjBv8LsAHSl0x2cRKYGYGLjzTli2DCZMgFdfhUqX3xtafNzhwxARATNnmu+N9HTo2hVGj4ZRoyClJflN0h/mMOMYx2Y2c45zVKc6oYTyNE/Tk54AhBBCDDF5vj6aaEJKsk4jqyJkD/p/RKy3lXx/VsALBscCgDHA18U/RVmjZF9E5DTwBfAuZgbPhlk7WdBWXAGYkW8HpqnNQ8DtQA13BipSRjkcZgb/oYegenX48kvoq2nQMu2PP0xyP2sWrF9vlnD06WOS+/DwSxs0dgPWYwZ1vYkNeAfzf4iINwjDTHrk8bNi+eAYwG9A15KdoixRsi8iZdcJzPrcbzGJfXHfBGbNxlQA7sKUwNUscXQiAnDyJNx/P/zvfzB2LEyfDjVqWB2VeJrdDhs2mOR+5kzYvRsqV4YhQ8wM/tChULOAX7z/AW71VLBFUAk4ClS3OhCRTP/GNCz1Nn5AG8xyKlXBFJqSfREpm37ENNFKIN+1acXiDwQBnwPDXXhekbLol1/g7383ZdkffQQ3qItZmZKWZsryZ840ZfpHjkCtWjBypJnB79+/8Ms4MjDbIEZRcNWWp/0LmGB1ECI5pGA6858FvC1L/AwzqSKFpmRfRMqWU8B9wP/I7qrtan6YKoGxmKUBmrERKZrz5+Gxx+DTT82M7aefQn0XNEET73fhAixYYBL8X36B+Hho1MjM3o8eDd27F3/XhT2YmcFUVwZcTAGYni+rUKMx8T7eNrsfALTFlPBrL7kiUbIvImXHIaAPEI1rZ/Pz4w+0AhZDZuNaEbmcVavg9tvhxAl46y245x6wqWbTp508CXPmmBL9hQshJQXats1usNeuneu+B94CHnfNqUqkArAdaGZ1ICJ5cADhmC1IPfF+6XICMM0DW1kdSOmjZF9EyoZDmAZNx/BsCac/0BhYjRJ+kYKkpMCUKTBtGnTrBl9/DVdrs3qfdfBg9vr7VatME8aePU1yP2qU2VbRHTKAIcBSrE1iPgXutvD6IpdzFGgJnMfacn4b8CrwlIUxlGJK9kXE953CJPoHsWatZgBwDaZcM9CC64t4u+3b4bbbYNcuePFFmDQJ/C/dE11KMYfD/DtnddDfsgXKl4cBA0xyP3Ik1PHQiGgi0B/TcdyKhP81lLhI6RABjMa6ZN8f6A3MR+X7xaRkX0R8mwO4AZiNtbM4/sAdmOYyImJkZJhS/eeeg2bN4NtvoX17q6MSV8nIgDVrTHI/axYcOACBgTBsmCnRHzwYqlWzJrYEYBiwBs9ux/d/wJMevJ5ISX2BNU3x/IFOwBKgqgXX9xFK9kXEt/0A3GJ1EDlEAoOtDkLEC0RHwx13mBLuxx+Hl16CihWtjkpKKjkZliwxM/izZ5v1+PXqZZfn9+ljZvS9QRLwD+ArshurukMAUA6YgRn0FSltviQ74fdE5ugHdAfmoorIElKyLyK+6wTQAojHO7aP8cOs2/8DdeiXssvhgC++gEcegSuugK++guuvtzoqKYn4eJg718zeR0aajvrNmmV30O/aFfy8uOX8L5hE5gzuqQDrg5kdbeSGc4t4ylzgdsx7KndVSmYNuo0D3gcKubOm5E/Jvoj4rruAr/GOTrJZ/IGHMR2hRcqa48fh3nvNjO+4cfD226asW0qfo0chIsIk+EuXQloadO6c3UH/mmtK1y4KZzFd+r/K/HNJZvmzEpYamLL9ezFNxkRKu1PAeEzVpKu3L/YHamEGxoa48LxlnJJ9EfFNp4H6QJrVgeShCnA887NIWTFrlkn0AT75BMLDLQ1HimHPnuwO+uvWmSaKvXub5D48HBo0sDhAFzgMfAJ8AJzElN8X5v8RP0zykwF0wQzq/hXQyhTxRf8DHsHsdORPySZV/DJv44BpmEEycRkl+yLim97ANEHyZOOlwrIBHwH3WB2IiAckJJiS/S+/NB3XP/nEc13XpWQcDoiKyu6g//vvUKmSaaw3erRptBcUZHWU7pGG2WN8FbAe2ARcyOfYK4FQTJI/GOjgiQBFLJaB6ZL/HrAg8zF/Lr/rkS3HcVdiKgXuAuq6J8yyTsm+iPgeO2Zv+1irA8mHDbgW2IZKO8W3LV9umvCdPg3Tp8Odd5au0u6yKC0NVqzI7qB/6JBJ6EeMMAn+gAFQubLVUXqeHbN96xlMYz9/zKx9Q0zpsUhZdgDTgDgKWAfsJu/Jliswg2JdMA34BmB+lsRtlOyLiM8JqR9CzLEYpjCF3vSmD30AiCaaEEKsDS6n34FrrA5CxA2Sk812em+9BT17miZ8jRtbHZXkJzERFi40M/i//AJnz0LDhtkd9K+7DgK0ybWIFFISpsQ/CTODXwmoiZm913ivR+k3t4j4ntTsu4EEEkooABWo4JbLTWISb/AGAKGEso51zud605vlLL/kNT3owaqNq5Tsi+/ZsgXGjjXru6dNg0cfNWu7xbucPg1z5pjZ+4ULISkJWreGBx80M/gdOqgKQ0SKpxLQzOogBJTsi4gvSsm+25GOuZJvV1vKUt7kzcse14Qm1Ka288/X+l1ryt3Gui00Ec/KyDDJ/ZQp0KoVbNwIbdpYHZXkFBNjOujPnAkrV4LdDt26wYsvmhn8pk2tjlBERFxIyb6I+J4cyf4yll1Sxu/AwfM8zwxmkE46t3EbgQTyGq8B4CjkXjJnOMPt3E4TmlCd6mxiU77HTmYyd3Jn9gN2TNMnEV+wb59Zm79uHTzxBEydChXcU0kjReBwwM6d2Q32Nm2C8uWhXz/44APTMLFePaujFBERN1GyLyK+xcFlO8G+x3u8zMsABBPMT/xEIolFvtS93MtxjrOKVTzJkwUe+yiPch/3EUww/enPS7xE3Z1qPSulnMMBH38Mjz8Odeuaxm49elgdVdlmt8Patdlb5O3fD9WqwdChZiBmyBAIDLQ6ShER8QAl+yLiW1Ivf8g0pgHQjW4sZzmppNKJTuxmd6Ev8xmf8V/+y8u87OwJkJ8qVKEBDTjDGaKJ5hM+YTGL2Z60nSpUKfQ1RbzK0aPw97/DvHlwzz3w5psmqRTPS0mBpUtNcj97Nhw/bgZfwsPh3Xehb19VWoiIlEFK9kXEt1wm2U8ggUMcAmA0oymX+TGMYYVO9uOIYwIT6EUvnubpAo99m7dpRSvKUx4HDp7lWV7jNaKJZmbaTG7jtkJdU8Sr/Pwz3H+/6dA+Zw4MH251RGVPQoIZaJk1y3w+fx6uvto0Rxw9GkJD1RhRRKSMU7IvIr6lCJNXthz7vxR2nT7AfvZzgQv8xm8EYsphk0gCYAMbqEpV1rKWNrShPe1zXe9WbnX2Boj1jy18sCLe4Nw5eOgh+PZb+Mtf4MMPoXbty75MXOTYMTNzP3MmLFkCaWnQsaMpzx81Cq69Vh30RUTEScm+iPiWcgU/HUggDWhAHHHMYQ4TmEAqqcxjXpEvlZL5kZMdO4kkkkEGJzjBt3zLPdxDNUx58w/84Dw2pHxIka8pYpklS2DcOIiPh6++MjPISizdb9++7PX3a9eCnx/06gVvvGES/IYNrY5QRES8lJ/VAYiIuJSNyyb8k5gEwApW0IQmNKYxccQV+hK96Y3jTx/Xcz0AoYTiwEF72nORizzO4wQRxDVcQ0MaOhsDXsM1/KX9X4r1JYp4VFISTJgA/fubrdm2b4fbb1ei7y4Oh+maP3my2bqwWTNzv3Zt+OILsx5/6VJ4+GEl+iIiUiDN7IuI76kApOX/9HjGc4ITfMAHxBPPGMZQnvK8y7tUpKLLwqhNbZ7lWRawgAMcIIkkWtKSUYziiYAnqBjmumuJuEVUlJnBP3AA/vUvk2D6aZ7A5dLTzb73s2aZW2ws1KwJI0bAiy/CwIFQRc08RUSkaGwOh6PwC1VFREqDGcCDkN8y/HjiSSGFOtQBIJlkutCFHewgjDDWstYzcX4L/M0zlxIpkvR0eO01k2i2bQvffAOtWlkdlW+5eBEWLTLl+XPmwJkzcNVVpjR/9Gi47jood5kyJRERkQJoZl9EfE8X8k30AaKJJowwutKVGtQgiiiOcAR//HmBFwDTqf8oR/N8/UxmUp/6rolTxNvs2WPK9DdsgGeeMSXk5ctbHZVvOHMGfvnFzN7Pn2+WSLRqBf/4h0nyO3XS8ggREXEZJfsi4ns6Ak2AaPJM+mtTm170YjObOcc5qlOdoQzlaZ6mJz0B2MxmYojJ8/R/bspXZH5AW6B5yU4j4lIOB8yYARMnQnAwrF4NYWFWR1X6xcVBRISZwV++HDIyzN/r1KkmwW+uXwQiIuIeKuMX8TgHcBCIArYBCUAKpqtcJUwG2Am4lst2mpP8vQ08RoEz/NZxwOc2GGd1HCKZDh+Gu+6ChQvNLPPrr2uNeHE5HLBrl0nuZ82CjRtNOX7fvia5Dw+H+i6oDBIREbkMJfsiHpEKzAQ+B9ZhEnwwxTV/LtnM6ixXDmgD3ATcBWgv6yI5C9SHkk7Cu4V/IsxcB8P7qmRXrPf99/DAA1CxInz+OQwebHVEpY/dDr/9lr1F3t69ULUqDBli1t8PHQrVq1sdpYiIlDFK9kXc6hDwEaZj3GlM/ba9iOfwy7zdDIwHVFZbaPcBnwEZVgeSg58D6n4DR++A7t1NKW///kr6xfPOnIEHHzTJ/k03wQcfwBVXWB1V6ZGaCr/+apL7iAg4dsxsjxcebmbw+/UzAygiIiIWUbIv4hZ24F3gSSAd12SbAZnnGgu8A9R0wTl93GmgBWaWv6hjLO7gB1wJ/O6AVfNNor9+PfToYe7366ekXzxj4UIYN850hH//fRgzRt97hXH+PERGmhn8uXMhIQEaNzaz96NHQ7du4O9vdZQiIiKAkn0RN9gH3AGscdP5/YErMEsChrnpGj4kAhhldRA5LAX6ZN53OExHbiX94ikXL8ITT5gEv39/+OILs92b5O/4cbM13syZsHixmdFv394k96NGQZs2+nkVERGvpGRfxKVmAmMwM/npbrxO1nKAh4F/Zf5Z8nUr8CPWlvP7YZYVfJDHc1lJ/5QpZrszJf3iDuvXw9ixEBsL06aZEn4//e7I04ED2Q32Vq82P4fXXWeS+1GjICTE2vhEREQKQcm+iMt8DdyZed+TP1a3AV9iZvwlT+eAnsBu3DsGkx9/zAYLS4GCGpw7HKZEeOpUk/T37Gnu91UjPymBtDR4+WV45RXo2BG+/hpatrQ6Ku/icMCWLdkN9rZvhwoVYOBAM4M/fLhZjy8iIlKKKNkXcYmfMA30rPhxsmH2cPuUSzv7i9NxoAcQg2cTfn+gJbCSwrdZyEr6p0wx23Yp6Zfi2rXLzOZv2QKTJ8Mzz5ht4ATS082sfdYMfkwM1KhhEvtRo2DQINNRX0REpJRSsi9SYhuA7pgacSt/nF4BnrHw+qXAcaAf8AeeKen3AzoD8yleP0WHA+bNM4n+xo2mjHjqVOjTR0m/FMxuh/fegyefhEaN4JtvoEsXq6OyXlKSWXc/c6ZZh3/qFFx5pUnuR4+G66/XYIiIiPgMJfsiJZIMtAUOYP3+bgFAFCYeyVc8ptXB15ht8OxuSJqzWir8A3idgkv3C0NJvxRFXJzptL9kCTz0EPzf/0HlylZHZZ2zZ03n/FmzTG+MxESzjCGrwV7nzupdICIiPknJvkiJPIXJ5rxhX7cA4BpMwq+ZqcuaC9x0Hi5WwvzduUjW9npfAX1dd1pASb8UzOGAf/8bxo835edffAEDBlgdlTUOH4aICDODv2yZKdkPDc1usKeeBSIiUgZoKFuk2DYC0/CORB/MQvQdmJjksprvhdSrodsmqIxpd1DcfDnrN2k1YCLwO65P9MEk9MOGma7qc+aYbdT69YPeveHXX02yJ2XT6dNw001mff6wYabBXFlL9P/4A157zST1V10FjzxifmamT4dDh2DdOnjqKSX6IiJSZmhmX6TYRgNzsL58/8+qAccwGazka/Ro2LTJJAjpleDfwDuY9fxgiiPSCnh9zufbAo8At+DZv3aHw5QnT50KUVHQq1f2TL+UHZGRcNddkJICH35okv6ywG43u1ZkddDfvRuqVIEhQ8zs/bBhpuGeiIhIGaVkX6RYDgGN8J5Z/T/7DLjL6iC816+/ms72330HY8ZkP+7AzMpvxKyGWAfsAlIwiX05oCLQGgjFbKfXBWiOtRshOBzwyy8m0d+0yTQZmzrVzPiL77pwASZOhI8+gsGD4bPPTLM5X5aaCsuXm+Q+IgKOHIFatWDkSDOA168fVKpkdZQiIiJeQcm+SLFMwXS/97ZZfTA15a2BLWgrvjxkZJiGXBUrwpo1hV/r7sD7/zqV9Jcda9eakv2jR+HNN+G++3y3b8OFC6ax3qxZ5vs7Pt7sMDB6tLl17w4BLuy7ISIi4iOU7FviLLA183MyZiPuikATzIbcetPi3exAPeCk1YFcxgbMvm+Sy2efwd//bpKlsDCro3EPJf2+KzUVXnjBdNjv0sVsqdesmdVRud7Jk6YvxcyZsGiRWaLQtm12B/127Xx3cENERMRFlOx7xCnMguA1wFogroBjK2AWAIcBw4H+qI+idwkJCSYm5ghTppjcKWt5dHQ0hIRYGVlOfphGfY9bHYh3OX/eJEZZJfy+zuEwCdPUqbB5s/mGnTrVJP9S+uzcaWbzt283/45PPulbM9rR0Wb2ftYsWLXKfP/27JndQb9JE2vjExERKWWURbqNA7PgdyxmH67HgP9ScKIPZnHwBmAGMAi4GngLOOO2SKWoUp33AgNN4+fQUKhQwbVXWbgQevQw22MHBsKgQWa3tSxffmkmtvK+2Vm2bJ5rA/IF//d/pgT4//7P6kg8w2Yza5mjosz65vj47BGq5cutjk4Ky26Ht96CTp3MDPdvv8Gzz5b+RN/hgK1bTaVC+/YmmX/qKahWDT7+GI4dgxUr4LHHlOiLiIgUg2b23WIPpjnaakxJfnoJz2fDdAabAjyByvytFRJSnZiYBKZMMZNr7hAZCSNGmOXlwcHm/f2pU6bv1Nq1poJ17lx46aXcr4uNNUt4AXbtCqZly0PuCbA0iomBFi1g0qRL/+LKCocDZs8237hbtpikf+pU08VfvFNMDNxxhxmcefRReOWV0t2ALiPD9MqYOdPM4EdHm9HM4cPN7P3gwSbZFxERkRLTzL5LZQD/AtoAv2U+VtJEH0yVQCrwHKb19w4XnFOKL8V5b9my7Nn0gwfNYw4HTJ5sGkTXqAHjx8Mzz2QfVxhPPGHeE4eFmfMeOGCWCCQlwXPPmWOGDTPbRue81aplnhswAFq2PAycd8lX7BOeegqCgkzpc1lls0F4uFnHP2sWnD1rSvr79jUzqOI9HA5TvtOmjfkFsHSpmd0vjYl+crLpIfH3v0P9+mZw6fvvTbnS/Plmff6//w033qhEX0RExIWU7LvMcaAnplw/Fdck+X/mALYDHTAbgos1Cu7A/9578PLLcPo0VK0KP/0E06cX/uyHD8OOzPGckSNNpW61aiaBB1iyxAwE/Nn8+WYpL5jJa+N04S/sy9asMcnFK6+Yf5SyLmfSP3NmdtLfr5+Sfm9w8iTccAOMG2ca0m3fnt0cpLQ4d870xbjxRjMKOWKE+d4aN86UJx06BDNmmIS/fHmroxUREfFJSvZdIgbTUG/j5Q50gQzMQMIE4FnMAIB4VsF/59Ommc/dupkK1QMH4KqrCn/2uBxtHerUyb5ft675nJRkcoE/e/1187lt2+yBAUgq/IV9ld1uyp87dDDl0JLNZjOl01lJ/+nT2Un/ypVWR1c2zZkDrVubxPjnn+Grr6B6daujKpwjR7IT+Dp14G9/M6VJzzxjmgvu3g3//KcpWfLT2w8RERF30/+2JXYEuA44hHtm8wvyKjDZw9eUgiQkmAkrMBNy5cpBlSqm5L6w8uuikfPxPy8H2LLFVPlCzll98P6N4T3gP/+B9evhX/9SgpGfvJL+Xr2U9HvS+fOmzH3kSLOl3o4dZnbf2+3Zk53ABwfDQw+Z0qO33jJNRDZsMMl+q1baKk9ERMTD9M63RBKBvsBRPJ/oZ3kFeM+ia5ctCQkJ7NixI99k/M9yvq8tShvMhg2z7x8/nn3/xAnzuVKl7LX5WbJm9Rs0gJtvzn48I6OMl8devGjW6v/lL9purjD8/LKT/v/9z3SF7NUL+vc3W6GJe6xcabpufv+96UI/Zw7Uq2d1VHlzOEwC/+yzJoFv0cJ007/ySvj6a/OLavFi06ykQQOroxURESnT1Na9RJ4C9gJ2i+N4DOgPtLQ4jtIrPT2do0ePEhsbS2xsLDExMc77Wbf4+Hggu5w+L4GB5v1tXJx5vz5hAqSmwrwi7IIXHGyqeHfsMLulPfGEyVkXLjTP9+8P/v7Zx8fGwo8/mvuPPGKqCcBMrtWo0Zw6dRoREhJC48aNL/lcr149/Hx5tvvNN03ykbW2QgrHz8+UpoSHm2/CqVPhuuvMTP/UqWbvcym5lBR4/nkzWte9OyxaBFdfbXVUl0pLM8sKsjroHz5sml2OHAmvvWbWDVWubHWUIiIi8ifaeq/YlgO9rQ4iUwCmad9awP8yx5ZN8fHxlyTvOW+HDx8mI0fXu5o1a9KwYcM8b2PGDCY29jxTpmRvWQ5mfX5ICLz7Ljz8sHmsQQPzfv7CBZOwQ+Fm+SMjzU5Udnv+W+9leewxU6FevbpJ/AMDzeMXLjTgm2+eJjo6mujoaA4ePEh0dDSnT2c37atQoQKNGjXKcyCgcePG1KpVC1tpLb09cgSaNYMHHsgufZDisdtNkvfCC7BtmxlxmjJFSX9JbNsGY8fCrl1mK8iJE3OP4lktMREWLDD/7r/8Ypo4NmxoKj9Gjzb/9gGaLxAREfFmSvaLJRFoBRzmcp3ZPWsaMOmyR/matLQ0jhw5UmAyn5CQ4Dw+ICCAq666Ks9EvlGjRjRo0IBqBWz/FBJSh5iYk/km+w6Hmaz74ANIT4cxY0yz6XffhYoVTYO9wliwAF580VRUBwSYhn+vvGKW82aJjzcDCufPm7X62RPYAcBtwBeXnPf8+fMcPHjQmfxnfc665fy7qlKlCiEhIXkOBISEhFCzZs3CfTFWuPNOmDsX9u0rPQ3OvF1eSf/UqdCjh9WRlR4ZGfDGG2Z/zpYt4Ztvco/eWenUKZPYz5xpSomSk02Z0ejRJsnv0EHr7kVEREoRJfvF8ibwBNaX7/9ZJeAYEGh1IC7jcDiIj4/Ps6w+63bkyBHs9ux/i6CgoHxn5Rs2bEi9evXwL9EM2kGgcb7PxsebmfisTvrJydn9tsLCzMy8+9mA6cD4Ir/y7NmzeQ4EZN2/mFWiAFSvXj3fgYDGjRtT1apt7qKioHNnM+Lyj39YE4Mvy0r6p04128Ip6S+c6Gi4/XZYvdrM5L/0ElSoYG1MMTHm33LWLFOq73CYJQWjRplb06bWxiciIiLFpmS/yOxAE8x2e97GhmnW94DVgRRaWloahw8fLnBW/vz5887jy5UrR4MGDfJN5Bs0aOCBBNOB+R44mOezW7aYpL5rV6hRw+SdR46YCt1582DgQDNRdvRo3mefORPq13dFnLtwdR8Hh8PBqVOn8h0IiImJISUlxXn8FVdcke8SgUaNGlGpUiWXxpcZpGnGd+aM+cdQqbH72O3mG/aFF0zSP2CASfq7d7c6skx2YB8QBWzC7J6SjPkZrgTUBzoCnYBmuK1nrcMBn39umnjUqmW20+vVyz3XKkwsO3aY5H7mTNi82ZQe9etnfjGNGOG9zQFFRESkSJTsF9kCYLDVQeTDBjTHJHnWl1o6HA7Onj1bYCJ/5MgRcn4LXnHFFfmW1zds2JC6det6SUO5/Ks7Dh+GcePMe+hz50wFeWgoPP109hLnkBAzoZaXrOUAxecP9MD0lfAsu93OsWPH8hwIOHjwILGxsaSnZ+9cUa9evXwrAxo2bEj58sXYTeC//4W//tWsgxg40IVfneQrK+mfOtUkkpYm/XZgIfABsBSz7AqgXOZzWT+zfpm3tMw/VwL6YAZLB+Oy/ifHj8M995iOnXfdZRpsBHq4+iojA9aty26wt38/VKtm9gQdNQqGDPF8TCIiIuJ2SvaLbAQQiXet1f+z5YD7Z41SU1M5fPhwgSX2iYmJzuPLly9/2Vn5KlWquD1u1zgNXAmkWh1IPn4C/mp1EJdIT0/nyJEj+VYGHDp0yDn4Y7PZCA4OzneJQHBwMAF/nrVPSTHbgbVsadbri2fZ7WbLvhdeMEn/wIEm6e/WzQMXjwc+wVQ3xWCS9aL+ns56zVWYJTD3AiXoSzFzJtx7r1nn/sknZncDT0lJgSVLTHIfEWF2pahb18QwerRpNmL1EgIRERFxKyX7RRASEkJMTEy+jdm8QwAwEXitRGdxOBycOXOmwFn5o0eP5pqVr127doFr5evUqeMls/KuMg74Fki/3IEeVhvTPLKc1YEUWWpqKnFxcflWBhzNsfYhICCABg0a5BoIGLpzJx1++okTixdTu1cvH/t+K0U8nvRHYn4eT+K6Xip+wBXAZ5hB3iKIjzf7YH71lUmuP/44u4mHO8XHm608Zs40a4YuXDBr7rMa7IWFmW0VRUREpExQsl8EISHBxMQcYcoUs73wA5lL4123xtpYuNC8R968OXcX9s6dcx+3bZvp1r58uXmPV7u26Y/14499gSUFXiMlJYVDhw4VmMznbMRWvnz5fJP4Ro0acdVVV1G5zO2zvA9oDaRc7kAP+xi4x+og3CIpKYnY2Ng8BwLO79/Pb2fO8BXwMOZ7Nmtbwbz6BtSuXbv0bitYWrg96T8HPIbZdcIP1zdNzTrnbZiGl4WY5V+2DO64w2xV9847ZlcId36fHTtmZu5nzTIz+Wlp0KlT9hZ5rVqpg76IiEgZpWS/CEJCahETc5opU8z7VXeIjDT9kTIyCt5ffdUq8745KckstWzSxEzixMZCcnI1Tp3aR2xsXL6J/LFjx3Jdt06dOgXOyteuXVuzpHl6G3jU6iAyBQC9MeuVy+Cb+/vvx/HDD/wxZw778thR4ODBg5w7d855eOXKlXNtK/jnAYGaNWtqMMBV7HbTS+GFF2DnThg0CKZMKWHSvxfoCxzF/cuq/DEVM0uBa/I+JDkZnn3WrMm/7jozq++ukq+9e7Mb7K1bZ2bre/XK7qDfsKF7risiIiKlipL9IggJCSQm5vxl91efMcPsr37bbSYRfy2zor4wf9Nt2mRv0bZypUnm27aFgwdh+HDT48nhMJM1f/wBf/ubWQqa1dT8/HnTd6lxY/MagIoVKxaYyF911VXu6YpeJtiBnsAGrC/nr4xpzlgG3+hv3w7t25v9yx/Nf/Dl3Llz+S4RiI6OztVjIjAwMN+BgMaNG1OtWjUPfGE+Jq+kf+pU8wuvSHZgBrbO4bn+Kf5ANeBXoH3upzZvhrFjTRL+6qvme9CVg6MOB2zalN1gb+dOqFjR/P2NHm3+c7jiCtddT0RERHyCkv0iCAmpTExMUr7J/rvvwsMPm8eCg001ZWKiucHlk/3Dh+Gqq8z9V1813dvB9HfKSujPnzeDAe3bm+duvdUMCiQkQMeO8PrrpoJz5coXqVx5KA0bNqRWrVqaoXSrfUAH4CKuLyMuii+AOy28vkUcDpP0HDxofjiK08Ef06fi9OnT+Q4EHDx4MNe2gkFBQfkOBDRq1KgMLmspArsdfv7ZJP2//w6DB5uZ/kIl/fuAbsBZPN8o1R8IBNYCLcyo7rRpZsCiVSv49lto3do1l0pPN/vez5plbnFxULOmKf0aNcqUdpWahqYiIiJiBW1AXSQFZ+vTppnP3bqZdfSpqSbx3r27cGePi8u+n7OXU9265nNSEpw8mft8330HLVqYNfu//moGIbZvh+uu64LZO1rcrykwH+iH2cbLioT/Zcpkog+mEdmiRWbdcjETfTDd/2vVqkWtWrXo0qXLJc/b7XaOHz+e50DA5s2biYmJybWtYN26dfOtDGjYsCEVynIndD8/uOkms0ViVtLfrVshkv6LwCA8O6OfUwZwHhgA++fA2H/Ab7/Bk0+ahL8E338AXLxomrbMnAm//AJnzpgR4Kz199ddB+VKX+NNERERsYaS/SLJP9lPSIBDh8z90aPN+7Fy5cw2xoVN9vOb+c/5uM1mJnyy3HUXfPaZqS5o1sys2//yS5g61Zu3BvRFPTAdwYdhtuPz5N//FOAZD17Pi6SlweOPQ9++ZsbTjfz8/Khfvz7169enex77x2dkZHD48OE8KwLWrFnDoUOHsNvNQJDNZuPKK6/MtzLgqquuunRbQV9UUNI/dSqEhv7pBc8BB7G2giYd7IdgcSc43sjMvvfoUfzTnTljEvuZM2HBAjOq26oV/OMfJsnv1EkN9kRERKRYysC7SVcq3BuunO/LirJIImdPpePHs++fOGE+V6oEtWqZJQJZunY1nxs3Nt34jx3LWquvNfie1weznncEcAZ3JvwZGbbMsvNnqVt3qtuu4/VmzIA9e+CHHyxPiPz9/Z19MHr16nXJ82lpacTFxV0yELB//36WLFnCkSNHcp2rQYMGeQ4EhISEcOWVV/pWw8yspP+GG7KT/rAwGDLEzPSHhgKrMA0xvWDlmZ8D7suA29+FSsVI9OPissvzly83HVm7dTMDHKNGQfPmro1XREREyiQl+0Xin+8zgYHQoIF5DzdnDkyYYMr4580r/NmDg81yzx07TEXyE09kV3UC9O8P/v4mwQ8MNNUEGzfCffdBTIwp8Qczww9Bxf0ipURCgd3AI8A3uGc7MBvQihtuSOTAgQh+++2Zsrk+/MwZkxzdfXf2NhVerFy5cjRp0oQmTZrk+XxycjIxMTGXVAbs3LmTX375hZNZP+BkbyuY10BA48aNqVOnTuns0+HvDzffnHumPywMRgyEH3dART+sKd/Piz9Uug/Yw2UHVx0O05sgq4N+VJQp/erbF95/3+zl6sr9W0VERERQg74iCQkJIibmbKEa9DVoYLbNu3DBJOxQuFn+yEjTWNluL3jrvX/9Cx57zNxv2RKOHjXr9uvVg23b/Kld+yJQwvWjUkK/AHcDJ3BN0u+XeXseeIqdO/fQtWtXbrrpJr744osSnrsUmjDBrGHZu9d84/u4CxcuEBMTk2/zwLNnzzqPrVSpUr4DASEhIQQFBZWOwYCMDPjpJ9j4OLxx5PLHWyKfxph2u1nPn9VBf+9eqFoVhg41s/dDh0L16p4NVURERMoUJftFEBJSj5iY45fdeu+DD8y6+jFjTL+md981uyQlJRXuOgsWwIsvmp2WAgJMdecrr8Cfe4Z99hm8/bZ5D1m7tonntdcgOLgdsMVlX7eURBLwEzAdiMIU0xRliz5/zExmXeBB4O9A9gzgN998w+23387nn3/OuHHjXBW099u925TBvPQSPPWU1dF4haxtBfMaCIiOjubChQvOY6tVq5bvQEDjxo0JDAy08CvJg6MbOH4z5fNexQ9oB2wyf0xNhaVLTXIfEWHWVdWuDeHhpplL377mPwMRERERD1CyXyQJQP4zMfHxZiY+q5N+crJJ0HfsMJWoa9e6P8K0NFi2rDGxsc8yePBggnMu8BeLRQFfAauB7ZjO/WDK8v0wa5HtOR5rDIQBNwAjyW/VzT333MO3337Lb7/9Rtu2bd0WvVcZOdJsO7Frl5KnQsjaVjC/gYCDBw+SnJzsPD4oKCjfgYBGjRpRxaNbvm3DJNRebNEr8Pl2s24rIQGaNDHJ/ahRZrTWP/8lYCIiIiLuomS/yFoDv5NXk6gtW0xS37Ur1KhhlmUeOWLe582bZ7ZFHj3alNznZebMki/bdDjgxRev5oUXDuBwOGjbti2DBw9myJAhdO/enfIl3RpKXCQN2IlJZBKAZMyyi0pAc6ADZj/vy0tKSqJbt25cvHiRjRs3et+srKstXgwDBpimfDfdZHU0PsHhcOS7rWB0dDSxsbGkpaU5j69Tp06+lQGNGjVy8baCDwMzKFpFjAelYSr5P+yQvUVe69aWN4wUERERUbJfZO8DD5FXsn/4MIwbB5s3w7lzZjlmaCg8/TT07GmOCQkxzfTykrUcoGSqAcc4fTqJRYsWERkZyfz58zlx4gTVqlWjX79+DBkyhMGDB9MwZ/t/KdX27t1Lp06dGDJkCN9//33pWI9dHBkZ0KGD6VC5cqUSKg/JyMjgyJEjeQ4EHDx4kLi4uEu2FcyvMqBBgwaF3lYwJCSEmJiYfJdOeY20ECgXbXUUIiIiIrko2S+yBMz66eTLHWgBf8ws2Fu5HrXb7WzZssWZ+K9du5aMjAxatWrlTPyvu+46F8/Giaf9/PPP3Hjjjbz33ns8+OCDVofjHh9/bLafWL/+0iYWYpm0tDQOHTqUb2XAn7cVvOqqqwrcVtA/s+w9JKQRMTGxTJliVm488IA5hyuqoHJauNA0/t+8OXeflM6dzfPnz8PkyWZ8KSYGEhNNE9abbza7plSrZsP831DVdUGJiIiIlJCS/WL5B/Ap3llWuhtTBp6/s2fPsnjxYubPn09kZCRHjx6lSpUq9O3b11ny37hxY8+EKy71yCOPMGPGDFavXk0XX0uGExLMvpIDB8I331gdjRRBSkpKntsKZt0/ceKE89hy5co5txXcuHE1584lMWWK2WXRHSIjYcQIUzSS3w4oBw9C48ZmSVazZub5U6fM64cMydpidSXQ0z1BioiIiBSDkv1i2Y1Zu+9NyX4AMAL4X5Fe5XA42LZtmzPxX716Nenp6bRo0cKZ+F9//fVUVBO0UiE1NZXrrruO48ePs3nzZmrWrGl1SK7z1FMwfTrs2QNXXWV1NOJCiYmJeW4ruGTJLM6dy7jsDigzZpgdUG67zazweO01c1xh/ndr0ya7ierKlWbXlLZtTYI/fDjMmWOa6n/7rSkqqVbNNF/t0wfWrTPnOHMGatacjlniJSIiIuIdlOwX2/8Bz5DX2n3Ps2Gauf0BlGy/8YSEBJYsWUJkZCSRkZEcOnSISpUq0bt3b4YMGcKQIUNo2rSpK4IWN4mJiaFDhw707NmTiIgI31i/Hx0NLVuaBhjumuIVrxMSUpOYmHP5JvvvvgsPP2weCw42u5EkJpobXD7ZP3w4e9zo1VfNtxfAvffCJ5+Y2f3z5/Nupv/ww+b6fn5w7lwA1ao9B0wp4VcsIiIi4jp+VgdQek0EOpLfdmie5cB0qy5Zog8QGBjI6NGj+fjjj4mNjWXHjh28+OKLpKSk8Pjjj9OsWTOaNm3KQw89xLx587h48WKJrymu1ahRI7755hvmzJnDG2+8YXU4rvHkk1CrFkyaZHUk4lEFZ+vTppnP3bqZAYADB4pW9BEXl30/a8tUgLp1zeekJDh58tLXHT4MP/1k7t9yS9aa/aTCX1hERETEA5TsF1sA8DVmVt3KmVN/YBRwi8vPbLPZuPbaa5k4cSJLlizhzJkzREREMGDAAObMmcOwYcMICgpi0KBBvP322/zxxx+oUMQ7DBs2jKeeeoqnn36aVatWWR1OyaxcaTKr114Dj+7vLtbL/3drQgIcOmTujx4N5cqZb49hwwp/9vx+XeV8/M+FMfv3mwqDY8egRw/48MOsZ/KY/hcRERGxkJL9EmkF/Ix1yb4/0J7sQQf3qlq1KiNHjmTGjBlER0eza9cuXstcHPvUU09xzTXX0KRJEx544AFmz57NhQsX3B6T5O+ll16ie/fu3HzzzbkaoJUqdjs8+qhpi37bbVZHIx5XuN9rORPyoow35tx99Pjx7PtZPy6VKpmCkixr15q1/Xv3mqZ+CxeaNfymAkF9TURERMS7KNkvsZHAvzF/lZ5M+v0xgw0LgGoevK5hs9lo2bIljz76KAsWLOD06dPMnTuX4cOHs3DhQsLDw7niiivo378/b7zxBjt37tSsv4cFBATw/fffk56ezt/+9jcyMjKsDqnovv0WoqLgrbfM4mgpY/KfLQ8MNNvfgWmil54OFy9mdcYvnOBgaN3a3I+IMOdISDBJPED//tnr9X/+Gfr2NV34H3oIZs2CypWzzpQONCrC1yUiIiLifmrQ5zJzgL8Cdtzfpd8GdAfmAtXdfK3i2bdvn7PJ36+//kpycjINGjRwdvjv168fgYGBVodZJixZsoQBAwYwZcoUpkwpRQ3EEhOheXPo3j17gbSUKSEhwcTEHClUg74GDcy2eRcumKQfCjfLHxlpuu7b7flvvXfkiOkF4HBA+fLQoUPuc3zwAXTsuAO41kVfuYiIiEjJKdl3qd3AHcBvbjp/VjPAqcATQDk3Xce1kpKSWLFihTP537NnDwEBAfTo0cPZ4b9Nmza+0TXeS7344otMnTqVhQsX0r9/f6vDKZypU806/T/+MJuci887e/Ysa9euZc2aNaxevZrly5fjcDguu/XeBx+YWfkxY0wy/u67ULGiabBXGAsWwIsvwqZNEBBgGv698gp06WKeP3iw4G/BX38tR+/eSWjdvoiIiHgTJfsulwG8AzyN62b5/TLP1Q74BmjjgnNa58CBA8yfP5/IyEiWLl3KxYsXufLKK52z/v3796dGjRpWh+lT7HY7Q4YMYfPmzWzZsoUrr7zS6pAKduiQmdV/+GH4v/+zOhpxA4fDwd69e52J/Zo1a/j9998BqF27Nj169KB79+7ce+93BAZuuaRRHkB8vJmJz+qkn5xsEvQdO8za+rVrPfXVdMV9g7wiIiIixaNk320OAO8CnwIXyE7YiyIAM1jQEXgI+BulZTa/sFJSUli5ciWRkZHMnz+f33//HX9/f7p168aQIUMYPHgw7du3x0/rtUvs5MmTdOjQgSZNmrB06VICArxh28h8jB1rFk7v3WsWZ0upl5SURFRUlDOxX7NmDadOnXLu+pGV3Hfv3p2rr746R6XP68BT5PX7c8sWk9R37Qo1apj2DkeOmHX28+bBwIGmU//Ro3nHNHMm1K9f0q/MD3gBeK6kJxIRERFxKSX7bpcIfA98CGzBJO82TLmnPfNmI7vBX1YlQE0gHHgQ6OzRiK0UExPD/PnzmT9/PosXL+bChQvUrVuXwYMHM3jwYAYOHEhQUJDVYZZaq1ev5vrrr2fixIn8n7fOmK9fD6Gh8NFHcO+9VkcjxXT06FFnUr969Wo2bdpEWloaVapUISwszJnYh4WFXaaS5xRwJZB2yTOHD8O4cbB5M5w7B9Wrm2+dp5+Gnj3NMSEhEBOT95mzlgOUjD9wCKhX0hOJiIiIuJSSfY9KAbYDUZjE/yyQhJnBrwQ0Bjpl3q7Cui39vENqaiqrV692lvxv374dPz8/QkNDnSX/nTp10qx/Eb3xxhtMmjSJOXPmMHz4cKvDyc3hgOuuMy3RN2/OboUuXi0jI4MdO3bkKsmPjo4GoFGjRs7EvkePHrRp06YYVSW3A//B/c1PiyoA+Avwg9WBiIiIiFxCyb6UGocOHWLBggVERkayaNEiEhISqFWrFoMGDWLIkCEMHDiQ2rVrWx2m13M4HIwePZoVK1awadMmQko+tek6P/4IN98MixaZfc/EKyUkJPDbb785E/t169Zx/vx5AgIC6NChQ66S/ODgYBdc8TcgzAXncYdlwPVWByEiIiJyCSX7UiqlpaWxbt0651r/zZs3Y7PZ6Ny5s7PDf5cuXfDXzHCezp49S8eOHalduzYrV66kQoUKVodkuqu1bAlt28Ls2VZHI5kcDgfR0dG5SvK3b9+Ow+EgKCgo16x9586dqZy9+byL/QWYjWmC6g0CgIHAL5T1KiwRERHxTkr2xSccPXqUBQsWMH/+fBYuXMjZs2cJCgpi4MCBDBkyhEGDBlG3bl2rw/QqGzdupEePHtx3331Mnz7d6nDMNnvPPw87d5pO/GKJlJQUNm/enKsk/9ixYwC0bNnSmdh3796d5s2be3AZzXGgJRAPWP3flg2oCvyB6ScgIiIi4n2U7IvPSU9PZ/369c61/hs3bgSgY8eOzrX+YWFh3t2N3kM++OADHnzwQX788UduvPFG6wI5dgyaNYO774a337YujjLo5MmTrF271pnYb9iwgZSUFCpWrEjXrl2diX23bt244oorLI72B+AWi2PI8jUw1uogRERERPKlZF983okTJ1i4cCGRkZEsWLCA06dPU716dQYMGODc3s/r9513E4fDwa233srcuXPZuHEjza2aUb/nHvjvf2HfPtBuC25jt9vZtWtXrpL8vXv3AnDllVc6E/sePXrQrl07ypcvb3HEf+YAbsPscFLUrUxdxQ8YDfyEyvdFRETEmynZlzIlIyODqKgoIiMjiYyMZP369TgcDtq2betc69+9e3fKlStndagec/78eTp37kzFihVZt24dlSpV8mwAW7dChw5mRv/hhz17bR+XmJjI+vXrnYn92rVrOXfuHH5+frRr1y5XSX7Dhg1z7G3vzVKBEcBiPJ/w+wPXAZFARQ9fW0RERKRolOxLmXb69GnnrP/8+fM5efIk1apVo3///s5Z/wYNGlgdpttt376d0NBQbr31Vj799FPPXdjhMF33Dx+G7duhDA2yuENcXJyzHH/NmjVs2bKFjIwMAgMD6datmzOxDw0NpWrVqlaHWwJJHDvWgzp1NuO5nTf9gD5ABFDFUxcVERERKTYl+yKZ7HY7mzdvds76r1u3DrvdzrXXXuuc9e/Ro4d3dK53gy+//JJx48bx5ZdfcscddxTx1cmYBmpJmFLrisAVQGDBL5s9G8LD4ZdfYNiwYkRddqWlpbF169ZcJfmHDh0CoGnTprm65Ldq1cqDjfTcb8OGDfTv34uZM+vTt280JhF31yx/1rlvBz4GfPPnX0RERHyPkn2RfJw9e5ZFixYxf/585s+fz9GjR6lSpQr9+vVzzvp71R71LnDXXXfx/fffs379elq3bl3AkXuBRUAUsA7YTd5booVg9kfvBPQCuuBc55yaCtdeC40bw4IFUCpKyK1z5swZ1q1b50zs169fz8WLFylfvjydO3d2JvbdunXz6Z0nYmJiCAsLIyQkhKVLl1Kp0lIcjruw20/g+p02/YGawGfASFefXERERMStlOyLFILD4WDr1q3ODv+rV68mIyODli1bOhP/Xr16UbFi6V7He/HiRcLCwkhNTWXjxo1/KvVOx+wp/h6wBJO0+2c+XhB/zGy/HWgNPAzcCv/6GCZONGv2CxxYKHscDgd79+7NVZL/+++/A1CnTp1ca+07derks9UmfxYfH0/Pnj1JTExk3bp11KlTB4CFC3/k8OGbufNOsNn8yXvgqSiyzvE3YDqgppEiIiJS+ijZFymG+Ph4lixZ4iz5P3z4MJUrV6ZPnz7O7f2uvvpqq8Mslt27d9O5c2dGjBjBv//978wJ96+Bp4GjZCdCxWEDHOCoAv+XAYdug/c/cU3gpVhSUhIbN27Mtbf96dOnsdlstG7dOldJfpMmTUpJIz3XSk9PZ/jw4axbt461a9dyzTXXAGY5Q9u2balfvz5LlnyMzfYxptw+Hgjg8oNRWbKOrQbcA9wPNHP51yEiIiLiKUr2RUrI4XCwc+dOZ+K/atUq0tLSaNq0qXOt//XXX0/lypWtDrXQfvjhB2655Ra+/vo1xo5dgek+npmou4odsDeHgO+BDq47bylw9OjRXIn9pk2bSEtLo0qVKoSFhTkT+9DQUGrUqGF1uJZzOBw88MADfPrpp8yfP59+/fo5n3v//fd56KGHiIqKokOHrO+jJOBnYCmwFrPsJL81/X5AU8xykz7ATUDp+VkVERERyY+SfREXO3/+PEuXLnUm/7GxsVSsWJHrr7/eWfLfvHlzr5+d/eKLgfz1r4uoWtUfm62kZdH5yVpk/RzwPCbx8i0ZGRns2LEjV0l+dHQ0AI0aNXKW43fv3p02bdoQEBBgccTe56233uLxxx/n008/5e6773Y+fu7cOZo2bcrIkSP5/PPPCzjDRWArcAzTTNIBVALqAu1Qd30RERHxRUr2RdzI4XDwxx9/OLf2W758OampqTRu3NiZ+Pft25cqVbwt2XgDmITdjge3NrsN+AJTTl16JSQkOBvprVmzhnXr1nH+/HkCAgLo2LGjM7Hv3r07wcHBVofr9WbNmsVf/vIXnnzySV577bVcz02aNIkZM2awZ88errzySosiFBEREfFOSvZFPCgxMZFff/3V2ejvwIEDlC9fnl69ejnX+l9zzTUWz/r/E3jKguvagL8C/yF7xt+7ORwOoqOjc5Xkb9++HYfDQVBQUK5Gep07dy5VSzm8wcaNG7n++usZNmwY33//fa7tA/fv30+rVq147rnnmDx5soVRioiIiHgnJfsiFsnquJ6V+C9btozk5GQaNmzoTPz79etHtWrVPBjVJ8C9Hrzen9kyr/+hhTHkLyUlhc2bN+cqyT927BgALVu2zFWS36JFC69fquHNYmNjCQ0NpVGjRvz6669UqlQp1/M33ngja9euZc+ePRpEEREREcmDkn0RL5GUlMTy5cuda/337t1LQEAAPXv2dDb6a926tRsTyF2Y9ctpbjp/UfwX+IvVQXDy5ElnUr969Wo2btxISkoKlSpVomvXrs6Z+7CwMK644gqrw/UZCQkJ9OzZk/Pnz7Nu3Trq1q2b6/lVq1Zx3XXX8fXXXzN27FiLohQRERHxbkr2RbzU/v37nbP+S5cuJSkpieDgYOesf//+/alevbqLrpaB6Ua+hcJvVeYuNqAmsBuo5bGr2u12du3alaskf+/evQBceeWV9OjRwzlz3759e8qVK+ex2MqS9PR0RowYwZo1a1i7di2tWrXK9bzdbicsLAy73c769etzlfaLiIiISDYl+yKlQHJyMitXrnQ2+tu1axf+/v50797d2eivffv2JZj1fx14wpUhl5A/Zv3+9267QmJiIuvXr3cm9mvXruXcuXP4+fnRrl27XCX5DRs2VEm+BzgcDsaPH89HH31EZGQkAwYMuOSYf//739x2220sX76cXr16WRCliIiISOmgZF+kFDp48CDz589n/vz5LFmyhAsXLlCvXj0GDx7M4MGDGThwIDVr1izk2U4BwUCqGyMuruWAaxK62NjYXCX5W7duJSMjg+rVq9OtWzdnSX7Xrl2pWrWqS64pRfP222/z6KOP8vHHH3PPPfdc8vzFixdp0aIFXbt25b///a8FEYqIiIiUHkr2RUq51NRUVq1a5Sz537FjB35+foSFhTlL/jt27FhAufM04GnA7sGoCyMAGA38WORXpqWlsXXr1lwl+YcOHQKgadOmubrkt2rVSqXgXmD27NmMGjWKSZMm8c9//jPPY15++WVefPFFfv/9d5o2berhCEVERERKFyX7Ij4mLi6OBQsWEBkZyeLFi0lISKB27doMGjSIIUOGMHDgQGrVyloLnwE0BuIsjLgg/pjY6hd41JkzZ1i7dq1z5n79+vVcvHiR8uXL07lz51wl+XXq1PFI5FJ4UVFR9OrViyFDhvDjjz/mOfhy9OhRmjVrxn333cebb75pQZQiIiIipYuSfREflpaWxtq1a50d/rdu3YrNZqNLly4MGTKEW26pTsuWj1kdZgH8gKlA9j7qDoeDPXv25CrJ37VrFwB16tRxJvY9evSgY8eOVKhQwZLIpXDi4uIIDQ2lQYMG/Prrr/luo/f3v/+dWbNmsW/fPmrUqOHZIEVERERKISX7ImXIkSNHnLP+ixYtwt//HKdPw5Qp0Ls39OljjouOhpAQKyPNZre3ZPXqj3OV5J8+fRqbzUbr1q1zleQ3adJEjfRKkfPnz9OzZ0/OnTvHb7/9Rr169fI8buvWrXTo0IHp06czfvx4D0cpIiIiUjop2Rcpo9LT07n66orExmYwZQqMHAkPPGCemzkT6hdcOV8kCxfCCy/A5s0QEADdusErr0DnztnH9O4Ny5df+tru3WHTJggIqEpoaKgzsQ8LC3Ph1oPiaenp6YSHh7Nq1SpWr15N69at8zzO4XDQv39/Dh8+zPbt27XloYiIiEghBVgdgIhYIyAgDZstw/nnjh1h3TrXXycyEkaMgIwMCA6GlBST/K9cCWvXQrt2uY9v0gRq187+c+vW8MUXX9OkyRgCAvQry1c8+uijLFiwgHnz5uWb6APMnTuXpUuXMmfOHCX6IiIiIkWgFtQiZda2XH9atgxsNnM7eNA85nDA5MlQqxbUqAHjx8Mzz2QfVxhPPGES/bAwc94DB8wSgaQkeO65S4+fPNkMOmTdPvrIRvPm55Xo+5Dp06fz3nvv8cEHHzBw4MB8j0tLS2PixIn069ePYcOGeTBCERERkdJPyb5ImbXnske89x68/DKcPg1Vq8JPP8H06YW/wuHDsGOHuT9ypCnhr1YNBgwwjy1ZYgYCcnr0UahQwczw33svHD/uX6hYpXT45ZdfePTRR5k4cSL33ntvgcd+9NFH7NmzhzfffFO9GERERESKSMm+SJl18bJHTJtmPnfrZpr2HTgAV11V+CvE5djRL+eOd3Xrms9JSXDyZPbjVapAgwamjD86Gj75BLp1SycxMaHwFxWvtXnzZm655RbCw8P55z//WeCxZ8+eZcqUKdx11120+/NaDxERERG5LCX7ImVWWoHPJiTAoUPm/ujRUK6cScaLUk2dX/vPnI9nTdi+/TacOQPbtplBgqefNo9HR8PMmfsKf1HxSocOHWL48OFcc801fPvtt/j5Ffzfz8svv0xKSgovvfSShyIUERER8S1K9kXKrIqFPjJnBXVR9u9o2DD7/vHj2fdPnDCfK1Uy/QAA2reH8uWzr3frrdnHx8amFP6i4gIOIB44BhwCTgHJxT7bhQsXGDFiBP7+/syePZvKlSsXePy+fft49913efrpp6nvym0hRERERMoQJfsiZVa1Ap8NDDQl9QBz5kB6Oly8CPPmFf4KwcGmmz5ARIQ5R0KC6cYP0L8/+Pub5P+tt+D8+ezX/vBD9v2QkBzt+cUNEoCZwLPAQCAIqAHUBxoAtYHKQFPgNuBtYB1mUKBgGRkZjBkzhv379zN37txCJe9PPvkk9erV47HHHivOFyMiIiIiaOs9kTKs7WWPmDQJHn4YVqwwDfNSUuDChaJdZdo0GD4c1q83XfhTUuDUKTOrn1WhffEiPP44PPkkNG0KiYnZ6/2vuQZ69+6Dw+FQkzaX2w58AHyJmbkPADLIO4l3APuBGOA/gB2T/D8M3A5Uz/MKjz32GJGRkfzyyy+0adPmshGtWLGC//3vf3z77bdUqlSpqF+QiIiIiGTSzL5ImdUcKDh5Hj/ebI8XFATx8Wbt/t13m+cqFnIVwJAhphqge3fT1T852XTjX74csvqu1a4Nzz5rSvlPnDCDAS1bwlNPwerV0K/fROrVq8fQoUOZPHkys2bNIjY2FkdR1hRIDlFAT8yAz6dkl+inc/nZ+nRMog8m+X8EqAc8CpzPdeR7773H9OnTeffddxk8ePBlo7Lb7Tz22GN06dKFMWPGFPJrEREREZG82Bx6tyxShvUA1uT7bHy8mYnP6qSfnAxdupjt9MLCYO1a90dot1dk9uxviYrawqZNm4iKiuJ4ZgOAWrVq0alTJzp16kTHjh3p1KkTjRo1UgVAvlKAl4DXMAM9GQUfXiR+wJXAV0Bf5s6dy8iRI5kwYQJvvvlmoc7wzTffcPvtt7Ny5Up69uzpwthEREREyh4l+yJl2iTM+uv0PJ/dssUk9V27Qo0aEBUFR46Ydfbz5sHAgWa2/+jRvM8+cyaUrL+aH2ZAYoXzEYfDwZEjR4iKinIm/1FRURzNDOKKK65wJv5Znxs3bqwBALYCtwC7Kcxa++LxA+ycOnUjLVrM5brrBvDf//4Xf3//y77y4sWLNG/enG7duvHTTz+5KT4RERGRskPJvkiZtp2C1u4fPgzjxsHmzXDuHFSvDqGhZlu8rInXkBCIicn79dHR5vmS+RK447JHHT161Jn4Zw0CHD58GICaNWteMgBw9dVXl6EBgOXAUMzMvitn8/OWkQFbtlSlZcu9VKlSr1CvefHFF3nllVf4/fffufrqq90coYiIiIjvU7IvUub1AH7DE0lg0VUHjgLFa9R2/PjxSyoA4jI7/1WvXv2SAYCmTZtedv/30mcZMIjca+3dz+Hww2brDCwBqhZ47JEjR2jWrBkPPPAAr7/+ukfiExEREfF1SvZFyrzvAW9shuaPafrm2uTv5MmTlwwAxGSWJgQGBtKhQwdn8t+pUyeaN29eigcANmEa8aXgyUQ/mz/QG4gEyuV71F133cWcOXPYu3cvNWrU8ExoIiIiIj5Oyb5ImZcKNAaOYU1CmJ8AzPryJm6/0qlTp9i0aVOuAYDo6GgAqlateskAQIsWLQq1Dt1aF4HWQCzWVm3YgBeAyXk+u3nzZjp16sR7773HAw884NHIRERERHyZkn0RARYBA60OIgcbpmP8k5ZFcObMmUsGAPbv3w9AlSpVaN++fa4BgJYtWxIQEGBZvJd6DHgH7xjACcBs95e7P4TD4aBfv34cO3aMbdu2ednfn4iIiEjppmRfRDLdA3yB9Wv3A4B2wLrM+97j3LlzbN682Zn8R0VFsXfvXgAqVapEu3btnMl/p06daNWqlUUJ7GrgOtzXdb+oAoBrMAl/djn/7NmzCQ8PZ+7cuQwdOtSq4ERERER8kpJ9EcmUgEnIjmNdwm/DJIZbM2PxfvHx8WzZsiXXAMCePXtwOBxUrFiRtm3b5hoAuPbaaylXLv/16yXnANoAf2D9wE1ONmA6MB6A1NRUWrduTUhICAsWLChDOyOIiIiIeIaSfRHJYRPQC0jCmvJvG/AdZj/40uv8+fOXDAD88ccfOBwOypcvf8kAQOvWrSlfvryLrr4KM6vvbWzA1cAewMb06dN59NFH2bJlC23atLE4NhERERHfo2RfRP5kFWb9fiqenxn+GLOcwPdcuHCBrVu35hoA2LVrF3a7nXLlytGmTZtcAwBt2rShQoUKxbjSGOBnzFZ73mgpZ860o2nTpvz1r3/l448/tjogEREREZ+kZF9E8vAbJuG/iPuTRn9M6flXwG1uvpZ3uXjx4iUDAL///jsZGRkEBATQunXrXAMAbdu2pWLFigWc8ThwFd6b6AcA4Tz6aAM+/fRT9u7dS7169awOSkRERMQnKdkXkXxEA3cAK914DT+gEfAN0MON1yk9kpKS2LZtmzP537RpEzt27CA9PR1/f3+uvfbaSwYAKleuDEBISC1iYk4zZQr07g19+phzRkdDSIhlX1IuDoc/1arBs8++xNNPP211OCIiIiI+y7taXYuIF2kMLANmABMxs8WumTHOyAA/Pxs22wTgJaCyS87rCypVqkRoaCihoaHOx5KTk9m+fXuuAYBvv/2WtLQ0/P39ueaaa+jUqRNpaRecrwkMhKxTFGs1QAEWLoQXXoDNmyEgALp1g1degc6dLz32/Hlo3x4OHDB/njEjgz596jJhwgTXBiUiIiIiuSjZF5EC+AEPAkOAV4B/Y9byQ/G2dfPH4bAzb56D+vXfoXPnh1wUp2+rWLEiXbp0oUuXLs7HUlJS2LFjR64lAJDifL5jR1i3zvWxREbCiBFmwCY4GFJSTPK/ciWsXQvt2uU+fvz47EQfwG6H558fRqVKlVwfnIiIiIg4+VkdgIiUBk2Az4CjwFtASI7nAjCd1vOSc4u5msATwAGmTu3IxIn/dUOcZUeFChXo1KkT9957Lx999BEbN64m545+y5aBzWZuBw+axxwOmDwZatWCGjVMIv7MM9nHFcYTT5hEPyzMnPfAAbNEICkJnnsu97E//ghffw033ZT9mMORdwWAiIiIiLiWkn0RKYKawARgP2ZN/8/AJKAv0AzTHO5KzOBAN+AfwNfATuAk8Co2WwiTJ09m+fLlrFixwuNfge+KvuwR770HL78Mp09D1arw008wfXrhr3D4MOzYYe6PHGlK+KtVgwEDzGNLlpiBAIC4OLjvPujUyVwzi78/2Gy/F/6iIiIiIlIsKuMXkWKwYWb3Q4AbivzqkSNH0rZtW1566SUWLVrk2tDKrIuXPWLaNPO5WzdYvhxSU00yvnt34a4QF5d9v06d7Pt165rPSUlw8qR5buxYSEuD774jV8WBkVi4C4qIiIhIsWlmX0Q8zs/Pj+eee47Fixezdu1aq8PxEakFPpuQAIcOmfujR5sEvEoVGDas8FfIb++WnI/bbPDOO2Yw4Z13oHnzoscqIiIiIiWnZF9ELHHDDTfQqlUrXnrpJatD8RGFb7mfc31+UTZfbdgw+/7x49n3T5wwnytVMv0Atm41f37kEbNc4Nprs4+dMAG6d89RIiAiIiIibqFkX0Qs4efnx7PPPktkZCQbN260OhwfUK3AZwMDoUEDc3/OHEhPh4sXYd68wl8hOBhatzb3IyLMORISTDd+gP79zZr8LImJ5nYxxwqDlBS4eLGQ3QBFREREpNiU7IuIZW6++WaaN2+u2X2XaHzZIyZNMp9XrIAmTaBx49zr8Atj2jTw84P1600X/quvhpgYM6uf9c/45ZemYiDrFp2jd+CMGX5s2TKuaBcVERERkSJTsi8ilvH39+eZZ55h9uzZbNmyxepwSjl/oHyBR4wfb7bHCwqC+Hizdv/uu81zFSsW7ipDhphqgO7dTVf/5GTTjX/5cmjXrjBnsAPae09ERETE3WwOR1FWbIqIuFZaWhotW7akQ4cO/Pzzz1aHU8o9AswA0vJ8Nj7elNFnddJPToYuXcx2emFh4LleiduB1p66mIiIiEiZpGRfRCz36aefcs8997B9+3Zat1YSWHz/Bm7L99ktW0xS37Ur1KgBUVFw5IhZZz9vHgwcaGb7jx7N+/UzZ0L9+iWNsRKQgHZ+FREREXEvJfsiYrnU1FSaNWtG9+7d+c9//mN1OKXYeaAecDHPZw8fhnHjYPNmOHcOqleH0FB4+mno2dMcExJi1uDnJTraPF98AcBdwEclOYmIiIiIFIKSfRHxCjNmzODBBx/k999/p2XLllaHU4qNxyTT6VYHko/NQHurgxARERHxeUr2RcQrpKSkcPXVV9O3b1++/vprq8MpxXbinevh/YAuwDqrAxEREREpE9SNX0S8QoUKFXjiiSf47rvv2Ldvn9XhlGLXAn3xvjXxduBxq4MQERERKTM0sy8iXiMpKYnGjRszbNgwPvvsM6vDKcX2YWb3U6wOJFMAMACYC9gsjkVERESkbNDMvoh4jUqVKjFp0iS+/vprDh48aHU4pVhT4J9WB5HJhunA/ylK9EVEREQ8RzP7IuJVEhMTCQkJ4YYbbuDDDz+0OpxSzA5cB6zH+mZ9XwJ3WByDiIiISNmimX0R8SpVqlTh8ccf5/PPPycuLs7qcEoxP+AHoC7Wrd+3AfcDt1t0fREREZGyS8m+iHidBx98kGrVqjFt2jSrQynlrgKWAUF4PuG3AbcC76PyfRERERHPU7IvIl6nWrVqTJgwgU8++YSjR49aHU4p1xRYC9QH/D143XuBr9B/MyIiIiLW0LswEfFKDz30EBUrVuT111+3OhQf0ATYCIzM/LO7ZtoDMM34PgBm4NnBBRERERHJScm+iHilGjVq8PDDD/Phhx9y4sQJq8PxAXWA/2HW8VfHlYm4w5E1eNAD+B34ByrdFxEREbGWkn0R8VoTJkzA39+fN9980+pQfMhNwG5M47zKmKS8eIl5Wpr5bLM1Bz4DlgIhJQ9RREREREpMyb6IeK2goCDGjx/P+++/z6lTp6wOx4fUAd4DjmFK7lvmeK5cAa/zJ+u/jYwMWLIkkPT0pcAu4C70X4rI/7d353Fazvsfx1/3LO2bJcvJSVQqQops6ZRDG6UahHB+dCxHI0QilbIeKUuL5WQ5wpGltGcJlSUhkVKUFvuuvaZm5v79cTVKWmZq5r7u5fV8PO7Hvcw11/cTY/S+Pt/v95IkKX74NzNJca179+5Eo1Huu+++sEtJQhUJOvzzgEUEU/yvAZoRbOi3B1AB2AuoCZwJ/Js33+zHXntBWtrzZGQ0xyn7kiRJ8ScSjUajYRchSTty3XXXMXz4cJYuXcoee+wRdjkpLScnh8MOO4xDDjmESZMmhV2OJEmStsPOvqS4d91117FhwwYGDx4cdikpb9iwYSxdupSBAweGXYokSZJ2wM6+pIRw1VVXMWLECJYtW0alSpXCLicl/fzzz9SqVYvzzjuPBx54IOxyJEmStAN29iUlhOuvv561a9cydOjQsEtJWbfccgvRaJT+/fuHXYokSZJ2wrAvKSFUq1aNLl26cM8997B69eqwy0k5CxYs4IEHHqB3795UrVo17HIkSZK0E07jl5QwvvzyS2rVqsXtt99Ojx49wi4npbRt25Z58+bx6aefUqZMmbDLkSRJ0k7Y2ZeUMKpXr84//vEPBg4cyNq1a8MuJ2VMmTKFCRMmcNdddxn0JUmSEoSdfUkJZfHixRxyyCEMHDiQq6++Ouxykl5eXh4NGzakQoUKvPXWW0QikbBLkiRJUiHY2ZeUUA4++GDOP/98BgwYwPr168MuJ+n997//Zc6cOdxzzz0GfUmSpARiZ19Swvn888+pV68egwcPpmvXrmGXk7RWrVrFIYccwsknn8zTTz8ddjmSJEkqAsO+pITUuXNnpk+fzqJFiyhdunTY5SSlPn36MHDgQD777DOqV68edjmSJEkqAqfxS0pIN910E9988w1PPPFE2KUkpa+++oqBAwfSvXt3g74kSVICsrMvKWGdffbZvP/++3z++edkZmaGXU5SueCCC3j11VdZuHAhFStWDLscSZIkFZGdfUkJq3fv3ixdupSnnnoq7FKSynvvvcdTTz3FbbfdZtCXJElKUHb2JSW0Dh06MHfuXObPn09GRkbY5SS8aDTKSSedxMqVK5k9ezbp6elhlyRJkqRdYGdfUkLr06cPixYtYuTIkWGXkhRGjRrF22+/zT333GPQlyRJSmB29iUlvNNPP50vvviCuXPnGlB3Q05ODvXq1ePQQw9lwoQJYZcjSZKk3WBnX1LC69OnDwsWLOCFF14Iu5SENmTIEL788kvuvvvusEuRJEnSbrKzLykptGzZkm+//ZaPP/6YtDSvYxbVTz/9RK1atbjgggsYOnRo2OVIkiRpN/k3YklJoW/fvsydO5cxY8aEXUpC6t+/P5FIhH79+oVdiiRJkoqBnX1JSePkk0/mt99+48MPPyQSiYRdTsKYP38+hx9+OHfddRfXXntt2OVIkiSpGBj2JSWNqVOn0rx5c8aNG0fbtm3DLidhnH766cyfP59PP/2U0qVLh12OJEmSioFhX1LSiEajNG3alJycHGbOnGl3vxBeffVVWrRowQsvvEBWVlbY5UiSJKmYGPYlJZWC8Dp58mRatWoVdjlxLS8vjwYNGlClShWmT5/uxRFJkqQkYtiXlFSi0SjHH388aWlpvP322wbYHRg+fDiXXnop7733Hsccc0zY5UiSJKkYGfYlJZ1JkyZx2mmnMWXKFP7+97+HXU5cWrVqFbVq1aJFixY8+eSTYZcjSZKkYmbYl5R0otEoxxxzDBUqVGDq1KlhlxOXbrrpJu69914+++wz/vrXv4ZdjiRJkopZWtgFSFJxi0Qi9OnTh2nTpjF9+vSwy4k7y5YtY9CgQVx33XUGfUmSpCRlZ19SUopGozRo0IB99tmHV199Nexy4krnzp15/fXXWbhwIRUqVAi7HEmSJJUAO/uSklJBd3/KlCnMmDEj7HLixsyZM/nf//7H7bffbtCXJElKYnb2JSWt/Px8Dj/8cA488EAmTZoUdjmhi0ajNGnShDVr1jBr1izS09PDLkmSJEklxM6+pKSVlpZG7969mTx5Mu+//37Y5YTu+eef55133mHQoEEGfUmSpCRnZ19SUsvLy+PQQw+lbt26jB07NuxyQrN+/Xrq1avH4Ycfzrhx48IuR5IkSSXMzr6kpJaens5NN93EuHHj+Oijj8IuJzSDBw/m66+/5u677w67FEmSJMWAnX1JSS83N5c6depw1FFH8cILL4RdTsz9+OOP1K5dm3/84x8MHjw47HIkSZIUA3b2JSW9jIwMevXqxahRo5g7d27Y5cRcv379SEtL4+abbw67FEmSJMWInX1JKWHDhg3Url2bE044gWeeeSbscmJm3rx5HHHEEQwcOJBrrrkm7HIkSZIUI4Z9SSnjoYce4oorruDTTz+lbt26YZcTE23atGHhwoXMmzePUqVKhV2OJEmSYsSwLyll5OTkULNmTU4++WRGjBgRdjkl7uWXX6ZVq1aMHj2aDh06hF2OJEmSYsiwLymlDBkyhKuvvprPPvuMWrVqhV1OicnNzaVBgwbstddeTJ06lUgkEnZJkiRJiiE36JOUUv75z39StWpV7rzzzrBLKVGPPvoo8+bN45577jHoS5IkpSA7+5JSzj333EPPnj1ZuHAhNWrUCLucYrdy5Upq1apF69ateeKJJ8IuR5IkSSGwsy8p5Vx22WVUqVKFf//732GXUiLuvPNOVq9eze233x52KZIkSQqJYV9SyilfvjzXXXcdjz32GF999VXY5RSrpUuXcu+993L99ddzwAEHhF2OJEmSQuI0fkkpadWqVdSoUYPzzjuPIUOGhF1OsTn33HOZNm0aCxcupHz58mGXI0mSpJDY2ZeUkipWrMg111zD8OHD+e6778Iup1jMmDGDkSNHcscddxj0JUmSUpydfUkpa8WKFRx44IFcfPHF3HPPPWGXs1ui0SgnnHACOTk5fPDBB6SleS1XkiQplfm3QUkpq3Llylx11VU89NBD/Pjjj2GXs1ueffZZ3n33XQYNGmTQlyRJkp19Sant119/5cADD+SKK67grrvuCrucXbJu3Trq1q3LUUcdxZgxY8IuR5IkSXHA9o+klLbnnnty5ZVXMmzYMH7++eewy9kl999/P99++y0DBgwIuxRJkiTFCcO+pJR3zTXXEI1Gue+++8Iupch++OEH7rjjDrp27cohhxwSdjmSJEmKE4Z9SSmvatWqXHHFFQwZMoTffvst7HKK5OabbyYjI4O+ffuGXYokSZLiiGFfkoBrr72WDRs2MHjw4LBLKbS5c+cyfPhwbr75Zvbcc8+wy5EkSVIccYM+Sdrk6quv5oknnmDZsmVUqlQp7HJ2qlWrVixevJi5c+dSqlSpsMuRJElSHLGzL0mb9OjRg7Vr1zJ06NCwS9mpl156iZdffpm7777boC9JkqQ/sbMvSVvo2rUrzz77LEuXLqVChQphl7NNubm5HHnkkeyzzz68/vrrRCKRsEuSJElSnLGzL0lb6NmzJytXruTBBx8Mu5TtGj58OPPnz2fQoEEGfUmSJG2TnX1J2sqll17K2LFjWbJkCeXKlQu7nD9YsWIFtWrV4vTTT+fxxx8PuxxJkiTFKTv7krSVG2+8kV9++YX//Oc/YZfyJ3fccQdr167l9ttvD7sUSZIkxTE7+5K0DRdddBEvv/wyixcvpkyZMmGXA8CSJUuoW7cuN910E3379g27HEmSJMUxw74kbcPChQupW7cugwcPpmvXrmGXA0CnTp14++23+eyzzyhfvnzY5UiSJCmOGfYlaTvOP/98pk2bxqJFiyhdunSotbzzzjuceOKJPPHEE1x44YWh1iJJkqT4Z9iXpO2YP38+hx12GA899BCXXnppaHXk5+dz/PHHk5uby/vvv09amtutSJIkaccM+5K0A506deK9997j888/JzMzM5QannnmGc477zymTp3K3/72t1BqkCRJUmIx7EvSDnzyySccccQRPPbYY1x00UUxH3/dunXUqVOHo48+mtGjR8d8fEmSJCUmw74k7UTHjh355JNPmD9/PhkZGTEd+4477qBfv37MmzeP2rVrx3RsSZIkJS4XfkrSTvTp04dFixYxcuTImI77/fffc+edd5KdnW3QlyRJUpHY2ZekQmjbti2LFi1i7ty5pKenx2TMSy+9lFGjRrFo0SL22GOPmIwpSZKk5GBnX5IKoU+fPixYsIAXXnghJuPNmTOHRx99lH79+hn0JUmSVGR29iWpkFq1asU333zDxx9/XKK3v4tGo7Ro0YIvv/ySuXPnhnYXAEmSJCUuO/uSVEh9+/Zl7ty5jBkzpkTHmTx5MlOmTGHgwIEGfUmSJO0SO/uSVAR///vf+fXXX/nwww+JRCLFfv6NGzdyxBFH8Je//IUpU6aUyBiSJElKfnb2JakI+vbty0cffcSECRNK5PzDhw/ns88+Y9CgQQZ9SZIk7TI7+5JURE2bNmX9+vXMnDlzG4E8CnwNzAFWAeuBTKAccAhQF9j2bv7Lly+nVq1anHHGGTz66KMlVr8kSZKSn2FfkopoypQpnHrqqUyePJlWrVoBXwBPAjOA94HfdvDdZYAGwLFAFtAECC4Y9OjRgwcffJCFCxey//77l+CfQJIkScnOsC9JRRSNRmnS5HhOOGE5AwZUJxJ5laBbn0/Q2S+MDCCXoNPfjcWLT6Bevcb06dOH3r17l1DlkiRJShWGfUkqsjdZu/ZsypX7nvz8NNLS8nfjXEFXf/36NPr1q0jfvl9Trlz54ilTkiRJKcuwL0mFthboBQwmGk0jEskrtjNHoxAs/28OPA4cWGznliRJUuox7EtSocwF2gJfEkzXLykZBBv6PQ50KsFxJEmSlMy89Z4k7dR7wInAV5Rs0IdgHf864FzgwRIeS5IkScnKzr4k7dCHQFOCW+gV37T9wvsPcEkI40qSJCmRGfYlabuWAUcBKwkn6EOwgd8YoF1I40uSJCkRGfYlaZuiwN+BNwmm1oclAlQBPgOqhliHJEmSEolr9iVpm4YDbxBu0IfgosNKoGvIdUiSJCmR2NmXpD9ZBtQj2CgvnjwPnBl2EZIkSUoAdvYl6U9uBTaGXcRWIsB1lPzdACRJkpQM7OxL0h8sB/YDckKuY3teAlqGXYQkSZLinJ19SfqDJ4ANYRexHRnA0LCLkCRJUgKwsy9Jv4sCtYHFm17HowiwBDgw7EIkSZIUx+zsS9ImNWr8lUjkC/r1izJ1KkQiwWPp0rAr21IUeDXsIiRJkhTnDPuS9LvN0/crVYJjjw0epUsX7yivvAInngjlygXjtGwJH3zwx2Muvhhq14YKFaB8eahZE7p1g19/zQBmFW9BkiRJSjpO45ekTWrUqMyyZSu5+Wbo169kxpg8Gdq2hbw8qFYNcnLg55+hbFmYMQOOPDI4bq+9oHJl2HNP+Okn+PLL4POWLeGll44CPiyZAiVJkpQU7OxL0u82d/a3NY0/GoU+fWDvvaFKFcjOhl69Nh9XGNdfHwT9444Lzrt4MdSoAevWQe/em4/75pvgax98AMuWQZMmwedvvw0wl/i7NaAkSZLiSUbYBUhS/Mjd4VeHDoXbbgteV6sGzz8Pa9YU/uzffANz5wav27WDjAyoWBFOPRWGD4fXXgsuBKSnQ5kyweyCyZPhhx+CwA8FoX8j8CNQrWh/PEmSJKUMO/uS9Lsdr2oaMCB4Pv54WLIk6LwfcEDhz/7VV5tf77PP5tf77hs8r1sXTNkvsGgRvPfe5qB/yinw3HMFX11f+IElSZKUcgz7kvS77Yf9lSvh66+D1x06QGZmsHHeaacV4ezbOf2Wn2+5HOCpp2DDBpg9G+rXhylToGvXgq/mFX5gSZIkpRzDviT9rnAL77cM5EXZ4rR69c2vf/hh8+sffwyey5YN9gPYUmYmNGgAl1wSvH/ySfj8c4CyhR9YkiRJKcewL0m/S9/uVypVgr/+NXg9fjzk5sLatTBpUuHPXq1a0KEHGDs2OMfKlcGt+CCYpp+eDu+/H2wQWGDDhqCrXyDYJ2CPwg8sSZKklGPYl6TfldrhV3v0CJ6nT4eDD4aDDvrjOvzCGDAA0tKCtfg1akDNmsGa/LJl4dZbg2PmzYPmzYPb7jVoAPvvH1xggOD9kUfWACoUbWBJkiSlFMO+JP2u9A6/mp0d3B5vzz1hxYpg7X6XLsHXypQp3AitWwezAU44AX75BdavD3bjnzYNjjwyOKZ+fWjVKjjnp58GMwjq1YPrroPXX08jLe343fgzSpIkKRVEotGirDiVpGQ2G2i43a+uWAE5OZt30l+/Ho45Jrid3nHHwYwZsagxDRgAXBuLwSRJkpSgMsIuQJLiR32gIrBqm19dsiQI9Y0bQ5UqMGsWfPttsM6+f//gmA4d4Lvvtn32F18MpuTvnnygye6eRJIkSUnOsC9Jv8sEugBD2Nat7apWhaZNg1vhLV8OlStDmzZw443QZFP+nj07WIO/LTk5u1tfBKgHNN7dE0mSJCnJOY1fkv7gc6BO2EVsRwR4CLg07EIkSZIU5wz7kvQnfwemsa3ufrjKA9/jTvySJEnaGXfjl6Q/6U38Bf0IcA0GfUmSJBWGnX1J2qZLgUcJNsQLWzpQE5jDzm4PKEmSJIFhX5K2YyXBZnjfE37gTwPeBY4JuQ5JkiQlCqfxS9I2VQKeAMK+HhoBbsCgL0mSpKIw7EvSdp0CDAtx/DTgLOCWEGuQJElSIjLsS9IO/QsYGMK4EeA04EmCNfuSJElS4Rn2JWmnrgX+Q/Ars6SDd2TT8wXAaKBUCY8nSZKkZOQGfZJUaB9A3nkQWVhCl0rTgfIESwc6szn4S5IkSUVjZ1+SCu1o6HEqDMqEaBqQUUznLZgt0ApYAJyPQV+SJEm7w7AvSYX1wQdw34OQdidE5gBdgDIEwXxXwnn6pu9rAUwCxgP7F1e1kiRJSmFO45ekwsjLg8aNITc3CP2ZmZu+sIJgE71HgU+APIIAnwHkQ35ecPe+9AyCF3mbvq86cC5wGXBQDP8gkiRJSgXFNQdVkpLbsGEweza8884WQR+gMpC96bGeIPDPAuYAq+C9abB6A5xyOnAI0AhoCOwR2/olSZKUUuzsS9LOfPMN1KsHnTvDgw8W7XsvvBCWLoXp00ukNEmSJGlbXLMvSTtz1VVQtizccUfRvzczEzZuLP6aJEmSpB1wGr8k7cjEiTBqFDz9NOyxC1PvMzIM+5IkSYo5O/uStD1r10J2NpxyCpx77q6dw86+JEmSQmBnX5K255Zb4Lvv4JVXILKL97037EuSJCkEdvYlaVvmzoVBg6BXL6hde9fPY9iXJElSCAz7krS1/Hy4/HKoWRN69ty9c2VmQm5u8dQlSZIkFZLT+CVpa489Bm+/Da+/DqVL79657OxLkiQpBHb2JWlLP/4I118PF14IzZvv/vkM+5IkSQqBYV+SttSjR/B8993Fcz7DviRJkkLgNH5JKvDGGzBiBAwfDvvsUzznNOxLkiQpBHb2JQkgJyfYlO/EE+Hii4vvvIZ9SZIkhcDOviQBDBgAixfDqFGQVozXQQ37kiRJCoGdfUlauBBuvx2uvRbq1y/ec2dmBrfyy88v3vNKkiRJO2DYl5TaolG44grYf3/o27f4z5+ZGTzb3ZckSVIMOY1fUmp75hmYMgUmToRy5Yr//FuG/dKli//8kiRJ0jbY2ZeUun77Da65BrKyoE2bkhnDzr4kSZJCYNiXlLp69YJ16+D++0tuDMO+JEmSQuA0fkmp6d134eGH4b77oFq1khvHsC9JkqQQ2NmXlHo2boTLLoOGDaFr15Idy7AvSZKkENjZl5R6Bg+GuXNh5kxITy/ZsQz7kiRJCoGdfUmp5csvg1vsde0KRx9d8uNlbLqmatiXJElSDBn2JaWWK6+EKlXgtttiM56dfUmSJIXAafySUsfYsTBuHDz/PFSqFJsxDfuSJEkKgZ19Salh9eqgq9+6NWRlxW5cw74kSZJCYNiXlBpuvhl+/hmGDYNIJHbjGvYlSZIUAqfxS0p+H30E998frNM/6KDYjm3YlyRJUgjs7EtKbnl5cNllUKcOdO8e+/EN+5IkSQqBnX1Jye0//4H33oM334RSpWI/fkHYz82N/diSJElKWXb2JSWv77+HG2+ELl2gSZNwarCzL0mSpBAY9iUlr+7dg7B9113h1WDYlyRJUgicxi8pOb3yCjzzDPz3v7DXXuHVYdiXJElSCOzsS0o+69bBFVdAs2Zw4YXh1mLYlyRJUgjs7EtKPnfcAV9+CRMmQCQSbi3p6cGzYV+SJEkxZGdfUnJZsCBYo3/DDVC3btjVBBcbMjMN+5IkSYopw76k5BGNwuWXQ/XqwS788cKwL0mSpBhzGr+k5DFiBEybFmzOV7Zs2NVsZtiXJElSjNnZl5QcfvkFrrsOzj0XTj017Gr+yLAvSZKkGDPsS0oOPXsGgfqee8Ku5M8M+5IkSYoxp/FLSnxvvgmPPgoPPAD77Rd2NX9m2JckSVKM2dmXlNg2bAg25Tv2WLjssrCr2TbDviRJkmLMzr6kxHbPPfDZZzBrFqTF6fXLjAzDviRJkmIqTv9mLEmFsGQJ3HILXHUVHHlk2NVsn519SZIkxZhhX1Jiikaha1fYe2/o3z/sanbMsC9JkqQYcxq/pMT0wgsweTKMGQMVKoRdzY4Z9iVJkhRjdvYlJZ6VK4Op+2ecETzinWFfkiRJMWbYl5R4evcOAv/gwWFXUjiGfUmSJMWY0/glJZYPPoChQ+Huu6F69bCrKRzDviRJkmLMzr6kxJGXB5ddBkccEUzjTxSGfUmSJMWYnX1JiWPYMJg9G955J7h3faIw7EuSJCnG7OxLSgzffBOs1b/sMjjuuLCrKZrMTMjNDbsKSZIkpRDDvqTEcNVVUK4c3Hln2JUUnZ19SZIkxVgCzYOVlLImToRRo+B//4MqVcKupugM+5IkSYoxO/uS4tvatZCdDaecAuecE3Y1u8awL0mSpBizsy8pvt1yC3z3Hbz6KkQiYVezawz7kiRJijE7+5Li19y5MGgQ3HQT1KoVdjW7zrAvSZKkGDPsS4pP+flw+eVQsyZcf33Y1ewew74kSZJizGn8kuLTY4/B22/D669D6dJhV7N7DPuSJEmKMTv7kuLPjz8G3fwLL4TmzcOuZvcZ9iVJkhRjhn1J8adHj2AzvoEDw66keBj2JUmSFGNO45cUX954A0aMgEcegapVw66meGRkGPYlSZIUU3b2JcWPnJxgU74TT4SLLgq7muJjZ1+SJEkxZmdfUvy46y5YvBhGj4a0JLoWadiXJElSjCXR36YlJbSFC+GOO+C66+Cww8KupngZ9iVJkhRjhn1J4YtG4YorYP/9oU+fsKspfgVhPxoNuxJJkiSlCKfxSwrfM8/AlCkwcSKUKxd2NcUvMzN4zssLNuuTJEmSSpidfUnh+u03uOYaOPNMaNMm7GpKRkHYdyq/JEmSYsSwLylcvXrBunVw331hV1JyDPuSJEmKMeeTSgrPu+/Cww8HQb9atbCrKTmGfUmSJMWYnX1J4di4ES67DBo2hK5dw66mZBn2JUmSFGN29iXtko3AfOAnYD0QAcoCBwA1KcSVxMGDYe5ceO89SE8vyVLDZ9iXJElSjBn2JRXKemAMMB2YCXxCEPi3pTzQEGgMtABOYavw/+WX0LcvZGdDo0YlVXL8KAj7ubnh1iFJkqSUYdiXtENLgIc3PZYDmWw/5BdYA7wJzAAGATWAK4H/A/YEuPJKqFIFbr21BCqOQ3b2JUmSFGOu2Ze0Tb8CFxJMyR9IEPRh50F/SwV97GVAD2B/4OYFC9gweTLcfz9UqlRM1cY5w74kSZJizLAv6U/GAXWA/wFRIG83zxcF8oENwK2HHMJRCxcyOytrN8+aQAz7kiRJijHDvqTfrSfo5p9B0Nnf3ZC/LdG0ND6rXp1jIhHuILgQkPQM+5IkSYoxw74kAFYBLYGnN73PL8Gx8iIR8oCbgK4lPFZcMOxLkiQpxtygTxJrgVYEu+zHOng/uGnMBwlu35eUDPuSJEmKMTv7UorLB84C3qVkpu0XxsNAv5DGjgnDviRJkmLMsC+luIeASYQ/lf5W4J2Qaygxhn1JkiTFmGFfSmFLgGvDLmKTNOACYF3YhZSEjE0rpgz7kiRJihHDvpSi8oF/ALlhF7JJHrAU6BNyHSXCzr4kSZJizLAvpaiJwJvET9iH4ALEPcCXYRdS3Az7kiRJijHDvpSihgDpYRexDWnAf8IuorgZ9iVJkhRjhn0pBX0BvEp4u+/vSB7Bbfg2hF1IcTLsS5IkKcYM+1IKeoj47OoX+BUYFXYRxSk9HSIRw74kSZJixrAvpZgaNWowMBIhr18/mDo1CKGRCCxdGnJlm6UDE8IuorhlZhr2JUmSFDOGfSnFRLd8U6kSHHts8ChduvgGeeYZaNwY9toLSpWC/feHNm1g+vTNx6xaBVdfDY0awd57Q9mycMgh0KcPeatW8W7xVRMfDPuSJEmKoYywC5AUW39YC9+wIbxbArF65sxgpsABB0A0CvPnw+TJ8MYbwesaNeCXX+D++4Mp7rVrB7MLFi6E226DWbNYMmkSq4CKxV9dOAz7kiRJiiE7+1KKydnyzbam8Uej0KdP0G2vUgWys6FXr83HFca//w0//ggffwxz5sBDDwWfr18Ps2YFr8uUgbvvht9+Cy4AfPUVHHdc8LXJk4n+9huzd/tPG0cM+5IkSYohO/tSisnd2QFDhwbddYBq1eD552HNmqINUqYMvP8+XHklrF0LCxZs/vzoo4PX++0H1133x+855phgpkFaGmRksARoWrSR45dhX5IkSTFkZ19KMdGdHTBgQPB8/PGwZAksXhxMxy+qFSuC6fyffBKE3KpV4eWX4cADt338N98EFxYAzjkHKlZkXdFHjV+GfUmSJMWQYV/SZitXwtdfB687dAgCavnycNppRT/XKacESwK+/x6uugp++gk6d4Yvv/zzsV98Ac2bB8eeeCI89BARCjELIZEY9iVJkhRDhn0pxRRy1f0f1+dHdzofYPv23RduuSV4/fXXm9fvF5gxI1irv3AhtG0Lr7wCFSsSBcru+qjxJzMTcpPq8oUkSZLimGFfSjE7/I++UiX461+D1+PHB+F07VqYNKlogwwb9sd1/hMnbn695ecvvAAnnww//xys7x8zBsqV+/3LexRt1PhmZ1+SJEkxZNiXUkypnR3Qo0fwPH06HHwwHHRQsFN+UWRnw557wqGHBrfVO++84POMjM2vv/0Wzj472KG/VCl47z044YSgy3/ccfDhhzQo2qjxzbAvSZKkGHI3finF7DTsZ2cHt8174IFgk71zzw3C+JAhwY75hfF//wfvvBOsz8/JCXbeP/54uP56OPbY4JgNGzYvD9iwIdjMbwtlV67koKL8weKdYV+SJEkxFIlGd2cxrqREEwUqA6u2d8CKFUFA32ef4P369cEt8ebODTruM2bEpM7mwOsxGSlGmjaFGjVgxIiwK5EkSVIKsLMvpZgI0AR4Bcjb1gFLlgShvnFjqFIFZs0Kptynp0P//sExHTrAd99te4AXX4T999+tGtM31ZhU7OxLkiQphgz7Ugq6BJi8vS9WrRp0oWfPhuXLoXJlaNMGbrwRmmyK4LNnw7Jl2/7+nJzdri8fuGi3zxJnMjIM+5IkSYoZw76UgtoC+wI/bOuL1aoFt7/bkaVLi72mAulAC0iu9fpgZ1+SJEkx5W78UgrKALoSn78A8oDssIsoCYZ9SZIkxVA8/l1fUgxcCpQjWMMfL9KBQ4GWYRdSEgz7kiRJiiHDvpSi9gUGE+zOHy+iwJMEoT/pGPYlSZIUQ4Z9KYX9H9CK+Ni8IwLcBDQMu5CSYtiXJElSDBn2pRQWAR4ByhDuL4MMoC7QO8QaSpxhX5IkSTFk2JdSXDVgApCWlwd5ebEvIDeXPfPymAyUiv3osWPYlyRJUgwZ9iXx0wsvkH/GGUSiUdKisVvFnx6NElm+nModOlD+559jNm4oDPuSJEmKIcO+lOJGjRrFOeecQ6dKlXg1EqFMJBKTNfzpQLVIhMm//cbyd9/l1FNP5ddff43ByCEx7EuSJCmGDPtSChs1ahSdOnXirLPOYsSIEfw9PZ1PgONLcMyCXzoXAB8DLWvX5vXXX+frr7+mRYsWLF++vARHD5FhX5IkSTFk2JdS1OjRoznnnHM466yzePLJJ8nICPr5BwNTgaEEG/cVZ5c/DagKTAQeB6ps+rx+/fpMmTKFJUuW0LJlS1asWFGMo8YJw74kSZJiyLAvpaAXX3yRTp06ceaZZ/4h6BdIA7oC84FsoMIWnxdV+qbnvwC3AguANts47sgjj+TVV1/l888/p3Xr1qxatWoXRotjhn1JkiTFkGFfSjEvvvgiZ599NllZWdsM+luqAdwLfE9wi74GbP6lkca2u/6ZW71uDowFvgR6sbmbvy0NGzbk1Vdf5dNPP6VNmzasXr26UH+mhGDYlyRJUgxFotEYbr0tKVRjxozhrLPOomPHjjz99NM7DPrbs4Zgrf0HwIfAT5s+SwfKAQcARwONgMP4Y/gvrJkzZ3Lqqady1FFHMWnSJMqXL78LZ4kzgwZB//6wcmXYlUiSJCkFxGLTbUlxYOzYsZx11ll06NBhl4M+QHnghE2PknLsscfy0ksv0bJlS9q2bcuECRMoV65cCY4YA3b2JUmSFENO45dSwNixYznzzDN3O+jH0gknnMCkSZOYOXMm7du3Z/369WGXtHsyMyE3N+wqJEmSlCIM+1KSGzduHGeddRbt27fn6aefJjNzVybWh+Okk05i4sSJvPXWW3To0IGcnJywS9p1BWHflVOSJEmKAcO+lMTGjx/PmWeeSbt27fjf//6XUEG/QLNmzRg/fjxTp04lKysrcQN/wT97u/uSJEmKAcO+lKTGjx9PVlYW7dq145lnnknIoF/g73//O2PHjmXKlCl06tSJjYm49r3gn38i1i5JkqSEY9iXktCECRPIysqibdu2CR/0C7Ro0YLRo0czefJkzj333MQL/AX7JCRa3ZIkSUpIhn0pyUyYMIGOHTty+umnM3LkyKQI+gXatGnDCy+8wLhx4zj//PPJTaQp8Xb2JUmSFEOGfSmJTJw4kaysLE4//XSeffbZpAr6Bdq2bcuzzz7L6NGjufDCC8nLywu7pMIx7EuSJCmGDPtSkpg0aRIdO3akTZs2SdfR31qHDh145plneO6557jooosSI/Ab9iVJkhRDhn0pCUyaNIkOHTrQunVrnn32WUqVKhV2SSXuzDPP5Omnn+bpp5/mkksuIT8/P+ySdsywL0mSpBjKCLsASbtn8uTJvwf95557LiWCfoFOnTqRm5vLBRdcQHp6Og8//DBpaXF6DdOwL0mSpBgy7EsJbPLkybRv355WrVqlXNAv0LlzZ3Jzc7nooovIzMxk2LBhRCKRsMv6M8O+JEmSYsiwLyWol156iQ4dOtCqVSuef/75lAz6Bf7xj3+Ql5dHly5dyMjI4P7774+/wG/YlyRJUgwZ9qUE9PLLL9O+fXtatGiR8kG/wMUXX0xubi6XXXYZ6enp3HPPPfEV+A37kiRJiiHDvpRgXn75Zc444wxOPfVUg/5WLr30UnJzc+natSuZmZncdddd8RP4DfuSJEmKIcO+lEBeeeUVzjjjDE455RReeOEFSpcuHXZJceeKK64gNzeXq666ioyMDG6//fb4CPyGfUmSJMWQYV9KEFsG/VGjRhn0d6Bbt27k5uZy7bXXkpmZSf/+/cMuybAvSZKkmDLsSwng1Vdf5YwzzuDkk0826BdS9+7dyc3NpWfPnqSnp9O3b99wCzLsS5IkKYYM+1KcmzJlCu3atePkk09m9OjRBv0iuP7669m4cSO9e/cmMzOTG2+8MbxiDPuSJEmKIcO+FMemTJlC27Ztad68uR39XXTTTTeRm5tLr169yMjIoEePHuEUYtiXJElSDBn2pTj12muv0bZtW5o1a8bo0aMpU6ZM2CUlrL59+5Kbm8v1119PRkYG11xzTeyLMOxLkiQphgz7UhzaMui/+OKLBv3dFIlEuOWWW9i4cSPdu3cnIyODK6+8MrZFGPYlSZIUQ4Z9Kc68/vrrtG3blqZNmxr0i1EkEuHOO+8kNzeXbt26kZGRwb/+9a/YFVAQ9nNzYzemJEmSUpZhX4ojr7/+OqeffjpNmzZlzJgxBv1iFolEuPvuu8nNzeWKK64gIyODSy65JFaDQ3q6nX1JkiTFhGFfihNvvPEGp59+OieddJJBvwRFIhHuvfdecnNzufTSS0lPT+fiiy+OzeAZGYZ9SZIkxYRhX4oDU6dO5bTTTqNJkyYG/RiIRCIMHjyY3Nxc/vnPf5KRkcGFF15Y8gNnZhr2JUmSFBOGfSlkWwb9sWPHUrZs2bBLSglpaWk88MAD5ObmctFFF5GRkcF5551XsoMa9iVJkhQjhn0pRNOmTeO0007jhBNOMOiHIC0tjf/85z/k5eVxwQUXkJ6eTqdOnUpuQMO+JEmSYsSwL4Vk2rRptGnThhNOOIFx48YZ9EOSlpbGI488Qm5uLp07dyYjI4OsrKySGcywL0mSpBhJC7sAKRVNnz6dNm3acPzxx9vRjwPp6ek8/vjjnHXWWZxzzjmMHTu2ZAYy7EuSJClGDPtSjL355pu0adOG4447jnHjxlGuXLmwSxKQkZHBk08+SYcOHTjrrLOYMGFC8Q9i2JckSVKMGPalGHrzzTdp3bo1xx57LOPHjzfox5mMjAyefvppTj/9dLKysnjppZeKdwDDviRJkmIkEo1Go2EXIaWCgqDfuHFjJkyYYNCPYxs2bODMM8/klVdeYfz48Zx66qm7dJ41wEfALGARsG70aHL335/Sxx9PZeBIoBFQG6+8SpIkqXgZ9qUYeOutt2jVqpVBP4Hk5OTQsWNHXn/9dSZOnMjJJ59cqO/7EvgP8DxBwM8nCPLpQHTjRkhLI5KeDkBBj78ccCzwTyALKF2cfxBJkiSlJMO+VMLefvttWrVqxdFHH82ECRMoX7582CWpkNavX88ZZ5zBm2++yeTJk/nb3/62zeOiwKvAEGAiQbjPK+JYaQQXBvYALgf+Bfx1VwuXJElSyjPsK6VFgcXAMmA9QdgqA+wJHMbud1gN+olv3bp1tG3blnfffZeXXnqJJk2a/OHr3wKXAJMI7mWaWwxjpm86113AlTjFX5IkSUVn2FdKiQJTgcnAe8CHwKrtHJsBHEowvboJcCbBdOvCeuedd2jZsiWNGjVi4sSJBv0EtnbtWk477TQ++OADXnnlFY4//niiwJNANrCWonfyC+sE4AmgVgmdX5IkScnJsK+UsAIYAQwmWEddlA5swbEVgS4E06sP2cn3vPPOO7Rq1YqjjjqKSZMmGfSTwJo1a2jdujUfffQRk6dMYVjjxjwDRAguIpWUDIJO//NA2xIcR5IkScnF2aFKarnAncB+wFXAF1t8XpRzQDADYAhQB+gIfL+d42fMmPF70LejnzzKly/PxIkTOaxRI5otX87ITddJS/pqaS6wAWgPPFPCY0mSJCl52NlX0poHXEBw67Pi/iFPByoADwDnEnR3IQj6LVu2pEGDBkyaNIkKFSoU88gKUy5wxsaNTEpPh7TYXytNA14AOsR8ZEmSJCUaw76S0n1AD4KQX1JrqQumb58BPAXMffddWrRoYdBPYt0JfrbC+qUZIbjQ9D7QIKQaJEmSlBgM+0oqUeBGgl3MYyUdqL16Nd/Ur8+Rf/0rkydPNugnoTeBvxFe0C+QDtQl2FyyVMi1SJIkKX4Z9pU0okBP4O4wBs/NpdySJXy2334cULFiGBWoBK0luBXjV5TcTJGiiAA3b3pIkiRJ22LYV9IYBFwX4vjp0SjHRyK8QbCDupJHd+B+ID/sQraQTtDdPyLsQiRJkhSXDPtKCh8DjQi/6xoBbidYSqDk8AuwP7Ax7EK2kgF0ItgvQpIkSdqat95TwttIsOt+PIgCfYG5YReiYvM44V9E2pZc4Fngp7ALkSRJUlwy7Cvh3UkQruMpkF1IEMaU2PKBIcTX9P0t5QOPhV2EJEmS4pLT+JXQfgb+QvxNsQYYQfzMONCumQKcGnYRO/FXYBnBEhJJkiSpgJ19JbTHiK+OfoE0go6wEtuZNWpAJAL9+sHUqcHrSASWLg23sC18BXwbdhGSJEmKO4Z9Jaw8YCjxOcU6H3ifYLd0Ja6cLd9UqgTHHhs8SpcuvkHmzYMOHaBatc0XE2644c/HPfwwNGkC5cptPm7BAgA+KL5qJEmSlCQM+0pYLxN0NeNVBjAs7CK0y6LAhi0/aNgQ3n03eOy/f/ENtHAhjB0bXEzYkcmTYfZs2GefP3ycAcwqvmokSZKUJAz7SljnxPkU61xgXNhFaJd9x1azRrb1MxaNQp8+sPfeUKUKZGdDr16bjyuM5s1h+XKYP3/Hxz3wAKxcGfy8byEPw74kSZL+zLCvhBVXU6wB5syBM8+EqlWhVCmoVo2fzz7b9dQJqlC3tBs6FG67DX75BSpUgOefh8GDizZQ5co77+oD/OUvkJ7+p4+jwPdFG1GSJEkpICPsAqRdsZHtTLEubgVTrOvUgW93ENvfegtatIB164LgdthhsHo1jB3LLII7Bij2otEoGzduZMOGDdt93t7X5lWosPMBBgwIno8/HqZNgw0boFEj+Oyzkv2DbWVdTEeTJElSIjDsKyF9uvUHU6cG06EBliyBGjWCKdZ9+8KDD0JuLpx/fhDE77wzOK4wd50smGJdqdL2p2VHo3DJJUHQ79wZhg+HsmUBSF+1illA2yL/CeNLfn4+GzduLHRQLkqoLsnz5ebm7vof+phjdvz1lSvh66+D1x06QGZm8DjttJiHfadoSZIkaWuGfSWkxYU5qGCKNQTT8J9/HtasKdpAlSvv/Jg5c37fFZ1oNJgFsHIlNGxI9O67WdKo0e+HFoTmsMLvrp4vL6/4bnCYmZlJqVKldvq89WcVKlTY6TFFOd/OjllYujRNjjyycH+oLS8EFeYiUjErG/MRJUmSFO8M+0pI6wtzUKymWG95vv/9Lwj7K1bAG2+Q36wZTw8cyHNXX83GjRuLNTTvaqAtU6bMLoXf4jg2IyODSGE3rgvZTn85VqoEf/0rfPUVjB8PV18d/IxNmhSD6jZLA2rEdERJkiQlAsO+EtJOI3Msp1hvOVX84ovh0UeDpQS1a8Pq1ew5dy6977qrWLvO6enpCROaE9WeQDo7+Vnr0QO6dYPp0+HggyEnJ9iroShmzgyWf2zp4YfhhRfggAOCJSoAPXvCqFGwatXm41q2JD8zk5xu3YI6JEmSpE0M+0pIZYpycElPsa5WbfPrxo2D54MOCnbl//57KqxaRTeDWEIqDazd0QHZ2fDjj8Ft8VasgHPPDe7EMGQIlCnkT+m6dfDFF3/8bPny4LHlhaQffvjzcV9+CUCVX38t3FiSJElKGYZ9JaQ9dnZALKdYN24cjLdyJXzwAVx2GSxbBj8FN2/bp3bt4h9TMdFn6VJ6s0V3f+uLRStXwpVXwq23Bu/Xr9+8sV+DBoUbpFmzwl2E+u9/g8c23Fe4kSRJkpRC3MRZCalBYQ7q0SN4LphifdBBQfgvipkzoVat4FHg4YeD982aBe/LloV+/YLXjzwC9erBkUdCXh7stx+dLr20aGMqbrRkJ9P4lyyB6tWhaVNo1w5q1oS5cyE9Hfr3D47p0AGOO27bj+++26360gj+W9hzt84iSZKkZGRnXwlpLwqxnjqWU6yvuSbo7t93HyxcGEzhb9cO7ryT5lWrFuFPpnhyFNAImA3kb+uAqlWDoD97dvAzUbkytGkDN94ITZoEx8yeHcz02JacnN2qLx9wgYgkSZK2JRKNhnCfKKkYZAFj2UHgX7EiCFP77BO8L5hiPXdu0FWdMaPEa8wE1mx6VmIaAfwj7CK2oxLwHVAu7EIkSZIUd+zsK2GdCIzZ0QFLlgShvnFjqFIFZs2Cb7/98xTr7U2lfvFF2H//Xa4vjaArbNBPbGcDVwHLQ65ja+nRKF0iEYO+JEmStsmwr4R1PtCT7UyvhriYYn35bp1B8aAMcAtxNl0+P5/8NWuo8dJLRM8809swSpIk6U+cxq+E1hl4Dsjd2YEhqEwwxbps2IVot+UDTYGZxM/P2nEPPMC7XbvSpEkThg4dypFHHhl2SZIkSYoj7savhNaV+AlfW0oHLsGgnyzSgCeIj6lQGcBpwDtXXMHrr7/OL7/8QsOGDbnqqqtYsWJF2OVJkiQpThj2ldCOB44jPkLYljIILkQoedQE7g+5hnSgCjAciADNmzfn448/5q677uKxxx6jTp06PPnkkzhhS5IkSYZ9JbQI8F/i7wd5AFAj7CJU7C4F+oY0djpQHngd2HLbyMzMTK677joWLFhAs2bNuPDCC2natCmffPJJOIVKkiQpLsRbRpKKrA5wR9hFbJIBnABkh12ISkw/4KYYj5kOVCQI+odv55hq1aoxcuRIpkyZws8//8xRRx3FNddc49R+SZKkFOUGfUoKeQS34ptFeGv4I0Bp4BOgVkg1KHbuB64DogQ/fyUlDagOTALqFfJ7NmzYwP3330///v2pWLEiAwcO5LzzznPXfkmSpBRiZ19JIR0YAxxAOOv3IwT/MY3GoJ8qriK4uHQowb//4pa+6bzdgHkUPugDlCpVih49erBgwQKaNm3K+eefT7NmzZg7d24JVCpJkqR4ZNhX0tgPmEqwnjmWgb8g6I8EWsdwXIXvCILA3x9Iy8uDvDzY3clSucHclOrAdOBeoNwunuqAAw7g2Wef5dVXX+WHH36gQYMGdO/enZUrV+5ejZIkSYp7hn0llQOBGQQ7p8fihzsDKAWMA86MwXiKP5lA9zVrqHz44TSeOJH9Nk2VL+oFp9+PnzaNHjNm8DnQpJhqPOWUU5gzZw633347Dz/8MHXq1OF///ufu/ZLkiQlMcO+kk41gm5rwa3v0ktwrIbAx0CbEhxD8e/hhx9m1cKFPHfkkXwFvAh0Jtg8cutfsltP+S8PnESw/n8B0KRfP97v1avYZ6eUKlWKnj17smDBApo0aULnzp1p3rw58+bNK+aRJEmSFA/coE9JbRpwIfAVwUZqxSGDIMDdAVxNyV5MUPxbv349Bx10EG3atOHRRx/909fXElwQWgSsAzYCZYFKwJH8eRbKs88+yznnnMMnn3xC/fr1S6zuV155hSuvvJLFixdz1VVXcfPNN1OxYsUSG0+SJEmxZdhX0lsDPAQMBZYShPWi7thf0I0tC1xEEPLdiE8ADzzwAFdeeSULFiygdu3au32+DRs2UKNGDc444wwefPDBYqhw+3Jycrj33nu59dZbqVKlCoMGDaJTp07u2i9JkpQEDPtKGfnAFILQ/wqQs+nzTIJu65YyNh2fTxD0Dwf+RTA1296nCmzYsIHatWvTpEkTnn766WI7b//+/RkwYADffPMNVapUKbbzbs+XX35J9+7dGTVqFM2bN2fo0KEceuihJT6uJEmSSo5hXykpl2B99CzgA+BLgunWeQQ7n+8FHAUcDTQAKoRSpeLdY489RpcuXZg7dy6HHXZYsZ33u+++o3r16gwcOJCrrrqq2M67My+//DJXXnklS5Ys4ZprrqFPnz5O7ZckSUpQhn1J2gW5ubnUq1ePI444glGjRhX7+c8991xmzZrFggULSEuL3V6qOTk5DBo0iNtuu40999yTQYMGcfbZZzu1X5IkKcG4G78k7YLnnnuORYsWcdNNN5XI+bOzs1m4cCGvvvpqiZx/e0qXLk2vXr2YP38+jRs35pxzzuGUU05h/vz5Ma1DkiRJu8fOviQVUX5+Pocffjg1atRg4sSJJTJGNBqlYcOGHHDAAYwfP75ExiiMyZMnc+WVV7Js2TK6d+9Onz59qFDBhS2SJEnxzs6+JBXRmDFj+PTTT+ndu3eJjRGJRMjOzmbixIksXry4xMbZmdatWzN37lxuvvlmBg8eTN26dXn++efxOrEkSVJ8s7MvSUUQjUZp1KgRe+yxB6+99lqJjrV27VoOOOAAunTpwt13312iYxXG0qVLufrqqxk7diynnHIKQ4YMoW7dumGXJUmSpG2wsy9JRTB58mRmz55dol39AuXKlaNLly48+uijrF27tsTH25kaNWowZswYJk6cyJIlSzjiiCO48cYbWbNmTdilSZIkaSt29iWpkKLRKCeeeCIAb7/9dkx2qF+8eDG1atVi+PDhdOnSpcTHK6z169dz9913c8cdd1C1alXuuecesrKy3LVfkiQpThj2JamQ3njjDU4++WQmTpxImzZtYjZu27Zt+frrr/nwww/jLkwvWbKEq6++mnHjxnHqqacyZMgQ6tSpE3ZZkiRJKc9p/JJUSLfddhtHHXUUrVu3jum42dnZfPTRR7zzzjsxHbcwDjroIMaOHcv48eNZtGgRhx9+OL169XJqvyRJUsjs7EtSIbzzzjuceOKJjBo1io4dO8Z07Pz8fOrWrUujRo145plnYjp2Uaxbt44BAwZw5513ss8++3DffffRoUOHuJuNIEmSlAoM+5JUCKeddhpLly7lk08+IS0t9pOiBg8ezLXXXsuXX37J/vvvH/Pxi2Lx4sVcddVVTJgwgZYtWzJkyBBq164ddlmSJEkpxWn8krQTH374IZMmTeKmm24KJegD/OMf/6B06dI8/PDDoYxfFAcffDDjx49n3LhxfPbZZ9SvX5/evXvHxR0FJEmSUoWdfUnaiaysLObMmcP8+fPJyMgIrY4rrriCF198kWXLllGqVKnQ6iiKdevW8e9//5u77rqLfffdl/vuu4/27ds7tV+SJKmE2dmXpB2YN28eo0eP5sYbbww16AN07dqV77//ntGjR4daR1GULVuW/v37M2/ePOrXr0/Hjh057bTTWLRoUdilSZIkJTU7+5K0A507d+att95i4cKFcdFNP/nkk9mwYQNvvfVW2KUUWTQaZfz48XTr1o3vvvuOnj17csMNN1CuXLmwS5MkSUo6dvYlaTsWLlzIyJEj6dmzZ1wEfQhuw/f2228ze/bssEspskgkQrt27fj000/p2bMnd911F4ceeihjx47F686SJEnFy86+JG1Hly5dmDRpEkuWLKFMmTJhlwNAbm4uBx10EC1btuSRRx4Ju5zdsnDhQrp168ZLL71EmzZtGDx4MDVr1gy7LEmSpKRgZ1+StmHZsmWMGDGCHj16xE3QB8jIyOBf//oXTz/9NL/++mvY5eyW2rVrM2nSJF588UXmzp3LYYcdxs0338y6devCLk2SJCnhGfYlaRsGDBhA5cqVueyyy8Iu5U/++c9/kp+fz2OPPRZ2KbstEonQvn175s+fz3XXXce///1vDjvsMMaPHx92aZIkSQnNsC9JW/nuu+949NFHueaaayhfvnzY5fzJPvvsQ6dOnXjggQfIy8sLu5xiUa5cOW677Tbmzp3LIYccQrt27Wjbti2LFy8OuzRJkqSEZNiXpK0MGjSIMmXKkJ2dHXYp25Wdnc2SJUuYPHly2KUUq9q1azN58mRGjRrFxx9/zKGHHkr//v2d2i9JklREbtAnSVv4+eefOfDAA+nevTu33npr2OXsUOPGjdlzzz156aWXwi6lRKxZs4bbb7+dgQMHcsABBzB48GBOP/30sMuSJElKCHb2JWkL9913H5FIhKuuuirsUnYqOzubl19+mc8//zzsUkpE+fLlueOOO/jkk0+oVasWbdu2pV27dixZsiTs0iRJkuKeYV+SNlm+fDlDhgzhX//6F3vvvXfY5ezU2Wefzd57780DDzwQdiklqk6dOrz88su88MILzJ49m0MPPZRbbrmF9evXh12aJElS3DLsS9ImQ4cOJScnh2uvvTbsUgqlTJkyXHLJJTz++OOsXr067HJKVCQSISsriwULFnD11Vdz2223Ub9+fSZNmhR2aZIkSXHJsC9JwOrVq7n33nu55JJL2G+//cIup9Auv/xyVq9ezVNPPRV2KTFRvnx57rzzTubMmcNBBx3EaaedRvv27Vm6dGnYpUmSJMUVw74kAQ899BCrVq2iR48eYZdSJNWrV+eMM85g6NChpNJ+q3Xr1uWVV17hueee44MPPqBevXrcdtttTu2XJEnaxLAvKeWtW7eOgQMH8o9//IPq1auHXU6RZWdnM2/ePKZNmxZ2KTEViUQ466yzWLBgAd26daN///4cfvjhSXt3AkmSpKIw7EtKeY8++ig//fQTN9xwQ9il7JLmzZtTr149hg4dGnYpoahQoQJ33XUXc+bMoXr16rRu3ZqOHTuybNmysEuTJEkKjWFfUkrbsGEDd911F+eddx41a9YMu5xdEolEyM7OZsyYMXz11VdhlxOaevXqMWXKFEaOHMnMmTOpV68et99+Ozk5OWGXJkmSFHOGfUkpbcSIEXzzzTfceOONYZeyWy644ALKlSvHww8/HHYpoYpEInTq1IkFCxaQnZ1Nv379OPzww3n55ZfDLk2SJCmmItFU2tFJkraQm5tLnTp1aNiwIc8//3zY5ey2bt26MXLkSL766itKly4ddjlx4dNPPyU7O5s33niDjh07cu+99ybkvgySJElFZWdfUsoaOXIkixcvplevXmGXUiyuuOIKfvrpp6S4cFFcDj30UF577TWeeeYZZsyYQd26dbnzzjud2i9JkpKenX1JKSk/P5/69etz8MEHM2HChLDLKTYtWrRg5cqVvPvuu2GXEndWrVpF//79ue+++6hZsyZDhgyhRYsWYZclSZJUIuzsS0pJL774IvPnz+emm24Ku5RilZ2dzcyZM3n//ffDLiXuVKxYkYEDB/LRRx+x//7707JlS84666yU3tRQkiQlLzv7klJONBqlYcOG7LXXXkyZMiXscopVXl4eNWvWpFmzZvz3v/8Nu5y4FY1GeeaZZ7j22mtZuXIlffr0oXv37pQqVSrs0iRJkoqFnX1JKWfSpEl89NFH9O7dO+xSil16ejpXXHEFI0eO5Keffgq7nLgViUQ477zz+Oyzz7j88svp3bs3RxxxRNJd/JEkSanLzr6klBKNRjn++OPJyMjgzTffJBKJhF1Ssfvll1844IADuPnmm7nhhhvCLichzJ07l65duzJ9+nTOPvtsBg0axAEHHBCz8fOAz4FZwIfAj8B6IAKUBaoBjTY9amz6XJIkaUcM+5JSymuvvcYpp5zC5MmTadWqVdjllJiLL76Y1157jS+++IKMjIywy0kI0WiUp59+muuuu47Vq1fTt29frr766hKb2p8LTAAeAN4kCPcAmUD+pgcEU/DSgI2b3lcCWgJXAH/D4C9JkrbNsC8ppTRv3pxVq1bx/vvvJ2VXv8CHH35Io0aNePHFF2nfvn3Y5SSUFStW0K9fP4YMGULt2rUZOnQof//734vt/D8DDwHDgO+BdILOflFkEFwsOAToBlwElCu2CiVJUjIw7EtKGW+99RYnnXRSygTgE088kbJly7oOfRfNmTOHrl278tZbb9GpUycGDRpEtWrVduuczwOXASvY3LnfHQWXq6oDTwInFcM5JUlScnCDPkkp4/bbb6d+/fq0a9cu7FJiIjs7m9dee4358+eHXUpCOuKII5g+fTojRozgjTfeoE6dOgwcOJCNGzfu/Ju38iOQBZwNLKd4gj5AdNPja4Ip/VcBa4rp3JIkKbEZ9iWlhA8++ICXXnqJm266ibS01PjVl5WVxb777suwYcPCLiVhRSIRLrjgAj777DO6dOlCz549adCgAW+88UahzzEbOBQYu+l9SUyny9t03qHAUQThX5IkpbbU+BuvpJR3++23U7t2bc4666ywS4mZUqVKcdlll/HEE0+wcuXKsMtJaFWqVOH+++/nww8/ZI899uDkk0/mvPPO49tvv93h980AmhJ084u6Ln9X5ANLgOOAxTEYT5IkxS/DvqSk98knnzBmzBh69epFenp62OXE1GWXXcb69esZMWJE2KUkhSOPPJLp06fz3//+l9dee406deowaNCgbU7t/whoAawlNkG/QC7Bxn/NgB1fipAkScnMDfokJb1zzz2XGTNmsHDhQjIzM8MuJ+Y6derExx9/zPz585P6DgSxtnz5cvr27cuwYcOoW7cuw4YNo1mzZgD8CtTd9BzLoL+lDOAw4H2C2/lJkqTUYmdfUlL7/PPPefbZZ+nZs2dKBn0INur77LPPeO2118IuJalUqVKFwYMHM2vWLCpXrkzz5s3p3Lkz3333HVcSbtCHoMM/B7grxBokSVJ47OxLSmoXX3wxL730EosXL6ZMmTJhlxOKaDRKgwYNOOiggxgzZkzY5SSl/Px8RowYwfXXX8+qk09m/ciRYZf0uwxgFnBE2IVIkqSYMuxLSlpLly6ldu3aDBgwgGuuuSbsckI1fPhwLr/8cr744gtq1KgRdjlJa9lvv1E3M5P1ZctCnOwPkUFwN4DZOJ1PkqRU4v/3JSWtAQMGUKVKFS699NKwSwndeeedR6VKlXjooYfCLiWpvbjHHuRUqBA3QR82T+d/OexCJElSTBn2JSWlb7/9lkcffZTu3btTvnz5sMsJXfny5bn44ot55JFHWLduXdjlJKUoMCTsIrYjAxgWdhGSJCmmDPuSktLAgQMpV64cXbt2DbuUuPGvf/2LX3/9lZFxtJ48mbxOcG/7eFwblwtMApaGXIckSYodw76kpPPTTz/x0EMP0a1bNypVqhR2OXGjVq1atG7dmiFDhuB2LcXvPwQd9HiVBjwWdhGSJClmDPuSks69995Leno63bp1C7uUuJOdnc3s2bN59913wy4lqdSoUYPnIhFy+/WDqVMhEgkeS5eGXNlmecC0sIuQJEkxY9iXlFR+++03hg4dyhVXXMFee+0Vdjlxp2XLltSsWZOhQ4eGXUpSydvyTaVKcOyxwaN06eIbZN486NABqlXbfDHhhhuKdIoPic9lBpIkqfgZ9iUllSFDhrBx40a6d+8edilxKS0tja5du/L888/z/fffh11O0tiw5ZuGDeHdd4PH/vsX3yALF8LYscHFhF20Gvii+CqSJElxzLAvKWmsWrWK++67j0svvZR999037HLi1v/93/+RmZnJ8OHDwy4lafwh7G9rGn80Cn36wN57Q5UqkJ0NvXptPq4wmjeH5cth/vwdH3fBBVC7NlSsCKVKwYEHQrdusHIlEHT3JUlS8jPsS0oaDz74IKtXr6ZHjx5hlxLX9thjD84//3weeughNm7cGHY5SSFvZwcMHQq33Qa//AIVKsDzz8PgwUUbpHLlwnX1X3wRfvsNataEv/4VvvwShgyBLl2IAL8WbVRJkpSgDPuSksLatWsZNGgQF110EQcccEDY5cS9rl278u233zJmzJiwS0kKO10HP2BA8Hz88bBkCSxeDCX1c/rWW/Dzz/DRR/DFF3DTTcHnY8YQWb+edSUzqiRJijOGfUlJ4ZFHHuGXX36hZ8+eYZeSEI444giaNm3qRn3FZIcT8VeuhK+/Dl536ACZmVC+PJx2WskU89prUL8+lC0bLBG4/fbg89xcoj/9RHrJjCpJkuKMYV9SwsvJyWHAgAGcd955HHzwwWGXkzCys7OZPn06c+bMCbuUhFfIVfd/XJ8fLYF98Z9+Gq67Lti5f489oHFj2OK/iWheHmWKf1RJkhSHDPuSEt6IESP49ttvufHGG8MuJaG0b9+ev/zlLwwbNizsUhLeDrvllSoFa+cBxo+H3FxYuxYmTSr+Qt59N3iuWDFYLjBzJrRo8YdDqhf/qJIkKQ4Z9iUltNzcXO68807OPPNM6tWrF3Y5CSUzM5PLL7+cp556it9++y3schJa6Z0dULBp5PTpQaf9oIPgq6+KNsjMmVCrVvAo8PDDwftmzYL3RxwRPK9aFYxz8MHw3HN/OE2joo0qSZISlGFfUkJ75plnWLJkCTcVbEKmIrnkkkvYuHEj//3vf8MuJaGV2tkB2dnQuzfsuSesWBGs3e/SJfhamUJOrF+3Lthw74svNn+2fHnwvuAWf126wDXXBLf4W7UquAhwyy2/H15100OSJCW/SDRaEosGJank5eXlUb9+fWrXrs24cePCLidhde7cmZkzZ/L555+TluY14F2Rk5PDwRs38m2FCts+YMUKyMmBffYJ3q9fD8ccA3PnwnHHwYwZJV5jGtAOeLHER5IkSfHAv9VJSlijR49mwYIFdvV3U3Z2Nl988QUvv/xy2KUklDVr1jBq1Cg6d+7MPvvsw7cPPRSsx9+WJUugenVo2hTatYOaNYOgn54O/fsHx3ToEAT/bT2++263640CrXf7LJIkKVHY2ZeUkKLRKA0aNGDffffllVdeCbuchBaNRjn66KPZb7/9mDhx4jaPyQPWb3ouA2RShB3ok8iKFSuYMGECo0ePZvLkyaxbt44jjjiCjh07cvS553L6IYds+xu/+QYuughmzw6m3leuDMceCzfeCE2aBMfUqAHLlm37+5csCb6+G8oD3wPbmXsgSZKSjGFfUkIaP3487dq1Y9q0aTRt2jTschLe448/TpcuXfj888/Zv1Yt3gI+2PSYCWzdVy4DHAEcCxwNnADUIjn9/PPPjBs3jlGjRjFlyhQ2bNhA48aN6dixIx07dqR27dq/H3sq8AbBRZF4kgH8CxgcdiGSJClmDPuSEk40GuW4446jdOnSTJ8+PexyksK6dev4S/Pm1Lj7bhaedBJrCG4nFwXyd/B9mcDGTa9PBK4EOlCIDevi3LfffsuYMWMYNWoU06ZNIz8/n5NOOun3gP/XglvpbWUs0D6mlRbep4D3q5AkKXUY9iUlnFdffZUWLVrw8ssv02Kre4ir6GYB1wLTIFhznpGxS+dJI7gwsBfQA+hOcDEgUSxdupTRo0czatQoZsyYQXp6OieffDIdO3akffv27Lvvvjs9Rz7QBHgf2M7q/ZhLBy4EHgu7EEmSFFOGfUkJ529/+xvr1q1j5syZRCKpuHK8eOQAtwJ3Eqy/L86p5xGCaf5PAocX43mL24IFC34P+B9++CGlS5emRYsWZGVl0bZtW/bcc88in3MRUJ/gn2/Y0oB9gAVA5ZBrkSRJsbVr7RtJ2kVRgjA0a9NjJvA5sI4gbJYCKgENCdaCN9r0vNem758+fTrTp09n7NixBv3d8BFwLvAZwb+T4hYF5hH8e+wH3Eh83P4lGo0yZ84cRo0axahRo/j0008pX748bdq04frrr6dNmzZUrFhxt8aoBdwFXF0cBe+mfIKOvkFfkqTUY2dfUkysAp4i2CBswabPtlzvvbU0NnebI0BbIBu4u1UrfvjuO2bPnu094XfRa8DpBP/sY7WR3HnAfwlnWn9+fj7vv/8+o0aNYvTo0XzxxRdUrlyZdu3akZWVRYsWLShbtmzxjkmwWd80wtusLwJcDjwQ0viSJClchn1JJeonoD9Bd3H9ps925ZdOBpvWQC9eTJdff+U/Rx8dF53iRPMq0IYgjO5o473iFiHYuO45YjOlLC8vj7feeuv3gP/NN99QtWpV2rdvT1ZWFs2bN6dUqZLdRnAV0JxgFkWsA38am/95p8d4bEmSFB8M+5JKzAvApcBKijHs5OdDWhrHA08AtXd2vH73LkH43EBsg36BCMFGcY9vel3cNmzYwBtvvMGoUaMYM2YMP/30E9WqVaNjx45kZWXRpEkT0tNjG31/A1oRLFmJVeCPAFnA0yT+XREkSdKuM+xLKnY/A1cAzxMEj5L4JZNB0LH8N9CN+FgPHs9WEtx27XvCCfpbehz4v2I617p163jllVcYNWoU48ePZ/ny5Rx88MFkZWWRlZXFMcccE/pyjzVAZ4Lb8pXUfw+w+W4IVwL3YkdfkqRUZ9iXVKyWEnSPvyJ2ncwLCJYJuOPo9l0GPEL4QT8ClCfYt6HaLp5j1apVTJo0iVGjRjFp0iTWrFnDoYce+nvAP+KII+Ju88Yo8D+Ci2BrKP7/NtKA/YERwMnFfG5JkpSYDPuSis1i4ATgF2J7j/EI0AF4FgP/tkwh2CwuXmQApwCTKPx0/t9++41x48YxatQoXnnlFXJycmjUqBEdO3akY8eO1K1bt+QKLkbfEWyaN44t9qHYDekEF3AuBe4Gdu8+ApIkKZkY9iUVi++AYzc9xzLoFyhYD/4YTunfUh5Qk2CmRdhd/a2NJrhIsz0//PADY8aMYfTo0bz++uvk5uZywgknkJWVRceOHalRo0aMKi1+nwAPEtyhYD3Bz2xhu/0FFwn2AP5FEPQPLP4SJUlSgjPsS9pt+QRT998hnKC/pQcIApACEwlusxdv0oEmwNStPv/qq68YPXo0o0aN4q233iItLY2//e1vZGVl0b59e/7yl7/EvtgStBJ4huAWfe8CS3ZwbBpQBziOYGZEFlC6pAuUJEkJy7Avabc9SLAWOR6UBT4FaoRcR7xoRTCNP6x7ve/Mp0DmokW/3yLvvffeIzMzk1NPPZWsrCzatWvH3nvvHXaZMbOC4FZ9PwHrCAJ+WeAvwJGbXkuSJBWGYV/SblkCHEowFTkeZAAnAq/jdP4vCG5NGK+/5CN5eew5ciS/nH8+ZcuWpXXr1mRlZXHaaadRuXLlsMuTJElKaIZ9SbulNUHnOOzp+1t7gmANfyrbo0YNli9bBjffDM2aQfPmwReWLIE4We9eavVq/vfKK7Ru1Ypy5cqFXY4kSVLSSPXGl6Td8DnwEvEX9NOAgcRvRztWNmz5plIlOPbY4FG6GFd6z5sHHTpAtWoQiQSPG27483EXXwy1a0OFClC+PNSsCd26sWHDBo7u2NGgL0mSVMwM+5J22UMEG63Fm3yC3c7fC7uQkOVs+aZhQ3j33eCx//7FN8jChTB2bHAxYUfGjoW8PKhbF/beGxYvhiFD4LzzmFV81UiSJGkTw76kXbIWeIT43fgtAxgWdhEh+oWt/t1Mnbq58750afBZNAp9+gThu0oVyM6GXr02H1cYzZvD8uUwf/6Oj/vmmyDgf/ABLFsGTZoEn7/9tmFfkiSpBGSEXYCkxDQRWBV2ETuQC4wEhpOatyfbSfQODB0Kt90WvK5WDZ5/HtasKdpAhd1Ir0wZ6NcPJk+GH34IAj9AkyaFq1WSJElFYmdf0i75Z40aQfe3X79td43jwEaC6fypaG1hDhowIHg+/vhg077Fi+GAA0quqEWL4L33Ngf9U06B555jdcmNKEmSlLLs7EvaJX9YD16w+RsU7+ZvzzwD994LX3wBq1bBXnvBUUcFG8A1bfrHY+fMgVtugWnTYMUKqFoVTjyRWc89x9HFV1HootEoGzZsYO3atb8/1qxZ84f3a9euZcZee+34RCtXwtdfB687dIDMzOBx2mnw2WclU/xTT8Hjjweb+l1wAUyZAl27sn7EiJIZT5IkKYUZ9iUVWZStdnov2PytuM2cGcwUOOCAYH35/PnBNPA33gheF9w+7q23oEULWLcuuPBw2GGwejWMHRvT9eB5eXmsW7duhyG8OB75+fk7L+bUUwtf+Jbr80v6bqyZmdCgAVxyCVx1FTz5JPm9e8Mhh5TsuJIkSSnGsC+pyL5mq9vaTZ3653u4R6PQty88+CDk5sL55wdB/M47g+MKEyr//W+4777N7x99FP75T1i/HmbN2jzOJZcEQb9zZxg+HMqWDY5ftYrZFL4bvruPnJycbfwh/iwSiVC+fHnKlSu33cfee++9w6/v7Ptnly1L05o1t19EpUrw17/CV1/B+PFw9dWwYQNMmlSoP0ORvP9+sBdAs2bB+w0bgq7+JmWKuk+AJEmSdsqwL6nICrUxX3Fs/lamTBAUr7wS1q6FBQs2f370psn5c+Zs/jwahTp1ginqDRvC3Xczq2JFMurVK1w3HChduvRuhfDCBPNSpUoRKexu97vo8MIc1KMHdOsG06fDwQdDTk4wI6IoZs4MLrJs6eGH4YUXghkZU6cG0/Yvugj22AOqVw8uMPz6a3BsgwaccOSRRRtTkiRJO2XYl1Rkhepfb7n527RpQTe3UaOirwdfsSIIlAWqVg2C5IEHBu+3PN///heE/RUrgqn+zZpR8e23GfDAA4UK5WXLliUjIzl+LVYB0tnJrRGzs+HHH+GBB4J/ZueeC6VKwZAhwQWVwli3LthTYUvLlweP3Nzgff360KoVfPwxfPoppKdDvXrB/gC9enF0mnvFSpIkFbfk+FutpJgqtbMDinPzt1NOCTr2P/wQLAG4//6gk/z220GXuCBQAlx8cTDVf8kSqF0bVq8mY/RoLuvXr2hjJomspUsZxRaBf+ulEytXBrMmbr01eL9+PRxzTPC6QYPCDdKs2c6XZBx9dLDXwnY0KtxIkiRJKgLbKZKKrHxRDi6uzd/23TfYbR+CCwkPPRS8rlZt8zGNGwfPBx0UzAAAonF0K8BYa8xWeytsbcmS4IJJ06bQrh3UrAlz5wad9/79g2M6dIDjjtv247vvdrvGqsBfdvsskiRJ2pphX1KR7fRO7AWbv0Gw+VtubrDmvqibvw0b9sd1/hMnbn5d8HnjxsF4AB98EDwvWwY//QTAgbVrF23MJNJpZwdUrRoE/YK7HOTkQJs2wTr7Fi2CY2bPDpZRbOtRyA0JtycduAgo2d0LJEmSUlMkGi3p+yxJSkala9Rgw7JlcPPNwVTurXfjHzIk2PwNguBfsPnb2rXBZ4X51ROJBGvIa9aEjRth0aLg84yM4HZ7xx4bvL/3XujePXhdt27QcV6xAvbbj9vmzOGmTV3+VNQemAjk7uS4MESARcDBYRciSZKUhOzsS9olpXd2QHY29O4Ne+4ZBO8OHaBLl+Brhd387f/+L7hw8OWXsHQp7LdfcJ4339wc9AGuuQYeeSTYCG7JEqhYES64AD74gL+lcNAHyCY+g3460BKDviRJUkmxsy9plzwB/N+ODlixIujm77NP8L5g87e5c4P13jNmlHiNacAKoEKJjxS/okA9gg76DnfmD8FLBIFfkiRJxc/d+CXtkrYEu/Jv2N4BS5YEob5xY6hSBWbNgm+//fPmb9vb5O3FF2H//Xe5vgygFakd9CGYKv8Y0CTsQraQDnTEoC9JklSSDPuSdsmewHnAU2xnmnjB5m+zZwf3XK9cOdj87cYbocmm6Dl7drCZ3rbs5uZvuQRT2AUnAN2Be4H8kGuJAJWAYSHXIUmSlOycxi9pl30AHBN2EdtxILAYNyYpsA6oDywj/On8LwBZIdcgSZKU7Px7sKRddjTQiGBadjyJAN3wF9yWygJjgHKE++/rGgz6kiRJsWBnX9JumQU0Jvzp4QXSgdrARxTijgEpaAbwd4K9FmLd4b8YeITgYowkSZJKlo0vSbulEXAj8RPgogT7CBj0t+14YCrBxoWx3LQlGxhO/PycSJIkJTvDvqTd1geoQ/jT+SNAL4ILENq+xgT7LRxdwuNkECwfeAgYjP/DkSRJiiWn8UsqFnMJdn1fSzgbwKVvGv9V7OoXVh5BCL+BYBnGNu+qsAsiBDMsmgGPAzWK6bySJEkqPBstkopFfeBloBSx7/CnAw2AiRj0iyKdYMO8ucAZBP9D2J1/dwXLAmoQTNl/HYO+JElSWOzsSypW7wItiF2HP41gHfokgvu3a9d9SxDSHwB+JOjQp7P9jn/apkfupmPbEazNPxmvJEuSJIXNsC+p2H0KnEPQMS6pXzBpBFPPLwXuJbilnIpHLjCb4E4Lswgu4CwFcggu4JQGKhOs+S+4/WJjYJ8QapUkSdK2GfYllYiNwL+BWwgCf3F2+dOA/YAngFOK8bySJElSsnCmpaQSkUmwS/8s4KRNn+3Ord4imx5lgCuBBRj0JUmSpO2xsy8pJhYQ3ILtUWA1QfDPY8fT/DMIpurnA7WBbsAFBFPIJUmSJG2fYV9STK0B3iDo+L+36fHzVsekATUJNt5rBBxLsCY8ErsyJUmSpIRm2JcUutXAeoJ1/mUINtvzFnqSJEnSrjPsS5IkSZKUZNygT5IkSZKkJGPYlyRJkiQpyRj2JUmSJElKMoZ9SZIkSZKSjGFfkiRJkqQkY9iXJEmSJCnJGPYlSZIkSUoyhn1JkiRJkpKMYV+SJEmSpCRj2JckSZIkKckY9iVJkiRJSjKGfUmSJEmSkoxhX5IkSZKkJGPYlyRJkiQpyRj2JUmSJElKMoZ9SZIkSZKSjGFfkiRJkqQkY9iXJEmSJCnJGPYlSZIkSUoyhn1JkiRJkpKMYV+SJEmSpCRj2JckSZIkKckY9iVJkiRJSjKGfUmSJEmSkoxhX5IkSZKkJGPYlyRJkiQpyRj2JUmSJElKMoZ9SZIkSZKSjGFfkiRJkqQkY9iXJEmSJCnJGPYlSZIkSUoyhn1JkiRJkpKMYV+SJEmSpCRj2JckSZIkKckY9iVJkiRJSjKGfUmSJEmSkoxhX5IkSZKkJGPYlyRJkiQpyRj2JUmSJElKMoZ9SZIkSZKSjGFfkiRJkqQkY9iXJEmSJCnJGPYlSZIkSUoyhn1JkiRJkpKMYV+SJEmSpCRj2JckSZIkKckY9iVJkiRJSjKGfUmSJEmSkoxhX5IkSZKkJGPYlyRJkiQpyRj2JUmSJElKMoZ9SZIkSZKSjGFfkiRJkqQkY9iXJEmSJCnJGPYlSZIkSUoyhn1JkiRJkpLM/wNqsWpyuJ6qvQAAAABJRU5ErkJggg==" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 134 + "execution_count": 3 }, { "metadata": {}, @@ -2543,8 +223,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:30:59.149154Z", - "start_time": "2026-10-30T11:30:58.832652Z" + "end_time": "2026-10-30T11:41:11.836079Z", + "start_time": "2026-10-30T11:41:11.796501Z" } }, "cell_type": "code", @@ -2556,7 +236,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "" ] @@ -2565,13 +245,13 @@ "output_type": "display_data" } ], - "execution_count": 178 + "execution_count": 4 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:30:59.970338Z", - "start_time": "2026-10-30T11:30:59.925648Z" + "end_time": "2026-10-30T11:41:11.857553Z", + "start_time": "2026-10-30T11:41:11.849161Z" } }, "cell_type": "code", @@ -2587,18 +267,18 @@ "{'score': 0.008208499862389881}" ] }, - "execution_count": 179, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 179 + "execution_count": 5 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:31:00.966094Z", - "start_time": "2026-10-30T11:31:00.915591Z" + "end_time": "2026-10-30T11:41:11.895987Z", + "start_time": "2026-10-30T11:41:11.869351Z" } }, "cell_type": "code", @@ -2611,7 +291,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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RpDl16pQ0zh8WFnbixAl7e3scx2E+d+/ePWkMJAM+fPhAlJM1d2no5OQEV2Jubq70LGw9tCOURWiU4PHjx5AYaWFhUVVVJY0hXrx4ceLEiZycnOfPn0OIXxqjyJKcnJzffvsNrkMTE5PExMTGj+fz+R07dmzjoRgayjl69Cj4whMnTkjj/EVFRR4eHnZ2durq6gwG4927d9IYRTaIxWIulwvz+O7du3t7ezflp4ig6K1bt6RtYSuhHaFMHSGO4xEREVCjM2nSpJKSEtLPn56e7uTkZG5uLi8v37t3765duzYrqEg6ubm5Bw8eZLPZIpEIw7D169dHR0e34DxeXl49evRACCkoKLDZ7Jqami8d+euvvyKEZs6c2Qqrv09qa2vhAYZh1FrSRiA2LPbs2UPWOd+8eXPo0KEJEyYwmUyYvUEVv6GhIaSwUYW3t/eJEycgcez58+ct2DtPTEyEKkwGg2FnZ1dWVtbIwRiGTZ8+HSE0Z86clhstK2hHKGtHiON4UlJSz549EUIjR44k69qIiYnhcDiSHf6UlJSgSXefPn0onI1iGCYWiw8cOFBQUHDx4kUulxsSEtKyU5WUlLBYLLhzDR069OXLlw2P8fb2Rgipqamlpqa2zvDvjZMnT7LZ7KdPn7q5uUE2Iw2O41evXoW2lGw2u8UnqaqqCgwMZLFYkuLRTCbTxMTE3t4+ICAAEqEHDhxI1T4FhmFJSUkRERFXrlypqKiwt7dvWeIeJLIpKSkhhHr37h0YGPilI8+fP/9NBEUB2hHiISEh1dXV8FgkEgUHB8tg0PT0dND11tfX//DhQ8tOIhKJQkND2Wy2ZEcFVVVVc3Nzd3f3kpISgUAwYcIEhJC2tnZ4eDi5H6HppKam2tvb5+fnz5kzh8PhtLKM5NmzZ6CUJicnV29aWlpaCqtGKW38fNPk5uauXbv27du3OI7LIHv5G+LmzZsgtrBu3bpmrZV5PB5EXyQ7znfp0sXGxsbDw0My3lNUVDR69GhwHi2+3lvPkSNHUlNTQWupNY4/MTERPs6Xlobp6emwB/StZOrRjhBPSUlRVVX99ddfZTxuUVHR2LFjIR0rNja26T9YVVXl5eVlZ2cnKdAAl5+Xl1e9mGFlZSU0OVJXV4dUUhkTGxs7atQoe3v7zMxMHo937dq1Bw8etPKc1dXVHA5HUVERdiw8PT3h9dWrV0PdpEgkarXh3xsYhoWHh7u6uuK0I2yAt7c3OLNVq1Y1npAlFAph9inZLExOTs7IyIjNZoeGhhKutK6u7vHjx0SOjEAgAEUbXV1dyVoF2SAWi3fs2HHhwgU+n3/p0iUnJ6dWpnHWWxoGBQURb31bQVFA6o4wNjbW3t6exWLV1NSw2WwHBwfpJUy2DHd3d4TQvHnzZD90eXn5zz//jBDq2LHj8+fPGz8Y9t5tbGwk5Qr19fVZLFZoaChx9VZUVNy9e9fGxsbFxQVeEQqFv//+OwRLpV07JUtiY2MJ3UgrKytfX185OTlFRUXZ32XaPhiGOTg4cDicd+/eeXl5sVistLQ0qo1qWzx58kRdXR0htGjRoob3qLy8PHd3dysrK01NTeLq09bWtrKycnJyysnJIY7Mz8+H6xTyRHr16kW8VVFRAVU9Xbt2bdbct/XU1dUFBgYGBgYSf3cej9f60yYkJDRcGoI827cSFAVktCLcvHmzr68vm812cXFpaxv1a9asQQgdPXqUktFramogJVJVVdXPz6/hAUTyi6RUpoGBAYfDkcyfLCwshAsVLmZIxiHexTBs8+bNsG9BOMjvAKFQePz4cahzgtyEXbt2UW0UzbfKs2fPoIWhhYVFTU2NWCyOioqyt7c3MTGRbFsBs8/AwMC6ujr4QbFYHB4e/s8//xgZGUkeOXTo0K1bt0oGaSorK2G11LFjRwp3K0hEcmnYp0+fq1evwkz925pzy8IRent7e3p6PnjwwNvb+/Lly1FRUTIYtOkYGhoihL66IJMeIpFo1apVCCFFRUUi4z8hIaHeFQh771wuNysri/jZtLQ0LpdrampKuEmI0nA4nKSkpHoDEbVTR44ckd3Hw/FGMjxJITU1VV9fH9xhly5dOBxOaWlpa05YVVXF5XL37dtXW1vr4OBw8ODB7OxssqylaZvk5+dnZWW9fPkSam/09PQ6depEuDQ1NTVoXiGZ7VJeXg6bFN27dyeOVFFRMTU15XK56enpnx2otrb2l19+QQhpampSeNshl7i4OElBYwsLC6otah5Sd4QhISG2trZOTk6FhYU7duzYuXNnZWWltAdtOmVlZUwmU0FBQUpVfU0Ew7CtW7eCGzM1NZVsotuhQ4eFCxfevHlT8ube0E0qKSnB5df4Lfvs2bOEwI0MlubXr183MDAYMmSIvr6+tbW1NMpFcByPi4tTUFBgMBj9+vWD30bnzp0PHDjQ4uGEQmFhYeHOnTtzc3OXL19+/fr1Z8+ekWszVdy5c6eoqIh46u7uLu1pyjdBeXn52bNnWSxWenr669ev1dXVIetKX1/fzs6u3tY7j8erN/uExZCdnZ2Hh0dThG1FItHSpUshDiSDnfv09PR169aNHz/ezMzM3t6eWMiSi1AonDt3LgRmFBUV7ezsWp8im5ub+/z5cwzDgoODAwMDIdVLGrT3ZBlo10KhLLVAIFi5cuXVq1dxHOdyufLy8hCc6dy5c73kFyJHtH///pITVcgRbfoa6N69exDHWLp0qVT3a8PDw3V0dGBhWldXt3z58pUrV5I+ilgshhyEDRs24DgeGBhINNnR0NBgsViS+zdN5927d3/99VdaWtr27ds5HE6LSz7aGoMGDXrz5g3xVLJ2iMbe3j47O7umpkZFRYXBYERERBBvNV4g0YIol6QCOJHtJQ0EAkGvXr2OHz+en5//7t27mTNnwpVCOmlpaRAUNTExgdm2srLyH3/80ZpoioeHh7u7+507d16/fn337l3p9S1o745w3759CKE///yTKgPev3+/fv3669ev4zienZ0Nk8QXL14QyS9EjqiOjg5xBRI5okSJdLMgBG4sLS2ltxTeuHGj5I5dYWGhgoIC6esPBwcHhFD37t0l13+hoaHm5ubEWtnOzq5ZqgKFhYVnz579448/4uLiVq1adfLkSaneqmQJ7Qi/xP3796ElBfQOHDZsGLx+79696dOnSxZIdOvWbcWKFXfu3GllBB7DMOiFK1UF8PPnz5ubmxNPCwsLNTQ0CgoKyB0FwzDI+1u4cCGO44mJiTY2NpKrw5a5w6KiImtr6/fv3+M4fvDgQendrGTnCD08PIigKGhIymzoRpg5cyZqAwpABw4cyM3NvXPnDkLIzMwMXrx9+7aFhYVkf9ohQ4bs2LEjIiKi9VFNQuBm8uTJrbyev4SFhcWVK1ckX9HR0SE3uJGRkQHJQZ/tqhMTE2NlZQXRY0VFRRsbm6YLC5SWlkKMq6qq6ntyFYMGDbpz507cJ1RVVb+nT9diMjMzt2/f7uTklJGRsX//fiLAgH9SYpMskCBXNP+ff/6BxeXFixdJPC3Bxo0b9+3bJ/nK0KFDQ0NDyR3l9OnTMEGXVAiRdIcwH22uO+Tz+e/fvz99+nRpaamDgwO5NksiO0coqfVVV1eHEKI8fbQtyFJDsfmePXsIUXyixmvt2rWNJ7+0kqSkJNgIMTIyIkvgprS09ObNmwsWLIiJibG2tj537pzku2pqauQ2JLOwsCAmoV8iPj7exsYG1EPk5OTMzc1bpvH2fTBo0KCpU6fO+4S8vDy1jhAUnDEM+/jxo1S7sjQdWNkQleBpaWmXL1+Wqno7kcV28uRJUk4oFosjIiI4HM6LFy9YLFY9Rzhs2DBy97yJoOidO3cavpuQkGBjYwPBUlVV1WbtVty4ceP48eNFRUWvX7+WqkBdu3aEiYmJEFWj1gwC2NwiNACjoqKcnZ2lWouTnp4OqjStEbjBcTw/P9/d3d3c3Bx2HxFCu3btcnBw+O2334hjwsLCevbsSeIf/cqVKwghbW3tpvyKeDwei8UC8xgMhrm5eb3k9YULF0reHaZMmQIBme+MNhUajYmJmT59Oo7j+/btu3Hjxv79+6myhEAoFGpoaDAYDBnXwB07dozBYDAYjH///Vfy9bNnz+7bt+9LCaj1qKiogG2UH374AS7DDRs2nD9/fu7cucQxJSUlGhoaJDoVsVg8efJkhJC1tXUjh8XHxxPhGTU1NRaL1aaa9MrUEYaEhLx9+/bt27cJCQltwRFeuHABITR//nxqzQBqamqUlZUZDEZrerK3gLy8PMh71tXVjYuLa9bPJicnHz582NjYGKZ7EOGZMmUKl8vNyMioqqoaMGDA1q1bX758eefOHX19fRLbQRQWFoKSKhFQSklJWbhwYXZ2dkBAgJOTk52dXcOfysjIYLFYUGgBu/pEi7VJkyZJduLu16/fd1mY36YcIf5J42bLli2PHz9etGgRhZYAL1++RAgNGjRI9kOfP38eriNC/EwsFv/9999FRUWNKwHFx8cfOXJk0qRJEPYgsljXrl0bEhJSUVHRo0cPd3f3uro6gUCwaNEicnPWTp48CUHRhotm0NmXfCUuLq6eO6w34XB1dZWckTs4OMimk5pMHeG0adNmz549e/ZsEP2i3BHa2toihKQaem46YWFhCKGhQ4fKfmhJgZsXL1589fiEhAQOh2NgYEBcdSoqKubm5k5OTvVmeYWFhUeOHFmyZMnmzZtBxFUkEllbW9+9e7eVNi9ZsgQhNGXKFMlvkbOzM0S54+PjG8k+yM/P53A4IPxBuMOxY8cOGDCAOKZnz55TpkxppZFtkM86Qicnp8Y7CUgPuMVXVVXFxcW1Rv2SLCBKuXr1akpGb6gAfuXKlVOnTjWUiSCyWHv37i2ZxQrbKFFRUZLXRVJSkpWV1aBBg0aOHLl161ZIOQkNDW39llBqaips0hNXdFJS0pw5c4qKioKCgg4cOODj49Pwp2JjY7/kDidNmiTZFqNv375fbbtGCu06NAq38jai7/Dvv/8ihFatWkXJ6ITAjZqa2mcFbqB4g8ViEVEXCEuCvnBTaqcAiGfKy8u3pjm4n58feN964VzCEXI4nK9WhhQVFe3Zswc2iRFCgwcPVlRUzNDUzFdTy1dTS2cwLnTu3GIL2ywpKSmExDyO40lJScePH0cIGRsbyzgUgeN4cnIyi8W6ceNGcHDw8ePH20IsGubo165do8qAW7duQXni2rVrxWLx3bt3d+3aFR8fD++mpaU5OTlJCkghhDp37mxlZeXu7i4QCJo4SmRkpLq6ur6+fmuatIjF4kmTJiGEFi9eLPm6o6NjUVHR+vXrT5061UiV5Js3bwh3qK6uzmazi4qKxo8fv3btWuKYXr16HTx4sMUWNp326wgFAoGcnJySklIbKSgGP+Tm5kaVASKRaOXKlZBgefPmTXixsrLSy8vLxsZGUmKxd+/eUGXcsspcIjWgZbJ2lZWV+vr6qIEqXl5e3qZNm1xdXcvLy+tlqzZCWVnZ0aNHZ82aZWxsnCAnhzMYOELw717Hji0w75sjPT0dhAgMDAxaVnPZAsQYFvq2+KR/mqN/WnBSoVDUJmQXRSIRxAmobeH58OFDQgG8srKypqamocw3/L1anMVKigK4o6MjQqhbt26SEg24hCOEBMDGTxIeHj5jxgz4RJqamgYGBtra2m8XLXq3cOG7hQuPqqj8KRGnkR5UOsLa2totW7aQXtHSRHx9fSEsRsnoDYGVlvSkE5oCIXDDZDKtra2nT58OHR6AESNG7Nmz5/Xr160f6PTp08R2SHPnQ3/++SdC6McffyRXIMPY2LgMIRyhLdOnXzM0bD+OEMdxPp8/dOhQ2BuTQQZ1UEJh9/WPO9gGqCz3V1nu18E2oMvqQK9oKpvWAlFRUQihvn37Um0IHhAQADvZ/fr1A4UNQEtLa/78+ZcuXWp9tksrFcAbBkWB9PR0W1vb8+fPv3jx4vDhw5cvX27K2V6+fGlubt67d28jI6ODiorYp8kojtCtrl2ba1sLkA7575kAACAASURBVJ0jjI+Pl7xzxcTE/P3333DtZWRkyMwMAijf+fvvv2U/dEPS09MRQp06daI8XIzjOJfLZTAYILRIaGeQ3tr32rVrEAJatmxZ0wVuIiIimEymvLw86Yq1xsbG5QwGjlDqwYP58+e3K0eI43heXh50de7Tp49UG+bdDuerLvdHE7ajmSeRtQ+y9kHz76LxW1R/93cNpnIdhn/anlixYgW1ZgChoaGqqqqQEUbIfLdMQONLtFgBnAiKLlmyhER7oGujGCEcoffW1vmjR3+HjrAhubm5I0aMQC3KV2wZfn5+RDKIqakpQojoFkYtN27cQAjNnj2bKgNEIhEhdyAWi9XU1BBCXC4Xtm0qKiqkoRAbFBQEk8o5c+ZIblx9CaFQCF8YaWRVbNiwoVwiLooj5KmtDW+JRCKIGRYXF/N4PCkpplKOQCCABpk9e/aU0nZdbkmN+gp/ZO2DNHshdR00/x6y9kFzLyMVbWTto7rcPzWfSiHiOXPmIIRas3vdSvh8/uXLlyFylp+fz2AwlJWVU1JSpDcioQCurq7+5MmTJv4Ul8uF+3a9oGjrIRzhJhbr7uTJMnOEcog6dHR0nj17ZmpqyufzJ0+eDInL0oPP5+vq6l69erW2thbDsMjISIQQXPmUA5+dEMmUPa9fv9bU1LSyskIIJSQkVFZW6unpaWhoXLp0qbi4mMPhPH36lPRBp02b9vjx406dOj148GDmzJllZWWNH29vb//69Ws9PT1YzZPIP//8o6SkFKikhObMgX9FEya8/5SP4O7ufvfu3evXr58+fTo6OrqgoIDc0dsIWlpagYGBU6dO/fjx408//RQXF0f6EOeCMoVi/NN4+ijhhuS7QjHG9UsnfdAmguP48+fPEUKw1qGEgICApUuX/vHHHwghqGqdMGECISUvDRQVFT08PJYuXVpRUWFubg7ay42Tlpa2a9cuhNCZM2eIXDPSOXHx4i/h4VI6eUOodIQIIXV19YcPH/7666/FxcU///xzQECA9MbS1dWtqKioqKiA3q2lpaV6enq6urrSG7HpQO3E+PHjKTRAJBKBPARhzIoVKxQUFLS1tSGRRxqMGTPm2bNnPXr0CAkJmTp1aiM+5v379wcPHmQwGM7OzrBgJYv3798fPXrUwcHBffp0dP8+/Mt3di6YPx8OmD9/flxc3MCBA1VVVfl8fnFxMYmjtynU1dW9vb3NzMzy8vImT54cERFB7vl93+TXCrH/ngxdiNJDUGkG8a5QjPvHUjbJiI+PLyoq6tmzZ58+faiyAWROJ06ciBCCqSc8lipMJtPNzW3FihVVVVUWFhb3799v5GAMw5YvX15RUbF06dJ58+aRa4mXlxeIPyOEUHk5qqlBCMEGikgkunDhgoODg1gsRgi5urrGxsaSODTFjhAhpKSkdOvWLVtb28rKSktLSw8PDykNxOfzBw8erK+vn5+fT7njkaS6ujouLo7JZEKvZ0qQXJLKeHlqYGAQGhrav3//6OjoiRMnZmZmNjwGx/G1a9fW1NSsWLECYtoksmXLlrq6uhUrVkjeAgYPHnzs2DF4rKCgcODAAX9//40bN/7xxx8Qx/5eUVVV9fb2njdvnkAgMDMzgyuFLIorRP//REEV/bgURZ5BCCdeK6kWkjhcswAnBCIpVAHOD5akko+lDZPJvHDhwsaNG2traxcsWACix5/F0dHx2bNnurq6IHZPIh8/frS2tv7tt9+G9eyJSkvh3xNPz3P6+gihurq6X375pby8PC8vLzIyMjU1NSMj46vnbAYyCL82BQzDIHeGyWSeP39eGkPU1NS4uro+fPiwqqoKbqanT5+WxkDNBb7xI0aMoNAGaC4DidTQ5ik6Ovr+/fscDofH4504ceLo0aMikUh6BjS+YXzu3DmEULdu3UivdXv8+DFCSENDo5HKAVdX10OHDiUnJ9+4ccPe3r4FKXbfHEKhECQL1NTUAgMDW39CXl6l0+OMTnaB/yXIaPZCFi7I+iHqOhSNsIU9QmTt8+M2ksWgmw6EPVxcXKgyICcnByHUoUMHkUhUXFwMxV1N2TsnkcYVwHk8HgRjpNGMBX7/CxYskCxKlsxdSEhIWL16dV5e3sqVKx88eNDEfNQm0lYcIQD5ikg62RDFxcUeHh42NjYQAOzcufPcuXOl2pCviUBd3bp166gyANo/dejQQSwWFxQUMBgMNTU12f9mysvLYYKira0dFhZGvJ6TkwPVXYQOMlmIRCJDQ0OE0OHDh8k983eAZMO8zzb3+CpVtSK/N/kbLiXo/xmMrH0UbHwH/hWiuNRPwhH6oFlnkZImOEKVZf6HHkgxYbURMAyDNmcUFvVfu3YNITRr1iwcxx88eIAQmjhxouzN+JICuFgs/umnnxBCy5YtI33QoKAghJCqquqXVFWLiopu3769ffv2169fe3h42NvbHzt2jEQD2pYjxHH80qVLIDL0xx9/kKJGn56e7ujoOHXqVEKIj8FgGBgYQMmqlZUV5QX1kKvW9Bpw0rl9+zZCCOSPvby8EEKTJ0+mxJKamppff/0VFiKE8idsRUgjpfb8+fMIIT09PRnPu78VMAxjsVgIIUVFxc82FvgsuSU17s+yrBxjNFYEIGufLmsCrRxj3J9lCSrrqmpFuuuCGIslHKG1Dxr8CzhC9RX+JZXUTEyTkpIg5EDJ6MDq1asRQvb29jiOb968GSH0zz//UGLJZxXAobZEV1eX9KiMUCgcNmwY8dm/REpKCuEmKysrmy6j0xTanCPEcfz+/fvgpZYsWdLioumEhAR7e3sTExNYYqJPJXFcLhdKhl+9egWlclOmTJFSQ74mAlNRHo9HlQFw1e3ZswfH8e3btyOEduzYQZUxIpEINGAVFRVv3boFTrpDhw6kF3qXlZV169ZNGgvN7wmiOxiTyWykrkAkxqJSSzh33hvtfM5Y7CO32Ndo53P2jbehb4vF/1sa+zqtVO13f6bFebTw/n+OcIGn3OwzSkv95Jb4uj/Lkv5n+gwwJVqwYAElowODBg1CCL18+RLH8VGjRiGEgoKCqDKmngL4u3fvoDGqNIKisBnfr18/CtckbdER4jgeHBwMegrm5uZN70osFoujoqI4HM7AgQOJTVBVVVVzc/PPCvElJCSAnsuoUaNko3HekJSUFIRQV5nUynwJqCEBrVvYnPf29qbQHskNYwiKnj17lvRRYIhx48a1BRGDNg4RLjt16pTk6wVltR7hOTZn33Rc9QiWdObHIp0eZ+QI6t/RCspqr4RmLTr9WtvuEbL2Gbr1mdJSvw62AR1sA5SW+s08EvGeX7HsXCxjsY+jf5rsPtgnFi1aJKWvWROBqkFVVdXa2tqysjJ5eXkFBYWKigqq7MElFMC3bt0KQdHly5eTPgqfzwf5Rl9fX9JP3nTaqCPEcTwqKqpLly4IIWNj48Y7xVRXV4MQu2QtROfOnW1sbLy8vD47y3B1dYVdqLS0NMgNGTRoECUCg5cvX0YISTYMkzE1NTVKSkpycnIlJSVCoVBdXZ3BYFA1LZDk0KFDDAZDQUFBXV39/PnzTZ8PEWRlZeE4Xltb+/r163qSHDweDz51REQEaRZ/10CjdgaDceLEiYSP5fZeH0z2hMkt9kXWPvp/BrPcEwPjC+oaSIbCkaaHXsnb/M+RtUJxRY3odXppTFppWfV/4VAMwzdcSmAs9jnu03Il6JbRs2dPIlmMEiDyYWpqin9Sfxw3bhxVxhDcvHkTqhcgKEpuNBJYvHgxQmjevHmkn7lZtF1HiON4cnIyZDMOGTIEbmqS1Et+AfT09ECLqJFcj2fPnjGZTKLNQm5u7vDhwxFCvXr1kr3UJ7ShP3LkiIzHJXjx4gVCaNiwYfgnrcUBMlG5bQogrg107dp1+/btTVfji4yMnDNnDo7jbDb78ePH27Ztk3wX1DSkse3//eHi4gJt4U6dOsVgMHQGjUWLHqr97m95POp8UEZmYf3t1cpaUWB8Acs9sceGx8jaR2W5v+mhV/ZeH97mfH19g2H4pitJyNqHfUN2V+KHDx9g6kxhbGDDhg0IIWhNzGazEUL1vrFU4e3traKiAitUGxubFswV8vPzMQzj8Xg8Hq+eKw0NDWUwGCoqKq1pgkEKbdoR4jienZ0N+6h6enogNZSRkeHk5GRubi6pB21gYABduJpyTpFItGrVKtiFglaxAoEA1v7a2toQo5cZIPAo2RtdxkAXHmhje+rUqbbjHioqKuDyc3FxgcoKhJCcnJypqamXl1dT7lnQ687BwWH9+vXOzs7E68HBwRAzp7bJwDcB9PQICAiAX3j//v1R58FHrwTV1NVPZIMCCfNjkUrL/JC1T5+NT+wuxHuE55RXN7vqhnPnPbL22Xo9mZzP8DVcXV0RQr/88otshvsskL389OlTHMehhJfaUCFBfHw8Qgh6hkNIYNasWY8ePWripMHb23vlypUikSgwMPDkyZO3bt0i3hKJRHD327t3r9TMbypt3RHW1tYWFBRApXnHjh1BIx9QVFQ0MzM7e/Zsw8XiV8EwjMPhEKEeHMdrampglaCmpibZGVKqEPd6aSh5NhHI0oSyIWtra4SQlOo4mwtU+I0ePRqeRkVF2dnZwY49bK3b29s3HjMHR/jnn39C9iO8KBaLoZ3Nvn37pPwJvgfevXv3119/eXp63rx5s7y8XF5eXl5enujiW10nDowvYN94O/jvp8jah7nE12RPmL3Xh6jU1sqx2nt9QNY+a1zjxdJfpS1btgwh5OjoCE9jY2MlexfLgKKiIqJqsLKyUlFRkclkthFJ2zNnziCEFi9enJKSwmKxCFGnAQMGcLncpty44DKEB5LJj9DFSV9fvy3kbLdpR/j48WPI46qoqBg9ejRsATaS/NJcHB0dJTOjiMIp0N8j4QN8DaieGTNmDDwVi8UyvgLxT+2foLkEiEvJRgD9q+zfvx8htHHjRskX8/Ly7O3tCREsZWVlGxubz3aGevfuHYfD8ff3DwgIcHBwIBLwLly4gBDq0aMHhZOPbwihULhu3To2m/3mzRvohzx27Fh4yyM8R3W5P7L26bYu6Hen2Nuv+KVVZBY/nHmUzljss+TsG+l1K4R20507d0YIwbcoNTW1a9euHTt2fPXqlZQGbQhIGk2aNAnHcVD7HDVqlMxGb5z58+cjhJycnOBpSUkJl8uFHSuEUJcuXdhsduMZ3eAI8/Lyzpw5Q7yYl5cHeXBeXl7SNL+ptGlHiEvMJiCGbmNjQ26K7eXLlyEzat26dWKxGMOwLVu2QL6iZDBNSmzcuFHyXr9p0ybJprgyIC0tDX1q/5Sbm4sQ0tDQkKqCTNOBXuGf/W2IxeLAwEBzc3OiNsbIyMjJyemrCTXl5eUwnaKwBfm3CHwloLSGELt4l1Ox9+77SF6J9NZszk8y5Rb7LjgZ0zANpzUUFhZ6eHjY2dlB5ZKSkpKiouLWrVtxHK+trSUqWR8/fkzioI0A9Uu7d+/GP2m7fLWfrczo3r07atAnVSQSeXl5ERKVioqKVlZWn23k9Pz5c9i0Cg4OlqxS+/333xFCZmZmUv8ATeObcYQQHZXGV9PLywsCbosWLYKVO5EsToxOLgkJCRwOBwJ03bt3nzlzJvhgoinuuXPnpDFuQ65fv44QMjc3x3H87t27CKGff/5ZNkM3DoZhME8nsmOuXr0Kt6p9+/adOHEiNzcXx/GUlBQ2mw31oJBQw2az09LSvnRauJWPHTuWLploAXDj8/HxkeWg119ky9v4mh+LrG6wK9ksMAyLjo7ev3//2LFjIQ5EhPhmzZoFs+G//voLwzCRSATBUlVVVdnsksCtAIIWoLL94MEDGYz7Vd69eweXFXG9ZGZmCgSCioqKwMDAiIiIqKgoGxsbQqvExMTEw8PjqzPpyMhIOTk5RUVF0ructpg27QifP39uZ2f36NGjqqoqiJuXlZXV1dVt2bKF3D6CISEhUMsybdo0ULq7ePEi/HVZLBYpAje1tbUBAQFr166FUCSgpaUFKT8LFiyA/H6pisw1BBq+HDx4EMdxWArDtJRykpOTYZYg+SKHw8nOzl68eDGXy5UssaqurnZ3d4fU30YSajIzM1VVVRkMxvPnz2X0Mb4jiL0raeTQN45XdJ7SMr8ZRyKqapsdq6isrPTy8rKzs+vRowdx3cnLy0O76RcvXkRFRUVFRd29exdEPOzs7GBWCiEoRUVFabcsLS0tZTKZUDVYU1OjrKwsJyfX+Oa3zHB2dkYIWVlZwdOCgoIdO3YEBgaGhYU5OTkRbiw7O5vD4XTs2BF+vfr6+vb29l/6nojF4jFjxiCEdu7cKaOP0QTatCMkAGH44cOH4zgeHh6OEDIwMCB3iPj4eAgCjBkzBr6Fnp6ecG3Y2Ni0WHgTrkMbGxsIiAO9evWys7Pz8vKqq6sLDw+HBc3UqVMhB8Hd3R188IYNG0jxwY0AAhbQkNPExAQh5OHhsXfv3tTU1MLCwsOHD7u5uUnVgC/h5uaGEPrtt98kX+RwOBkZGQcOHIiPj79w4ULDn6qXUNO/f397e3uidyjsdixevFgWH+C7A/azjYyMKBnd902+ynL/n/a+bOI2JI/Hg9xyJSUl4rrT0dGxsbHx8PAgYnQ5OTmBgYHLli0rKyvz8/ODb461tbVQKMQwbNOmTRChIVffuR4+Pj4IofHjx+M4HhISghD68ccfpTdcswDhdcnmBKGhoYGBgdHR0ceOHauniFZeXu7k5AT6OLDJwmKxGoZnQEC/Z8+e1MoF1OPbcISSstTQ/sPW1pb0UXg8Xt++fcHLwvbvkydPoEjRwsKiWQXdBQUF7u7u9a5DAwMDNpsdGhoKKxV7e3sOhxMREUEI3IwePbqgoADH8QcPHoAPXrx4cYtF5r5KVVWVgoICk8ksLy+vq6tTUVFhMBh8Pv/BgwevXr2yt7fPzMw8cOAAn8+XkgGNAPUtklKHvr6+dnZ20JVw165djcQ/IaGmd+/e8GuHhBp3d3eoWPqSqi9N4+zevRshtGnTJqoMCEkq0lgRMGrX88Ly2s8eAMIabDZ78ODBxEXHZDKNjIxgm+qz8fCqqqpDhw7B46dPn8L1Pn/+fLjuILecyWS6urpK6XNJVg1CNz4iw5lyIClGMnsOHGFRUZFYLGaxWA1/pWKx2MvLi+iVJicnB/1+4d2ioiLY8rh9+7bsPkYT+DYcoaQsNczrP7sgaD05OTlQ0NOnTx/QoY+MjASBm0mTJn01oTk1NZXL5ZqamhJBczk5OQjCNCzVX79+fUlJyZ9//gk/CH2oBw8eDMVthMjc7NmzpZTfCNPPkSNH4v+7zn748OGrV6/YbHZ1dbWTk1NSUpI0Rm+cIUOGoE+6iy1DJBLdu3fP1NSUSKhBCP3yyy/1JGZomgho77WsDQVZRPBKtO0ejdgRml/6/3/E3Nxcd3d3KysrSWGNTp06WVlZubu7f1Uh2sXFRXJuFBERAV3XZ82aBXNfImOAy+VK40OBwCGIe0ybNg0h1HR9c6mSmpqKENLW1ibiUrW1tY6Ojlwul8fj/fvvv43353r9+rWdnR1M6OE+4+7uDhPcadOmyeQTNINvwxGCOPKHDx/wT2JIiYmJUhqruLgYgoQ6OjoxMTE4jiclJcGgQ4cOzc7ObvgjkskvxCrE1NSUy+U2spx6/vy5i4sLIW/N5/Nhl6t3797gNaOiorp27YqaIDLXMg4fPowQWr9+PY7jJ06cQAitXLmyurr6wIED586dCwwM/Pfff9esWSO9JemXEAgEUFZFSobw+/fv7ezsCHeoo6PDZrPpdWGzgL0rBoNB+d5VdFpJ59WBg7Y8vesXsnXrVsnCYgaDYWRk9M8//4SHhzd9T6FhWkpMTAzMfSdPngwZA2fOnIHvD+m1p8TOa2lpaV1dnZqaGoPByMvLI3eUlnHx4kXUavXHnJycXbt2we8TFgZMJlPGoiVN4RtwhCCABLLU0DlPS0tLqptnlZWVM2fORAipq6vDrCcjIwOEvPX19cEfQwUSi8WS3ITv2LEjzEOJiuNG8PDw4HA4kv3PBALBhAkTYBYGuchEtPazInOtxNLSEiF09epVHMetrKwQQq6urkKhkMfjQeAxJyeHkmI7f39/9GnXhBRAMWfUqFGw3EcIycvL//LLL0FBQXT6aFOATXrQ4aOcuMwy7ZV+Xc3Y8KeEwmInJycS+5MkJSVBxsCECRNgQ9HZ2Vmy5pgsJKsGQeyQ9OyHFgMVDqA30kpqamrc3d07deqkqqqKEFJSUrKxsZHeYqYFfAOO8MqVKwgh0I28desWQmjGjBnSHrS2tnbhwoXwN7t79y6O4/n5+bDm69q1q7m5uWTyS+/evVks1uPHj1vfzLayshLq59TV1R89eoTjeE5ODojMEdFasuBwOKNHj4b2T+DOKYmCNgQ2ZkgspTI3N0cIQeIPJNQQEZt6CTU0nwXEDTZs2ECVARiGSYYHfhgwHDGYy5cvf/z4sZQiFqmpqXp6egghIyMj2Lm/fv06bHn8/fffZM2f6urqXrx4AVVhEKFZs2YNhmEHDx7ct28f1AhRBSj9RkdHk3VC0MEYP348LK/l5ORmz54dGBjYFiaj34AjXLduHfrUs/HPP/9EstKmk0wbgy3J8vJyMzMzrWGz4R5aL/mFLIRCIczFlJSUYEu5uLgYSrh0dHQ+q6LSSjIzM2Wwzm46P//8M4k7JbW1tdBSQzKsnZub2zChRvayPt8K8BehsHFjQkKCiorKokWL8E9f144dO0r765qRkQE79wYGBvDl8fLygvS3NWvWkD76jBkzEEI3btyoq6tbsWKFk5MTbM1QQlZWFkJIU1OTLHmNN2/eIIR0dXUxDGuxWpv0+AYcIeycgSy1sbExQqjxTVpSuHPnzvHjx3k8HiTLDRkyBJIs3mWVoEUPf//rgFT76Ep2QwWBm4qKCrhOtLS0SFfohjX3zJkzyT1tyxCLxVDTSVYoGPL+P5uS/lmFGnd3d9lvi7ZlhEKhhoYGg8GgcIECipfW1tY4jru7uyOELC0tZTAun8+HbciBAwdC6NXX1xeqLBYvXtz6CBAARcZw2uzs7OLi4n/++Wffvn2hoaGknL8FXLt2DSE0e/Zssk4IQYVVq1YRr4BaG6RfoE9qbaRvADWRtu4IJWWpJTvnSXtcOzu7+Ph4UHhxdXUlth+uvchG1j4fi2ShEkukq8FquLa2dsGCBZLR2lZC9LFSUVHp1q3buHHj2sKKMC4uDuLAZJ0QGvCCKs2XeP/+PZvNhnRB9Cmhpuktn75vXr58iRAaNGgQhTbANx/k4EEQWLK0Rqrk5eURWWyQHxASElKvyqLFZ4Z8V5j5aWpqysvLOzg45OXl2dnZHThwgMIO9XZ2dojU9nBQRN9QWbSurs7DwwMabqBG1dqkSlt3hJItCJ4/f44QMjQ0lMG427dvZ7PZDf9sGy4l9PxDRgqEOI6fPXsWtuihZEckEq1Zswa1rrApPT3d0dFx6tSpRI0Hg8GAgA8hMkchTk5OYAlZJ4Qd1qaI84FCDbSGQRIlUBD6Tk9P9/f3Jw5OTEykcMIuS2BCtnr1agptgNSV5ORkHMchXBkZGUm8e+7cOS6XKxKJduzYsXPnTtJXFQKBAO7Uurq60JCPqLKYPXt2s5oniESisLCwnTt3jhgxQrKwx9DQ0MzMDF45fPhwbW0ttaFCqIsnyyHl5eXJycmpqKg08qFaptZGFm3dER44cAB9qjCFHtkyuCAxDFu2bFlgYCBRaUtgtPP5gpMyDdzfu3cPMjuWLl0KoRhipXjs2LGmnychIcHe3t7ExIS4/JhMpomJCZfL/fjx49OnTwmRuaakvLaSRnZVly9fjhA6efIkKQPl5OQwGAw1NbVmVWI8ffp0wYIFRG9uAwOD06dPe3h4SGZpnTp1auXKlaQY2caB7C0KZcpB8bJLly4YhuXk5CCENDQ06oUlQZvi0qVLHz58kEbBX0VFBRT5de3aNTY2FsfxyMhI0IRqSoFBUVERyHyD5jugoqICRVZE7OHKlSvgCWSjsPgl8vLy4Koha1oMLV8sLCy+emRqaiqbzSbU2vr27duIWhuJtHVHOHv2bNhAxnF83rx5CKFLly7JYNzi4uLY2Nh6ocKqWpGCjS/XL00GBkjy+PFjCMVYWlpCkS8hSdp4/oJYLI6KiuJwOFD7AXypjxUhMkcI3EgJDw+PtWvXgnnLly+vp181YMAAhFATeyx/Fei52pQrsCGQUAPiGp07d26fjlAkEkGCNIVNjF1cXNAnvT2QiW+4n83hcF69enXlypW0tDRSMv4bUllZOX36dIRQx44dYamUlJTUp0+fhw8ffulHeDweKGwQkyqEkJ6eHigsfnYpeePGDTh4/fr10kunLCkpOXjw4Pv370EDBLRKCDw8PBCp+vugiNL0fj5lZWVOTk7EXatDhw4sFgtqf0eNGkVoswkEArKKrNq0I6zXggAmU+SWEDSLkKQiZO3z6oOsRYdxHI+IiIBfxaRJk6Cw6dq1azNnzvzsQge0plgsluT0s3PnzjY2Nl5eXo2sjQiBG0JkTkpAWw9nZ2dvb29JR1hYWAhCaGRNRUGHSFIssbkIhcLbt2+7ubl5enpOmDAh7hPbt29vD44wKioKJuYU2mBjY4MQOnXqFI7jsDVw+PBhyQPc3NzMzc0/fPiwZcuW7du3S08toba2Ftp3q6urg0Jvw6upsrISrj4iDYSIvtjb2zdlhuft7S2pAC6ND1JdXe3r6/vs2bOCgoKKiop6om4gOH7gwAFSxqqpqYG07eaGrEUi0d27d6EdB0JIXl7+5s2bioqKhFRsQUGBhoYGKUa2aUco2YIA9H46d+5MYdHJoQcflJf51QqpySghBG6MjIw+qz1BJL9Iak3p6emxWKzAwMAmZrjx+XzYJOvdu7f0mqRwOJyqqqoFCxbs379/y5YtxOve3t7oU4fS1iMSiWAjB3IcWomnp6eOjs68oLgcHwAAIABJREFUT/z444/twRGC5NDvv/9OoQ1Q5QIBSdARDQsLo8oYkUi0dOlSiKxApS/wWZnvrl27gsx3c/P76imAk/0hcBzHAwMDIf/cy8tr+/btkm/BtjpZ2emgKt6aVsMxMTF2dnaampp8Pl9KjlAetWEgXQ1K6ODxuHHjJHeYZW1PimCUvqaivNzXD5UCgwcPDg0NnT59enR09Lhx4x49egSiM5mZmf7+/t7e3o8ePaqrq4ODDQwMrKysLCwsJIXfmkK3bt2Cg4MtLCxevHgxceJEPz+/ESNGkPtBnjx5IhAIUlNTb968mZOTk5SURLxF/JVJGSgiIqK4uLhfv37wi2o9I0aMuHfvHjw+ffp0bGws8VZVVZWSkhKTyaysrCQKpL4Dnj59ihACoVFKSE9Pz8jI0NbWHjp0aEFBwdu3b1VVVZv7rSYRJpPp5uYmLy/v5uZmYWGxffv2kpISHx+flJQU4oBx48bNnj171qxZw4cPb9n9asaMGf7+/hYWFtevX6+oqPDw8JD0r62nrq4uJiZGSUlJQUEBw7CioiLireLi4sTERGVlZWgB23pgdmthYdHiM4wYMcLJyen48eMwxd+4cSNEj2tra0mxECHUpleEki0IYLXeMHtFZmAY3mVN4NbryVQZABQVFUExpY6Ozvr160eOHEn8KRUVFc3MzM6ePdv6rLmGInOyYcqUKYi8rqSgUEOWHoqnp+eX9ghLSkqmTZsWGRl55syZ06dP//PPP6SMSDkYhkE+SGpqKlU2XLp0CX0Slrp9+zZCyNTUlCpjCDAMW79+PUIIlPERQtra2lZWVk5OTjk5OWSNQuTjEArgZCESiXg8XmpqKoZhHz58kEzm9PT0RAhNnjyZlIEwDAPVKrLEARQVFR8+fPjkyZMnT57cv3+/XYRGoQUBhEHgjh8cHEyVMe/5FcjaxzOSStEjoLy8fMqUKYRI2JeSX1qJUCiEBE4lJaV6Ii9BQUGQ1Xny5El7e3uystUzMzNhokdW4TbMGLy9vUk5WyOOcM+ePVevXo2MjIS9Fugo8h0ANZ09evSg0AbJqkHoI0268nXLIDqXbdy48fnz51JK9CcUwCdNmiSZzh0cHLx//354fOfOHVIKiwGQ04Jd/NYTHR2NEOrevTtZW1pfCo2Wlpbev38/Ly+vtrbW29v7s90RGoGaKF9TKCsrS05OVlJSGjlyZFVVVVxcnLy8PDSSpYSXKSUIobH9tL56pLRRV1ffvHlzTU1Njx49fHx8oqOjHR0d58yZIyl/2nog+LN582Yo5IcEaMDY2BhiKdnZ2ZaWltBMscVgGObl5TV79uxRo0YJhUJ9fX0dHZ3WWo+QQCCIiopSVFScPHly68+GEJo2bRrUOALW1tZ79+5FCPF4vMTExKCgoIiICAzDEEI4jpMyIuVAXJT4BeI4XlVVRYkNEJulPE4ryZs3b6qrq/v378/lcsPDw11dXWHeQC4jRox49uzZDz/88PTp05kzZ5aWlsLr48ePF4lECKGMjIzc3FzJ/YUWw+Px1qxZA41ziRSVVgJxUUtLS2lvacXGxhoaGh44cODff//t3bs31N01nba7R/jy5UsMw4yMjJSUlEJCQkQikZGRkbq6OmX2pAj0u6p20yIzUt9iIJdvwYIFZmZmISEhiYmJPXv2hHw2EmEwGP/++2/Xrl23bdtmZ2dXXFy8detWhBDxV5g3b15MTExcXBwIfzSXjIyMQ4cOhYWFpaSkQLifyWSmpaUNGTJkyJAhBgYGRkZGQ4YM0dPTa8FVFBAQIBaLJ0+eTNZ3RkNDQzILiZCh6du3r4eHR3h4uLa29ogRI44dO9ZG7tStB+QsiHvili1bQkND/f39ic8ubfh8Po/H69Chw/Dhw4uLixMSEpSUlECjhHLAK8Mvp7S0VElJCVorkM6gQYNCQ0NNTU1fvHgxderUgICAzp07Kyoqwrt79+61tbW9efNmi88POhI3b95MSkoqKChACMnJyRUVFX38+FEy8bVltH6DsB5Tpkwhiu4VFRWJa+2nn346ceLE4MGDVVVV/fz8MjIymndeUpar0kCyBcGhQ4cQpeL3OI4bbnu2+Az5gtctw8zMDEnIUnM4HCmllgFnzpyRFLhJSkqysbFJTU11dnY+evSoZO5cUxCJRJ6enrNnz5as7mgETU3N8ePH29nZOTo6BgUFfTVwWl5e7urqCnHdo0eP8vn8W7duteLTty+g9hS0FxQVFVVVVaHILD8/H7I3jYyMZNaVEKoGZ82aheP4/fv3EXkZxa0HWpoQFXhisXjjxo3SGy4jI6N///7okwJ4XFzcb7/9FhsbGx0dHR4e3oJ7o1gs9vb2Njc319fXh6sbkCx51NTUNDExYbFY7u7uUVFRzW1qnZ2dzWAwVFVVZaCSk5KSIhaLWSyWSCSqq6vbtGlTs3687TpCyXs9TCgo1LYoqxYyl/ieftQmurmKxWJQXoDNuezsbNBElSrXr1+HK2Tp0qU5OTk8Hk8gEJSWljZrP+/9+/e///67gYGB5MUmCcz1li9f7u7uzmaz4SpteJiWlpaRkZGNjQ2Xyw0MDKzX/TgzM7Nr164gDhAXFxcWFjZixAiyfx/fG0VFRdevX1+8eDGUqwKQqaiiogL90zMzM+FePHjw4ObuwbSM1atXo0+dZzZv3owQaiOJSGKxGHYioMT52rVr9vb28FuSHnw+HwobBg4cmJKSUlxcTGTQNEvmLTk52c7Obvjw4Y0sYfv370900yVQVVU1NjZevXr1+fPnX7161Xj+jrOzM0ilz507t6ampunV9C0jMTGRy+UmJSUlJyefPHmyuULBbdQR8vl8iGjBvb6srCwoKEiqcieNExhfgKx9YtJKqTJAkvj4eIRQ79694amPj48MRNFwHA8KCoLY4Jw5c5p14dXW1rq4uEycOPGzm3+dO3ceMmTI9OnTt23bdvnyZQaDoaysLKljkp+f/+TJk1OnTq1evXrChAmE/JIk3bp1MzU13bhxo7Ozc3JyMsTuoOcL7QgboRHpEw8Pj7KyMshPUVRUhHSM3NxcuBfr6enJIJUUqgahoTmUTFCoQy3J69evEanS8E2kqKgIqhoIBfCmIxAIDh48aGJiIjnXaXglQlqilpaWQCDIzs4ODAzkcrk2NjYGBgaSC0eEEJPJ1NfXNzc353A4Xl5e9ebEKioqIMTj6upaVFSkpqZG6m+CZNqWI0xLS4PLUl5eHtoZk5gN1Rr23UtR+91fKKK+gSSO487OzgihhQsXyn5oQuBm8uTJRO5WIyQlJa1YsWLw4MFEWB+unx49ehgZGc2dO/f06dP15m6w3Whra9vIaYuLi0NDQ52cnFgslomJieTWHUIoISEBprqWlpYRERGurq6SjhDDMEK2prmhnu+DpkifVFdXHz58+OHDhxiGwVKMyWSCumFxcTHs0vXq1SslJUV6dubn50Ngrba2trS0lMlkKigoEPJa1MLlchFCy5Ytk/3QDRXAG0csFj948MDCwqJv37719tpVVVV79+5tbGxsZWV18uTJhIQEyO2E9Kg9e/bUO1VJSUlISIiDg8OyZct+/PHHhnGdXr16WVpacjictLQ0FRUVFRUVOTk5Pp9PO8Kvg2FYZGTkzp07oe8XoKysDEEYeXl5d3d3qm3EZx6JmHJA1p1BvgS07XV0dKRk9MTERCgMMjIyys/P/+wxNTU1Tk5OkyZNIuaeqqqq/fv3nzRp0pIlS+7cudNIpUdqaqqioiKTyWzKRU6Qlpbm4+Nz5MgRFouVmZkJU9cJEyZYW1ubmZkRjrC2tvbIkSNr165NTU09ceLEli1bXr161ayP32bx8/MD0S/g5s2b9XqLN1f6JDMzE/pR45903uXk5OCVkpISkLno1q1bfHy8lD6RZNWgr68vQmjcuHFSGqu5gO6xm5sbJaNLKoA30k06MTFxzZo1hoaGIFIDs5lu3boZGhqam5tv27bt6dOnnxVchD4/mpqaRUVFjZhRV1eXkJDg7u7OYrFMTU0lU6iio6MhnWfo0KFRUVFPnjyhHeHnEYlEoaGh9aalHTt2tLKycnd3h9UG0Wbh6NGjVNmJ4ziG4dp2j3bcekuhDZKAFq1kGxoZk56eDtLYgwYNqreeS0hIWLFixaBBg5hMZseOHYcNGzZjxgwWi/X06dOmL7+gVBlqqFtAZmZmly5d/Pz84OZeLzT67t07W1vb4uLi7Ozs1atXU1gqTi5///03UViG4/iiRYsuXrwoFApDQ0PZbLaBgQFxlcnJyRkZGbHZ7NDQ0EaquyQdIY7jR44cgYvRwcEBx/GKigpTU1O4F79+LZUkMojKwodis9kIoW3btkljIEmKiorMzc1xHHdxceFwOJ+djWEY1rVrV0SSdF/LaKgATiAQCPbv329iYgLF+FpaWnAZ2tra3rlzp3HfRgApGvWk1xpHLBa/e/fu1q1b27Ztq66uhiDQsGHDFixYMG/ePMIRYhjm6OjIZrPfvn17+vTpXbt2tYXJqKwdYVVVlZeXl52dHXyTgJ49e4IcO9wrk5KStmzZsmHDhrq6utOnT8Psns1mU6UympRVjqx9vGM+I+9JLlFRUXCXwTBs27Ztn5W9JmSpqQ3r5ebmgvSarq5uXFxceXn5sWPH+vbtq6mpaWhoOGfOnH///bfFPiY/Px8EO5rV8C8lJQWax0KyTEhIyLRp0/AGjhDDsMuXL4MPePLkyfXr11tmZFujoSN0c3ODtTvQqVMna2vra9euNTHn88SJE0uXLpXcfia6Y+7duxfH8ZqaGugqoKWlBdt45GJoaIgQevr0KY7jEAyUdjYKjuO7d++GTPWKiopLly4RHSi1tbWLi4vhMQgB/vDDD9I2pnHqKYALhcKbN2+OGzdOS0ure/fuEydOhMlQy/QuIiMjoRNTc9UtiLu0iopKcHAw3MTqhUZLS0uPHDkSHR0tFoszMjKk1C2kWZBTR/j27Vs1NTVibffmzZvu3btLurqioiIfH5+HDx/6+vpWVlbCi7DRamVlJdkkDyH04MGDTZs2PXny5M2bN+vXr+/YsePy5cuPHDmSm5t74cIFyd0maSMU47y8Ss+oXAZD6qX0IpHo3r17EHa/du0aQqisrAzekpeXr66uhrd8fHxwHB89ejRRSEQJOjo6bm5ulpaWHz9+tLS01NLSGjhw4MGDB3/++efWF5l16dJl06ZNN27cgJLhJoJhmGS5t1gsrq6urndMVVXVqVOnKisrZ8yY4eDgUFZWtmzZslZa23ZITk6GGgOEUFZWFoPBMDExiY+Pt7CwMDU1nTRp0peSdT8LKIxIsnbtWg0NjeXLl4Nmur29vYeHh7W19d27d01NTb28vKZOnUrWZ5GsGqyqqoqOjgYNT7LO/1mePHlSW1ubn5+fmZmpra0tFAqJrxCEEOExRA4prxZVVFTcv39/cnJycnLyypUrVVVVu3fvPnfuXGdn58GDBzOZzNacfNSoURYWFl5eXsePHz927FgTfyonJ8fQ0LCwsBCeisVic3PzN2/e1DuMwWB06tQpLy9PLBa7ubnB0p9ayHEqXC7XwMCAxWLB0127dq1YseKXX35JT09/8ODBw4cPoSIefQrLmJubL1y4EJogN0RZWbm6urq6uhpUxKytrXV0dObOnevu7l5SUnLz5k1CXUx6ZAtq/rqW/CAqT4EpVyPEEELsm++OLBzYWUNa7sfBwcHU1PT+/fvZ2dlRUVF9+/YlVCSgFQs8hlJ6ad8RGgHH8cDAQEdHR39/fxBSSU9P79u376lTpxrmW7eYbdu2jR49GnoBAnfu3Pnpp5+aIjrTvXv3iIgIHo8HT0eOHOnl5QWPVVVVIciGEIIckG8OoVCYm5sLk86srKyOHTsSGt+ZmZlhYWHwGG5GV69eJXfiuGTJEnl5+aVLlx45cqSiouLUqVO3bt2ytbV1d3e3sLDw9PSEkFrrqa6utrW1raurU1ZWDgoKqqurGzVqFPSODgsLGzBgwJdSH1vDyJEj+/Tp4+joqKmpefHixeLiYijCqQf8kil0hEKh0NPT8/z58yEhIXBnSE1N/eGHHwICAj5rcMvYt2/fwIEDQUODRDAMc3Z2zs/PNzQ0XLhw4ZgxY969ewdbzlRCyrpy9erVkrkbs2fPvnXrFnTzAZSVlc3NzV1cXD7bP6gefD5/9+7dBw4ckIyFvnr1CkLeU6ZMaUq+YmuISSvVXBmgYOOLrH2If0r/1959hzWRrQ0AP0novUuTIqiIuCogKiq6V9ZrAZdVsQLWBSviqosFBXVVsCKrIFgQxYZiQQERXAVB6aAUAem9E0oCgSTz/XHW+XJRkZIGnN9zn/skk2TmZGXyzpzyvnbhCg6RuZX/P2nt9OnTbFwck5ycHBkZaWtrW1paGhkZefDgwbCwMPgSAADvCJ0xYwYA4MmTJ+w6bu/BDBT4nCZhYWFbW9vExMRp06YBAFgLZrLF4sWLWcsOGxoa9lx8Jzc3V1hYeM4XkyZNYlfRTr7i7e197ty5Bw8eeHl53bt3D58r8c0xQg61oVvBPCaTCXPiCwkJPXr0iO2Hc3FxAV9ya+Tl5a1evbpPfeb9xloUU0xMzNTU1MzMzMzMDHbGfPrEg/z7FRUV7u7ueN+biIgIPAfhvfjkyZPZew7u2bPn1atX+NO1a9f2PDeqoqJCXl4ePv78+fP79+8nTpyIYRiDweDoBOOBY9vVIo1Gw7vy6HS6gICAjIyMrKysubm5hYWFlZUVnqb9h5SVlWEWR1YmJiYxMTH//e9/X79+PXfu3LCwMDbef+CyytseJVUde5zfxeieLpJGZ3ZRuua5J+adnS0kQHz//v3IkSNLS0vZdWi4TMrIyEhWVnbkyJHTpk1j7fycPXs2gUDAMCwxMREAAGMP1xQVFfn6+l65cqWxsREAoKKiYm9vv337dnhV/uzZM1NT0+Tk5BUrVjx58oSbfdfdKCgo3Lp1Cz5+9+7dhQsXeNUSztmyZUtCQkJhYWFcXNy0adM4cWP0QxYWFo8fP16yZImfn19bW1tAQICXl5egoOD58+etra39/f1hKV12ef36NQDAzMwMw7Dr169zrTe7W0/ylStXZGRkCgsLZ82aRSAQ1NXVZ8yYsWjRoiVLlnyvc4td4Hi2n5/f48ePYdeanp7eunXr7O3t4bLaBw8eTJ8+PS0tzc7O7sGDB90W/PVbbm4ua0K7jIyM1tbWnj/S3NyMZ4SmUqnwR4xIJMKK33yLbb9Zf//9N57vrqCgwN7ePjAwcMSIEX0aluiZvr7+27dv582bl5ycbGZmFhERwdp11m8MJvbuc9OT5JonyTWFtVRBEpH5nZzJTCbW0NZ1513lOjN1V1dXS0tLtqfZxVeLd8uQGRwcLCgomJGRMXfuXBKJxGQyVVRUfvnlF0tLywULFnAuBWtsbKyXlxd++hkZGTk6Oq5atYr1n1VBQSE8PNzU1DQ0NHTbtm2smakHqKioKDU1FT7+esDva3CFIt4qdjWDr+Tm5sbExOzevTshIcHJyWnPnj1wJj2XwYJ5FhYWeMG8c+fOSUlJHTlyZP369V1dXbBqRL8xGIz09PRnz549f/48JydHRERESkqqqqpKTk4uPDx89OjRM2fOZNd36SVlZWU5OTlYZpZIJL548eLdu3fv3r07ePDg96Y7DFxNTc2NGzd8fX2LiooAAEJCQtbW1vb29nPnzmU9EPzPMnXq1EePHh04cADOt+cJKSkpmKIWAJCamvr1SDOf6tP9Y2Bg4JEjR3Jych4+fOjr6wtrRmPf6hrl3EJ4fL6iqqrqx48f+72f9k5GZEadY0CWytYosDpUzv6l9YXUgJjyn/bFsPaIfv0/8xP/TvalUqmczhsEgS9dozBlkYiISEBAAP4vKCYmtnjxYj8/PzZmvero6AgICIDT9sCX06/nnsmEhAS4jB0mxBq4xYsXm5qaLv9CVlb2h12jGhoa+NNXr14Nya5Re3t7d3d3WI/t1KlT+NTzrq4u1kpAnZ2dHCoMxCopKQnOjcIL5uFLnjw9Pfuxw+rqan9/f2trazgcCMFuWFlZWTg3tby8vFtSPS4QExODCw9sbGwAAIKCgu3t7TAvAeu4tYaGhqOjY2Rk5MBz/yYnJ9vb2+NLAHV0dNzd3b+3cheKjo6Gd2C+vr4DPDoEr7N3fKGoqNjzOcjaNYphWEJCAuwa5X99C4RNTU2VlZXu7u6urq6xsbH4YkxuBkIMw1pbW+EaJjk5uZ7/Yb7W2NYZFF9p650utTECrA7VdPzH/mpGSEpN55esMUqbI1nDntSKhyd+Xi294gG+ZfQfbzjwnXqCB0J4EkpISGAYlpmZCTMjs3aD6Ovr/3B9WM9qamr279+P307BbupeTqEOCQkhkUgEAuHmzZv9Ozqrvo4R1tTUsK4zy8nJ8fDwGHgzkJ7hBfPmzJkD11pcunQJ3qz0vnDgN/+YR40aBYNKW1vbsmXLAADi4uLcLBPNytHREWaOhr1Qq1evxl/Cl0Sz1iOTl5e3tbXFl4T1SWxsLL7uk0QiWVlZvXjxAs6Y+6Hr16/DOM06ttdvlpaWBw4cCPtCV1cXBcJ/eXh4FBYWFhQUJCcn48stuRwIMQzr6OiAa2jExcUjIiJ++P7S+nbfVyUWp5OE7MLB6lD9vdHOd3Pe5jR2ixf1rTTF/w2EE5b4pE4ZFzrdVGn5HbjF+GAsp77Vd2zfvh1e3cMM1Bs2bGB9tba2NiAgwNramnUUVlFREWYM6ZaGtKysLDg4GA+TycnJBQUF8HFcXByGYXl5efDHyNDQ0NfXt085RTEMg8XMBAUFB/6D1ddAiPBKdnY2jAEzZ86EE9kuX75MJBIXLFjQw11pW1sbXFLMOtFRRETE3Nzc3d2921QUOp2Ol4l++vQpx7/SdxQWFsLr7++FpczMTFdXV5jyApKRkYFJQlpbW+F7bt++PWvWLDyVj6OjIz73B6YULygoIBKJysrKzs7OxcV9TvS/d+9e2Mjc3Nz+fEkWlpaWfToHm5ubDx8+jD8tLS2FFZX5X98C4fr160+ePJmamnrnzp3Lly///fffcHtGRkZRURH+tqSkJC70XdDp9I0bN8KOu+/V2SmooXiGF81we0dYE0pcEzbD7Z17SH5OZfeJVSX1VBgm4UxRwv/2hY5eeiVu6qQ3U420lt0Qtgt3CRron1f/1NTUwMD/vV4XvK+GdegU/rJ4enrCNNZBQUEAgGvXrsGP2NvbX758GT52cHCADzw8PAYScuCyBCkpKbznvH9QIBxECgsLtbW1AQBGRkYwOX5YWNg3qxPgab5Z54JpaWnhab6/dwgmk9ktAzj3+fv7AwCsrKx++M709PTDhw/DBOX4EMbSpUvv3Llz7tw5OTm5bdu2wXcuXLjw+fPnhYWF7u7u06ZNgyMLMTEx/e5cZTAYVlZW8JZ6gIUK+hoIB68+d402NjZSKBQGg9FDukiuYTKZ8PKHRCLhP+iQ76sSnV2vwepQ0XUvfj2bfP1NWV1L9z6KzLJW95B8GCZJNv+GyYiPdaLrX3QbF1RfdvPVtCnxUyYYrrxS0di3myR2efz4MQAAZkv5ofT09L/++mvq1KmsfU1Tpky5e/fu7NmzNTQ04BkCA2FNTc2hQ4eMjIwOHTr0dc7JvmIymWvWrAEAqKmpsRaR6KuKigr8IhrDsOLi4u/dnra3t/v6+oaEhDCZzBs3buBlGhFuKikpgTMDYcE81peoVCq8RIMVDfFOPyMjI1dX1+Tk5F725H+dAZzLYJrfPmVCgYUE8Ek0pqam586d27Fjx+jRoxMTE7EvgZBOp7e1tW3atIkt6x+oVCqc7WlmZjaQFFR79+5l7WJdt24d51LL8hbvk24PnLu7O/wjc3Z2xjdef1O21ufDo6QqCu1/OmcYTObbnEbnuzljd7+BYdLidJLvq5Jq8v8nnz0fVij2VSxUXHE3dMasnBkG7amJ3PtuLODKVhcXlz59qq6uLigoyNbWVlpaes6cOUFBQatWrTp69Oi6deuw/70j7Gspyx7QaDS4sMnAwIALF0wlJSUtLS3btm2LjIx89uyZl5fXwDuFkH5gLZhXVlZWWFjo6+trbW3NOqtZQUEBdhX2+w+jWwZwboLLJGCBjr4qKyvz8vKC02v37t375MkTQ0NDOp0OAyF8DxtHQCsrK+EM6vXr17Nrnz0oLi6+cOFCSEhIU1PT2bNn8T6nwWIoBEIMw27cuAGXr+3YseOb3ff4HFHlL3NEbb3Tg+IrW9u/PYYRGFsuvSlCalMEySaMZBMmvSlCzv7ls5jP5RuWFUwb0/aK4zkPvwZDC37O9FVHR0dJSQkMhO3t7bq6um/evGENhOzV0NAAfzV+/vnngadFhZfVV65cwTCMRqNt3bq1W83riIgIBweHysrKvXv3bt26FWbcQLivtrZ20qRJAADWaZ9EItHExOTIkSNJSUm9nPTRs24ZwLmGSqX+888/A5yOCwMhhmGLFi26ePEiayBkr9TUVJh1iAtFC0pLSzs7O7dv3x4REXHnzp0///yTLf/QXDNEAiGGYU+ePIHTrG1sbPCUEA2t/84Rldzw7TmiPaDS6E+Sq/96/PnE0/zQtFpaFwPDMGY7tXLH+nxjnZYn3x6V5Bwajfbu3TvW3sJ+gIEQw7CwsLCJEydu3LiRQ4EQw7CioiJlZWU4xW6ACdMbGxsbGxsdHR0xDDt37tzu3btZu3BzcnKys7MdHBwoFMrnz5/37t2L50dGuK+xsVFJSUlFRUVUVNTCwsLX15cT5ey7ZQDnHJgCHn+ampqqr68/kB3igbCgoEBdXd3U1JRDgRDDsOfPn5NIJCKR+Pjx4wHuKjMz8+zZs9u3b6+srPTw8Ni0aRNr5h0Mw4KCglxdXdPS0nbv3r1p06bBVeyTPQkI+MGvv/76/PlzCQmJwMDAhdZrz4UW/Od4gtKWqOVeaVnlbXsWaaefnFl84WffjQaWhkqCpB/s9oOZAAAgAElEQVQvehUVIv1qNOKgle7+xToLJykKCRABAAQRUeXzfpIWS2qP7SPfusL5rwW0tbXz8vIAAEJCQtOnT5eVlWVNLd1vCxYs0NHRefr06cB39T1aWlqhoaESEhJ37txxdXUdyK5kZWXT09OFhYU/ffoExx3h1CFIR0enqKjIycmJRCJ9+vRpx44d36xij3CHqKhoS0tLTU0NnDTY0tJSW1vL9qNs2bIlICBAQEDA1dV13759bN8/F4waNcre3h5PD8sJixYtOn78OJPJXL16NUxK1W/jx483NjYWFRWFqTyEhYVZ83rHxcUpKyvX1NR8+PDB2NhYUlIST5U8OPA6ErPH2bNn9+7dm5ycnJSUJC0trT7ThrTsDpz8klfFgZLWTGb9ueP5hlr1Xu4Yh4tDaWpq5uT8fylEEok0kOF0/I4Qw7DS0lIJCQnO3RFCYWFhsNf60qVL/d5JYmJiUFDQpk2bysvLIyMjN2/ePMApqQjnwIxokyZNwjBs+/btDAYDnyHJdnfv3oVJjrZt28ahMm1svyOE2UjgY7gMrFtBQbZzcHAAAKioqHyzslvvNTc3b9u2jU6nUygUZ2dn1npeXV1dSUlJsJ8mNTW1f7WfeGiIBEIMw9LT0+FEMpjIPPAexydYN/n75Btq1bjuYdIHmkWiB+wNhI2NjaxlAj9//lxRUREdHc3RtVlXr14FAJBIpIHkCi8vL8dnjZLJ5ME1AjGsuLm5AQB27tyJYZiTkxOGYX/88QfnDtctAzjb919bW0sikUy+MDAwGGAghBoaGq5fvx4YGIhh2MePH5csWXLgwIGB7/abOjs7YR6+gWTlTktLO3PmjJubW1FR0alTp/bv39+ta3RQGyKBsLm52cXFhUql0ul0SUlJAAB3kjA1P7iVbzyq6g97Jq3jx+/uF01NzYkTJ079AgAw8AnWTCZTR0dHTEyMRqO1tbUBAERFRdnS2u+BBQTExMQ4ffGL8Byc1QWX+h04cCAuLm7Pnj0cPWJ4eDhMRbZ69eqB5zbrpra2VlJSsuGLN2/e9CMQFhYW3r9/v729nUwmw6V42dnZAABYShr2jk6bNo29LWfV0NAwZswYAMCiRYu4kHtv0BkKgZBOp1tZWXl5eSUlJaWlpQEAdHR0uHb0tn9eFEwbW2G/itE2oGks36OpqZmQkICfh/24I4yIiDh79uytW7cSExM3b94MN8J0HrCrBM5u52hxKyaTCTODKCoqvnnzBk5+6fmEZDKZL1++dHd3v3jxIuoIHSxoNJqYmBiBQIAF12g0WmxsbLcpvpwQHR0NMystXrwYT/3IFmzpGn3x4kVKSsrp06cjIyPhTBk4yK2oqIhhWG5uLgBg9OjRbGz21woKCmDqRNZlZgjEs4o5bEQikeBicwCAt7c34G7dWvGf/6vidb36D/vKzatVvG6QZP+tz44x6BiFQhARJQy4lLy0tPRAyr7PmTNn3rx5u3btsrGxgbnzAQDKysqVlZXV1dXq6urKysr5+fnV1dW9L5XVVwQCwc/Pr7Ky8uXLl3PmzGF9SURERFRUlEQiwaNLSUktXbr04MGDK1asqK6utrW1bWlpWbRo0ZEjRwZY0ADhgqSkJCqVOn78eCUlJQCAkJAQrKDJaWZmZi9evFi4cGFISMiyZcuePHkywBLt7PXf//73xYsXY8eONTc3hwXu5eTkCAQCXEwJK602NDRwtA2jRo169OiRubm5h4fH/fv3dXV1JSQkxMXFxcXFZWRkxMXFxcTEpKSkpKSkxMTETExMlJWVHz165OPjU1JSoqqqamtrCzN5DU28jsRsBtNSD2ReRv90ZH4o+s/kkiVzu6orO7I/VmxeXTB1TMG0MflTdEtXzG97/bLfe2bLGGFwcDDMyOrq6gq3LFy4EADw7NkzDMNgRZvo6Oh+N/JrME0+hUJhTZbY0tIC6/oSiURpaenvVU3btm0bLLWDd3NlZWUpKCjg+bfy8vLCwsIYDEZ5efnTp0/7mhAV4Zzjx48DALZu3cqTo8MM4K6urtevX8/KysK3nz9/vt/TN9ra2liTuZeXl/ejvkpERAQcDsRYzkG41LKpqYnBYBCJRCKRyIVOS5iV+4cePnwYGhqqoaERHx/PZDIzMzPHjRt39+5dTjePV4bCHSGr9+/fAwDgfBluEh7/k6rfvarta8tWLmB00hgdHfh/2fa8nKqDO6XnWSoedgd9r1W2YcMG1tvBnTt39rXEo5eXV2JioqWlZV5eXkpKSkRExH//+1+4wq+6uhoAwPqYLZ48eRIREeHt7e3v79/S0jJlyhRYLURSUvLFixc6Ojo0Gs3MzCwkJAQA0N7e3tHRwWAwYGHn5uZmBQWFGzduWFpa4jV+9fX1R4wYkZWVBcdRsrOz6XR6cHCwtLS0goLC5cuXnZyc2NV4ZCBiYmIAAGZmZjw5+uTJkz9+/KisrLxgwQJZWVm8hsO1a9dmzpzJWh2i98TFxU+ePIk/VVNTc3Z27utOKisrOzs73759S6fTVVVVMzIyJkyYIC8v39zc3NDQICMjIyMj09jYSCaT4d3hwPn5+ZWWljo5OR07dszS0hKegACA9evXJyQk+Pr6EonE8+fPy8nJUSgUMplMoVAoFEpbW1tzczOFQhk5cuSxY8cOHToE5yWMHz/+9OnThw8fXrlyJQCARqP5+/uLi4vb2NjcunWLQqE4ODiwqxowTwypQFhbW1tQUCAuLg5vO7hMSGeM/C6X2kO7QmrJkWTqxVGKcPvV2ua6rsbDhOcC2jqydvZ93e3hw4dZn549e7ave3B0dMQfP3v2DD7gaCC0srJKT08nEAjbtm179uwZawXRESNGSEtL19bWEonEuro6AQEBERERuOyPtZRuQ0NDt7WAsrKydXV1AAACgfDrr7/6+/tPmDBh/PjxTk5OcPQR4Tk6nQ7nfcyaNYtXbYB/zPyG9U/0559/hg/k5eULCwsbGhp0dHTk5eUbGxsbGhrYFQjt7e3d3NwUFBSWL19OoVBYXzp+/Lifnx+TyRQQEIBdaN+Um5sLkwRBhoaGsGuKQCA0NzcvX7787NmzdXV15ubmgYGBZWVlrIlkB50hFQjh7eDUqVPxOwkua7rqhdG7vvkSs51K9vOUtrYhiopxuVXfBKuJwhF71sfslZycXFpaum3bNnyLt7c3XF799OlT1uX8sLMUACApKblnzx4tLa0PHz7gr2IYVlxcDOsbAAAeP34sKytrbGycn5/v7u5+7tw5bo4KI9+Tmpra2to6ZswY1spKvAJLFsPHsLOhH8LCwqqrq9euXfvx48f379/b2trCSelswTo0KC8v//nzZ04PE0KwUpKwsPCDBw9CQ0PFxcVlZWXFxMTExcUlJSWlpKTExcWtrKwEBAQYDAb+KTqdLiAgAC9qlZSUYEeOkpJSRkZGdnY2a0a9wWgIBkJe/SYyGuq6ykt6egdRoCM5XmzWf3q5w/r6+qdPn5qZmSkrKwcHBxsaGuIl4weOo3eE0dHRAICkpKSHDx/q6Oikp6fDS8va2lqYYkZZWVlMTKylpYXBYMAzislkNjU1AQCampqoVOqqVauOHz9eUFCgo6MDAAgMDFRUVIQ9XTQaraioSEJCIi0trampKTw8fOfOnexqOTIQ8N999uzZvG4IAADk5uYymUz4uH/JmLq6ujQ0NGpqalJTU69du3bixAl3d3c4CMoW3QIhYOt8mZs3bxYWFubk5Dx58oTBYJibm8Ouy6SkJF9fXwKBQKPR3rx5872PFxYWGhgYJCQkwK5RAEB8fDxeVaqqqsrZ2Xnjxo3V1dUVFRW6urrl5eUyMjLsajz3DalACLtleBUIu8pLiYLCDBoNABDX2m7xqRJub6QzFsqKAwCwrs6uitLe77CkpGTFihX79u3btGnT4sWLDx48CMvesgUMflVVVYADgXD27Nnw13DKlCms2/fv308mk+EMum4fwccIW1paZGRkpKWlz549O3v2bGNj45aWloqKigcPHsAzWVhYGNbigeCqNYQf8HaAsBs7OztYlg8AEBYW1o89CAoKSkhIREZGmpubKygoyMnJtbe3s7GFHA2EdnZ2dnZ2AIDTp0/jGxkMhoODA4PB2Lx586ZNm1paWigUCpVKJZPJbW1tVCq1ra2NTCZTqVRZWdl9+/ZZWFiMHj165syZ6enpzs7OcE4+AADDMDs7u66uLklJSWVlZSsrK56MRrHR0AmEXV1dKSkpBAIBv4ThMqKYOAb+7YqZLilyVuvfMcLrtc1NdCYAAJBIBBHR3u/QyMjo77//1tHRmTRpEplMZm/+zK/vCGFQ5JyUlJQbN24ICQl5eXl9/SqJRIJfEP+adnZ2q1at+vz5s5iYmJaWFkfbhgwck8mECwP4JBAOXEdHR2Vl5YIFC3Jzc+vq6rKystg1gAdxNBB+k5eXV1pamoaGxpkzZ2Bhih4YGhreuXPHx8fn4MGDWlpanp6e8+fPhy+pqqrivd+s44iD19AJhOnp6VQqVU9Pj3XOBTcJautidDp8TAQEUeK/M0QE/n+qCCbyk2Hvd5ienr527dqDBw+WlJR4e3uzd2IkR7tGv8ZkMmE2yL1798IMF70hKCiIT/xD+NyHDx/IZLK2traGhgav28IeIiIiDAZDUVHR3Nx8zJgxKSkpsCYou3A5EFZVVR05cgQAcPHixR9GQWjOnDndVv0OVUMnEPJ2gBAAQBAQkFqysiX4DgCUb7xKJAppjxEaNbr3O1RSUnr06NEff/xRXl6uo6MTHh7OxhXlcEicQqG0traOGDGCSCTW1tYyGAwOLUO+fv16QkKCurr6/v37ObF/hOf4aoAwJCSE9S85NTW1fxPo8OmvGhoabA/wXA6E27dvb25uXrp0qaWlJeeOMkihQMhOclv3UN/+M7atnXW1oKG4cBsGCKLiI05c6NPeVFVV4axrbW1tTqTnGDFiRGFhYXV19ejRo+Xk5Orr6+vr6+EMUvZqamo6cOAAAODs2bO9vBRFBh2+GiDstta2r0tvuYObgTA8PPzRo0eSkpKenp4cOsSgNoiXQHYDZ8pwfyk9K6KomPrNJ5Nnzf5VVYEgIAgAAATiVCWFeQb66jcfC47kr3U2XOsdPXz4cF1d3axZs6ytrTmxf4TnMAyLi4sDfHNHiKPT6bAwHpPJJJPJvG7O/+BaIKRSqXAJ09GjR9XV1TlxiMFuiNwRVlZWlpaWSklJjRs3jrctIUrJqF66ScvNpsb901VWKqCgJDpluqjxdMB/aRe6BcLMzMzq6uqJEyey9yhZWVmXL18WEBC4dOkSoe+JdZBBISsrq7a2Vl1dfdSoUbxuy/+j0+nnzp2rrq5et27d1atXR44cqaenxz8dg1wLhEeOHCkqKvrpp5+2b9/Oif0PAXz369w/+MIJPknzIzxWX3bD9sYVG/w7CBGVdQ1NTe7u7nfu3OF1u/4Hd+4It2/fTqfTd+zYgS9CQoYe2C/Kb7eDAgICf/7557Rp0+AigWnTpsGiwXyCO4EwKyvr/PnzRCLR19eXV5lG+B9fhI2B44cBwq9JSkru3r379evXd+/eXbVqVV5eXr8zXHACa0IZDgXCO3fuvHnzRklJqVuiOGSIgTNl+GSAkFVaWlpNTc306dOPHTvGZDI5MQTebxISEsLCwhQKpaOjg0OBEMMwBweHrq6uLVu2TJs2jb07H0pQIOQgNTW1jx8/6unpLVq06ObNmxkZGR0dHbxu1P9jDX79y7JWUlIC86XV1dUVFxd3e7WtrQ1ON/fw8BjUWSeQH1q2bJmdnR2/JTcgk8mnT5+mUqnp6ekxMTEJCQn29n1O9stRMJ9+Y2OjqKioqKhoR0dHn5LgUCgUWOCXTqdnZWV1dnZ2e8PVq1fj4uKUlZX/+usvNjZ76BkKd8o0Gi01NZVIJPJqKf33vHz58vbt2xs2bBATEzMyMiKTybBIG59gDYQ6OjoGBgZwCWZkZGRVVZWdnR3Mpbtt27Zv1iksKSl5+/ZtVFTUqVOnjh49qqmpaWpqyjq79dixYxUVFVOmTIEZLpChZ+vWrefPnxcWFra2tp4/f/6hQ4f4alKijIwMPh7Bn+u+5eXlq6qqGhoaVFVVT5w4ISQkRCAQsrKyTp486evrGxERUVhYKC0t/fvvv3/z4/fv3wcAJCcn5+fnT58+/datW+7u7virDQ0NcLa2p6cnuhL9Ad5VgGKbjIwMERERAwMDXjekO1gNo6ioqKurKzs7G6+uxycSEhIAAEZGRt2219XVubq60un06upqf3//d+/e9bCTXbt2vXnz5uHDh1VVVax12j5//iwsLEwkEmHuY2RIEhQUbG1thY9ramqkpaV5255BB46qvnnzptv206dP49U3d+3a1cMe4uLi7t275+TkhGEY/H8cvACdN28eW5s8NA3irtGOjo7GxkYAgIGBQXNz87179+A8af6hqKg4atQoLS0tAQGBcePG8dtI9ffGBeF9IYlE6ujoSEtL+15aKQzD/Pz8rK2tVVRUKioqKioqWAdgHB0daTTa77//bmJiwrFvgCCDG7xRS0lJwfODd+Pt7d1Dh0pGRkZCQsLy5cvpdDoAgLVYRExMzK1bt4SFhb+Z0RDphr9+mvskPDz86tWroaGhAAAhIaF//vknMzPT19eX1+0aNAgEAoFAqK6uXrJkiamp6fTp042MjERERD5+/FhYWFhSUhIdHa2lpVVeXv7NpGipqakwPemKFSskJSVjYmI2b94MX3ry5El4eLisrCwamRjybt68KSwsDABobW3ldVsGmczMzLi4OA0Njd27d7u5uU2dOtXc3HzGjBlKSkrv37+XkZEpLy8vKSkRExP7Xr/u7du3R40aFR8fv3HjxjNnzqxZswZu7+zs3Lx5M4ZhLi4uY8eO5eJ3GqwGcSBEBiInJ2fBggUYhjGZzMePHz9+/BgAICAgMGbMmJ9++mncuHH19fW//PILlUqFhZC+ZmRkZGRkBB+vX7+e9aWjR48CAP766y9e5X1FuKa+vl5ISAgA0K36K9Kz8PDwlStXtrS0aGtra2pqlpSUREVFRUVFAQAkJSVnzpxZX18/b968KVOm9JAWh3VEkDVYXrly5dOnT3p6env37uXotxgyUCAcjuLj4y0tLevr66dOnern55ebmxsbG5uSkpKYmJidnQ3nobm6uqqoqBgZGc2cOXPGjBnGxsYiIiK93H9ERMSlS5ccHBw4+SUQvvDHH39ISEgAAGpra//++29eN2dw8PPz27ZtG51OX758+Y0bN0RFRSsrK+Pi4qKiomJjYz99+hQeHh4eHg4AEBMTmzx58syZM+HNoqhor8rX2NvbUygUExMTeLOO/BiPxygH4NGjR2JiYrpfKCoq2tvb87pRg0BwcDA8naysrCgUCutLra2tb9++dXd3t7Cw6DY0KCgoaGRk5OjoGBAQUFxc3G2fc+fOXb16NXzc3NysoaHBpS+D8BSfT5YpKyvDMKympqagoKDbnzqv0Ol0R0dHAACBQHB2dmYymV+/p7q6OigoyNHR0cjIiDUZk4CAgJGRkbOzc0hICJlMZv2Ih4fHjBkzOjo64FNLS8uMjAxufJ+hYnAHwoULF+JPvby8UCD8IU9PT5h8Z8eOHXQ6vec3FxQUBAQEwBOyW8oeFRUVa2trT0/Pt2/f0mg0ExMTVVXV0NBQDMPIZLKUlBRXvg3CY9LS0m1tbfBxbW2tiooKb9vD6v379yYmJhiG7dmzJygoqKamhtctwlpaWiwsLAAAwsLCt27d6s1HamtrQ0JCnJ2du52DJBIJXpgGBQU1NjY6OzsrKCi4ubnBTxkaGiYnJ3Pyqww1KBByyocPH65cuYJh2L17986fP0+j0XjbHiaTCZe3EwgEV1fXvn68sbExNDT00KFDc+fOlZSUZA2Kp06dMjExuXLlio6ODoVCQYEQ4TkajbZ37174d+7i4uLh4VFSUoJhWHNzs4SEBP62rKwsPT097jSprKwMDuMpKCjExMT0Yw+NjY0hISG7d++eMmUK6xT0efPmOTs7Hzp0aOTIkXl5eRgKhH03iJdP8DMGgxEUFFReXp6VlVVXV2djYwMnFGRlZcHamFBMTAx3hlVoNNqqVatOnTolLCwcGBjo5ubW1z3IysouXLjw6NGjUVFRTU1N6enp3t7etra2urq6MHWToaHh3Llz0TRRhB+Eh4eLiYmlpKRkZma6ubmtW7cOrj3HMIz+pXr21085Jz4+3tjYOD093cDAICkpCS9z2CeysrKWlpZnzpxJTExsbGwMCwvbt2+fqanp3LlzAQDS0tKHDx/eunUru9s+PPA6EvdfXl7ew4cP8acpKSkRERE8bA+rc+fORUZGOjo6Pn782MXFZc+ePTk5ORiGRUVFzZw5E3/bjRs3Vq5cyenGNDQ0wBNPVlb266W7bGFiYpKSklJfX6+qqpqYmIjuCIcDOOrW3t7e2NjY3t7O6+Z8Q1BQEIZhFy9e/OuvvyoqKjAMI5PJIiIi+BsyMzN1dXU53Yz79+/DUfl58+Z1G9tjF2dn5zNnzjAYjOnTp9+/fx/dEfbVIJ41Onr06NGj/7/gu6GhIQ8b082kSZPq6+urqqrGjh377t07MTExDlV+/6GioqKFCxfm5OSoqamFhYX99NNPnDuWvLz80aNH0Yzt4eDSpUsNDQ0TJ07MyMhQUlKaPn06H1YXgfUvYSk+XGdnJ54IsL29naMNwDDs1KlT+/fvxzDM3t7+0qVLHM2qQSQSvb29ly1bhiaL9tUgDoT87OeffwYAzJgxQ1VVdefOnQAANTU1+FJlZeX58+fh46SkJI42IykpydLSsqamZsKECWFhYVyoyblhw4Zr165x+igIz23btu3Tp09v374lEolw/JvXLeotISEhuGoWAJCbm7thw4ampiZnZ+fVq1ebmZmxsY4bjUbbtGlTYGAgiUQ6d+4cnCzKaZMmTVq0aBHKJtNXgzUQPn36tKSkxN7e/sKFCxiGGRsbm5ub87pR3amqqgKWEAgRCAT8ek1QULCzs7OoqOjDhw/z58/v/UK93nj58uWyZctaW1vNzc2Dg4O/mTibXebOnSsrKwsAIBAIPj4+586d49yxEH5QXV394MEDZ2dnmCf6zz//PHXqFK8b1Vt47vu6ujoAQFBQ0JUrV65cuaKmprZ06dK1a9cOvHupvr5+yZIlb9++lZSUvHPnDpwsyjm6urp4Wu2jR48WFBRw9HwfgnjbM9s/sOZIVFTU48ePMQwLDw/vOTE0//jmGKGLiwsAQExMzMLCIigoCF8MNBDXrl2DnTDr1q3r7Owc+A57LzY2dunSpWgZ09D2+++/e3t7x8XF3b59293dfbCcgN8cI8zPz3dxcdHW1sZ/FSdOnHj69Ony8vL+HSUjI0NLSwsAoK2tnZmZyaa291ZBQcHevXv/+ecfLh93UBuUgRA6evQo/EuFXfCDwjcDob+/P+sVqIyMzNq1a0NDQ/sXwJhMpqurK9zV91bsctSOHTsAAL///juXj4sgP9Te3s5azKGqqurIkSP40+TkZEdHR/x+kUgkzpgxw9fXt7m5ufeHiIiIkJaWBgBMmzaturqana3vnZMnTwIAfv31V+4fevAalIGwq6vLxcXl6dOnzc3NHz9+DA4O5nWLeuvt27esf6D379+HuXExDCsuLvb09GSt5ycjI2NraxsSEtL7iEij0WxsbAAAAgICvr6+7P8CvfD582cikSgqKlpfX8+TBiDIQNDp9MjISFtbW3FxcXgmioiIwN6aH64GvnjxIpwWt2bNGl7NpK2rqxMWFiaRSF9ngEK+Z1AGwvb29sjIyMjIyNLS0qKioh9mSBlcioqKukVEWVnZb0bEkpISPMFVZ2dnaWnpypUrAQCSkpK8XUmycOFCAMDJkyd52AYEGSAymRwQEGBhYYFP+YZnYmRkJOxoMTEx2bhxI3xzeXn5+PHj/f39e8idxjXwd+Dw4cM8bMPgMigD4TBRWFjo7u6ur6+PR0Q5OTkYEWGNXy0trfnz58M35+Xl6erqJicn6+rqpqSk8LTh2IsXLwAAampq3ByeLCkpOXXqFIPB8PPzO3HiBM9T+SBDRmlpqbu7u4GBAX4m6ujoBAcH6+npjRw5Eo7GlZaWqqurYxiWnp7O6/Zi0dHRAAAVFRUuzw8YvFAgHAQ+fvzo4uLCWhRQWVn54cOHWlpaEyZMgD3DMBBiGAZjJG8xmcxx48YBAOCKZu44ePDggQMHOjo6ampq7t+/HxcXx7VDI8NEZmamq6srnFYTFhamp6cXEBCgp6fX0dGBB0I+AcM2a8oRTiOTyTDz/uXLl2/fvs2147IFSrE2CEyYMOHYsWO5ubnwPBw7dmx1dTVcm3HmzJldu3Y1Nzfjb+boit1eIhAIcMoM29czTZ06NSYmBn+qpqZWWFgIALh9+/aECRPa2tqYTKa8vHxycvLkyZPZe2gEGT9+vJubW35+/qtXr3755RcAAKxQxodLR+zt7QEAly9fZu9uU1JSWCswv3//vrOzEz729PQcMWIEAGDDhg2fP39m73E5DQXCwQSehzk5OSkpKTDDp76+vpWVVT9yh3La2rVr5eTkYmNjk5OTuXC4iRMnysvLNzU11dbWrlmzxsjIqKWlhQvHRYYhIpH4n//8B7/iPHPmjLe3d0lJCet7Ojs7mUwmAIBOp3MnnWk3dnZ24uLir169ysvLY+NuN27cCOuVQosXL4ZrMd++fQsAoFAotbW1/HAt3lcoEA5KhoaGeKGyY8eOBQcHZ2Rk4K8ymcynT5/Gx8cDAOCsIu63UExMDJatv3jxIhcOZ2BgYG5ufuHCBWVl5RMnTkyZMgWf8ocgHDVixIiDBw/u27cP35KXl/f333+vX7++vr7+9OnTmzZt4v5lmbS09KpVqzAM8/X15cLhRo8ejScNePnyZXZ2dn19PReOyy4oEA56UlJS7u7uBw8exLeUlpbq6urevHmzpqZGQkb9K5sAABfTSURBVELi/fv3xcXF3G/Yjh07SCTSvXv3ampq2LjbzZs3T/0CXo3iZGVlhYWFR40aNWrUKFg2HUG4YOvWrV1dXfjTMWPG7N69W1FRUU5Obv/+/WPGjKHRaNxvFUyyev36dSqVysbd0mi09i/wjcrKykZGRp6enjDx7OXLl/FMN4MCCoRDwerVq+GQIaSlpTVu3DgSiQT/KFtbWxUVFbnfKk1NTUtLSxqN5ufnN8BdYRj24sWLjo4OAMDp06fDv1BQUGBHSxGkP5YvXw4zmRGJRB8fnzVr1uAvPXz4cO7cuUQi8Z9//tHU1OTJCThp0qQpU6aQyeSHDx+ycbcbN27Er0SbmppYX4JFNqSkpGRlZQdXBykKhIOYs7MzzGEBAPD19cXLPlAolBMnTuzbt49Op585c2b9+vW86ieEiYZ9fHzwEfW+amtr8/PzMzAwWLBgwd27dwEAkpKScl/g/cMIwn1HjhxRVFTs6Ojw8PC4fv26u7s73P7u3bvo6OiysrIPHz7cu3ePQqFUV1fzpIVbtmwBAPj4+AxwPzExMTY2NpWVlQCAwMDAj1/ADMNDAa+nrSIDxWAwfv/9dxMTE3zNUHZ2tq+vr6+vL/4AFmPjiYkTJwIAAgMD+/rB/Px8JycnPNJramoGBgaamJhER0fj71FVVS0oKGBrexGkbxgMBuyH58NUSlQqVU5ODgDQv7XFzc3Nvr6+eO22o0ePTpw4MT4+Hn+DgoJCvzOy8hUUCIcCWJeRH1byfu3KlSsAACMjo95/5O3bt9bW1ng6DyMjo4CAALg+8ty5c6yRz9XVlQ9/fZDhZurUqQAADlW9HiBYBs7BwaFPn0pJSbG3t8cH2pWVlZ2dnYuKilAgRPjXqlWrAABXr17ldUO+oaOjA2Yxfv/+/Q/fGRAQgNd3FRYWtra2Zj3rEIQ//f777wAALy8vXjfkGz59+kQgECQkJHqTOryjoyMoKIi1pB28DMV7m/7555/Gxkb8/WFhYbxKqcpeKBAOBWfOnAEAbNmyhbfNqKmpcXZ2Tk9Pz87OPnr06KVLl+D2/fv3AwDgZO5vqqysdHV1lZeXxy8/XV1da2trudVwBBkQmDhi06ZNvG1GYWHh7t27KRTK+/fvT58+/eDBA7gd1gnHz8dvysvLc3Z2xmefSUtL29vbD59iaigQDgWvX78GAJiYmPC6IdirV6/evHnz119/1dfX79q1C9ZWrKioEBQUFBQULCsr6/b+5ORkW1tbfIJZt8tPBBkUYG5Pnp+AVCr17NmzZDL52LFjSUlJBw4cgNvv378PANDX1//6I3Q6PSQkxNzcHJ93ZmRk5Ovr29bWxt228xiaNToUGBoaEonEjx8/si5m4qFNmzb5+/sXFBTAp6qqqr/99ltXVxe+trezs/PBgwfTp083Nja+desWkUi0traOi4tLTk62s7MTFBTkXdsRpM9++uknAoGQkZHBYDB42Ay4egEAoKSklJycTCT++/P+22+/qaqqZmdnx8XF4W+urKz08PDQ1tZevHhxVFSUsLCwra1tampqcnKyvb39cMtHgQLhUCAlJaWrq9vR0ZGVlcXDZnR0dISGhoaGhlIoFCEhIVNTU2FhYfgSXEfh6+tbUlICT7/ly5fHx8crKSk5OzsXFBQEBQWZmprysPEI0m8yMjIjR45sb2/HL/54oqam5sOHD9HR0eXl5YqKingKYkFBQZjmycfHh8lkRkVFLV++XFNTc9++fWVlZWPHjnV3d6+oqLh58+bwzdDL61tShD1Wr14NALhy5QqvG/Kvr+uxwXoU3eaCwr5TBBnsYIIxfFiOJ6hUakFBQXl5OZPJLCkpYR1iKCkpIZFIJBJJQ0MDnoDCwsKrV6+OiYnhYYP5B7ojHCKMjIwAACkpKbxuyL/wIQfYCzpz5sxPnz5JSkoyGAwLC4vIyEjYC4rfMiLIoAYX2338+JGHbRAVFR01apSamhqBQNDQ0MCHGFJSUo4fPw4AEBcXh+WiXF1dS0tLb9++PWvWLB42mH8Mpiw4SA/4LRACAGpqavz8/Hx8fKqqqgAA8vLyMEfa3r17zczMeN06BGEnfgiE3TQ3N9+6dcvHxwfWiyASiS0tLcrKykVFRYMr/xkXoDvCIQLOl/nw4UO/k5mxUVpamoODg7a29uHDh6uqqsaOHevp6VlaWrpnzx7AgSKFCMJzfBUIU1NTHRwc1NXVd+zYkZ2dDZfDf/78WVtbu7q6+tWrV7xuIP/hdd8swjZjx44FAKSmpvKqAQwGA07Fhn9aRCLR3Nw8JCQEHy+sqakRFhYmkUiFhYW8aiSCcAKdThcVFSUQCL1Zt84hXy+HnzFjRlBQED5YeOLECQCAlZUVr1rIt1AgHDpg8ns/Pz/uH5pMJnt6empqasLTT0pKyt7ePicn5+t32tjYAAD+/PNP7jcSQTgKDk/ExsZy/9BwOTyekgIuh8/MzOz2ttraWnglWlxczP1G8jPUNTp08GSYMDc3d+fOnaqqqk5OTiUlJaNHj/b09KyoqPD19YV3qN04OTkBAK5cuUKhULjZTgThNNg7+uHDB64dkcFgPHv27Jdffhk7dqyHh0dDQwNcDl9ZWenr6zt+/Phu71dUVLSysmIwGP7+/lxr5ODA60iMsA1Mb2FsbMyFYzEYjMjISAsLCzg7lEAgdOsF7cH06dMBAJcvX+ZCOxGEa86fPw8A2Lx5M1v2xmAwWBcX0el0Go2GP62oqHB3dx85ciT8GRcREYHL4X+42zdv3gAAVFRUUP4mVigQDh2tra1EIlFISIhdi/OWL1/OWtFi9uzZVVVVsDKLnp4ePAMlJCTs7e2zsrJ6v1tYVlBfX783URNBBgs4CcXU1JQte4uNjWW9qL137x68mYuMjLS2tsanferp6bm7uzc0NPR+z/BOMTg4mC3tHBpQ1+jQISEhMWbMmM7OTnbll8nPz6dSqfjTT58+JSUlqampOTg45OTk6OjonD9/HvaC6uvr9363y5YtU1dXz87ORrPXkKFk8uTJ+/btw+tjc8LatWt/+eWXBw8ekEikNWvWvH379tOnT87OzrDoYC/Z29sDAC5fvsyxZg4+KBAOKdevX8/NzZ00aRKH9j9+/HgJCQk4FS03N9fJyUlKSqqvOxEQENi8eTMA4MKFCxxoI4LwQFFR0eHDh0+ePGllZQUASE5OPnLkCNuPYmVlpaOj4+7uXlZWFhgYOHPmzH7sZO3ateLi4lFRUXl5eWxv4SCFllUOHdbW1mvXroUjcAAAU1PTixcvGhoaDmSfJ06cgNUEAQAtLS2CgoLZ2dmysrIDbKqDg8Px48dDQ0Pz8vLGjBkzwL0hCM81Nzez9nBUV1ezZrjun6ysLLw8Z3Nzs5GR0W+//fbbb7/h2bT7R1paeuXKldeuXfPz84MV3BB0Rzh0UKlU1uoTFApl4LnwFy5cuPYLmNt+4FEQAKCgoADLE3p7ew98bwgyJOnr6yd9AZcAEonEAUZBaNu2bQCAGzduwGRPCAqECAAAFBYWxsbGMpnM58+ft7e349snTZpk9gV7qyPt2rWLQCBcv369paWFjbtFEF5paGg4+cWDBw96/8GcnJxdu3YBALy8vI4cOVJcXAy3EwgEkS/Ye/ZNnjzZ2Ni4oaGhT+0cwlAgHFLc3NzMvygsLOzlp5hM5rVr1yIjI4uKimJjY5uamjjaSMjAwMDMzKy1tTUgIIALh0MQTiORSCO+6H3HCZPJvHnzpqSkZE1NTUtLi5OT07Vr1zjaTgiO0/v4+HDhWPwPBcIhZdu2bde+wNcY/ZCfn5+trS2BQNDR0dHW1sa3T506VUZGBn9qZmYmIiLCxtbCIoVeXl5MJpONu0UQnpCRkdnwBWues575+fmpqKgUFxdXVlaKi4uLiIjQaDQAwMiRI2ERQUhfX3/ZsmVsbO3q1atlZWXfv3+flpbGxt0OUigQDimKioqaX/S+L6Wjo+Pp06exsbHt7e1UKrW1tRVu9/b2hkUEoQcPHigqKrKxtb/++qu2tnZ+fn54eDgbd4sgg8jSpUsXLVokKys7ZsyY4uLi48eP//rrrwAADQ2NrVu34m+bMGECzKHILqKiora2tgAAPz8/Nu52kEKBEAFOTk7Ozs4eHh7Nzc3i4uJcyxFFIpHgqY7qUSCDnZCQkJqaGv5UTExMWVm5Nx9UVFQcNWrUhQsXxMXFL1y4cOjQoRkzZnCsmf9jy5YtBAIhMDAQjdMTMAzjdRsQ9rhw4YKZmdnkyZPhUzc3t3Xr1mlpafG0UT9AJpPV1dUpFEpGRoaBgQGvm4Mgw8ucOXOio6O9vb23bNnC67bwEsnNzY3XbUDYY9q0aSoqKvjTOXPmsI7w8ScREZGysrLk5GQAwKJFi3jdHAQZXkRFRR8+fFhWVjbMAyG6I0R4LDs728DAQFJSsrq6Gi5VRJDBCybLFhcXBwC0tbWJi4vDxPT8qbOzU0NDo6amJi4uztTUlNfN4Rk0RjgE5eXl5ebmksnkBw8ewGTz/ExfX9/b2zs9PR1FQWQI8PT0dHV1BQCUlZXNmTOnvr6e1y3qiZCQ0MaNG2fNmsXrhvAYCoRDDY1G8/f3DwkJSUtLk5aWNjY25nWLfiwuLi4nJwd/am5uzs2ibgjCLh8/flRRUZGQkAAA+Pr6wmmZfM7a2trV1RW/HXz37l1CQgJvm8R9KBAONZ6enhs2bAAAyMrK5ubmwhppfK6xsZE11VNdXV1nZycP24Mg/ePj49Pa2pqSkvLmzZvm5uaEhAT+v6SLi4uDldGgZ8+eDcPlTCjp9lDT2Nh47969jx8/rlixwt7efs+ePbxuEYIMFzBRi6io6Jw5c+bMmRMSEjJlyhReNwr5MRQIhxoPD4+urq60tDRBQcGrV69u3LiR1y3qFXd39xs3bsDHeK5FBBmM7Ozs4IPFixfztiW9VFdXFx8fDx9XVlayppcaJlAgHIIEBQVNTEzAlxzzg4KNjc3cuXPh4yVLlvC2MQgyrGRnZ1+6dAk+TktLQ4EQQXhDXV0dL3MvLCzM28YgyLAye/bsq1evwsf79+/nbWN4Ak2WQRAEQYY1FAgR3ps9e7a6ujr+dP78+fLy8jxsD4IgwwrKLIMgCDJ8tba20mg0BQUF+LSpqYlAIPB/dkb2QoEQQRAEGdbQZBmEvzCZzB07dmzatKmsrCw/P19RUXFQpOdAkKEhPz9fXFxcTk4uJiZm4sSJSkpKvG4RN6AxQoS/+Pv76+vrt7a2WlhYTJo0ic9TNSLIUNLa2urh4fHy5cv4+HglJaUjR47wukVcggIhwkdqamqeP39eW1ubl5dHJBKFhYVbW1t53SgEGS48PT03b94MAJg9e7aOjo6UlBSvW8QlqGsU4SPy8vLXr19///69qKhocHBwSUmJtLQ0rxuFIMNCR0dHV1dXREREQ0NDXV2dj4/P8FlTiCbLIAiCIP8ik8nl5eVlZWXFxcWSkpI2Nja8bhE3oECIIAiCDGtojBBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIEQQBEGGNRQIEQRBkGENBUIEQRBkWEOBEEEQBBnWUCBEEARBhjUUCBEEQZBhDQVCBEEQZFhDgRBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIEQQBEGGNRQIEQRBkGENBUIEQRBkWEOBEEEQBBnWUCBEEARBhjUUCBEEQZBhDQVCBEEQZFhDgRBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIOQLhYWFVVVVvG4FggxThYWFlZWVvG4FwjMkNzc3XrcBAaamptnZ2b/99ltubm5UVNT48eMJBMJAdtja2vr06VNBQUF5eXnW7VQq9cmTJwQCQVFRcWBNRpChY+bMmVlZWUuWLPn8+XNkZKSenh6ROKCbhJiYmKdPn2ZmZgoKCo4YMYL1pcTExKdPn6alpQEAVFVVB9RuhF0whA/o6enZ2tpiGHb69GkAQGdnZ793VVVV5erqKicnBwDYsWMHvr26utrV1RXGxQ0bNrCh0QgyVOjr69vY2GAY9vfffwMAWlpa+r2ruro6MzMzQUHBcePGKSkpEQiE7du3w5eam5vnz58PANDR0YEhcP369QwGgz3fARkA1DXKX3bu3Nna2iooKNjvPfz888/v3r07duyYpKQk6/b58+fHxsa6urqie0EE+R4HB4fW1tZu506fuLi4NDU1FRYWZmdnV1VV7dmz5+LFi9HR0QCAEydOJCYmvnv3Lj8/v7y8fP/+/f7+/sHBwexrPtJPArxuAPI/amtrCwsLZ86ciXeNlpWVJSYmdnR0aGlpYRimra2tpqbWwx4yMzNJJBIA4NChQ6zbk5KSBAQEAADwphNBkK/V19fn5+ebmprCkwgAUFVVFR8fT6FQNDQ0BAQE1NTUNDU1e9iDp6dnQ0MDPEmJROKqVatOnz6dnZ09e/bso0ePrlu3Tk9PDwBAIBD279/v7u4eHx9vbW3Nha+G9ADdEfKXu3fvmpmZ0el0AACTydy5c6eWlpa9vf2xY8f+85//zJo16/nz5z3vAT+Bu4FREEGQHgQHB5uZmVGpVAAAhmEHDhzQ0NDYsGHD8ePH582bN2PGjPv37/e8BxEREdZL1RcvXhAIBBMTEwCAkJAQjIKQkJAQkUiEx0J4CwVC/nX27FkvL69z5841NDTk5OQUFhbyukUIMoxcuXLl5MmThw8fbmxs/PTpU319fe8/m5aW5urqamFh4eHh4ePjY2Rk9PV7oqOjGQyGqakp+5qM9BMKhPzL29t7zpw5O3fuhE8HOI8UQZA+8fb2njx5souLCzz1+nQC1tXVffjwoaysDD7GMKzbGzo7Ow8cOKCvr79q1So2thnpH9RdxqeoVGpxcfGKFSt43RAEGaays7M3btzYvwvQefPmzZs3DwAQEBCwbt06DQ0NOzs7/FUGg7Fx48acnJxXr16hMQt+gO4I+ZSQkJCAgMD3BvwQBOE0UVHRgZ+Atra2YmJikZGR+Ja2traVK1cGBweHhIRMnTp1gPtH2AJdjPApAQEBTU3NpKQkfMvnz5952B4EGW50dXX7cQIWFxd3dXWNHj0aPiWTyTQaTVRUFD7Ny8tbunRpU1PT69evURTkH+iOkH+tX78+Kirq1KlTJSUlr1692rhxY28+VV5eHhUVFRUV1dXVVVZWFhUVlZiYCACoqqqC2zs6OiorK6Oiot6/f8/hb4Agg9j69esTExNdXV2Li4tjYmJsbGx686k///xz2rRp/v7+2dnZr1+/XrJkCQBg7dq1AIDHjx9PmTKlqqrKw8OjtbUVno/oNOQLPF7Qj2AYhmEGBgbr1q3DMOzs2bMkEqmrqwvDsK6urr1798IcMYqKiocPHwYAXL58ueddeXt7d/snNjY2xjAsICCg2/Zx48Zx4ashCP/76aef1q5di2HYpUuXSCRSa2srhmEMBsPV1VVBQQEAICcnd+TIEQCAh4dHz7tqbm7esmWLhIQEPMvGjBnz5MkT+JKxsfHXv8Djx4/n8JdDfoyAfTWdCeE3TU1NsrKypaWlmpqa169fX79+fU5OTnNz89fv1NLS6pbYEEGQAYInYHNzs4yMzPnz552cnAoKCr65mkJNTU1dXR0A0NnZWVtbKywsjBI5DQpojHAQkJWVBQDk5uYCAEaNGgUA2LFjR1RU1NfvvHDhgqOjI5ebhyBDG+sJqKOjAwA4fPjwnTt3vn7nwYMH//rrLwCAkJAQjIjIoIDuCPmXl5dXcnKylZWVmppaRUWFi4tLe3v7p0+fREREGhsbaTTa1x+RlpYWExPjflMRZOjx9/ePiIhYunTpyJEja2tr3dzcKisr8/LypKSkyGRye3v71x+RlJTEO0WRQQTdEfIvIyOjpKQkZ2fn8vJyRUVFExMTDw8PERERAAAcOEQQhHMmTJjw9u1bFxeX0tJSeXl5IyOjwMBAKSkpAICMjIyMjAyvG4iwDbojRBAEQYY1tHwCQRAEGdZQIEQQBEGGNRQIEQRBkGENBUIEQRBkWEOBEEEQBBnWUCBEEARBhjUUCBEEQZBhDQVCBEEQZFhDgRBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIEQQBEGGtf8DSuWIL27cFPUAAALgelRYdHJka2l0UEtMIHJka2l0IDIwMjUuMDkuNgAAeJzVkltIVFEUhtc5c3Xuzs0pHTveajRz0nwoy5lNFNnNRrMkzZjQdDAj0TLthpVEYVAkaFAa0YVIyxgayNKZbZaFaBZCNHR7CYUKFR8KQ7JzllgQvfbQhsX/7Z+1/r0PZ4/5b78Hfun4YmFm2flK4auWkRIbryLxP1P4UzleeYnlhfm9/YvIZkZEMkgQekW/DPmMsrPKoDLM/6JKYDwssG5G5GbFHlYk97BiuVsi9bCSELdU6ZaFeFiZwi0P4RRKD6vQgtLCqbSgMnKqMBurNnJqEy9mTqsRMxodaE2gCwWdHnQWMBgB5gAzF9hwEEWA2AqSSJDOAxkHmijQRENoDITGQmgc6OeDfgHobWCIB0MCGBaCKRHMi8CcBCvF/DWlwF9KxEglITKFUipRaU1qo1SjsygV2jAH/44YmH1L9mfPqbVsuEvYuNY8pqqJNqfAA9WttH3/uYDA2gON9MvyYfQtWYdoc24S+kvyPgdelSQSgTuKXvh7VWuRbdypwFlnvkPgvE3F9Me2DJx9fUdBP1Q1I1ec6HRcK4xH9makOF0SOc72tVhJVE0X5k/nljs6NkdQge8NLiN5/f50gYt2dDrL9vn8Ale+zCQNq59iTs6GY8Rbk4o5QV8DKXhwEP36R3py5GEd5gwUBp3VW5KRJ3zd6b1TFXiWOrookN2Tjz7X4iJZuyj67SlyQm7FYmb5hSm6aqcWfW/jCD1cG4Z+26SPOkbicHbx13pa0OdCP8ebSjvTgtj/bv0KunHvHvQt+Ve6mrY3IF+P8Qbejl/GnnVpZ8iNYBR+11VVNvl+Pxz92vNt5NuYAvv93clkhHiQR+0XSebN48jWylZyOlKPXPXkJCmxjmLOJEtJ/NAA/guurtQ52NOE91R/LCVbiwuR7UNLibP/LrLlTWuAU0nw3OqjlsCn8Uvo75420BJjOrL5J6uf1mSMXEKgAAAEGnpUWHRNT0wgcmRraXQgMjAyNS4wOS42AAB4nH1Wy24cRwy86yv6BzTgs7t5yMGSHCsILAGJkmuOgQEf/f9IsWc1M4KJrHTY4VSzi8XXfv/27z/Mdy0/fzz9/u1HOz7ydAc7/c9/RLS/lYjuvrb80h4+f/ntpT2+fXp4tzy+/vXy9mez0Zza+vuI/fT2+vXdwu2x3ffNOoeOdk8bszhzo43W5zwrO5JVB3njTQHDkZ+BmkDH+2nR2z1vLNOjcmmJtG2QkCpcjpiDrAB6AnUL6VM8WfbJJBWyJ5K3qbMHOG89FN8L4EggbUaDeDbd2NgQ18/AuXukrjYzCAGSqQDGTtID0SrejyldtADiNJCyce8hgrhFiaNyyZke2hThrve9cx+V5iwL2KMbPMK1uJb6cGZHkG+ZPDIumyQeFdKWS1EboAG6ZtLLwNmXz0ASSfJ9KPcy8MwOrhx4zam+ulmZcM70KC5nV8MRd0hQlRBnemwj5K4PkIBQIVW+ORaws4fl+xhh1Ks6pxWNIAgoiPJkG3NWogu3F3DrMVHrqdAYNE0qpACZwpjPVb9hweOqZXt8/vILH3gFHrAZzOZZVYa2i5KutddUyqM7Z0KdlKIk4UBmaUwkCF98yCzbUnp7bvcDt2unmTy8E41KVRk7VHQkUUGpDuFSrJlIR+cCsdRyFL9WlSeRUNt0KIozg0MfecUU/8/paorqtIy+x+CyjVFwCxomnI+YOhFcxaSSSNjHxF/WDDl5pSiG1nNeqkgmGgvfxMLLcYMqfoYrncK8KoB9kpfj04FEzFNIVpMgB1LmSTNPGLQo+JkJRUXNskY0s5TlOcwzdDR/9NLjXCxzcBNnW00lK9WM/WofmSF0y1RUajXiaXlE1B2NngO3O/VKIcsE9TTrGh6GrUCV6pb5QQvB44i1PzCYvJo3pkvKjhHfY81mR1OXm8iWQujziUGLMnVkUsvb/ZZzXCorImfUXCW79b2QPDC9KJkGYd5VJW9jr86cOd1XnbrphwnxDv388vRhg+87/eH15enc6RiZTc7FnY96rmdL07mDDSY/N20a4lynBtPxiG6AaZyrMR/5sgENED5u5nQ/LwDm3XAMOuwtxrELIukBdnGS7NPSL/smARhol8XCefSCGbtFzqsmwk7LETpWAqf7EyOpVlpO8fiGOfTLjb0uxNb79ePM5kXtkEdsR168LeagNi+zdrccGsvYLWd0GJe8qJ13LeY4fBmEeMTZy7hDznH0MtVSfDnuyV9cyK1eRxQjnXrcosl24ORl4DDSp2ctjT1mPTTVebMc7HVxxamDrNHNcrA1vlkOurZUDpy+NPOy2LVpd8vB2HxXyg7K1m+Wg7ONm+VsgaUveuLgbMkZCbWDs9PNcnDOPrx2XT6//wrH97v/AKyaMuk/6HRGAAAFDXpUWHRTTUlMRVMgcmRraXQgMjAyNS4wOS42AAB4nHVWTWscRxD9K4FAkMjuUN9d3cIQ0MUn2ZecjA5CmCSQ4GAEufjH51VL3hnWM6tlNVO811Xd9bqqPr1/fObnm0/vH2+//56f5e1Rn28+3N/cz5f5Mx+u3rev9789qt3fvPtw+/Aj7OHy/O7Tw6+PtvEpm2fG70/fbs5tya7BpzMtHkQtT3fnWCy4W9mYxZmnjVUbn3hRGAyWtggMfpJFo4nAYksjIQWm9WzToE3zpIuEqMOgi/f0fqKlZdlgkoUjuoAkStxh4YVCLQEStukcTjvDOR7EusM7wWlyLRTB0VpZoochDiworoinHpI6DN4ksTLiUPaKzx3s050tZOQNCKzVEY3DkWTWGg1MLNFaM8cOAitEMdLwH8RUsnoP9u6Io7duFHMJb7WCpPL0IcxRZ2nhFDjfWIhIAxzDcdGMtJMqDNSVo2LHyaWwzBR4VowVfQpNU1BKx9q8WAO+TOpmFZDADXB1VJYkMXnew8vipLUrxlElAjjX9ignS+AZLFvYWp0VNsRBrZepmYCPhaIRt1Nl0QsNWmahz744EqNFE0VMXGsbaJU9gsQgGS+iqeY04dBwFli8kxlPHWBJm0EhwzozSt08fe6vGxTwKgUsO0/BuWP3czUjRFZCg2BsWqBk/CFRQU4+iakZpYboWnsuRyasYCHXXaY8u0S+HnEyySsqpYI+16m1irQynPM8oS/v0xIZTSt27wLa7enp5cs/H79++XfQ8vHp68tfT3/f//n09Y/PC/3ys8Uda3jAeUvDceELXFEevrx8Rr4GX7HObzSIgbNxc9y5ZtQBvNBoyAFLnMRdIiC4wC0D8MLioQcRdu64R7jQXRqegFsjjGFHEeLOhfTSRYqnAXih6fADX2neOlTdHL4CsNVVjth3RUwoHDiOIGsQQxcgL7wc7YDm1oKlBcqdUCJzQF5ofeQBLRj3sBMjaYbLKQbkhdZG398ZcNaLy4rbHwncehwy+Foeb95EUHgI8kZhwO33Aq4po8HXAnlzlwGRpyPhrpCiF3Dl8eADiSDPmtAJilyVJCngmgIbfCASbAubapYMPffMAq7+YvChTHBBKfEtsWj2Qq7ENnhfKeRQv+I+KrIhKi0LuZ4oNnytlmPiRi2KUK/1ckzc6EUR6oFidva4UQxjnX3N7HncqiaH7BeVHaJsZKN9yL5s9ogb3Rgqy1FpwQc101GeraF8ZyHXPdqQfeFQoqKgvVgGg5XhhVxD1SHXyjkmbisMPB4oJ9Ci1AJ1Gv1MfALXSLHMvnCi96pMim7WjUMmcOXJkIM6Y97xaQYVWFZ7KOR6p/B6oBurc0R3SG6KqsYFXHnIxZFs0BPYuQp9x2imBVwD7UMPVJM4lxCrUQBpDJ3INfloEAeq2SFuVSNDr1VzTNyoxtBq9lVjIDb0lKgajqLaCrhuEbyDcoOKj5kSqUShUhTiXsj1TBHpvmgU0g6DP0Lq0WOigGugPnRfNDu8jWgMr0eiQalw1OKoGRYzqBRyDRS5uBbNm0MN9aiJiDAQomUUcHVoQ69F8/0CKwZb9M9s1RHRNAp5IaLNH8wvgUEE822reUnBLOB6C3nYwQSDZtbAbRhJa5xF2y/ohenD9jXDGNUIbZeqoKKdZgFXhzTsWjLHA8lGM3jbrzM/jCS2FUwfdi2Yw1nLtsPMsH3B/Dht2UYw0k7//V71Suzb//CgqWZrIGzqAAAC1npUWHRyZGtpdFBLTDEgcmRraXQgMjAyNS4wOS42AAB4nM2SW0gUURjHv5mdndmLO3txL26Zbbblete0UkL3pA9ZSIZSXkJYSnBrwQhCK7FMKSzRIHMzNMKHInKlbel+2RlBK6UyMLpKFwzsoeyhVxeb+WQJosceOvDx/50/3/mfj8P5ER7+ANIySEXD4sqQKluqVoolLkkVzD9X+FMdkkrilIT6vf2LcIsRCg6imiKfUTBRQ7WodFQpVIr631ULlJcGxkOxHlrhpWnOo1B7GI2XVnJSqTxsrJfmNA6V2kureFDHOTSxHi0PWrNDa3PRMWZHjEUSq4PXMZROD7wF9AbQG0EfByYzgB2oJUAvBUU8MMtAmQDsctA5QLcCDIlgWAkGJxhXgXE1GJPA5AJTMphSwJIK1jSwpkMRI83IAqOJZSmW5pQqtUKp5S0xZlanj1OreNsa6fNQEP1AgfRhsSqyR5A3oTeT4qWx+QKZG298FS4H1xKZ9zaNhGdObkPWZB0RH2VFwjJPhzoFw+5gocymhlHx/eYJt8zNmVpxYiaIHOkOFbb4i5FbB3LdHiePOeM19eLFXY3oMwE7qT59E2dw1CQU9pyxiDJvmFpP+nstmP+u7LY7YaET722+spXkZj3DsyK0EbN3HWY+KeohKVePor/zsJbcGmzDHF+VX9wxv4D+WMlz91R9Gvo6S07BvspKvLe/o0UY8leg3zVYSroO3UO/oVxNLkRcmF/ad0e0hW3YUz0XEY/1ncN5SoL5YrL1IPb4XgUelH/xI8+/zRPzbKOYU1t7V3hY141st8+K+08lYU+HsZN8zlFjTkNGGdmkNmDPt+kAkd4Re05k5xL7iA+5aHiAcPHtyN2TQ2TOa0J+6TtOUvlPmFORLZDXs+P4blzzFnfo6Vmc+ef2OnLguwe5/EUmaddfQ/648bogNI3i2cfF+UIvdx595327WJyYg2z9BdqgzmCCrlliAAAD/XpUWHRNT0wxIHJka2l0IDIwMjUuMDkuNgAAeJx9VstuHEcMvOsr5gc84KvZzUMOluRYQeAVkMi55hgYyDH/jxR7Vj0jhMhKAma4xe5i8aW/f/z1p8rDlp/fnn/98c+2PvL8ADv9z29EbH8oET182/Jhe/zy9Zfb9vT2+fHd8vT6/fb2+2a+WcAHPx+xn99ev71beHvaPrXdmIe07RPtwUYiG+00P6evJNJ3iyaU37OoiRVATSDvgwdR32QfOgIP/wVaAmnX3ofbpru0YewFsCVQ9+jsQ9LFwhprgfTjbgrWyJPUrFF1ZD+iYR2mvvHeBmmPAjgSKDt3610B1MFQoAAGgHnhABIP3Y24FTimCezUMgTZRSg6VUA+wna2mLKEi1l1NWduZMeFjIwgfmOEVUnOOi/nZm6ccal26lwhbZ45uojnSWxOpUCc2cGVKvNy2qWraXlkZgdpliBNKZ2UtKogzuzYTi4tOEk0YioDHxPoxDG/jxgepZYxo+Ee4LZ9wtlDxCqk0HZLgMeQSJZouDFGhWQgEQVzQ61BSw8dperop1uakRM4QSLUOffrmdvTy9efeOE1OWQ4pO5ZA0pepwmSv0JTjq5OM/XNRykq2vs1JQCgOR5Qn1YmVHx7yZnQfaT8WSQdJVoG1hPa99CmNseHhNOoyl5GQmUPyp4D5dEdXhUyEgm7MMVAejs3LYPH7zwT+VFqGX1TdGcVFAryZcpjg13yKXvFSqgcQYnwQBdAKeRKqphQxS/gh9Ax4ZIyq8aohEJGXrL0UQAUiWwuXvayNiBb9nL42NbgKYCZJsTOraFeMCYGaqVUqU+WI5wbZVthjtZyjnm1ooVGVoggRa2c8DFPZA/DQMl5rB1pr0Y8zSMjg8grNUyt3AWZH0zFoIBP9p9FLaXJlLJ57zZ7ozHyWClkOhXC3ZTpgaid3EqkHdWByhwyI8pOlmpxWDuqs0vqlExRHNyr8jA/yhMLFa2WTz2QherUL7fnD2v5WNSPr7fnc1Eb/uTcxvnazp2LQt36uVktTef+zNdxLsn8ni+70ADhNX7Qc8AseMzXuOwvg4tc9xTD5/TnJBeXC1AknIZFl5M850y6bBHLky7HemLk6tUPi5w3oUOmZYWKQc9534mRFCstp3Z8x+hlQHNSyKX388dBzJOsX+btRF5Oa2kB2aW++N2yJJR+WM54MQ45yV54TuZwvkw3vML3MsMYeZR1rMosC1m5ybWKVOp17jCSp+sWTbYdnpcxMkPUJYb2u2WJqpMsvM56i7tlsTW6W84inDIPeF9a97AswqaHxa6tOIWxRdna3bI4m98ti7MlZ2TRFmdLzsifLc4Wd8vinF137bF8f/9HGs8P/wI2qihCDD9d+QAABSt6VFh0U01JTEVTMSByZGtpdCAyMDI1LjA5LjYAAHicdVdNaxxHEP0rgUCQyOzQ9dHVVS0MAV18kn3JyeggFpMEkigIQS7+8XnVsnaGTc9F3u19r+vrVXX5y8fHM/19vjmd+Xzz5ePj7VnON5/ub+7Hl/FnfLj6vv96/8uj6P3Nh0+3D/+HPVw+f/jy8POjDhNvhnj3+e0v4e8P325ObWUmp4VXo+a83J1sJXHVhdbqRRwnulo4B04iwgMnArQGL2UNY9Wk8UpNWwNInKLihNYSpMCIai02TlSdbMEHIlaA8kcHD6hmWmgctVIrCVxiLtGWO1yt1Sx91KLwSPBLFIEtK4J/73QtxjWA8Fpouasr7gL13Slc4VQrL3DcBVeB4WEwB2Y0anlgoGRE4RZ5hViUvJK9WU0AWSgtp7KqNCuemBjXI4pQ0Tc7jTlPSK20SJpoRdJAI5HwNtyXxuOmamyZPmRGmDiPuIkK5VWIIY/wG2VBwENMMtKHfLgkzSjzicwEijZS1UJ0Qc2KM+tIJ8IYJ6aoX15TrbW6oIqVVCNZCAOxnpC3VmywqKpltLyKtAKvT5makskDUYp9P2uMPOXtUA/5EIIWLjWZDWppeVnRDHiE50oQ2hADPPZxJo6fbVwnrTkcQIqqD12BzFQC0cIEVWmD6uQlReviQcNoFMgWPBRL6pAo6mo+yhWaekqUOy4CvwpFGkTARHmE4Ah+8zhr5hBTho6s+OgJjZpyJxbNrKJvQioMZcECYqjL7fL0+vzX55fnf3pZPz+9vP7x9Of9708vv31dy08/qt0Vbi4FpaeKbiqNHMDkPDy/fl2FOk1pSgYPFWLyCo24AHdhWecr0umNZRU5LwE8B+RXEnhhUXSZ28oymVllSbFy4jZW6To3RrDWiCC8EK7qwF1Yrdc5qSB9hkaymv0RVQG8sGq3qYMkDNcMJcYgKUoK3JbC0tvcFloy+5KtVTQxBwF4oXn3uS1UGENOCcOLiWDLN1L0mJsSzCxvRVEtNJBEAi8s1k5zcWAGV4Q2LMJTaQncUl87XavjPfeUo0/QmYiR0AWJ3IjW6Voh7+l3N3c0TVRBC9eB3HIJT+camRF3KhG4ei2TY+JOKIKvB1KZxLjTCiFXc7XMLO71AuK1YI6JO8WId5prZkbcqUai04FuECMmnuItksBwa5TILUbpfDBV8I65MWYDtIb+VU3kZpE7z+fKjLhTjsDigXKsccGoxFvOhAGJ/PN+uOCeuXJawcNu3JSylO4tgRuPOh/MF62B1OBRcfQuY6IlcmsqeHogHMzqKgVjMAL7BhxP5EYsnY+EI84GEmYMelGLJ3JzFV8PhONYQ1BCvE8tjb8hL0SFxQPhTIg74SiScy2cY+JOOMpd5sJB9QxTHvMeI4vwwCZwCxG8g4mjrUbBGqfIas5vS+SWVBDnuhGMXwKl4j0LPMQDuDmqXeaymfB2stHa5Ug2pWIZwi6ZesVjOJCbo9TlWjbfDYpVvGZ48ZuHwngCN4N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"text/plain": [ "" ] @@ -2620,13 +300,13 @@ "output_type": "display_data" } ], - "execution_count": 180 + "execution_count": 6 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:31:01.947766Z", - "start_time": "2026-10-30T11:31:01.920788Z" + "end_time": "2026-10-30T11:41:11.917433Z", + "start_time": "2026-10-30T11:41:11.909230Z" } }, "cell_type": "code", @@ -2642,12 +322,12 @@ "{'score': 0.951229424500714}" ] }, - "execution_count": 181, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 181 + "execution_count": 7 }, { "metadata": {}, @@ -2662,8 +342,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T16:18:12.220434Z", - "start_time": "2026-10-28T16:18:12.203607Z" + "end_time": "2026-10-30T11:41:13.064971Z", + "start_time": "2026-10-30T11:41:11.928887Z" } }, "cell_type": "code", @@ -2682,8 +362,24 @@ "default_septop_settings.protocol_repeats = 1" ], "id": "43fc256c3e0ff239", - "outputs": [], - "execution_count": 139 + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/Bio/Application/__init__.py:39: BiopythonDeprecationWarning: The Bio.Application modules and modules relying on it have been deprecated.\n", + "\n", + "Due to the on going maintenance burden of keeping command line application\n", + "wrappers up to date, we have decided to deprecate and eventually remove these\n", + "modules.\n", + "\n", + "We instead now recommend building your command line and invoking it directly\n", + "with the subprocess module.\n", + " warnings.warn(\n" + ] + } + ], + "execution_count": 8 }, { "metadata": {}, @@ -2700,8 +396,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:32:10.898883Z", - "start_time": "2026-10-30T11:32:10.323090Z" + "end_time": "2026-10-30T11:41:13.432983Z", + "start_time": "2026-10-30T11:41:13.074892Z" } }, "cell_type": "code", @@ -2785,7 +481,7 @@ ], "id": "4b08f0258bbdd052", "outputs": [], - "execution_count": 182 + "execution_count": 9 }, { "metadata": {}, @@ -2796,8 +492,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T16:18:28.893208Z", - "start_time": "2026-10-28T16:18:21.554588Z" + "end_time": "2026-10-30T11:41:20.392419Z", + "start_time": "2026-10-30T11:41:13.434816Z" } }, "cell_type": "code", @@ -2828,7 +524,7 @@ ], "id": "fcc7a807d6edc1a8", "outputs": [], - "execution_count": 141 + "execution_count": 10 }, { "metadata": {}, @@ -2839,8 +535,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:32:55.471534Z", - "start_time": "2026-10-30T11:32:55.433358Z" + "end_time": "2026-10-30T11:41:20.402716Z", + "start_time": "2026-10-30T11:41:20.394110Z" } }, "cell_type": "code", @@ -2863,12 +559,12 @@ " openfe.protocols.openmm_septop.equil_septop_method.SepTopProtocol: 6}" ] }, - "execution_count": 183, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 183 + "execution_count": 11 }, { "metadata": {}, @@ -2885,13 +581,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:33:54.755101Z", - "start_time": "2026-10-30T11:33:54.719436Z" + "end_time": "2026-10-30T11:41:20.428842Z", + "start_time": "2026-10-30T11:41:20.414091Z" } }, "cell_type": "code", "source": [ "from collections import defaultdict\n", + "import json\n", "\n", "import numpy as np\n", "from openff.units import unit\n", @@ -3001,7 +698,7 @@ ], "id": "27d647049e330837", "outputs": [], - "execution_count": 184 + "execution_count": 12 }, { "metadata": {}, @@ -3020,8 +717,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-28T17:38:44.786590Z", - "start_time": "2026-10-28T17:38:39.810431Z" + "end_time": "2026-10-30T11:41:24.782669Z", + "start_time": "2026-10-30T11:41:20.436182Z" } }, "cell_type": "code", @@ -3398,12 +1095,12 @@ "" ] }, - "execution_count": 161, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 161 + "execution_count": 13 }, { "metadata": {}, @@ -3420,8 +1117,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T10:32:11.101280Z", - "start_time": "2026-10-30T10:32:10.360299Z" + "end_time": "2026-10-30T11:41:24.991799Z", + "start_time": "2026-10-30T11:41:24.785983Z" } }, "cell_type": "code", @@ -3647,12 +1344,12 @@ "" ] }, - "execution_count": 167, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 167 + "execution_count": 14 }, { "metadata": {}, @@ -3663,12 +1360,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:10:01.251639Z", - "start_time": "2026-10-30T11:09:59.615343Z" + "end_time": "2026-10-30T11:41:26.783378Z", + "start_time": "2026-10-30T11:41:24.994077Z" } }, "cell_type": "code", "source": [ + "%matplotlib inline\n", + "%config InlineBackend.figure_formats = ['svg']\n", "import json\n", "from gufe.tokenization import JSON_HANDLER\n", "from cinnabar import plotting\n", @@ -3705,18 +1404,26 @@ ], "id": "4401ca21e5bd73c9", "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/matplotlib_inline/config.py:59: DeprecationWarning: InlineBackend._figure_formats_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n", + " def _figure_formats_changed(self, name, old, new):\n" + ] + }, { "data": { "text/plain": [ "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 177 + "execution_count": 15 }, { "metadata": {}, From 50d2a4a82bf0ce23ce03c949cfa01da5ef6d3433 Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Wed, 4 Nov 2026 13:11:52 +0000 Subject: [PATCH 4/9] add PR feedback, update ligand file --- mixed_networks/inputs/ligands_AshGC.sdf | 2144 +++++++++++++++++++++++ mixed_networks/mixed_networks.ipynb | 481 +++-- 2 files changed, 2524 insertions(+), 101 deletions(-) create mode 100644 mixed_networks/inputs/ligands_AshGC.sdf diff --git a/mixed_networks/inputs/ligands_AshGC.sdf b/mixed_networks/inputs/ligands_AshGC.sdf new file mode 100644 index 0000000..1c3be68 --- /dev/null +++ b/mixed_networks/inputs/ligands_AshGC.sdf @@ -0,0 +1,2144 @@ +lig_02 + RDKit 3D + + 37 39 0 0 0 0 0 0 0 0999 V2000 + -38.0090 -45.6315 19.1393 C 0 0 0 0 0 0 0 0 0 0 0 0 + -39.9692 -45.1638 17.2217 C 0 0 0 0 0 0 0 0 0 0 0 0 + -35.2004 -46.0152 17.8190 C 0 0 0 0 0 0 0 0 0 0 0 0 + -35.7492 -44.0607 16.6787 C 0 0 0 0 0 0 0 0 0 0 0 0 + -33.6155 -44.8304 16.6899 C 0 0 0 0 0 0 0 0 0 0 0 0 + -37.6132 -45.2875 17.8281 C 0 0 0 0 0 0 0 0 0 0 0 0 + -36.2008 -45.1247 17.4552 C 0 0 0 0 0 0 0 0 0 0 0 0 + -39.3683 -45.7328 19.5198 C 0 0 0 0 0 0 0 0 0 0 0 0 + -38.6278 -45.0833 16.8841 C 0 0 0 0 0 0 0 0 0 0 0 0 + -40.3241 -45.4655 18.5293 C 0 0 0 0 0 0 0 0 0 0 0 0 + -37.8478 -46.5876 22.5205 C 0 0 0 0 0 0 0 0 0 0 0 0 + -39.9995 -44.7608 21.7798 C 0 0 0 0 0 0 0 0 0 0 0 0 + -38.7192 -44.0071 22.0925 C 0 0 0 0 0 0 0 0 0 0 0 0 + -39.7803 -46.0487 20.9633 C 0 0 1 0 0 0 0 0 0 0 0 0 + -41.0746 -46.8925 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Mixed Protocol Networks\n", "\n", + "\n", "## Introduction\n", "\n", "**openfe** provides a number of protocols to estimate free energy differences, each with their own areas of applicability and limitations, suited to answering different types of scientific questions. While traditionally these protocols have been used in isolation, it is possible, thanks to the interchangeable nature of **openfe**, to combine them into a single network of transformations to leverage the strengths of each protocol.\n", @@ -14,7 +15,7 @@ "\n", "## Overview\n", "\n", - "In this tutorial, we will demonstrate how to create a mixed protocol network to estimate absolute binding free energies for `three` distinct ligand scaffold series bound to $\\beta$-secretase 1 (BACE1) taken from [Baumann et al.](https://doi.org/10.1021/acs.jctc.3c00282). The dataset contains a total of 18 ligands with 6 ligands in each of the three unique scaffold series grouped as `biaryl`, `spirocyclic` and `amide`, examples of each are shown below:\n", + "In this tutorial, we will demonstrate how to create, execute and analyze a mixed protocol network to estimate absolute binding free energies for `three` distinct ligand scaffold series bound to $\\beta$-secretase 1 (BACE1) taken from [Baumann et al.](https://doi.org/10.1021/acs.jctc.3c00282). The dataset contains a total of 18 ligands with 6 ligands in each of the three unique scaffold series grouped as `biaryl`, `spirocyclic` and `amide`, examples of each are shown below:\n", "\n", "\n", "\n", @@ -22,27 +23,22 @@ "\n", "- Plan a single LOMAP-style hybrid topology binding free energy network with the Kartograf atom mapper and default LOMAP scorer\n", "- Identify edges within this network between the three distinct series that are not suitable for the hybrid topology approach\n", - "- Build the transformations for the network, assigning the appropriate protocol to each transformation\n", + "- Set up the calculations by configuring the protocols and building transformations for the network, assigning the appropriate protocol to each transformation\n", "- Analyze the results to estimate the absolute binding free energies for each ligand\n", "\n", "# Load and classify the ligands\n", "\n", "First, load the ligands from the provided SDF file and map each ligand name to its series. Convert the ligands to `SmallMoleculeComponents` for use with **openfe**.\n", "\n", - "
Note: Ligands should have partial charges assigned before planning the network. Here, we use pre-charged ligands, but charges can also be generated via the CLI or API.
" + "
Note: Ligands should have partial charges assigned before planning the network. Here, we use pre-charged ligands, but charges can also be generated via the CLI or API.
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" - } - } + "id": "6d660986567950a0" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:40:58.537893Z", - "start_time": "2026-10-30T11:40:56.725310Z" + "end_time": "2026-11-04T12:16:08.361601Z", + "start_time": "2026-11-04T12:16:06.307258Z" } }, "cell_type": "code", @@ -50,7 +46,8 @@ "from rdkit import Chem\n", "from gufe import SmallMoleculeComponent\n", "\n", - "ligands_file = \"inputs/ligands.sdf\"\n", + "# Load the pre-charged ligands from the SDF file\n", + "ligands_file = \"inputs/ligands_AshGC.sdf\"\n", "\n", "supplier = Chem.SDMolSupplier(ligands_file, removeHs=False)\n", "ligands = [SmallMoleculeComponent.from_rdkit(mol) for mol in supplier if mol is not None]\n", @@ -77,7 +74,7 @@ " \"lig_81\": \"amide\",\n", "}" ], - "id": "c2ec360a98734f5d", + "id": "878ed1f41247982c", "outputs": [], "execution_count": 1 }, @@ -93,13 +90,13 @@ "* **Scorer**: Given an atom mapping, provides an estimate of the quality of that mapping (higher scores are better). We will use `default_lomap_scorer`.\n", "* **Network Planner**: Creates the actual `LigandNetwork`; different network planners provide different strategies. We will create a `LOMAP` redundant network with the `generate_lomap_network` method.\n" ], - "id": "ab9b576aad42da72" + "id": "ca3f2dbf591ec0dd" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:11.646049Z", - "start_time": "2026-10-30T11:40:58.540451Z" + "end_time": "2026-11-04T12:16:20.625792Z", + "start_time": "2026-11-04T12:16:08.372122Z" } }, "cell_type": "code", @@ -120,7 +117,7 @@ " scorer=scorer\n", ")" ], - "id": "1df183ab22a81c3", + "id": "c4cd5b264631d89a", "outputs": [ { "name": "stderr", @@ -160,13 +157,13 @@ "\n", "Now we can visualize the `LigandNetwork` while highlighting the three distinct series using colours to match the examples shown above. The `biaryl` series is shown in yellow, the `spirocyclic` series in cyan, and the `amide` series in magenta. Edges between the series are shown in red while edges within the series are shown in black." ], - "id": "aac35319eaa125a7" + "id": "4047400ea15f2e1a" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:11.793622Z", - "start_time": "2026-10-30T11:41:11.648682Z" + "end_time": "2026-11-04T12:16:20.804763Z", + "start_time": "2026-11-04T12:16:20.639271Z" } }, "cell_type": "code", @@ -175,31 +172,37 @@ "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "\n", + "# Map the ligand series to colors for visualization\n", "series_to_color = {\n", " \"biaryl\": \"yellow\",\n", " \"spirocyclic\": \"cyan\",\n", " \"amide\": \"magenta\"\n", "}\n", "\n", + "# Build a networkx graph from the LigandNetwork for visualization\n", "network_graph = nx.Graph()\n", "for edge in network.edges:\n", " network_graph.add_edge(edge.componentA.name, edge.componentB.name)\n", - "pos = nx.spring_layout(network_graph, seed=1)\n", + "\n", + "# Generate positions for the nodes in the graph and assign colors to nodes and edges based on the ligand series\n", + "pos = nx.spring_layout(network_graph, seed=42) # change the seed for a different layout\n", "node_colors = [series_to_color[ligand_series_mapping[node]] for node in network_graph.nodes()]\n", "edge_colors = [\"black\" if ligand_series_mapping[edge[0]] == ligand_series_mapping[edge[1]] else \"red\" for edge in network_graph.edges()]\n", + "\n", + "# Draw the network graph with the specified node and edge colors\n", "plt.figure(figsize=(10, 8))\n", "nx.draw(network_graph, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors, node_size=900, font_size=10, font_color='black', font_weight='bold')\n", "plt.axis(\"off\")\n", "plt.show()" ], - "id": "e4cba9083f76e449", + "id": "7f92fa5e68ee95ef", "outputs": [ { "data": { "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -214,29 +217,32 @@ "## Inspecting the atom mappings\n", "\n", "\n", + "\n", + "\n", "Now we can compare atom mappings and scores for edges between and within the series. First, let's inspect an edge between the `biaryl` and `spirocyclic` series, connecting ligands `lig_03` and `lig_11`. The mapping and its score are shown below. The low score reflects the large number of alchemical atoms and suggests that calculations for this edge may be challenging to converge with the hybrid topology approach.\n", "\n", "
Reminder: Shared core (mapped) atoms are shown in black, and atoms unique to each end state are shown in red. Mapped atoms whose elements change during the simulation are shown in blue.
" ], - "id": "feadf6d35aa1a518" + "id": "f63d2a755692dbf9" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:11.836079Z", - "start_time": "2026-10-30T11:41:11.796501Z" + "end_time": "2026-11-04T12:16:20.857338Z", + "start_time": "2026-11-04T12:16:20.808671Z" } }, "cell_type": "code", "source": [ + "# Find an edge between the biaryl and spirocyclic series\n", "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_03\", \"lig_11\"}][0]\n", "edge" ], - "id": "738e5bff854de03f", + "id": "8edec480a7e5ea99", "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "" ] @@ -250,16 +256,16 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:11.857553Z", - "start_time": "2026-10-30T11:41:11.849161Z" + "end_time": "2026-11-04T12:16:20.885236Z", + "start_time": "2026-11-04T12:16:20.871688Z" } }, "cell_type": "code", "source": [ - "# Higher score is better\n", + "# Higher score is better, with scores going from 0 to 1.\n", "edge.annotations" ], - "id": "782559866e5eb3f5", + "id": "fd3f483f1e2602b6", "outputs": [ { "data": { @@ -274,11 +280,17 @@ ], "execution_count": 5 }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We can then inspect an edge within the `spirocyclic` series, connecting two similar ligands `lig_11` and `lig_32`. The mapping and its score are shown below. The higher score reflects the smaller number of alchemical atoms and suggests that calculations for this edge should be easier to converge with the hybrid topology approach.", + "id": "8624cb56880a1253" + }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:11.895987Z", - "start_time": "2026-10-30T11:41:11.869351Z" + "end_time": "2026-11-04T12:16:20.984945Z", + "start_time": "2026-11-04T12:16:20.905745Z" } }, "cell_type": "code", @@ -287,11 +299,11 @@ "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_11\", \"lig_32\"}][0]\n", "edge\n" ], - "id": "11976268a7459cc3", + "id": "91e92274b502426e", "outputs": [ { "data": { - "image/png": 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", + "image/png": 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RpDl16pQ0zh8WFnbixAl7e3scx2E+d+/ePWkMJAM+fPhAlJM1d2no5OQEV2Jubq70LGw9tCOURWiU4PHjx5AYaWFhUVVVJY0hXrx4ceLEiZycnOfPn0OIXxqjyJKcnJzffvsNrkMTE5PExMTGj+fz+R07dmzjoRgayjl69Cj4whMnTkjj/EVFRR4eHnZ2durq6gwG4927d9IYRTaIxWIulwvz+O7du3t7ezflp4ig6K1bt6RtYSuhHaFMHSGO4xEREVCjM2nSpJKSEtLPn56e7uTkZG5uLi8v37t3765duzYrqEg6ubm5Bw8eZLPZIpEIw7D169dHR0e34DxeXl49evRACCkoKLDZ7Jqami8d+euvvyKEZs6c2Qqrv09qa2vhAYZh1FrSRiA2LPbs2UPWOd+8eXPo0KEJEyYwmUyYvUEVv6GhIaSwUYW3t/eJEycgcez58+ct2DtPTEyEKkwGg2FnZ1dWVtbIwRiGTZ8+HSE0Z86clhstK2hHKGtHiON4UlJSz549EUIjR44k69qIiYnhcDiSHf6UlJSgSXefPn0onI1iGCYWiw8cOFBQUHDx4kUulxsSEtKyU5WUlLBYLLhzDR069OXLlw2P8fb2Rgipqamlpqa2zvDvjZMnT7LZ7KdPn7q5uUE2Iw2O41evXoW2lGw2u8UnqaqqCgwMZLFYkuLRTCbTxMTE3t4+ICAAEqEHDhxI1T4FhmFJSUkRERFXrlypqKiwt7dvWeIeJLIpKSkhhHr37h0YGPilI8+fP/9NBEUB2hHiISEh1dXV8FgkEgUHB8tg0PT0dND11tfX//DhQ8tOIhKJQkND2Wy2ZEcFVVVVc3Nzd3f3kpISgUAwYcIEhJC2tnZ4eDi5H6HppKam2tvb5+fnz5kzh8PhtLKM5NmzZ6CUJicnV29aWlpaCqtGKW38fNPk5uauXbv27du3OI7LIHv5G+LmzZsgtrBu3bpmrZV5PB5EXyQ7znfp0sXGxsbDw0My3lNUVDR69GhwHi2+3lvPkSNHUlNTQWupNY4/MTERPs6Xlobp6emwB/StZOrRjhBPSUlRVVX99ddfZTxuUVHR2LFjIR0rNja26T9YVVXl5eVlZ2cnKdAAl5+Xl1e9mGFlZSU0OVJXV4dUUhkTGxs7atQoe3v7zMxMHo937dq1Bw8etPKc1dXVHA5HUVERdiw8PT3h9dWrV0PdpEgkarXh3xsYhoWHh7u6uuK0I2yAt7c3OLNVq1Y1npAlFAph9inZLExOTs7IyIjNZoeGhhKutK6u7vHjx0SOjEAgAEUbXV1dyVoF2SAWi3fs2HHhwgU+n3/p0iUnJ6dWpnHWWxoGBQURb31bQVFA6o4wNjbW3t6exWLV1NSw2WwHBwfpJUy2DHd3d4TQvHnzZD90eXn5zz//jBDq2LHj8+fPGz8Y9t5tbGwk5Qr19fVZLFZoaChx9VZUVNy9e9fGxsbFxQVeEQqFv//+OwRLpV07JUtiY2MJ3UgrKytfX185OTlFRUXZ32XaPhiGOTg4cDicd+/eeXl5sVistLQ0qo1qWzx58kRdXR0htGjRoob3qLy8PHd3dysrK01NTeLq09bWtrKycnJyysnJIY7Mz8+H6xTyRHr16kW8VVFRAVU9Xbt2bdbct/XU1dUFBgYGBgYSf3cej9f60yYkJDRcGoI827cSFAVktCLcvHmzr68vm812cXFpaxv1a9asQQgdPXqUktFramogJVJVVdXPz6/hAUTyi6RUpoGBAYfDkcyfLCwshAsVLmZIxiHexTBs8+bNsG9BOMjvAKFQePz4cahzgtyEXbt2UW0UzbfKs2fPoIWhhYVFTU2NWCyOioqyt7c3MTGRbFsBs8/AwMC6ujr4QbFYHB4e/s8//xgZGUkeOXTo0K1bt0oGaSorK2G11LFjRwp3K0hEcmnYp0+fq1evwkz925pzy8IRent7e3p6PnjwwNvb+/Lly1FRUTIYtOkYGhoihL66IJMeIpFo1apVCCFFRUUi4z8hIaHeFQh771wuNysri/jZtLQ0LpdrampKuEmI0nA4nKSkpHoDEbVTR44ckd3Hw/FGMjxJITU1VV9fH9xhly5dOBxOaWlpa05YVVXF5XL37dtXW1vr4OBw8ODB7OxssqylaZvk5+dnZWW9fPkSam/09PQ6depEuDQ1NTVoXiGZ7VJeXg6bFN27dyeOVFFRMTU15XK56enpnx2otrb2l19+QQhpampSeNshl7i4OElBYwsLC6otah5Sd4QhISG2trZOTk6FhYU7duzYuXNnZWWltAdtOmVlZUwmU0FBQUpVfU0Ew7CtW7eCGzM1NZVsotuhQ4eFCxfevHlT8ube0E0qKSnB5df4Lfvs2bOEwI0MlubXr183MDAYMmSIvr6+tbW1NMpFcByPi4tTUFBgMBj9+vWD30bnzp0PHDjQ4uGEQmFhYeHOnTtzc3OXL19+/fr1Z8+ekWszVdy5c6eoqIh46u7uLu1pyjdBeXn52bNnWSxWenr669ev1dXVIetKX1/fzs6u3tY7j8erN/uExZCdnZ2Hh0dThG1FItHSpUshDiSDnfv09PR169aNHz/ezMzM3t6eWMiSi1AonDt3LgRmFBUV7ezsWp8im5ub+/z5cwzDgoODAwMDIdVLGrT3ZBlo10KhLLVAIFi5cuXVq1dxHOdyufLy8hCc6dy5c73kFyJHtH///pITVcgRbfoa6N69exDHWLp0qVT3a8PDw3V0dGBhWldXt3z58pUrV5I+ilgshhyEDRs24DgeGBhINNnR0NBgsViS+zdN5927d3/99VdaWtr27ds5HE6LSz7aGoMGDXrz5g3xVLJ2iMbe3j47O7umpkZFRYXBYERERBBvNV4g0YIol6QCOJHtJQ0EAkGvXr2OHz+en5//7t27mTNnwpVCOmlpaRAUNTExgdm2srLyH3/80ZpoioeHh7u7+507d16/fn337l3p9S1o745w3759CKE///yTKgPev3+/fv3669ev4zienZ0Nk8QXL14QyS9EjqiOjg5xBRI5okSJdLMgBG4sLS2ltxTeuHGj5I5dYWGhgoIC6esPBwcHhFD37t0l13+hoaHm5ubEWtnOzq5ZqgKFhYVnz579448/4uLiVq1adfLkSaneqmQJ7Qi/xP3796ElBfQOHDZsGLx+79696dOnSxZIdOvWbcWKFXfu3GllBB7DMOiFK1UF8PPnz5ubmxNPCwsLNTQ0CgoKyB0FwzDI+1u4cCGO44mJiTY2NpKrw5a5w6KiImtr6/fv3+M4fvDgQendrGTnCD08PIigKGhIymzoRpg5cyZqAwpABw4cyM3NvXPnDkLIzMwMXrx9+7aFhYVkf9ohQ4bs2LEjIiKi9VFNQuBm8uTJrbyev4SFhcWVK1ckX9HR0SE3uJGRkQHJQZ/tqhMTE2NlZQXRY0VFRRsbm6YLC5SWlkKMq6qq6ntyFYMGDbpz507cJ1RVVb+nT9diMjMzt2/f7uTklJGRsX//fiLAgH9SYpMskCBXNP+ff/6BxeXFixdJPC3Bxo0b9+3bJ/nK0KFDQ0NDyR3l9OnTMEGXVAiRdIcwH22uO+Tz+e/fvz99+nRpaamDgwO5NksiO0coqfVVV1eHEKI8fbQtyFJDsfmePXsIUXyixmvt2rWNJ7+0kqSkJNgIMTIyIkvgprS09ObNmwsWLIiJibG2tj537pzku2pqauQ2JLOwsCAmoV8iPj7exsYG1EPk5OTMzc1bpvH2fTBo0KCpU6fO+4S8vDy1jhAUnDEM+/jxo1S7sjQdWNkQleBpaWmXL1+Wqno7kcV28uRJUk4oFosjIiI4HM6LFy9YLFY9Rzhs2DBy97yJoOidO3cavpuQkGBjYwPBUlVV1WbtVty4ceP48eNFRUWvX7+WqkBdu3aEiYmJEFWj1gwC2NwiNACjoqKcnZ2lWouTnp4OqjStEbjBcTw/P9/d3d3c3Bx2HxFCu3btcnBw+O2334hjwsLCevbsSeIf/cqVKwghbW3tpvyKeDwei8UC8xgMhrm5eb3k9YULF0reHaZMmQIBme+MNhUajYmJmT59Oo7j+/btu3Hjxv79+6myhEAoFGpoaDAYDBnXwB07dozBYDAYjH///Vfy9bNnz+7bt+9LCaj1qKiogG2UH374AS7DDRs2nD9/fu7cucQxJSUlGhoaJDoVsVg8efJkhJC1tXUjh8XHxxPhGTU1NRaL1aaa9MrUEYaEhLx9+/bt27cJCQltwRFeuHABITR//nxqzQBqamqUlZUZDEZrerK3gLy8PMh71tXVjYuLa9bPJicnHz582NjYGKZ7EOGZMmUKl8vNyMioqqoaMGDA1q1bX758eefOHX19fRLbQRQWFoKSKhFQSklJWbhwYXZ2dkBAgJOTk52dXcOfysjIYLFYUGgBu/pEi7VJkyZJduLu16/fd1mY36YcIf5J42bLli2PHz9etGgRhZYAL1++RAgNGjRI9kOfP38eriNC/EwsFv/9999FRUWNKwHFx8cfOXJk0qRJEPYgsljXrl0bEhJSUVHRo0cPd3f3uro6gUCwaNEicnPWTp48CUHRhotm0NmXfCUuLq6eO6w34XB1dZWckTs4OMimk5pMHeG0adNmz549e/ZsEP2i3BHa2toihKQaem46YWFhCKGhQ4fKfmhJgZsXL1589fiEhAQOh2NgYEBcdSoqKubm5k5OTvVmeYWFhUeOHFmyZMnmzZtBxFUkEllbW9+9e7eVNi9ZsgQhNGXKFMlvkbOzM0S54+PjG8k+yM/P53A4IPxBuMOxY8cOGDCAOKZnz55TpkxppZFtkM86Qicnp8Y7CUgPuMVXVVXFxcW1Rv2SLCBKuXr1akpGb6gAfuXKlVOnTjWUiSCyWHv37i2ZxQrbKFFRUZLXRVJSkpWV1aBBg0aOHLl161ZIOQkNDW39llBqaips0hNXdFJS0pw5c4qKioKCgg4cOODj49Pwp2JjY7/kDidNmiTZFqNv375fbbtGCu06NAq38jai7/Dvv/8ihFatWkXJ6ITAjZqa2mcFbqB4g8ViEVEXCEuCvnBTaqcAiGfKy8u3pjm4n58feN964VzCEXI4nK9WhhQVFe3Zswc2iRFCgwcPVlRUzNDUzFdTy1dTS2cwLnTu3GIL2ywpKSmExDyO40lJScePH0cIGRsbyzgUgeN4cnIyi8W6ceNGcHDw8ePH20IsGubo165do8qAW7duQXni2rVrxWLx3bt3d+3aFR8fD++mpaU5OTlJCkghhDp37mxlZeXu7i4QCJo4SmRkpLq6ur6+fmuatIjF4kmTJiGEFi9eLPm6o6NjUVHR+vXrT5061UiV5Js3bwh3qK6uzmazi4qKxo8fv3btWuKYXr16HTx4sMUWNp326wgFAoGcnJySklIbKSgGP+Tm5kaVASKRaOXKlZBgefPmTXixsrLSy8vLxsZGUmKxd+/eUGXcsspcIjWgZbJ2lZWV+vr6qIEqXl5e3qZNm1xdXcvLy+tlqzZCWVnZ0aNHZ82aZWxsnCAnhzMYOELw717Hji0w75sjPT0dhAgMDAxaVnPZAsQYFvq2+KR/mqN/WnBSoVDUJmQXRSIRxAmobeH58OFDQgG8srKypqamocw3/L1anMVKigK4o6MjQqhbt26SEg24hCOEBMDGTxIeHj5jxgz4RJqamgYGBtra2m8XLXq3cOG7hQuPqqj8KRGnkR5UOsLa2totW7aQXtHSRHx9fSEsRsnoDYGVlvSkE5oCIXDDZDKtra2nT58OHR6AESNG7Nmz5/Xr160f6PTp08R2SHPnQ3/++SdC6McffyRXIMPY2LgMIRyhLdOnXzM0bD+OEMdxPp8/dOhQ2BuTQQZ1UEJh9/WPO9gGqCz3V1nu18E2oMvqQK9oKpvWAlFRUQihvn37Um0IHhAQADvZ/fr1A4UNQEtLa/78+ZcuXWp9tksrFcAbBkWB9PR0W1vb8+fPv3jx4vDhw5cvX27K2V6+fGlubt67d28jI6ODiorYp8kojtCtrl2ba1sLkA7575kAACAASURBVJ0jjI+Pl7xzxcTE/P3333DtZWRkyMwMAijf+fvvv2U/dEPS09MRQp06daI8XIzjOJfLZTAYILRIaGeQ3tr32rVrEAJatmxZ0wVuIiIimEymvLw86Yq1xsbG5QwGjlDqwYP58+e3K0eI43heXh50de7Tp49UG+bdDuerLvdHE7ajmSeRtQ+y9kHz76LxW1R/93cNpnIdhn/anlixYgW1ZgChoaGqqqqQEUbIfLdMQONLtFgBnAiKLlmyhER7oGujGCEcoffW1vmjR3+HjrAhubm5I0aMQC3KV2wZfn5+RDKIqakpQojoFkYtN27cQAjNnj2bKgNEIhEhdyAWi9XU1BBCXC4Xtm0qKiqkoRAbFBQEk8o5c+ZIblx9CaFQCF8YaWRVbNiwoVwiLooj5KmtDW+JRCKIGRYXF/N4PCkpplKOQCCABpk9e/aU0nZdbkmN+gp/ZO2DNHshdR00/x6y9kFzLyMVbWTto7rcPzWfSiHiOXPmIIRas3vdSvh8/uXLlyFylp+fz2AwlJWVU1JSpDcioQCurq7+5MmTJv4Ul8uF+3a9oGjrIRzhJhbr7uTJMnOEcog6dHR0nj17ZmpqyufzJ0+eDInL0oPP5+vq6l69erW2thbDsMjISIQQXPmUA5+dEMmUPa9fv9bU1LSyskIIJSQkVFZW6unpaWhoXLp0qbi4mMPhPH36lPRBp02b9vjx406dOj148GDmzJllZWWNH29vb//69Ws9PT1YzZPIP//8o6SkFKikhObMgX9FEya8/5SP4O7ufvfu3evXr58+fTo6OrqgoIDc0dsIWlpagYGBU6dO/fjx408//RQXF0f6EOeCMoVi/NN4+ijhhuS7QjHG9UsnfdAmguP48+fPEUKw1qGEgICApUuX/vHHHwghqGqdMGECISUvDRQVFT08PJYuXVpRUWFubg7ay42Tlpa2a9cuhNCZM2eIXDPSOXHx4i/h4VI6eUOodIQIIXV19YcPH/7666/FxcU///xzQECA9MbS1dWtqKioqKiA3q2lpaV6enq6urrSG7HpQO3E+PHjKTRAJBKBPARhzIoVKxQUFLS1tSGRRxqMGTPm2bNnPXr0CAkJmTp1aiM+5v379wcPHmQwGM7OzrBgJYv3798fPXrUwcHBffp0dP8+/Mt3di6YPx8OmD9/flxc3MCBA1VVVfl8fnFxMYmjtynU1dW9vb3NzMzy8vImT54cERFB7vl93+TXCrH/ngxdiNJDUGkG8a5QjPvHUjbJiI+PLyoq6tmzZ58+faiyAWROJ06ciBCCqSc8lipMJtPNzW3FihVVVVUWFhb3799v5GAMw5YvX15RUbF06dJ58+aRa4mXlxeIPyOEUHk5qqlBCMEGikgkunDhgoODg1gsRgi5urrGxsaSODTFjhAhpKSkdOvWLVtb28rKSktLSw8PDykNxOfzBw8erK+vn5+fT7njkaS6ujouLo7JZEKvZ0qQXJLKeHlqYGAQGhrav3//6OjoiRMnZmZmNjwGx/G1a9fW1NSsWLECYtoksmXLlrq6uhUrVkjeAgYPHnzs2DF4rKCgcODAAX9//40bN/7xxx8Qx/5eUVVV9fb2njdvnkAgMDMzgyuFLIorRP//REEV/bgURZ5BCCdeK6kWkjhcswAnBCIpVAHOD5akko+lDZPJvHDhwsaNG2traxcsWACix5/F0dHx2bNnurq6IHZPIh8/frS2tv7tt9+G9eyJSkvh3xNPz3P6+gihurq6X375pby8PC8vLzIyMjU1NSMj46vnbAYyCL82BQzDIHeGyWSeP39eGkPU1NS4uro+fPiwqqoKbqanT5+WxkDNBb7xI0aMoNAGaC4DidTQ5ik6Ovr+/fscDofH4504ceLo0aMikUh6BjS+YXzu3DmEULdu3UivdXv8+DFCSENDo5HKAVdX10OHDiUnJ9+4ccPe3r4FKXbfHEKhECQL1NTUAgMDW39CXl6l0+OMTnaB/yXIaPZCFi7I+iHqOhSNsIU9QmTt8+M2ksWgmw6EPVxcXKgyICcnByHUoUMHkUhUXFwMxV1N2TsnkcYVwHk8HgRjpNGMBX7/CxYskCxKlsxdSEhIWL16dV5e3sqVKx88eNDEfNQm0lYcIQD5ikg62RDFxcUeHh42NjYQAOzcufPcuXOl2pCviUBd3bp166gyANo/dejQQSwWFxQUMBgMNTU12f9mysvLYYKira0dFhZGvJ6TkwPVXYQOMlmIRCJDQ0OE0OHDh8k983eAZMO8zzb3+CpVtSK/N/kbLiXo/xmMrH0UbHwH/hWiuNRPwhH6oFlnkZImOEKVZf6HHkgxYbURMAyDNmcUFvVfu3YNITRr1iwcxx88eIAQmjhxouzN+JICuFgs/umnnxBCy5YtI33QoKAghJCqquqXVFWLiopu3769ffv2169fe3h42NvbHzt2jEQD2pYjxHH80qVLIDL0xx9/kKJGn56e7ujoOHXqVEKIj8FgGBgYQMmqlZUV5QX1kKvW9Bpw0rl9+zZCCOSPvby8EEKTJ0+mxJKamppff/0VFiKE8idsRUgjpfb8+fMIIT09PRnPu78VMAxjsVgIIUVFxc82FvgsuSU17s+yrBxjNFYEIGufLmsCrRxj3J9lCSrrqmpFuuuCGIslHKG1Dxr8CzhC9RX+JZXUTEyTkpIg5EDJ6MDq1asRQvb29jiOb968GSH0zz//UGLJZxXAobZEV1eX9KiMUCgcNmwY8dm/REpKCuEmKysrmy6j0xTanCPEcfz+/fvgpZYsWdLioumEhAR7e3sTExNYYqJPJXFcLhdKhl+9egWlclOmTJFSQ74mAlNRHo9HlQFw1e3ZswfH8e3btyOEduzYQZUxIpEINGAVFRVv3boFTrpDhw6kF3qXlZV169ZNGgvN7wmiOxiTyWykrkAkxqJSSzh33hvtfM5Y7CO32Ndo53P2jbehb4vF/1sa+zqtVO13f6bFebTw/n+OcIGn3OwzSkv95Jb4uj/Lkv5n+gwwJVqwYAElowODBg1CCL18+RLH8VGjRiGEgoKCqDKmngL4u3fvoDGqNIKisBnfr18/CtckbdER4jgeHBwMegrm5uZN70osFoujoqI4HM7AgQOJTVBVVVVzc/PPCvElJCSAnsuoUaNko3HekJSUFIRQV5nUynwJqCEBrVvYnPf29qbQHskNYwiKnj17lvRRYIhx48a1BRGDNg4RLjt16pTk6wVltR7hOTZn33Rc9QiWdObHIp0eZ+QI6t/RCspqr4RmLTr9WtvuEbL2Gbr1mdJSvw62AR1sA5SW+s08EvGeX7HsXCxjsY+jf5rsPtgnFi1aJKWvWROBqkFVVdXa2tqysjJ5eXkFBYWKigqq7MElFMC3bt0KQdHly5eTPgqfzwf5Rl9fX9JP3nTaqCPEcTwqKqpLly4IIWNj48Y7xVRXV4MQu2QtROfOnW1sbLy8vD47y3B1dYVdqLS0NMgNGTRoECUCg5cvX0YISTYMkzE1NTVKSkpycnIlJSVCoVBdXZ3BYFA1LZDk0KFDDAZDQUFBXV39/PnzTZ8PEWRlZeE4Xltb+/r163qSHDweDz51REQEaRZ/10CjdgaDceLEiYSP5fZeH0z2hMkt9kXWPvp/BrPcEwPjC+oaSIbCkaaHXsnb/M+RtUJxRY3odXppTFppWfV/4VAMwzdcSmAs9jnu03Il6JbRs2dPIlmMEiDyYWpqin9Sfxw3bhxVxhDcvHkTqhcgKEpuNBJYvHgxQmjevHmkn7lZtF1HiON4cnIyZDMOGTIEbmqS1Et+AfT09ECLqJFcj2fPnjGZTKLNQm5u7vDhwxFCvXr1kr3UJ7ShP3LkiIzHJXjx4gVCaNiwYfgnrcUBMlG5bQogrg107dp1+/btTVfji4yMnDNnDo7jbDb78ePH27Ztk3wX1DSkse3//eHi4gJt4U6dOsVgMHQGjUWLHqr97m95POp8UEZmYf3t1cpaUWB8Acs9sceGx8jaR2W5v+mhV/ZeH97mfH19g2H4pitJyNqHfUN2V+KHDx9g6kxhbGDDhg0IIWhNzGazEUL1vrFU4e3traKiAitUGxubFswV8vPzMQzj8Xg8Hq+eKw0NDWUwGCoqKq1pgkEKbdoR4jienZ0N+6h6enogNZSRkeHk5GRubi6pB21gYABduJpyTpFItGrVKtiFglaxAoEA1v7a2toQo5cZIPAo2RtdxkAXHmhje+rUqbbjHioqKuDyc3FxgcoKhJCcnJypqamXl1dT7lnQ687BwWH9+vXOzs7E68HBwRAzp7bJwDcB9PQICAiAX3j//v1R58FHrwTV1NVPZIMCCfNjkUrL/JC1T5+NT+wuxHuE55RXN7vqhnPnPbL22Xo9mZzP8DVcXV0RQr/88otshvsskL389OlTHMehhJfaUCFBfHw8Qgh6hkNIYNasWY8ePWripMHb23vlypUikSgwMPDkyZO3bt0i3hKJRHD327t3r9TMbypt3RHW1tYWFBRApXnHjh1BIx9QVFQ0MzM7e/Zsw8XiV8EwjMPhEKEeHMdrampglaCmpibZGVKqEPd6aSh5NhHI0oSyIWtra4SQlOo4mwtU+I0ePRqeRkVF2dnZwY49bK3b29s3HjMHR/jnn39C9iO8KBaLoZ3Nvn37pPwJvgfevXv3119/eXp63rx5s7y8XF5eXl5enujiW10nDowvYN94O/jvp8jah7nE12RPmL3Xh6jU1sqx2nt9QNY+a1zjxdJfpS1btgwh5OjoCE9jY2MlexfLgKKiIqJqsLKyUlFRkclkthFJ2zNnziCEFi9enJKSwmKxCFGnAQMGcLncpty44DKEB5LJj9DFSV9fvy3kbLdpR/j48WPI46qoqBg9ejRsATaS/NJcHB0dJTOjiMIp0N8j4QN8DaieGTNmDDwVi8UyvgLxT+2foLkEiEvJRgD9q+zfvx8htHHjRskX8/Ly7O3tCREsZWVlGxubz3aGevfuHYfD8ff3DwgIcHBwIBLwLly4gBDq0aMHhZOPbwihULhu3To2m/3mzRvohzx27Fh4yyM8R3W5P7L26bYu6Hen2Nuv+KVVZBY/nHmUzljss+TsG+l1K4R20507d0YIwbcoNTW1a9euHTt2fPXqlZQGbQhIGk2aNAnHcVD7HDVqlMxGb5z58+cjhJycnOBpSUkJl8uFHSuEUJcuXdhsduMZ3eAI8/Lyzpw5Q7yYl5cHeXBeXl7SNL+ptGlHiEvMJiCGbmNjQ26K7eXLlyEzat26dWKxGMOwLVu2QL6iZDBNSmzcuFHyXr9p0ybJprgyIC0tDX1q/5Sbm4sQ0tDQkKqCTNOBXuGf/W2IxeLAwEBzc3OiNsbIyMjJyemrCTXl5eUwnaKwBfm3CHwloLSGELt4l1Ox9+77SF6J9NZszk8y5Rb7LjgZ0zANpzUUFhZ6eHjY2dlB5ZKSkpKiouLWrVtxHK+trSUqWR8/fkzioI0A9Uu7d+/GP2m7fLWfrczo3r07atAnVSQSeXl5ERKVioqKVlZWn23k9Pz5c9i0Cg4OlqxS+/333xFCZmZmUv8ATeObcYQQHZXGV9PLywsCbosWLYKVO5EsToxOLgkJCRwOBwJ03bt3nzlzJvhgoinuuXPnpDFuQ65fv44QMjc3x3H87t27CKGff/5ZNkM3DoZhME8nsmOuXr0Kt6p9+/adOHEiNzcXx/GUlBQ2mw31oJBQw2az09LSvnRauJWPHTuWLploAXDj8/HxkeWg119ky9v4mh+LrG6wK9ksMAyLjo7ev3//2LFjIQ5EhPhmzZoFs+G//voLwzCRSATBUlVVVdnsksCtAIIWoLL94MEDGYz7Vd69eweXFXG9ZGZmCgSCioqKwMDAiIiIqKgoGxsbQqvExMTEw8PjqzPpyMhIOTk5RUVF0ructpg27QifP39uZ2f36NGjqqoqiJuXlZXV1dVt2bKF3D6CISEhUMsybdo0ULq7ePEi/HVZLBYpAje1tbUBAQFr166FUCSgpaUFKT8LFiyA/H6pisw1BBq+HDx4EMdxWArDtJRykpOTYZYg+SKHw8nOzl68eDGXy5UssaqurnZ3d4fU30YSajIzM1VVVRkMxvPnz2X0Mb4jiL0raeTQN45XdJ7SMr8ZRyKqapsdq6isrPTy8rKzs+vRowdx3cnLy0O76RcvXkRFRUVFRd29exdEPOzs7GBWCiEoRUVFabcsLS0tZTKZUDVYU1OjrKwsJyfX+Oa3zHB2dkYIWVlZwdOCgoIdO3YEBgaGhYU5OTkRbiw7O5vD4XTs2BF+vfr6+vb29l/6nojF4jFjxiCEdu7cKaOP0QTatCMkAGH44cOH4zgeHh6OEDIwMCB3iPj4eAgCjBkzBr6Fnp6ecG3Y2Ni0WHgTrkMbGxsIiAO9evWys7Pz8vKqq6sLDw+HBc3UqVMhB8Hd3R188IYNG0jxwY0AAhbQkNPExAQh5OHhsXfv3tTU1MLCwsOHD7u5uUnVgC/h5uaGEPrtt98kX+RwOBkZGQcOHIiPj79w4ULDn6qXUNO/f397e3uidyjsdixevFgWH+C7A/azjYyMKBnd902+ynL/n/a+bOI2JI/Hg9xyJSUl4rrT0dGxsbHx8PAgYnQ5OTmBgYHLli0rKyvz8/ODb461tbVQKMQwbNOmTRChIVffuR4+Pj4IofHjx+M4HhISghD68ccfpTdcswDhdcnmBKGhoYGBgdHR0ceOHauniFZeXu7k5AT6OLDJwmKxGoZnQEC/Z8+e1MoF1OPbcISSstTQ/sPW1pb0UXg8Xt++fcHLwvbvkydPoEjRwsKiWQXdBQUF7u7u9a5DAwMDNpsdGhoKKxV7e3sOhxMREUEI3IwePbqgoADH8QcPHoAPXrx4cYtF5r5KVVWVgoICk8ksLy+vq6tTUVFhMBh8Pv/BgwevXr2yt7fPzMw8cOAAn8+XkgGNAPUtklKHvr6+dnZ20JVw165djcQ/IaGmd+/e8GuHhBp3d3eoWPqSqi9N4+zevRshtGnTJqoMCEkq0lgRMGrX88Ly2s8eAMIabDZ78ODBxEXHZDKNjIxgm+qz8fCqqqpDhw7B46dPn8L1Pn/+fLjuILecyWS6urpK6XNJVg1CNz4iw5lyIClGMnsOHGFRUZFYLGaxWA1/pWKx2MvLi+iVJicnB/1+4d2ioiLY8rh9+7bsPkYT+DYcoaQsNczrP7sgaD05OTlQ0NOnTx/QoY+MjASBm0mTJn01oTk1NZXL5ZqamhJBczk5OQjCNCzVX79+fUlJyZ9//gk/CH2oBw8eDMVthMjc7NmzpZTfCNPPkSNH4v+7zn748OGrV6/YbHZ1dbWTk1NSUpI0Rm+cIUOGoE+6iy1DJBLdu3fP1NSUSKhBCP3yyy/1JGZomgho77WsDQVZRPBKtO0ejdgRml/6/3/E3Nxcd3d3KysrSWGNTp06WVlZubu7f1Uh2sXFRXJuFBERAV3XZ82aBXNfImOAy+VK40OBwCGIe0ybNg0h1HR9c6mSmpqKENLW1ibiUrW1tY6Ojlwul8fj/fvvv43353r9+rWdnR1M6OE+4+7uDhPcadOmyeQTNINvwxGCOPKHDx/wT2JIiYmJUhqruLgYgoQ6OjoxMTE4jiclJcGgQ4cOzc7ObvgjkskvxCrE1NSUy+U2spx6/vy5i4sLIW/N5/Nhl6t3797gNaOiorp27YqaIDLXMg4fPowQWr9+PY7jJ06cQAitXLmyurr6wIED586dCwwM/Pfff9esWSO9JemXEAgEUFZFSobw+/fv7ezsCHeoo6PDZrPpdWGzgL0rBoNB+d5VdFpJ59WBg7Y8vesXsnXrVsnCYgaDYWRk9M8//4SHhzd9T6FhWkpMTAzMfSdPngwZA2fOnIHvD+m1p8TOa2lpaV1dnZqaGoPByMvLI3eUlnHx4kXUavXHnJycXbt2we8TFgZMJlPGoiVN4RtwhCCABLLU0DlPS0tLqptnlZWVM2fORAipq6vDrCcjIwOEvPX19cEfQwUSi8WS3ITv2LEjzEOJiuNG8PDw4HA4kv3PBALBhAkTYBYGuchEtPazInOtxNLSEiF09epVHMetrKwQQq6urkKhkMfjQeAxJyeHkmI7f39/9GnXhBRAMWfUqFGw3EcIycvL//LLL0FBQXT6aFOATXrQ4aOcuMwy7ZV+Xc3Y8KeEwmInJycS+5MkJSVBxsCECRNgQ9HZ2Vmy5pgsJKsGQeyQ9OyHFgMVDqA30kpqamrc3d07deqkqqqKEFJSUrKxsZHeYqYFfAOO8MqVKwgh0I28desWQmjGjBnSHrS2tnbhwoXwN7t79y6O4/n5+bDm69q1q7m5uWTyS+/evVks1uPHj1vfzLayshLq59TV1R89eoTjeE5ODojMEdFasuBwOKNHj4b2T+DOKYmCNgQ2ZkgspTI3N0cIQeIPJNQQEZt6CTU0nwXEDTZs2ECVARiGSYYHfhgwHDGYy5cvf/z4sZQiFqmpqXp6egghIyMj2Lm/fv06bHn8/fffZM2f6urqXrx4AVVhEKFZs2YNhmEHDx7ct28f1AhRBSj9RkdHk3VC0MEYP348LK/l5ORmz54dGBjYFiaj34AjXLduHfrUs/HPP/9EstKmk0wbgy3J8vJyMzMzrWGz4R5aL/mFLIRCIczFlJSUYEu5uLgYSrh0dHQ+q6LSSjIzM2Wwzm46P//8M4k7JbW1tdBSQzKsnZub2zChRvayPt8K8BehsHFjQkKCiorKokWL8E9f144dO0r765qRkQE79wYGBvDl8fLygvS3NWvWkD76jBkzEEI3btyoq6tbsWKFk5MTbM1QQlZWFkJIU1OTLHmNN2/eIIR0dXUxDGuxWpv0+AYcIeycgSy1sbExQqjxTVpSuHPnzvHjx3k8HiTLDRkyBJIs3mWVoEUPf//rgFT76Ep2QwWBm4qKCrhOtLS0SFfohjX3zJkzyT1tyxCLxVDTSVYoGPL+P5uS/lmFGnd3d9lvi7ZlhEKhhoYGg8GgcIECipfW1tY4jru7uyOELC0tZTAun8+HbciBAwdC6NXX1xeqLBYvXtz6CBAARcZw2uzs7OLi4n/++Wffvn2hoaGknL8FXLt2DSE0e/Zssk4IQYVVq1YRr4BaG6RfoE9qbaRvADWRtu4IJWWpJTvnSXtcOzu7+Ph4UHhxdXUlth+uvchG1j4fi2ShEkukq8FquLa2dsGCBZLR2lZC9LFSUVHp1q3buHHj2sKKMC4uDuLAZJ0QGvCCKs2XeP/+PZvNhnRB9Cmhpuktn75vXr58iRAaNGgQhTbANx/k4EEQWLK0Rqrk5eURWWyQHxASElKvyqLFZ4Z8V5j5aWpqysvLOzg45OXl2dnZHThwgMIO9XZ2dojU9nBQRN9QWbSurs7DwwMabqBG1dqkSlt3hJItCJ4/f44QMjQ0lMG427dvZ7PZDf9sGy4l9PxDRgqEOI6fPXsWtuihZEckEq1Zswa1rrApPT3d0dFx6tSpRI0Hg8GAgA8hMkchTk5OYAlZJ4Qd1qaI84FCDbSGQRIlUBD6Tk9P9/f3Jw5OTEykcMIuS2BCtnr1agptgNSV5ORkHMchXBkZGUm8e+7cOS6XKxKJduzYsXPnTtJXFQKBAO7Uurq60JCPqLKYPXt2s5oniESisLCwnTt3jhgxQrKwx9DQ0MzMDF45fPhwbW0ttaFCqIsnyyHl5eXJycmpqKg08qFaptZGFm3dER44cAB9qjCFHtkyuCAxDFu2bFlgYCBRaUtgtPP5gpMyDdzfu3cPMjuWLl0KoRhipXjs2LGmnychIcHe3t7ExIS4/JhMpomJCZfL/fjx49OnTwmRuaakvLaSRnZVly9fjhA6efIkKQPl5OQwGAw1NbVmVWI8ffp0wYIFRG9uAwOD06dPe3h4SGZpnTp1auXKlaQY2caB7C0KZcpB8bJLly4YhuXk5CCENDQ06oUlQZvi0qVLHz58kEbBX0VFBRT5de3aNTY2FsfxyMhI0IRqSoFBUVERyHyD5jugoqICRVZE7OHKlSvgCWSjsPgl8vLy4Koha1oMLV8sLCy+emRqaiqbzSbU2vr27duIWhuJtHVHOHv2bNhAxnF83rx5CKFLly7JYNzi4uLY2Nh6ocKqWpGCjS/XL00GBkjy+PFjCMVYWlpCkS8hSdp4/oJYLI6KiuJwOFD7AXypjxUhMkcI3EgJDw+PtWvXgnnLly+vp181YMAAhFATeyx/Fei52pQrsCGQUAPiGp07d26fjlAkEkGCNIVNjF1cXNAnvT2QiW+4n83hcF69enXlypW0tDRSMv4bUllZOX36dIRQx44dYamUlJTUp0+fhw8ffulHeDweKGwQkyqEkJ6eHigsfnYpeePGDTh4/fr10kunLCkpOXjw4Pv370EDBLRKCDw8PBCp+vugiNL0fj5lZWVOTk7EXatDhw4sFgtqf0eNGkVoswkEArKKrNq0I6zXggAmU+SWEDSLkKQiZO3z6oOsRYdxHI+IiIBfxaRJk6Cw6dq1azNnzvzsQge0plgsluT0s3PnzjY2Nl5eXo2sjQiBG0JkTkpAWw9nZ2dvb29JR1hYWAhCaGRNRUGHSFIssbkIhcLbt2+7ubl5enpOmDAh7hPbt29vD44wKioKJuYU2mBjY4MQOnXqFI7jsDVw+PBhyQPc3NzMzc0/fPiwZcuW7du3S08toba2Ftp3q6urg0Jvw6upsrISrj4iDYSIvtjb2zdlhuft7S2pAC6ND1JdXe3r6/vs2bOCgoKKiop6om4gOH7gwAFSxqqpqYG07eaGrEUi0d27d6EdB0JIXl7+5s2bioqKhFRsQUGBhoYGKUa2aUco2YIA9H46d+5MYdHJoQcflJf51QqpySghBG6MjIw+qz1BJL9Iak3p6emxWKzAwMAmZrjx+XzYJOvdu7f0mqRwOJyqqqoFCxbs379/y5YtxOve3t7oU4fS1iMSiWAjB3IcWomnp6eOjs68oLgcHwAAIABJREFUT/z444/twRGC5NDvv/9OoQ1Q5QIBSdARDQsLo8oYkUi0dOlSiKxApS/wWZnvrl27gsx3c/P76imAk/0hcBzHAwMDIf/cy8tr+/btkm/BtjpZ2emgKt6aVsMxMTF2dnaampp8Pl9KjlAetWEgXQ1K6ODxuHHjJHeYZW1PimCUvqaivNzXD5UCgwcPDg0NnT59enR09Lhx4x49egSiM5mZmf7+/t7e3o8ePaqrq4ODDQwMrKysLCwsJIXfmkK3bt2Cg4MtLCxevHgxceJEPz+/ESNGkPtBnjx5IhAIUlNTb968mZOTk5SURLxF/JVJGSgiIqK4uLhfv37wi2o9I0aMuHfvHjw+ffp0bGws8VZVVZWSkhKTyaysrCQKpL4Dnj59ihACoVFKSE9Pz8jI0NbWHjp0aEFBwdu3b1VVVZv7rSYRJpPp5uYmLy/v5uZmYWGxffv2kpISHx+flJQU4oBx48bNnj171qxZw4cPb9n9asaMGf7+/hYWFtevX6+oqPDw8JD0r62nrq4uJiZGSUlJQUEBw7CioiLireLi4sTERGVlZWgB23pgdmthYdHiM4wYMcLJyen48eMwxd+4cSNEj2tra0mxECHUpleEki0IYLXeMHtFZmAY3mVN4NbryVQZABQVFUExpY6Ozvr160eOHEn8KRUVFc3MzM6ePdv6rLmGInOyYcqUKYi8rqSgUEOWHoqnp+eX9ghLSkqmTZsWGRl55syZ06dP//PPP6SMSDkYhkE+SGpqKlU2XLp0CX0Slrp9+zZCyNTUlCpjCDAMW79+PUIIlPERQtra2lZWVk5OTjk5OWSNQuTjEArgZCESiXg8XmpqKoZhHz58kEzm9PT0RAhNnjyZlIEwDAPVKrLEARQVFR8+fPjkyZMnT57cv3+/XYRGoQUBhEHgjh8cHEyVMe/5FcjaxzOSStEjoLy8fMqUKYRI2JeSX1qJUCiEBE4lJaV6Ii9BQUGQ1Xny5El7e3uystUzMzNhokdW4TbMGLy9vUk5WyOOcM+ePVevXo2MjIS9Fugo8h0ANZ09evSg0AbJqkHoI0268nXLIDqXbdy48fnz51JK9CcUwCdNmiSZzh0cHLx//354fOfOHVIKiwGQ04Jd/NYTHR2NEOrevTtZW1pfCo2Wlpbev38/Ly+vtrbW29v7s90RGoGaKF9TKCsrS05OVlJSGjlyZFVVVVxcnLy8PDSSpYSXKSUIobH9tL56pLRRV1ffvHlzTU1Njx49fHx8oqOjHR0d58yZIyl/2nog+LN582Yo5IcEaMDY2BhiKdnZ2ZaWltBMscVgGObl5TV79uxRo0YJhUJ9fX0dHZ3WWo+QQCCIiopSVFScPHly68+GEJo2bRrUOALW1tZ79+5FCPF4vMTExKCgoIiICAzDEEI4jpMyIuVAXJT4BeI4XlVVRYkNEJulPE4ryZs3b6qrq/v378/lcsPDw11dXWHeQC4jRox49uzZDz/88PTp05kzZ5aWlsLr48ePF4lECKGMjIzc3FzJ/YUWw+Px1qxZA41ziRSVVgJxUUtLS2lvacXGxhoaGh44cODff//t3bs31N01nba7R/jy5UsMw4yMjJSUlEJCQkQikZGRkbq6OmX2pAj0u6p20yIzUt9iIJdvwYIFZmZmISEhiYmJPXv2hHw2EmEwGP/++2/Xrl23bdtmZ2dXXFy8detWhBDxV5g3b15MTExcXBwIfzSXjIyMQ4cOhYWFpaSkQLifyWSmpaUNGTJkyJAhBgYGRkZGQ4YM0dPTa8FVFBAQIBaLJ0+eTNZ3RkNDQzILiZCh6du3r4eHR3h4uLa29ogRI44dO9ZG7tStB+QsiHvili1bQkND/f39ic8ubfh8Po/H69Chw/Dhw4uLixMSEpSUlECjhHLAK8Mvp7S0VElJCVorkM6gQYNCQ0NNTU1fvHgxderUgICAzp07Kyoqwrt79+61tbW9efNmi88POhI3b95MSkoqKChACMnJyRUVFX38+FEy8bVltH6DsB5Tpkwhiu4VFRWJa+2nn346ceLE4MGDVVVV/fz8MjIymndeUpar0kCyBcGhQ4cQpeL3OI4bbnu2+Az5gtctw8zMDEnIUnM4HCmllgFnzpyRFLhJSkqysbFJTU11dnY+evSoZO5cUxCJRJ6enrNnz5as7mgETU3N8ePH29nZOTo6BgUFfTVwWl5e7urqCnHdo0eP8vn8W7duteLTty+g9hS0FxQVFVVVVaHILD8/H7I3jYyMZNaVEKoGZ82aheP4/fv3EXkZxa0HWpoQFXhisXjjxo3SGy4jI6N///7okwJ4XFzcb7/9FhsbGx0dHR4e3oJ7o1gs9vb2Njc319fXh6sbkCx51NTUNDExYbFY7u7uUVFRzW1qnZ2dzWAwVFVVZaCSk5KSIhaLWSyWSCSqq6vbtGlTs3687TpCyXs9TCgo1LYoqxYyl/ieftQmurmKxWJQXoDNuezsbNBElSrXr1+HK2Tp0qU5OTk8Hk8gEJSWljZrP+/9+/e///67gYGB5MUmCcz1li9f7u7uzmaz4SpteJiWlpaRkZGNjQ2Xyw0MDKzX/TgzM7Nr164gDhAXFxcWFjZixAiyfx/fG0VFRdevX1+8eDGUqwKQqaiiogL90zMzM+FePHjw4ObuwbSM1atXo0+dZzZv3owQaiOJSGKxGHYioMT52rVr9vb28FuSHnw+HwobBg4cmJKSUlxcTGTQNEvmLTk52c7Obvjw4Y0sYfv370900yVQVVU1NjZevXr1+fPnX7161Xj+jrOzM0ilz507t6ampunV9C0jMTGRy+UmJSUlJyefPHmyuULBbdQR8vl8iGjBvb6srCwoKEiqcieNExhfgKx9YtJKqTJAkvj4eIRQ79694amPj48MRNFwHA8KCoLY4Jw5c5p14dXW1rq4uEycOPGzm3+dO3ceMmTI9OnTt23bdvnyZQaDoaysLKljkp+f/+TJk1OnTq1evXrChAmE/JIk3bp1MzU13bhxo7Ozc3JyMsTuoOcL7QgboRHpEw8Pj7KyMshPUVRUhHSM3NxcuBfr6enJIJUUqgahoTmUTFCoQy3J69evEanS8E2kqKgIqhoIBfCmIxAIDh48aGJiIjnXaXglQlqilpaWQCDIzs4ODAzkcrk2NjYGBgaSC0eEEJPJ1NfXNzc353A4Xl5e9ebEKioqIMTj6upaVFSkpqZG6m+CZNqWI0xLS4PLUl5eHtoZk5gN1Rr23UtR+91fKKK+gSSO487OzgihhQsXyn5oQuBm8uTJRO5WIyQlJa1YsWLw4MFEWB+unx49ehgZGc2dO/f06dP15m6w3Whra9vIaYuLi0NDQ52cnFgslomJieTWHUIoISEBprqWlpYRERGurq6SjhDDMEK2prmhnu+DpkifVFdXHz58+OHDhxiGwVKMyWSCumFxcTHs0vXq1SslJUV6dubn50Ngrba2trS0lMlkKigoEPJa1MLlchFCy5Ytk/3QDRXAG0csFj948MDCwqJv37719tpVVVV79+5tbGxsZWV18uTJhIQEyO2E9Kg9e/bUO1VJSUlISIiDg8OyZct+/PHHhnGdXr16WVpacjictLQ0FRUVFRUVOTk5Pp9PO8Kvg2FYZGTkzp07oe8XoKysDEEYeXl5d3d3qm3EZx6JmHJA1p1BvgS07XV0dKRk9MTERCgMMjIyys/P/+wxNTU1Tk5OkyZNIuaeqqqq/fv3nzRp0pIlS+7cudNIpUdqaqqioiKTyWzKRU6Qlpbm4+Nz5MgRFouVmZkJU9cJEyZYW1ubmZkRjrC2tvbIkSNr165NTU09ceLEli1bXr161ayP32bx8/MD0S/g5s2b9XqLN1f6JDMzE/pR45903uXk5OCVkpISkLno1q1bfHy8lD6RZNWgr68vQmjcuHFSGqu5gO6xm5sbJaNLKoA30k06MTFxzZo1hoaGIFIDs5lu3boZGhqam5tv27bt6dOnnxVchD4/mpqaRUVFjZhRV1eXkJDg7u7OYrFMTU0lU6iio6MhnWfo0KFRUVFPnjyhHeHnEYlEoaGh9aalHTt2tLKycnd3h9UG0Wbh6NGjVNmJ4ziG4dp2j3bcekuhDZKAFq1kGxoZk56eDtLYgwYNqreeS0hIWLFixaBBg5hMZseOHYcNGzZjxgwWi/X06dOmL7+gVBlqqFtAZmZmly5d/Pz84OZeLzT67t07W1vb4uLi7Ozs1atXU1gqTi5///03UViG4/iiRYsuXrwoFApDQ0PZbLaBgQFxlcnJyRkZGbHZ7NDQ0EaquyQdIY7jR44cgYvRwcEBx/GKigpTU1O4F79+LZUkMojKwodis9kIoW3btkljIEmKiorMzc1xHHdxceFwOJ+djWEY1rVrV0SSdF/LaKgATiAQCPbv329iYgLF+FpaWnAZ2tra3rlzp3HfRgApGvWk1xpHLBa/e/fu1q1b27Ztq66uhiDQsGHDFixYMG/ePMIRYhjm6OjIZrPfvn17+vTpXbt2tYXJqKwdYVVVlZeXl52dHXyTgJ49e4IcO9wrk5KStmzZsmHDhrq6utOnT8Psns1mU6UympRVjqx9vGM+I+9JLlFRUXCXwTBs27Ztn5W9JmSpqQ3r5ebmgvSarq5uXFxceXn5sWPH+vbtq6mpaWhoOGfOnH///bfFPiY/Px8EO5rV8C8lJQWax0KyTEhIyLRp0/AGjhDDsMuXL4MPePLkyfXr11tmZFujoSN0c3ODtTvQqVMna2vra9euNTHn88SJE0uXLpXcfia6Y+7duxfH8ZqaGugqoKWlBdt45GJoaIgQevr0KY7jEAyUdjYKjuO7d++GTPWKiopLly4RHSi1tbWLi4vhMQgB/vDDD9I2pnHqKYALhcKbN2+OGzdOS0ure/fuEydOhMlQy/QuIiMjoRNTc9UtiLu0iopKcHAw3MTqhUZLS0uPHDkSHR0tFoszMjKk1C2kWZBTR/j27Vs1NTVibffmzZvu3btLurqioiIfH5+HDx/6+vpWVlbCi7DRamVlJdkkDyH04MGDTZs2PXny5M2bN+vXr+/YsePy5cuPHDmSm5t74cIFyd0maSMU47y8Ss+oXAZD6qX0IpHo3r17EHa/du0aQqisrAzekpeXr66uhrd8fHxwHB89ejRRSEQJOjo6bm5ulpaWHz9+tLS01NLSGjhw4MGDB3/++efWF5l16dJl06ZNN27cgJLhJoJhmGS5t1gsrq6urndMVVXVqVOnKisrZ8yY4eDgUFZWtmzZslZa23ZITk6GGgOEUFZWFoPBMDExiY+Pt7CwMDU1nTRp0peSdT8LKIxIsnbtWg0NjeXLl4Nmur29vYeHh7W19d27d01NTb28vKZOnUrWZ5GsGqyqqoqOjgYNT7LO/1mePHlSW1ubn5+fmZmpra0tFAqJrxCEEOExRA4prxZVVFTcv39/cnJycnLyypUrVVVVu3fvPnfuXGdn58GDBzOZzNacfNSoURYWFl5eXsePHz927FgTfyonJ8fQ0LCwsBCeisVic3PzN2/e1DuMwWB06tQpLy9PLBa7ubnB0p9ayHEqXC7XwMCAxWLB0127dq1YseKXX35JT09/8ODBw4cPoSIefQrLmJubL1y4EJogN0RZWbm6urq6uhpUxKytrXV0dObOnevu7l5SUnLz5k1CXUx6ZAtq/rqW/CAqT4EpVyPEEELsm++OLBzYWUNa7sfBwcHU1PT+/fvZ2dlRUVF9+/YlVCSgFQs8hlJ6ad8RGgHH8cDAQEdHR39/fxBSSU9P79u376lTpxrmW7eYbdu2jR49GnoBAnfu3Pnpp5+aIjrTvXv3iIgIHo8HT0eOHOnl5QWPVVVVIciGEIIckG8OoVCYm5sLk86srKyOHTsSGt+ZmZlhYWHwGG5GV69eJXfiuGTJEnl5+aVLlx45cqSiouLUqVO3bt2ytbV1d3e3sLDw9PSEkFrrqa6utrW1raurU1ZWDgoKqqurGzVqFPSODgsLGzBgwJdSH1vDyJEj+/Tp4+joqKmpefHixeLiYijCqQf8kil0hEKh0NPT8/z58yEhIXBnSE1N/eGHHwICAj5rcMvYt2/fwIEDQUODRDAMc3Z2zs/PNzQ0XLhw4ZgxY969ewdbzlRCyrpy9erVkrkbs2fPvnXrFnTzAZSVlc3NzV1cXD7bP6gefD5/9+7dBw4ckIyFvnr1CkLeU6ZMaUq+YmuISSvVXBmgYOOLrH2If0r/1959hzWRrQ0AP0novUuTIqiIuCogKiq6V9ZrAZdVsQLWBSviqosFBXVVsCKrIFgQxYZiQQERXAVB6aAUAem9E0oCgSTz/XHW+XJRkZIGnN9zn/skk2TmZGXyzpzyvnbhCg6RuZX/P2nt9OnTbFwck5ycHBkZaWtrW1paGhkZefDgwbCwMPgSAADvCJ0xYwYA4MmTJ+w6bu/BDBT4nCZhYWFbW9vExMRp06YBAFgLZrLF4sWLWcsOGxoa9lx8Jzc3V1hYeM4XkyZNYlfRTr7i7e197ty5Bw8eeHl53bt3D58r8c0xQg61oVvBPCaTCXPiCwkJPXr0iO2Hc3FxAV9ya+Tl5a1evbpPfeb9xloUU0xMzNTU1MzMzMzMDHbGfPrEg/z7FRUV7u7ueN+biIgIPAfhvfjkyZPZew7u2bPn1atX+NO1a9f2PDeqoqJCXl4ePv78+fP79+8nTpyIYRiDweDoBOOBY9vVIo1Gw7vy6HS6gICAjIyMrKysubm5hYWFlZUVnqb9h5SVlWEWR1YmJiYxMTH//e9/X79+PXfu3LCwMDbef+CyytseJVUde5zfxeieLpJGZ3ZRuua5J+adnS0kQHz//v3IkSNLS0vZdWi4TMrIyEhWVnbkyJHTpk1j7fycPXs2gUDAMCwxMREAAGMP1xQVFfn6+l65cqWxsREAoKKiYm9vv337dnhV/uzZM1NT0+Tk5BUrVjx58oSbfdfdKCgo3Lp1Cz5+9+7dhQsXeNUSztmyZUtCQkJhYWFcXNy0adM4cWP0QxYWFo8fP16yZImfn19bW1tAQICXl5egoOD58+etra39/f1hKV12ef36NQDAzMwMw7Dr169zrTe7W0/ylStXZGRkCgsLZ82aRSAQ1NXVZ8yYsWjRoiVLlnyvc4td4Hi2n5/f48ePYdeanp7eunXr7O3t4bLaBw8eTJ8+PS0tzc7O7sGDB90W/PVbbm4ua0K7jIyM1tbWnj/S3NyMZ4SmUqnwR4xIJMKK33yLbb9Zf//9N57vrqCgwN7ePjAwcMSIEX0aluiZvr7+27dv582bl5ycbGZmFhERwdp11m8MJvbuc9OT5JonyTWFtVRBEpH5nZzJTCbW0NZ1513lOjN1V1dXS0tLtqfZxVeLd8uQGRwcLCgomJGRMXfuXBKJxGQyVVRUfvnlF0tLywULFnAuBWtsbKyXlxd++hkZGTk6Oq5atYr1n1VBQSE8PNzU1DQ0NHTbtm2smakHqKioKDU1FT7+esDva3CFIt4qdjWDr+Tm5sbExOzevTshIcHJyWnPnj1wJj2XwYJ5FhYWeMG8c+fOSUlJHTlyZP369V1dXbBqRL8xGIz09PRnz549f/48JydHRERESkqqqqpKTk4uPDx89OjRM2fOZNd36SVlZWU5OTlYZpZIJL548eLdu3fv3r07ePDg96Y7DFxNTc2NGzd8fX2LiooAAEJCQtbW1vb29nPnzmU9EPzPMnXq1EePHh04cADOt+cJKSkpmKIWAJCamvr1SDOf6tP9Y2Bg4JEjR3Jych4+fOjr6wtrRmPf6hrl3EJ4fL6iqqrqx48f+72f9k5GZEadY0CWytYosDpUzv6l9YXUgJjyn/bFsPaIfv0/8xP/TvalUqmczhsEgS9dozBlkYiISEBAAP4vKCYmtnjxYj8/PzZmvero6AgICIDT9sCX06/nnsmEhAS4jB0mxBq4xYsXm5qaLv9CVlb2h12jGhoa+NNXr14Nya5Re3t7d3d3WI/t1KlT+NTzrq4u1kpAnZ2dHCoMxCopKQnOjcIL5uFLnjw9Pfuxw+rqan9/f2trazgcCMFuWFlZWTg3tby8vFtSPS4QExODCw9sbGwAAIKCgu3t7TAvAeu4tYaGhqOjY2Rk5MBz/yYnJ9vb2+NLAHV0dNzd3b+3cheKjo6Gd2C+vr4DPDoEr7N3fKGoqNjzOcjaNYphWEJCAuwa5X99C4RNTU2VlZXu7u6urq6xsbH4YkxuBkIMw1pbW+EaJjk5uZ7/Yb7W2NYZFF9p650utTECrA7VdPzH/mpGSEpN55esMUqbI1nDntSKhyd+Xi294gG+ZfQfbzjwnXqCB0J4EkpISGAYlpmZCTMjs3aD6Ovr/3B9WM9qamr279+P307BbupeTqEOCQkhkUgEAuHmzZv9Ozqrvo4R1tTUsK4zy8nJ8fDwGHgzkJ7hBfPmzJkD11pcunQJ3qz0vnDgN/+YR40aBYNKW1vbsmXLAADi4uLcLBPNytHREWaOhr1Qq1evxl/Cl0Sz1iOTl5e3tbXFl4T1SWxsLL7uk0QiWVlZvXjxAs6Y+6Hr16/DOM06ttdvlpaWBw4cCPtCV1cXBcJ/eXh4FBYWFhQUJCcn48stuRwIMQzr6OiAa2jExcUjIiJ++P7S+nbfVyUWp5OE7MLB6lD9vdHOd3Pe5jR2ixf1rTTF/w2EE5b4pE4ZFzrdVGn5HbjF+GAsp77Vd2zfvh1e3cMM1Bs2bGB9tba2NiAgwNramnUUVlFREWYM6ZaGtKysLDg4GA+TycnJBQUF8HFcXByGYXl5efDHyNDQ0NfXt085RTEMg8XMBAUFB/6D1ddAiPBKdnY2jAEzZ86EE9kuX75MJBIXLFjQw11pW1sbXFLMOtFRRETE3Nzc3d2921QUOp2Ol4l++vQpx7/SdxQWFsLr7++FpczMTFdXV5jyApKRkYFJQlpbW+F7bt++PWvWLDyVj6OjIz73B6YULygoIBKJysrKzs7OxcV9TvS/d+9e2Mjc3Nz+fEkWlpaWfToHm5ubDx8+jD8tLS2FFZX5X98C4fr160+ePJmamnrnzp3Lly///fffcHtGRkZRURH+tqSkJC70XdDp9I0bN8KOu+/V2SmooXiGF81we0dYE0pcEzbD7Z17SH5OZfeJVSX1VBgm4UxRwv/2hY5eeiVu6qQ3U420lt0Qtgt3CRron1f/1NTUwMD/vV4XvK+GdegU/rJ4enrCNNZBQUEAgGvXrsGP2NvbX758GT52cHCADzw8PAYScuCyBCkpKbznvH9QIBxECgsLtbW1AQBGRkYwOX5YWNg3qxPgab5Z54JpaWnhab6/dwgmk9ktAzj3+fv7AwCsrKx++M709PTDhw/DBOX4EMbSpUvv3Llz7tw5OTm5bdu2wXcuXLjw+fPnhYWF7u7u06ZNgyMLMTEx/e5cZTAYVlZW8JZ6gIUK+hoIB68+d402NjZSKBQGg9FDukiuYTKZ8PKHRCLhP+iQ76sSnV2vwepQ0XUvfj2bfP1NWV1L9z6KzLJW95B8GCZJNv+GyYiPdaLrX3QbF1RfdvPVtCnxUyYYrrxS0di3myR2efz4MQAAZkv5ofT09L/++mvq1KmsfU1Tpky5e/fu7NmzNTQ04BkCA2FNTc2hQ4eMjIwOHTr0dc7JvmIymWvWrAEAqKmpsRaR6KuKigr8IhrDsOLi4u/dnra3t/v6+oaEhDCZzBs3buBlGhFuKikpgTMDYcE81peoVCq8RIMVDfFOPyMjI1dX1+Tk5F725H+dAZzLYJrfPmVCgYUE8Ek0pqam586d27Fjx+jRoxMTE7EvgZBOp7e1tW3atIkt6x+oVCqc7WlmZjaQFFR79+5l7WJdt24d51LL8hbvk24PnLu7O/wjc3Z2xjdef1O21ufDo6QqCu1/OmcYTObbnEbnuzljd7+BYdLidJLvq5Jq8v8nnz0fVij2VSxUXHE3dMasnBkG7amJ3PtuLODKVhcXlz59qq6uLigoyNbWVlpaes6cOUFBQatWrTp69Oi6deuw/70j7Gspyx7QaDS4sMnAwIALF0wlJSUtLS3btm2LjIx89uyZl5fXwDuFkH5gLZhXVlZWWFjo6+trbW3NOqtZQUEBdhX2+w+jWwZwboLLJGCBjr4qKyvz8vKC02v37t375MkTQ0NDOp0OAyF8DxtHQCsrK+EM6vXr17Nrnz0oLi6+cOFCSEhIU1PT2bNn8T6nwWIoBEIMw27cuAGXr+3YseOb3ff4HFHlL3NEbb3Tg+IrW9u/PYYRGFsuvSlCalMEySaMZBMmvSlCzv7ls5jP5RuWFUwb0/aK4zkPvwZDC37O9FVHR0dJSQkMhO3t7bq6um/evGENhOzV0NAAfzV+/vnngadFhZfVV65cwTCMRqNt3bq1W83riIgIBweHysrKvXv3bt26FWbcQLivtrZ20qRJAADWaZ9EItHExOTIkSNJSUm9nPTRs24ZwLmGSqX+888/A5yOCwMhhmGLFi26ePEiayBkr9TUVJh1iAtFC0pLSzs7O7dv3x4REXHnzp0///yTLf/QXDNEAiGGYU+ePIHTrG1sbPCUEA2t/84Rldzw7TmiPaDS6E+Sq/96/PnE0/zQtFpaFwPDMGY7tXLH+nxjnZYn3x6V5Bwajfbu3TvW3sJ+gIEQw7CwsLCJEydu3LiRQ4EQw7CioiJlZWU4xW6ACdMbGxsbGxsdHR0xDDt37tzu3btZu3BzcnKys7MdHBwoFMrnz5/37t2L50dGuK+xsVFJSUlFRUVUVNTCwsLX15cT5ey7ZQDnHJgCHn+ampqqr68/kB3igbCgoEBdXd3U1JRDgRDDsOfPn5NIJCKR+Pjx4wHuKjMz8+zZs9u3b6+srPTw8Ni0aRNr5h0Mw4KCglxdXdPS0nbv3r1p06bBVeyTPQkI+MGvv/76/PlzCQmJwMDAhdZrz4UW/Od4gtKWqOVeaVnlbXsWaaefnFl84WffjQaWhkqCpB/s9oOZAAAgAElEQVQvehUVIv1qNOKgle7+xToLJykKCRABAAQRUeXzfpIWS2qP7SPfusL5rwW0tbXz8vIAAEJCQtOnT5eVlWVNLd1vCxYs0NHRefr06cB39T1aWlqhoaESEhJ37txxdXUdyK5kZWXT09OFhYU/ffoExx3h1CFIR0enqKjIycmJRCJ9+vRpx44d36xij3CHqKhoS0tLTU0NnDTY0tJSW1vL9qNs2bIlICBAQEDA1dV13759bN8/F4waNcre3h5PD8sJixYtOn78OJPJXL16NUxK1W/jx483NjYWFRWFqTyEhYVZ83rHxcUpKyvX1NR8+PDB2NhYUlIST5U8OPA6ErPH2bNn9+7dm5ycnJSUJC0trT7ThrTsDpz8klfFgZLWTGb9ueP5hlr1Xu4Yh4tDaWpq5uT8fylEEok0kOF0/I4Qw7DS0lIJCQnO3RFCYWFhsNf60qVL/d5JYmJiUFDQpk2bysvLIyMjN2/ePMApqQjnwIxokyZNwjBs+/btDAYDnyHJdnfv3oVJjrZt28ahMm1svyOE2UjgY7gMrFtBQbZzcHAAAKioqHyzslvvNTc3b9u2jU6nUygUZ2dn1npeXV1dSUlJsJ8mNTW1f7WfeGiIBEIMw9LT0+FEMpjIPPAexydYN/n75Btq1bjuYdIHmkWiB+wNhI2NjaxlAj9//lxRUREdHc3RtVlXr14FAJBIpIHkCi8vL8dnjZLJ5ME1AjGsuLm5AQB27tyJYZiTkxOGYX/88QfnDtctAzjb919bW0sikUy+MDAwGGAghBoaGq5fvx4YGIhh2MePH5csWXLgwIGB7/abOjs7YR6+gWTlTktLO3PmjJubW1FR0alTp/bv39+ta3RQGyKBsLm52cXFhUql0ul0SUlJAAB3kjA1P7iVbzyq6g97Jq3jx+/uF01NzYkTJ079AgAw8AnWTCZTR0dHTEyMRqO1tbUBAERFRdnS2u+BBQTExMQ4ffGL8Byc1QWX+h04cCAuLm7Pnj0cPWJ4eDhMRbZ69eqB5zbrpra2VlJSsuGLN2/e9CMQFhYW3r9/v729nUwmw6V42dnZAABYShr2jk6bNo29LWfV0NAwZswYAMCiRYu4kHtv0BkKgZBOp1tZWXl5eSUlJaWlpQEAdHR0uHb0tn9eFEwbW2G/itE2oGks36OpqZmQkICfh/24I4yIiDh79uytW7cSExM3b94MN8J0HrCrBM5u52hxKyaTCTODKCoqvnnzBk5+6fmEZDKZL1++dHd3v3jxIuoIHSxoNJqYmBiBQIAF12g0WmxsbLcpvpwQHR0NMystXrwYT/3IFmzpGn3x4kVKSsrp06cjIyPhTBk4yK2oqIhhWG5uLgBg9OjRbGz21woKCmDqRNZlZgjEs4o5bEQikeBicwCAt7c34G7dWvGf/6vidb36D/vKzatVvG6QZP+tz44x6BiFQhARJQy4lLy0tPRAyr7PmTNn3rx5u3btsrGxgbnzAQDKysqVlZXV1dXq6urKysr5+fnV1dW9L5XVVwQCwc/Pr7Ky8uXLl3PmzGF9SURERFRUlEQiwaNLSUktXbr04MGDK1asqK6utrW1bWlpWbRo0ZEjRwZY0ADhgqSkJCqVOn78eCUlJQCAkJAQrKDJaWZmZi9evFi4cGFISMiyZcuePHkywBLt7PXf//73xYsXY8eONTc3hwXu5eTkCAQCXEwJK602NDRwtA2jRo169OiRubm5h4fH/fv3dXV1JSQkxMXFxcXFZWRkxMXFxcTEpKSkpKSkxMTETExMlJWVHz165OPjU1JSoqqqamtrCzN5DU28jsRsBtNSD2ReRv90ZH4o+s/kkiVzu6orO7I/VmxeXTB1TMG0MflTdEtXzG97/bLfe2bLGGFwcDDMyOrq6gq3LFy4EADw7NkzDMNgRZvo6Oh+N/JrME0+hUJhTZbY0tIC6/oSiURpaenvVU3btm0bLLWDd3NlZWUpKCjg+bfy8vLCwsIYDEZ5efnTp0/7mhAV4Zzjx48DALZu3cqTo8MM4K6urtevX8/KysK3nz9/vt/TN9ra2liTuZeXl/ejvkpERAQcDsRYzkG41LKpqYnBYBCJRCKRyIVOS5iV+4cePnwYGhqqoaERHx/PZDIzMzPHjRt39+5dTjePV4bCHSGr9+/fAwDgfBluEh7/k6rfvarta8tWLmB00hgdHfh/2fa8nKqDO6XnWSoedgd9r1W2YcMG1tvBnTt39rXEo5eXV2JioqWlZV5eXkpKSkRExH//+1+4wq+6uhoAwPqYLZ48eRIREeHt7e3v79/S0jJlyhRYLURSUvLFixc6Ojo0Gs3MzCwkJAQA0N7e3tHRwWAwYGHn5uZmBQWFGzduWFpa4jV+9fX1R4wYkZWVBcdRsrOz6XR6cHCwtLS0goLC5cuXnZyc2NV4ZCBiYmIAAGZmZjw5+uTJkz9+/KisrLxgwQJZWVm8hsO1a9dmzpzJWh2i98TFxU+ePIk/VVNTc3Z27utOKisrOzs73759S6fTVVVVMzIyJkyYIC8v39zc3NDQICMjIyMj09jYSCaT4d3hwPn5+ZWWljo5OR07dszS0hKegACA9evXJyQk+Pr6EonE8+fPy8nJUSgUMplMoVAoFEpbW1tzczOFQhk5cuSxY8cOHToE5yWMHz/+9OnThw8fXrlyJQCARqP5+/uLi4vb2NjcunWLQqE4ODiwqxowTwypQFhbW1tQUCAuLg5vO7hMSGeM/C6X2kO7QmrJkWTqxVGKcPvV2ua6rsbDhOcC2jqydvZ93e3hw4dZn549e7ave3B0dMQfP3v2DD7gaCC0srJKT08nEAjbtm179uwZawXRESNGSEtL19bWEonEuro6AQEBERERuOyPtZRuQ0NDt7WAsrKydXV1AAACgfDrr7/6+/tPmDBh/PjxTk5OcPQR4Tk6nQ7nfcyaNYtXbYB/zPyG9U/0559/hg/k5eULCwsbGhp0dHTk5eUbGxsbGhrYFQjt7e3d3NwUFBSWL19OoVBYXzp+/Lifnx+TyRQQEIBdaN+Um5sLkwRBhoaGsGuKQCA0NzcvX7787NmzdXV15ubmgYGBZWVlrIlkB50hFQjh7eDUqVPxOwkua7rqhdG7vvkSs51K9vOUtrYhiopxuVXfBKuJwhF71sfslZycXFpaum3bNnyLt7c3XF799OlT1uX8sLMUACApKblnzx4tLa0PHz7gr2IYVlxcDOsbAAAeP34sKytrbGycn5/v7u5+7tw5bo4KI9+Tmpra2to6ZswY1spKvAJLFsPHsLOhH8LCwqqrq9euXfvx48f379/b2trCSelswTo0KC8v//nzZ04PE0KwUpKwsPCDBw9CQ0PFxcVlZWXFxMTExcUlJSWlpKTExcWtrKwEBAQYDAb+KTqdLiAgAC9qlZSUYEeOkpJSRkZGdnY2a0a9wWgIBkJe/SYyGuq6ykt6egdRoCM5XmzWf3q5w/r6+qdPn5qZmSkrKwcHBxsaGuIl4weOo3eE0dHRAICkpKSHDx/q6Oikp6fDS8va2lqYYkZZWVlMTKylpYXBYMAzislkNjU1AQCampqoVOqqVauOHz9eUFCgo6MDAAgMDFRUVIQ9XTQaraioSEJCIi0trampKTw8fOfOnexqOTIQ8N999uzZvG4IAADk5uYymUz4uH/JmLq6ujQ0NGpqalJTU69du3bixAl3d3c4CMoW3QIhYOt8mZs3bxYWFubk5Dx58oTBYJibm8Ouy6SkJF9fXwKBQKPR3rx5872PFxYWGhgYJCQkwK5RAEB8fDxeVaqqqsrZ2Xnjxo3V1dUVFRW6urrl5eUyMjLsajz3DalACLtleBUIu8pLiYLCDBoNABDX2m7xqRJub6QzFsqKAwCwrs6uitLe77CkpGTFihX79u3btGnT4sWLDx48CMvesgUMflVVVYADgXD27Nnw13DKlCms2/fv308mk+EMum4fwccIW1paZGRkpKWlz549O3v2bGNj45aWloqKigcPHsAzWVhYGNbigeCqNYQf8HaAsBs7OztYlg8AEBYW1o89CAoKSkhIREZGmpubKygoyMnJtbe3s7GFHA2EdnZ2dnZ2AIDTp0/jGxkMhoODA4PB2Lx586ZNm1paWigUCpVKJZPJbW1tVCq1ra2NTCZTqVRZWdl9+/ZZWFiMHj165syZ6enpzs7OcE4+AADDMDs7u66uLklJSWVlZSsrK56MRrHR0AmEXV1dKSkpBAIBv4ThMqKYOAb+7YqZLilyVuvfMcLrtc1NdCYAAJBIBBHR3u/QyMjo77//1tHRmTRpEplMZm/+zK/vCGFQ5JyUlJQbN24ICQl5eXl9/SqJRIJfEP+adnZ2q1at+vz5s5iYmJaWFkfbhgwck8mECwP4JBAOXEdHR2Vl5YIFC3Jzc+vq6rKystg1gAdxNBB+k5eXV1pamoaGxpkzZ2Bhih4YGhreuXPHx8fn4MGDWlpanp6e8+fPhy+pqqrivd+s44iD19AJhOnp6VQqVU9Pj3XOBTcJautidDp8TAQEUeK/M0QE/n+qCCbyk2Hvd5ienr527dqDBw+WlJR4e3uzd2IkR7tGv8ZkMmE2yL1798IMF70hKCiIT/xD+NyHDx/IZLK2traGhgav28IeIiIiDAZDUVHR3Nx8zJgxKSkpsCYou3A5EFZVVR05cgQAcPHixR9GQWjOnDndVv0OVUMnEPJ2gBAAQBAQkFqysiX4DgCUb7xKJAppjxEaNbr3O1RSUnr06NEff/xRXl6uo6MTHh7OxhXlcEicQqG0traOGDGCSCTW1tYyGAwOLUO+fv16QkKCurr6/v37ObF/hOf4aoAwJCSE9S85NTW1fxPo8OmvGhoabA/wXA6E27dvb25uXrp0qaWlJeeOMkihQMhOclv3UN/+M7atnXW1oKG4cBsGCKLiI05c6NPeVFVV4axrbW1tTqTnGDFiRGFhYXV19ejRo+Xk5Orr6+vr6+EMUvZqamo6cOAAAODs2bO9vBRFBh2+GiDstta2r0tvuYObgTA8PPzRo0eSkpKenp4cOsSgNoiXQHYDZ8pwfyk9K6KomPrNJ5Nnzf5VVYEgIAgAAATiVCWFeQb66jcfC47kr3U2XOsdPXz4cF1d3axZs6ytrTmxf4TnMAyLi4sDfHNHiKPT6bAwHpPJJJPJvG7O/+BaIKRSqXAJ09GjR9XV1TlxiMFuiNwRVlZWlpaWSklJjRs3jrctIUrJqF66ScvNpsb901VWKqCgJDpluqjxdMB/aRe6BcLMzMzq6uqJEyey9yhZWVmXL18WEBC4dOkSoe+JdZBBISsrq7a2Vl1dfdSoUbxuy/+j0+nnzp2rrq5et27d1atXR44cqaenxz8dg1wLhEeOHCkqKvrpp5+2b9/Oif0PAXz369w/+MIJPknzIzxWX3bD9sYVG/w7CBGVdQ1NTe7u7nfu3OF1u/4Hd+4It2/fTqfTd+zYgS9CQoYe2C/Kb7eDAgICf/7557Rp0+AigWnTpsGiwXyCO4EwKyvr/PnzRCLR19eXV5lG+B9fhI2B44cBwq9JSkru3r379evXd+/eXbVqVV5eXr8zXHACa0IZDgXCO3fuvHnzRklJqVuiOGSIgTNl+GSAkFVaWlpNTc306dOPHTvGZDI5MQTebxISEsLCwhQKpaOjg0OBEMMwBweHrq6uLVu2TJs2jb07H0pQIOQgNTW1jx8/6unpLVq06ObNmxkZGR0dHbxu1P9jDX79y7JWUlIC86XV1dUVFxd3e7WtrQ1ON/fw8BjUWSeQH1q2bJmdnR2/JTcgk8mnT5+mUqnp6ekxMTEJCQn29n1O9stRMJ9+Y2OjqKioqKhoR0dHn5LgUCgUWOCXTqdnZWV1dnZ2e8PVq1fj4uKUlZX/+usvNjZ76BkKd8o0Gi01NZVIJPJqKf33vHz58vbt2xs2bBATEzMyMiKTybBIG59gDYQ6OjoGBgZwCWZkZGRVVZWdnR3Mpbtt27Zv1iksKSl5+/ZtVFTUqVOnjh49qqmpaWpqyjq79dixYxUVFVOmTIEZLpChZ+vWrefPnxcWFra2tp4/f/6hQ4f4alKijIwMPh7Bn+u+5eXlq6qqGhoaVFVVT5w4ISQkRCAQsrKyTp486evrGxERUVhYKC0t/fvvv3/z4/fv3wcAJCcn5+fnT58+/datW+7u7virDQ0NcLa2p6cnuhL9Ad5VgGKbjIwMERERAwMDXjekO1gNo6ioqKurKzs7G6+uxycSEhIAAEZGRt2219XVubq60un06upqf3//d+/e9bCTXbt2vXnz5uHDh1VVVax12j5//iwsLEwkEmHuY2RIEhQUbG1thY9ramqkpaV5255BB46qvnnzptv206dP49U3d+3a1cMe4uLi7t275+TkhGEY/H8cvACdN28eW5s8NA3irtGOjo7GxkYAgIGBQXNz87179+A8af6hqKg4atQoLS0tAQGBcePG8dtI9ffGBeF9IYlE6ujoSEtL+15aKQzD/Pz8rK2tVVRUKioqKioqWAdgHB0daTTa77//bmJiwrFvgCCDG7xRS0lJwfODd+Pt7d1Dh0pGRkZCQsLy5cvpdDoAgLVYRExMzK1bt4SFhb+Z0RDphr9+mvskPDz86tWroaGhAAAhIaF//vknMzPT19eX1+0aNAgEAoFAqK6uXrJkiamp6fTp042MjERERD5+/FhYWFhSUhIdHa2lpVVeXv7NpGipqakwPemKFSskJSVjYmI2b94MX3ry5El4eLisrCwamRjybt68KSwsDABobW3ldVsGmczMzLi4OA0Njd27d7u5uU2dOtXc3HzGjBlKSkrv37+XkZEpLy8vKSkRExP7Xr/u7du3R40aFR8fv3HjxjNnzqxZswZu7+zs3Lx5M4ZhLi4uY8eO5eJ3GqwGcSBEBiInJ2fBggUYhjGZzMePHz9+/BgAICAgMGbMmJ9++mncuHH19fW//PILlUqFhZC+ZmRkZGRkBB+vX7+e9aWjR48CAP766y9e5X1FuKa+vl5ISAgA0K36K9Kz8PDwlStXtrS0aGtra2pqlpSUREVFRUVFAQAkJSVnzpxZX18/b968KVOm9JAWh3VEkDVYXrly5dOnT3p6env37uXotxgyUCAcjuLj4y0tLevr66dOnern55ebmxsbG5uSkpKYmJidnQ3nobm6uqqoqBgZGc2cOXPGjBnGxsYiIiK93H9ERMSlS5ccHBw4+SUQvvDHH39ISEgAAGpra//++29eN2dw8PPz27ZtG51OX758+Y0bN0RFRSsrK+Pi4qKiomJjYz99+hQeHh4eHg4AEBMTmzx58syZM+HNoqhor8rX2NvbUygUExMTeLOO/BiPxygH4NGjR2JiYrpfKCoq2tvb87pRg0BwcDA8naysrCgUCutLra2tb9++dXd3t7Cw6DY0KCgoaGRk5OjoGBAQUFxc3G2fc+fOXb16NXzc3NysoaHBpS+D8BSfT5YpKyvDMKympqagoKDbnzqv0Ol0R0dHAACBQHB2dmYymV+/p7q6OigoyNHR0cjIiDUZk4CAgJGRkbOzc0hICJlMZv2Ih4fHjBkzOjo64FNLS8uMjAxufJ+hYnAHwoULF+JPvby8UCD8IU9PT5h8Z8eOHXQ6vec3FxQUBAQEwBOyW8oeFRUVa2trT0/Pt2/f0mg0ExMTVVXV0NBQDMPIZLKUlBRXvg3CY9LS0m1tbfBxbW2tiooKb9vD6v379yYmJhiG7dmzJygoqKamhtctwlpaWiwsLAAAwsLCt27d6s1HamtrQ0JCnJ2du52DJBIJXpgGBQU1NjY6OzsrKCi4ubnBTxkaGiYnJ3Pyqww1KBByyocPH65cuYJh2L17986fP0+j0XjbHiaTCZe3EwgEV1fXvn68sbExNDT00KFDc+fOlZSUZA2Kp06dMjExuXLlio6ODoVCQYEQ4TkajbZ37174d+7i4uLh4VFSUoJhWHNzs4SEBP62rKwsPT097jSprKwMDuMpKCjExMT0Yw+NjY0hISG7d++eMmUK6xT0efPmOTs7Hzp0aOTIkXl5eRgKhH03iJdP8DMGgxEUFFReXp6VlVVXV2djYwMnFGRlZcHamFBMTAx3hlVoNNqqVatOnTolLCwcGBjo5ubW1z3IysouXLjw6NGjUVFRTU1N6enp3t7etra2urq6MHWToaHh3Llz0TRRhB+Eh4eLiYmlpKRkZma6ubmtW7cOrj3HMIz+pXr21085Jz4+3tjYOD093cDAICkpCS9z2CeysrKWlpZnzpxJTExsbGwMCwvbt2+fqanp3LlzAQDS0tKHDx/eunUru9s+PPA6EvdfXl7ew4cP8acpKSkRERE8bA+rc+fORUZGOjo6Pn782MXFZc+ePTk5ORiGRUVFzZw5E3/bjRs3Vq5cyenGNDQ0wBNPVlb266W7bGFiYpKSklJfX6+qqpqYmIjuCIcDOOrW3t7e2NjY3t7O6+Z8Q1BQEIZhFy9e/OuvvyoqKjAMI5PJIiIi+BsyMzN1dXU53Yz79+/DUfl58+Z1G9tjF2dn5zNnzjAYjOnTp9+/fx/dEfbVIJ41Onr06NGj/7/gu6GhIQ8b082kSZPq6+urqqrGjh377t07MTExDlV+/6GioqKFCxfm5OSoqamFhYX99NNPnDuWvLz80aNH0Yzt4eDSpUsNDQ0TJ07MyMhQUlKaPn06H1YXgfUvYSk+XGdnJ54IsL29naMNwDDs1KlT+/fvxzDM3t7+0qVLHM2qQSQSvb29ly1bhiaL9tUgDoT87OeffwYAzJgxQ1VVdefOnQAANTU1+FJlZeX58+fh46SkJI42IykpydLSsqamZsKECWFhYVyoyblhw4Zr165x+igIz23btu3Tp09v374lEolw/JvXLeotISEhuGoWAJCbm7thw4ampiZnZ+fVq1ebmZmxsY4bjUbbtGlTYGAgiUQ6d+4cnCzKaZMmTVq0aBHKJtNXgzUQPn36tKSkxN7e/sKFCxiGGRsbm5ub87pR3amqqgKWEAgRCAT8ek1QULCzs7OoqOjDhw/z58/v/UK93nj58uWyZctaW1vNzc2Dg4O/mTibXebOnSsrKwsAIBAIPj4+586d49yxEH5QXV394MEDZ2dnmCf6zz//PHXqFK8b1Vt47vu6ujoAQFBQ0JUrV65cuaKmprZ06dK1a9cOvHupvr5+yZIlb9++lZSUvHPnDpwsyjm6urp4Wu2jR48WFBRw9HwfgnjbM9s/sOZIVFTU48ePMQwLDw/vOTE0//jmGKGLiwsAQExMzMLCIigoCF8MNBDXrl2DnTDr1q3r7Owc+A57LzY2dunSpWgZ09D2+++/e3t7x8XF3b59293dfbCcgN8cI8zPz3dxcdHW1sZ/FSdOnHj69Ony8vL+HSUjI0NLSwsAoK2tnZmZyaa291ZBQcHevXv/+ecfLh93UBuUgRA6evQo/EuFXfCDwjcDob+/P+sVqIyMzNq1a0NDQ/sXwJhMpqurK9zV91bsctSOHTsAAL///juXj4sgP9Te3s5azKGqqurIkSP40+TkZEdHR/x+kUgkzpgxw9fXt7m5ufeHiIiIkJaWBgBMmzaturqana3vnZMnTwIAfv31V+4fevAalIGwq6vLxcXl6dOnzc3NHz9+DA4O5nWLeuvt27esf6D379+HuXExDCsuLvb09GSt5ycjI2NraxsSEtL7iEij0WxsbAAAAgICvr6+7P8CvfD582cikSgqKlpfX8+TBiDIQNDp9MjISFtbW3FxcXgmioiIwN6aH64GvnjxIpwWt2bNGl7NpK2rqxMWFiaRSF9ngEK+Z1AGwvb29sjIyMjIyNLS0qKioh9mSBlcioqKukVEWVnZb0bEkpISPMFVZ2dnaWnpypUrAQCSkpK8XUmycOFCAMDJkyd52AYEGSAymRwQEGBhYYFP+YZnYmRkJOxoMTEx2bhxI3xzeXn5+PHj/f39e8idxjXwd+Dw4cM8bMPgMigD4TBRWFjo7u6ur6+PR0Q5OTkYEWGNXy0trfnz58M35+Xl6erqJicn6+rqpqSk8LTh2IsXLwAAampq3ByeLCkpOXXqFIPB8PPzO3HiBM9T+SBDRmlpqbu7u4GBAX4m6ujoBAcH6+npjRw5Eo7GlZaWqqurYxiWnp7O6/Zi0dHRAAAVFRUuzw8YvFAgHAQ+fvzo4uLCWhRQWVn54cOHWlpaEyZMgD3DMBBiGAZjJG8xmcxx48YBAOCKZu44ePDggQMHOjo6ampq7t+/HxcXx7VDI8NEZmamq6srnFYTFhamp6cXEBCgp6fX0dGBB0I+AcM2a8oRTiOTyTDz/uXLl2/fvs2147IFSrE2CEyYMOHYsWO5ubnwPBw7dmx1dTVcm3HmzJldu3Y1Nzfjb+boit1eIhAIcMoM29czTZ06NSYmBn+qpqZWWFgIALh9+/aECRPa2tqYTKa8vHxycvLkyZPZe2gEGT9+vJubW35+/qtXr3755RcAAKxQxodLR+zt7QEAly9fZu9uU1JSWCswv3//vrOzEz729PQcMWIEAGDDhg2fP39m73E5DQXCwQSehzk5OSkpKTDDp76+vpWVVT9yh3La2rVr5eTkYmNjk5OTuXC4iRMnysvLNzU11dbWrlmzxsjIqKWlhQvHRYYhIpH4n//8B7/iPHPmjLe3d0lJCet7Ojs7mUwmAIBOp3MnnWk3dnZ24uLir169ysvLY+NuN27cCOuVQosXL4ZrMd++fQsAoFAotbW1/HAt3lcoEA5KhoaGeKGyY8eOBQcHZ2Rk4K8ymcynT5/Gx8cDAOCsIu63UExMDJatv3jxIhcOZ2BgYG5ufuHCBWVl5RMnTkyZMgWf8ocgHDVixIiDBw/u27cP35KXl/f333+vX7++vr7+9OnTmzZt4v5lmbS09KpVqzAM8/X15cLhRo8ejScNePnyZXZ2dn19PReOyy4oEA56UlJS7u7uBw8exLeUlpbq6urevHmzpqZGQkb9K5sAABfTSURBVELi/fv3xcXF3G/Yjh07SCTSvXv3ampq2LjbzZs3T/0CXo3iZGVlhYWFR40aNWrUKFg2HUG4YOvWrV1dXfjTMWPG7N69W1FRUU5Obv/+/WPGjKHRaNxvFUyyev36dSqVysbd0mi09i/wjcrKykZGRp6enjDx7OXLl/FMN4MCCoRDwerVq+GQIaSlpTVu3DgSiQT/KFtbWxUVFbnfKk1NTUtLSxqN5ufnN8BdYRj24sWLjo4OAMDp06fDv1BQUGBHSxGkP5YvXw4zmRGJRB8fnzVr1uAvPXz4cO7cuUQi8Z9//tHU1OTJCThp0qQpU6aQyeSHDx+ycbcbN27Er0SbmppYX4JFNqSkpGRlZQdXBykKhIOYs7MzzGEBAPD19cXLPlAolBMnTuzbt49Op585c2b9+vW86ieEiYZ9fHzwEfW+amtr8/PzMzAwWLBgwd27dwEAkpKScl/g/cMIwn1HjhxRVFTs6Ojw8PC4fv26u7s73P7u3bvo6OiysrIPHz7cu3ePQqFUV1fzpIVbtmwBAPj4+AxwPzExMTY2NpWVlQCAwMDAj1/ADMNDAa+nrSIDxWAwfv/9dxMTE3zNUHZ2tq+vr6+vL/4AFmPjiYkTJwIAAgMD+/rB/Px8JycnPNJramoGBgaamJhER0fj71FVVS0oKGBrexGkbxgMBuyH58NUSlQqVU5ODgDQv7XFzc3Nvr6+eO22o0ePTpw4MT4+Hn+DgoJCvzOy8hUUCIcCWJeRH1byfu3KlSsAACMjo95/5O3bt9bW1ng6DyMjo4CAALg+8ty5c6yRz9XVlQ9/fZDhZurUqQAADlW9HiBYBs7BwaFPn0pJSbG3t8cH2pWVlZ2dnYuKilAgRPjXqlWrAABXr17ldUO+oaOjA2Yxfv/+/Q/fGRAQgNd3FRYWtra2Zj3rEIQ//f777wAALy8vXjfkGz59+kQgECQkJHqTOryjoyMoKIi1pB28DMV7m/7555/Gxkb8/WFhYbxKqcpeKBAOBWfOnAEAbNmyhbfNqKmpcXZ2Tk9Pz87OPnr06KVLl+D2/fv3AwDgZO5vqqysdHV1lZeXxy8/XV1da2trudVwBBkQmDhi06ZNvG1GYWHh7t27KRTK+/fvT58+/eDBA7gd1gnHz8dvysvLc3Z2xmefSUtL29vbD59iaigQDgWvX78GAJiYmPC6IdirV6/evHnz119/1dfX79q1C9ZWrKioEBQUFBQULCsr6/b+5ORkW1tbfIJZt8tPBBkUYG5Pnp+AVCr17NmzZDL52LFjSUlJBw4cgNvv378PANDX1//6I3Q6PSQkxNzcHJ93ZmRk5Ovr29bWxt228xiaNToUGBoaEonEjx8/si5m4qFNmzb5+/sXFBTAp6qqqr/99ltXVxe+trezs/PBgwfTp083Nja+desWkUi0traOi4tLTk62s7MTFBTkXdsRpM9++uknAoGQkZHBYDB42Ay4egEAoKSklJycTCT++/P+22+/qaqqZmdnx8XF4W+urKz08PDQ1tZevHhxVFSUsLCwra1tampqcnKyvb39cMtHgQLhUCAlJaWrq9vR0ZGVlcXDZnR0dISGhoaGhlIoFCEhIVNTU2FhYfgSXEfh6+tbUlICT7/ly5fHx8crKSk5OzsXFBQEBQWZmprysPEI0m8yMjIjR45sb2/HL/54oqam5sOHD9HR0eXl5YqKingKYkFBQZjmycfHh8lkRkVFLV++XFNTc9++fWVlZWPHjnV3d6+oqLh58+bwzdDL61tShD1Wr14NALhy5QqvG/Kvr+uxwXoU3eaCwr5TBBnsYIIxfFiOJ6hUakFBQXl5OZPJLCkpYR1iKCkpIZFIJBJJQ0MDnoDCwsKrV6+OiYnhYYP5B7ojHCKMjIwAACkpKbxuyL/wIQfYCzpz5sxPnz5JSkoyGAwLC4vIyEjYC4rfMiLIoAYX2338+JGHbRAVFR01apSamhqBQNDQ0MCHGFJSUo4fPw4AEBcXh+WiXF1dS0tLb9++PWvWLB42mH8Mpiw4SA/4LRACAGpqavz8/Hx8fKqqqgAA8vLyMEfa3r17zczMeN06BGEnfgiE3TQ3N9+6dcvHxwfWiyASiS0tLcrKykVFRYMr/xkXoDvCIQLOl/nw4UO/k5mxUVpamoODg7a29uHDh6uqqsaOHevp6VlaWrpnzx7AgSKFCMJzfBUIU1NTHRwc1NXVd+zYkZ2dDZfDf/78WVtbu7q6+tWrV7xuIP/hdd8swjZjx44FAKSmpvKqAQwGA07Fhn9aRCLR3Nw8JCQEHy+sqakRFhYmkUiFhYW8aiSCcAKdThcVFSUQCL1Zt84hXy+HnzFjRlBQED5YeOLECQCAlZUVr1rIt1AgHDpg8ns/Pz/uH5pMJnt6empqasLTT0pKyt7ePicn5+t32tjYAAD+/PNP7jcSQTgKDk/ExsZy/9BwOTyekgIuh8/MzOz2ttraWnglWlxczP1G8jPUNTp08GSYMDc3d+fOnaqqqk5OTiUlJaNHj/b09KyoqPD19YV3qN04OTkBAK5cuUKhULjZTgThNNg7+uHDB64dkcFgPHv27Jdffhk7dqyHh0dDQwNcDl9ZWenr6zt+/Phu71dUVLSysmIwGP7+/lxr5ODA60iMsA1Mb2FsbMyFYzEYjMjISAsLCzg7lEAgdOsF7cH06dMBAJcvX+ZCOxGEa86fPw8A2Lx5M1v2xmAwWBcX0el0Go2GP62oqHB3dx85ciT8GRcREYHL4X+42zdv3gAAVFRUUP4mVigQDh2tra1EIlFISIhdi/OWL1/OWtFi9uzZVVVVsDKLnp4ePAMlJCTs7e2zsrJ6v1tYVlBfX783URNBBgs4CcXU1JQte4uNjWW9qL137x68mYuMjLS2tsanferp6bm7uzc0NPR+z/BOMTg4mC3tHBpQ1+jQISEhMWbMmM7OTnbll8nPz6dSqfjTT58+JSUlqampOTg45OTk6OjonD9/HvaC6uvr9363y5YtU1dXz87ORrPXkKFk8uTJ+/btw+tjc8LatWt/+eWXBw8ekEikNWvWvH379tOnT87OzrDoYC/Z29sDAC5fvsyxZg4+KBAOKdevX8/NzZ00aRKH9j9+/HgJCQk4FS03N9fJyUlKSqqvOxEQENi8eTMA4MKFCxxoI4LwQFFR0eHDh0+ePGllZQUASE5OPnLkCNuPYmVlpaOj4+7uXlZWFhgYOHPmzH7sZO3ateLi4lFRUXl5eWxv4SCFllUOHdbW1mvXroUjcAAAU1PTixcvGhoaDmSfJ06cgNUEAQAtLS2CgoLZ2dmysrIDbKqDg8Px48dDQ0Pz8vLGjBkzwL0hCM81Nzez9nBUV1ezZrjun6ysLLw8Z3Nzs5GR0W+//fbbb7/h2bT7R1paeuXKldeuXfPz84MV3BB0Rzh0UKlU1uoTFApl4LnwFy5cuPYLmNt+4FEQAKCgoADLE3p7ew98bwgyJOnr6yd9AZcAEonEAUZBaNu2bQCAGzduwGRPCAqECAAAFBYWxsbGMpnM58+ft7e349snTZpk9gV7qyPt2rWLQCBcv369paWFjbtFEF5paGg4+cWDBw96/8GcnJxdu3YBALy8vI4cOVJcXAy3EwgEkS/Ye/ZNnjzZ2Ni4oaGhT+0cwlAgHFLc3NzMvygsLOzlp5hM5rVr1yIjI4uKimJjY5uamjjaSMjAwMDMzKy1tTUgIIALh0MQTiORSCO+6H3HCZPJvHnzpqSkZE1NTUtLi5OT07Vr1zjaTgiO0/v4+HDhWPwPBcIhZdu2bde+wNcY/ZCfn5+trS2BQNDR0dHW1sa3T506VUZGBn9qZmYmIiLCxtbCIoVeXl5MJpONu0UQnpCRkdnwBWues575+fmpqKgUFxdXVlaKi4uLiIjQaDQAwMiRI2ERQUhfX3/ZsmVsbO3q1atlZWXfv3+flpbGxt0OUigQDimKioqaX/S+L6Wjo+Pp06exsbHt7e1UKrW1tRVu9/b2hkUEoQcPHigqKrKxtb/++qu2tnZ+fn54eDgbd4sgg8jSpUsXLVokKys7ZsyY4uLi48eP//rrrwAADQ2NrVu34m+bMGECzKHILqKiora2tgAAPz8/Nu52kEKBEAFOTk7Ozs4eHh7Nzc3i4uJcyxFFIpHgqY7qUSCDnZCQkJqaGv5UTExMWVm5Nx9UVFQcNWrUhQsXxMXFL1y4cOjQoRkzZnCsmf9jy5YtBAIhMDAQjdMTMAzjdRsQ9rhw4YKZmdnkyZPhUzc3t3Xr1mlpafG0UT9AJpPV1dUpFEpGRoaBgQGvm4Mgw8ucOXOio6O9vb23bNnC67bwEsnNzY3XbUDYY9q0aSoqKvjTOXPmsI7w8ScREZGysrLk5GQAwKJFi3jdHAQZXkRFRR8+fFhWVjbMAyG6I0R4LDs728DAQFJSsrq6Gi5VRJDBCybLFhcXBwC0tbWJi4vDxPT8qbOzU0NDo6amJi4uztTUlNfN4Rk0RjgE5eXl5ebmksnkBw8ewGTz/ExfX9/b2zs9PR1FQWQI8PT0dHV1BQCUlZXNmTOnvr6e1y3qiZCQ0MaNG2fNmsXrhvAYCoRDDY1G8/f3DwkJSUtLk5aWNjY25nWLfiwuLi4nJwd/am5uzs2ibgjCLh8/flRRUZGQkAAA+Pr6wmmZfM7a2trV1RW/HXz37l1CQgJvm8R9KBAONZ6enhs2bAAAyMrK5ubmwhppfK6xsZE11VNdXV1nZycP24Mg/ePj49Pa2pqSkvLmzZvm5uaEhAT+v6SLi4uDldGgZ8+eDcPlTCjp9lDT2Nh47969jx8/rlixwt7efs+ePbxuEYIMFzBRi6io6Jw5c+bMmRMSEjJlyhReNwr5MRQIhxoPD4+urq60tDRBQcGrV69u3LiR1y3qFXd39xs3bsDHeK5FBBmM7Ozs4IPFixfztiW9VFdXFx8fDx9XVlayppcaJlAgHIIEBQVNTEzAlxzzg4KNjc3cuXPh4yVLlvC2MQgyrGRnZ1+6dAk+TktLQ4EQQXhDXV0dL3MvLCzM28YgyLAye/bsq1evwsf79+/nbWN4Ak2WQRAEQYY1FAgR3ps9e7a6ujr+dP78+fLy8jxsD4IgwwrKLIMgCDJ8tba20mg0BQUF+LSpqYlAIPB/dkb2QoEQQRAEGdbQZBmEvzCZzB07dmzatKmsrCw/P19RUXFQpOdAkKEhPz9fXFxcTk4uJiZm4sSJSkpKvG4RN6AxQoS/+Pv76+vrt7a2WlhYTJo0ic9TNSLIUNLa2urh4fHy5cv4+HglJaUjR47wukVcggIhwkdqamqeP39eW1ubl5dHJBKFhYVbW1t53SgEGS48PT03b94MAJg9e7aOjo6UlBSvW8QlqGsU4SPy8vLXr19///69qKhocHBwSUmJtLQ0rxuFIMNCR0dHV1dXREREQ0NDXV2dj4/P8FlTiCbLIAiCIP8ik8nl5eVlZWXFxcWSkpI2Nja8bhE3oECIIAiCDGtojBBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIEQQBEGGNRQIEQRBkGENBUIEQRBkWEOBEEEQBBnWUCBEEARBhjUUCBEEQZBhDQVCBEEQZFhDgRBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIEQQBEGGNRQIEQRBkGENBUIEQRBkWEOBEEEQBBnWUCBEEARBhjUUCBEEQZBhDQVCBEEQZFhDgRBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIOQLhYWFVVVVvG4FggxThYWFlZWVvG4FwjMkNzc3XrcBAaamptnZ2b/99ltubm5UVNT48eMJBMJAdtja2vr06VNBQUF5eXnW7VQq9cmTJwQCQVFRcWBNRpChY+bMmVlZWUuWLPn8+XNkZKSenh6ROKCbhJiYmKdPn2ZmZgoKCo4YMYL1pcTExKdPn6alpQEAVFVVB9RuhF0whA/o6enZ2tpiGHb69GkAQGdnZ793VVVV5erqKicnBwDYsWMHvr26utrV1RXGxQ0bNrCh0QgyVOjr69vY2GAY9vfffwMAWlpa+r2ruro6MzMzQUHBcePGKSkpEQiE7du3w5eam5vnz58PANDR0YEhcP369QwGgz3fARkA1DXKX3bu3Nna2iooKNjvPfz888/v3r07duyYpKQk6/b58+fHxsa6urqie0EE+R4HB4fW1tZu506fuLi4NDU1FRYWZmdnV1VV7dmz5+LFi9HR0QCAEydOJCYmvnv3Lj8/v7y8fP/+/f7+/sHBwexrPtJPArxuAPI/amtrCwsLZ86ciXeNlpWVJSYmdnR0aGlpYRimra2tpqbWwx4yMzNJJBIA4NChQ6zbk5KSBAQEAADwphNBkK/V19fn5+ebmprCkwgAUFVVFR8fT6FQNDQ0BAQE1NTUNDU1e9iDp6dnQ0MDPEmJROKqVatOnz6dnZ09e/bso0ePrlu3Tk9PDwBAIBD279/v7u4eHx9vbW3Nha+G9ADdEfKXu3fvmpmZ0el0AACTydy5c6eWlpa9vf2xY8f+85//zJo16/nz5z3vAT+Bu4FREEGQHgQHB5uZmVGpVAAAhmEHDhzQ0NDYsGHD8ePH582bN2PGjPv37/e8BxEREdZL1RcvXhAIBBMTEwCAkJAQjIKQkJAQkUiEx0J4CwVC/nX27FkvL69z5841NDTk5OQUFhbyukUIMoxcuXLl5MmThw8fbmxs/PTpU319fe8/m5aW5urqamFh4eHh4ePjY2Rk9PV7oqOjGQyGqakp+5qM9BMKhPzL29t7zpw5O3fuhE8HOI8UQZA+8fb2njx5souLCzz1+nQC1tXVffjwoaysDD7GMKzbGzo7Ow8cOKCvr79q1So2thnpH9RdxqeoVGpxcfGKFSt43RAEGaays7M3btzYvwvQefPmzZs3DwAQEBCwbt06DQ0NOzs7/FUGg7Fx48acnJxXr16hMQt+gO4I+ZSQkJCAgMD3BvwQBOE0UVHRgZ+Atra2YmJikZGR+Ja2traVK1cGBweHhIRMnTp1gPtH2AJdjPApAQEBTU3NpKQkfMvnz5952B4EGW50dXX7cQIWFxd3dXWNHj0aPiWTyTQaTVRUFD7Ny8tbunRpU1PT69evURTkH+iOkH+tX78+Kirq1KlTJSUlr1692rhxY28+VV5eHhUVFRUV1dXVVVZWFhUVlZiYCACoqqqC2zs6OiorK6Oiot6/f8/hb4Agg9j69esTExNdXV2Li4tjYmJsbGx686k///xz2rRp/v7+2dnZr1+/XrJkCQBg7dq1AIDHjx9PmTKlqqrKw8OjtbUVno/oNOQLPF7Qj2AYhmEGBgbr1q3DMOzs2bMkEqmrqwvDsK6urr1798IcMYqKiocPHwYAXL58ueddeXt7d/snNjY2xjAsICCg2/Zx48Zx4ashCP/76aef1q5di2HYpUuXSCRSa2srhmEMBsPV1VVBQQEAICcnd+TIEQCAh4dHz7tqbm7esmWLhIQEPMvGjBnz5MkT+JKxsfHXv8Djx4/n8JdDfoyAfTWdCeE3TU1NsrKypaWlmpqa169fX79+fU5OTnNz89fv1NLS6pbYEEGQAYInYHNzs4yMzPnz552cnAoKCr65mkJNTU1dXR0A0NnZWVtbKywsjBI5DQpojHAQkJWVBQDk5uYCAEaNGgUA2LFjR1RU1NfvvHDhgqOjI5ebhyBDG+sJqKOjAwA4fPjwnTt3vn7nwYMH//rrLwCAkJAQjIjIoIDuCPmXl5dXcnKylZWVmppaRUWFi4tLe3v7p0+fREREGhsbaTTa1x+RlpYWExPjflMRZOjx9/ePiIhYunTpyJEja2tr3dzcKisr8/LypKSkyGRye3v71x+RlJTEO0WRQQTdEfIvIyOjpKQkZ2fn8vJyRUVFExMTDw8PERERAAAcOEQQhHMmTJjw9u1bFxeX0tJSeXl5IyOjwMBAKSkpAICMjIyMjAyvG4iwDbojRBAEQYY1tHwCQRAEGdZQIEQQBEGGNRQIEQRBkGENBUIEQRBkWEOBEEEQBBnWUCBEEARBhjUUCBEEQZBhDQVCBEEQZFhDgRBBEAQZ1lAgRBAEQYY1FAgRBEGQYQ0FQgRBEGRYQ4EQQRAEGdZQIEQQBEGGtf8DSuWIL27cFPUAAALfelRYdHJka2l0UEtMIHJka2l0IDIwMjYuMDMuMQAAeJzVkltIVFEUhtc5c7+euTlO6djxVqOZpvlQljObKLKbjWZJmjGh6WBGomXaDSuJwqBI0KA0oguRljEkZOnMNstCNAshkm4voVCh4kNhSHbOEgui1x7asPi//bPWv/fmnDH/7fcgLINQEphZCUIlCVXNyIlD9KX/TOFP5QUVJEoQ5vf2L6KYGZEoIFbslfwylDPKziqDyjD/i2qA8bLAehiJh5V6WYnSy0qVHpncy8pUHrnGo1B5WYXao1Txao2XVXOgsfFaDrQWXhvsYHUWXhckiJXn9FJGbwAuCAxGMJjAYAOzBWAOMHOBDQFJKEjtIAsD+TxQ8KAPB30EGCPBGAXGaDDNB9MCMDnAHAPmWDAvhKA4sC4CazyslArXlINwKQkjl6kUao1cpuWCdBa53mDTqLlgJys8A2b/pYRnz6m9ZLhT3LjXPKbaiRaXyP2VzbR1/7mAyNyBevpl+TD6toxDtDE7Hv0lOZ8Dr4riiMjtBS/8Pdq1yA7+VOCsK9cpcs6mQvpjWxrOvr6jph8qGpHLTnQ4r+XHIPvSklxumRJne5vsJLyqE/Ons0ud7ZtDqcj3BpaRnD5/qsgFOzpcJfva/CKXv0wndaufYk7WhmPEV5WMOUNtdSTvwUH0ax+ZyJGHNZjTnz/kqtySiDzR1pXaM1WGZ+kiCgKZ3bno801ukrGLot+apCTkVhRmll6Yoqt2cuj76kfo4epg9Fsm26hzJBpnF3+tpXm9bvSzfMm0I2UI+9+tX0E37t2Dvi33SmfD9jrk65G+wNvxy9izLuUMuTEUju+6qs0k3++HoF99voV8G1Njv78rkYwQL/JowkWSfvM4sr28mZwOMyFXPDlJiuyjmDPJUhIz2I/fgq8pdg10N+A9dR+LydbCfOSEwaXE1XcX2famOcBrZXhu5VFb4NP4JfR3T5tpkSUV2foTsCnWZVfjQbsAAAQaelRYdE1PTCByZGtpdCAyMDI2LjAzLjEAAHicfVbLbhxHDLzrK/oHNOCzu3nIwZIcKwgsAYmSa46BAR/9/0ixZzUzgomsdNjhVLOLxdd+//bvP8x3LT9/PP3+7Uc7PvJ0Bzv9z39EtL+ViO6+tvzSHj5/+e2lPb59eni3PL7+9fL2Z7PRnNr6+4j99Pb69d3C7bHd9806h452TxuzOHOjjdbnPCs7klUHeeNNAcORn4GaQMf7adHbPW8s06NyaYm0bZCQKlyOmIOsAHoCdQvpUzxZ9skkFbInkrepswc4bz0U3wvgSCBtRoN4Nt3Y2BDXz8C5e6SuNjMIAZKpAMZO0gPRKt6PKV20AOI0kLJx7yGCuEWJo3LJmR7aFOGu971zH5XmLAvYoxs8wrW4lvpwZkeQb5k8Mi6bJB4V0pZLURugAbpm0svA2ZfPQBJJ8n0o9zLwzA6uHHjNqb66WZlwzvQoLmdXwxF3SFCVEGd6bCPkrg+QgFAhVb45FrCzh+X7GGHUqzqnFY0gCCiI8mQbc1aiC7cXcOsxUeup0Bg0TSqkAJnCmM9Vv2HB46ple3z+8gsfeAUesBnM5llVhraLkq6111TKoztnQp2UoiThQGZpTCQIX3zILNtSentu9wO3a6eZPLwTjUpVGTtUdCRRQakO4VKsmUhH5wKx1HIUv1aVJ5FQ23QoijODQx95xRT/z+lqiuq0jL7H4LKNUXALGiacj5g6EVzFpJJI2MfEX9YMOXmlKIbWc16qSCYaC9/Ewstxgyp+hiudwrwqgH2Sl+PTgUTMU0hWkyAHUuZJM08YtCj4mQlFRc2yRjSzlOU5zDN0NH/00uNcLHNwE2dbTSUr1Yz9ah+ZIXTLVFRqNeJpeUTUHY2eA7c79UohywT1NOsaHoatQJXqlvlBC8HjiLU/MJi8mjemS8qOEd9jzWZHU5ebyJZC6POJQYsydWRSy9v9lnNcKisiZ9RcJbv1vZA8ML0omQZh3lUlb2Ovzpw53VeduumHCfEO/fzy9GGD7zv94fXl6dzpGJlNzsWdj3quZ0vTuYMNJj83bRriXKcG0/GIboBpnKsxH/myAQ0QPm7mdD8vAObdcAw67C3GsQsi6QF2cZLs09Iv+yYBGGiXxcJ59IIZu0XOqybCTssROlYCp/sTI6lWWk7x+IY59MuNvS7E1vv148zmRe2QR2xHXrwt5qA2L7N2txway9gtZ3QYl7yonXct5jh8GYR4xNnLuEPOcfQy1VJ8Oe7JX1zIrV5HFCOdetyiyXbg5GXgMNKnZy2NPWY9NNV5sxzsdXHFqYOs0c1ysDW+WQ66tlQOnL4087LYtWl3y8HYfFfKDsrWb5aDs42b5WyBpS964uBsyRkJtYOz081ycM4+vHZdPr//Csf3u/8ArJoy6bRA4T8AAAUNelRYdFNNSUxFUyByZGtpdCAyMDI2LjAzLjEAAHicdVZNaxxHEP0rgUCQyO5Q313dwhDQxSfZl5yMDkKYJJDgYAS5+MfnVUveGdYzq2U1U7zXVd31uqo+vX985uebT+8fb7//np/l7VGfbz7c39zPl/kzH67et6/3vz2q3d+8+3D78CPs4fL87tPDr4+28SmbZ8bvT99uzm3JrsGnMy0eRC1Pd+dYLLhb2ZjFmaeNVRufeFEYDJa2CAx+kkWjicBiSyMhBab1bNOgTfOki4Sow6CL9/R+oqVl2WCShSO6gCRK3GHhhUItARK26RxOO8M5HsS6wzvBaXItFMHRWlmihyEOLCiuiKcekjoM3iSxMuJQ9orPHezTnS1k5A0IrNURjcORZNYaDUws0Vozxw4CK0Qx0vAfxFSyeg/27oijt24UcwlvtYKk8vQhzFFnaeEUON9YiEgDHMNx0Yy0kyoM1JWjYsfJpbDMFHhWjBV9Ck1TUErH2rxYA75M6mYVkMANcHVUliQxed7Dy+KktSvGUSUCONf2KCdL4BksW9hanRU2xEGtl6mZgI+FohG3U2XRCw1aZqHPvjgSo0UTRUxcaxtolT2CxCAZL6Kp5jTh0HAWWLyTGU8dYEmbQSHDOjNK3Tx97q8bFPAqBSw7T8G5Y/dzNSNEVkKDYGxaoGT8IVFBTj6JqRmlhuhaey5HJqxgIdddpjy7RL4ecTLJKyqlgj7XqbWKtDKc8zyhL+/TEhlNK3bvAtrt6enlyz8fv375d9Dy8enry19Pf9//+fT1j88L/fKzxR1reMB5S8Nx4QtcUR6+vHxGvgZfsc5vNIiBs3Fz3Llm1AG80GjIAUucxF0iILjALQPwwuKhBxF27rhHuNBdGp6AWyOMYUcR4s6F9NJFiqcBeKHp8ANfad46VN0cvgKw1VWO2HdFTCgcOI4gaxBDFyAvvBztgObWgqUFyp1QInNAXmh95AEtGPewEyNphsspBuSF1kbf3xlw1ovLitsfCdx6HDL4Wh5v3kRQeAjyRmHA7fcCrimjwdcCeXOXAZGnI+GukKIXcOXx4AOJIM+a0AmKXJUkKeCaAht8IBJsC5tqlgw998wCrv5i8KFMcEEp8S2xaPZCrsQ2eF8p5FC/4j4qsiEqLQu5nig2fK2WY+JGLYpQr/VyTNzoRRHqgWJ29rhRDGOdfc3sedyqJofsF5Udomxko33Ivmz2iBvdGCrLUWnBBzXTUZ6toXxnIdc92pB94VCioqC9WAaDleGFXEPVIdfKOSZuKww8Hign0KLUAnUa/Ux8AtdIscy+cKL3qkyKbtaNQyZw5cmQgzpj3vFpBhVYVnso5Hqn8HqgG6tzRHdIboqqxgVcecjFkWzQE9i5Cn3HaKYFXAPtQw9UkziXEKtRAGkMncg1+WgQB6rZIW5VI0OvVXNM3KjG0Gr2VWMgNvSUqBqOotoKuG4RvINyg4qPmRKpRKFSFOJeyPVMEem+aBTSDoM/QurRY6KAa6A+dF80O7yNaAyvR6JBqXDU4qgZFjOoFHINFLm4Fs2bQw31qImIMBCiZRRwdWhDr0Xz/QIrBlv0z2zVEdE0Cnkhos0fzC+BQQTzbat5ScEs4HoLedjBBINm1sBtGElrnEXbL+iF6cP2NcMY1Qhtl6qgop1mAVeHNOxaMscDyUYzeNuvMz+MJLYVTB92LZjDWcu2w8ywfcH8OG3ZRjDSTv/9XvVK7Nv/8KCpZmq2yc0AAALWelRYdHJka2l0UEtMMSByZGtpdCAyMDI2LjAzLjEAAHiczZJbSBRRGMe/mZ2d2Ys7e3EvbplttuV617RSQvekD1lIhlJeQlhKcGvBCEIrsUwpLNEgczM0wocicqVt6X7ZGUErpTIwukoXDOyh7KFXF5v5ZAmixx468PH/nT/f+Z9vDvMjPPwBpGWQSgGLK0OqbKlaKZa4ZJ/55wp/qkNSSZySUL+3fxFuMULBQVRT5DMKJmqoFpWOKoVKUf+7aoHy0sB4KNZDK7w0zXkUag+j8dJKTiqVh4310pzGoVJ7aRUP6jiHJtaj5UFrdmhtLjrG7IixSGJ18DqG0umBt4DeAHoj6OPAZAawA7UE6KWgiAdmGSgTgF0OOgfoVoAhEQwrweAE4yowrgZjEphcYEoGUwpYUsGaBtZ0KGKkGVlgNLEsxdKcUqVWKLW8JcbM6vRxahVvW0NL3wDRHyiQPixWRfYI8ib0ZlK8NDZfIHPjja/C5eBaIvPeppHwzMltyJqsI+KjrEhY5ulQp2DYHSyU2dQwKr7fPOGWuTlTK07MBJEj3aHCFn8xcutArtvj5DFnvKZevLirEX0mYCfVp2/iDI6ahMKeMxZR5g1T60l/rwXz35XddicsdOK9zVe2ktysZ3hWhDZi9q7DzCdFPSTl6lH0dx7WkluDbZjjq/KLO+YX0B8ree6eqk9DX2fJKdhXWYn39ne0CEP+CvS7BktJ16F76DeUq8mFiAvzS/vuiLawDXuq5yLisb5zOE9JMF9Mth7EHt+rwIPyL37k+bd5Yp5tFHNqa+8KD+u6ke32WXH/qSTs6TB2ks85asxpyCgjm9QG7Pk2HSDSO2LPiexcYh/xIRcNDxAuvh25e3KIzHlNyC99x0kq/wlzKrIF8np2HN+Na97iDj09izP/3F5HDnz3IJe/yCTt+mvIHzdeF4SmUTz7uDhf6OXOo++8bxeLE3OQrb8A3xPOYXKfImgAAAP9elRYdE1PTDEgcmRraXQgMjAyNi4wMy4xAAB4nH1Wy24cRwy86yvmBzzgq9nNQw6W5FhB4BWQyLnmGBjIMf+PFHtWPSOEyEoCZrjF7mLxpb9//PWnysOWn9+ef/3xz7Y+8vwAO/3Pb0RsfygRPXzb8mF7/PL1l9v29Pb58d3y9Pr99vb7Zr5ZwAc/H7Gf316/vVt4e9o+td2Yh7TtE+3BRiIb7TQ/p68k0neLJpTfs6iJFUBNIO+DB1HfZB86Ag//BVoCadfeh9umu7Rh7AWwJVD36OxD0sXCGmuB9ONuCtbIk9SsUXVkP6JhHaa+8d4GaY8COBIoO3frXQHUwVCgAAaAeeEAEg/djbgVOKYJ7NQyBNlFKDpVQD7CdraYsoSLWXU1Z25kx4WMjCB+Y4RVSc46L+dmbpxxqXbqXCFtnjm6iOdJbE6lQJzZwZUq83LapatpeWRmB2mWIE0pnZS0qiDO7NhOLi04STRiKgMfE+jEMb+PGB6lljGj4R7gtn3C2UPEKqTQdkuAx5BIlmi4MUaFZCARBXNDrUFLDx2l6uinW5qREzhBItQ59+uZ29PL15944TU5ZDik7lkDSl6nCZK/QlOOrk4z9c1HKSra+zUlAKA5HlCfViZUfHvJmdB9pPxZJB0lWgbWE9r30KY2x4eE06jKXkZCZQ/KngPl0R1eFTISCbswxUB6Ozctg8fvPBP5UWoZfVN0ZxUUCvJlymODXfIpe8VKqBxBifBAF0Ap5EqqmFDFL+CH0DHhkjKrxqiEQkZesvRRABSJbC5e9rI2IFv2cvjY1uApgJkmxM6toV4wJgZqpVSpT5YjnBtlW2GO1nKOebWihUZWiCBFrZzwMU9kD8NAyXmsHWmvRjzNIyODyCs1TK3cBZkfTMWggE/2n0UtpcmUsnnvNnujMfJYKWQ6FcLdlOmBqJ3cSqQd1YHKHDIjyk6WanFYO6qzS+qUTFEc3KvyMD/KEwsVrZZPPZCF6tQvt+cPa/lY1I+vt+dzURv+5NzG+drOnYtC3fq5WS1N5/7M13EuyfyeL7vQAOE1ftBzwCx4zNe47C+Di1z3FMPn9OckF5cLUCSchkWXkzznTLpsEcuTLsd6YuTq1Q+LnDehQ6ZlhYpBz3nfiZEUKy2ndnzH6GVAc1LIpffzx0HMk6xf5u1EXk5raQHZpb743bIklH5YzngxDjnJXnhO5nC+TDe8wvcywxh5lHWsyiwLWbnJtYpU6nXuMJKn6xZNth2elzEyQ9Qlhva7ZYmqkyy8znqLu2WxNbpbziKcMg94X1r3sCzCpofFrq04hbFF2drdsjib3y2LsyVnZNEWZ0vOyJ8tzhZ3y+KcXXftsXx//0cazw//AjaqKELdsbL6AAAFK3pUWHRTTUlMRVMxIHJka2l0IDIwMjYuMDMuMQAAeJx1V01rHEcQ/SuBQJDI7ND10dVVLQwBXXySfcnJ6CAWkwSSKAhBLv7xedWydoZNz0Xe7X2v6+tVdfnLx8cz/X2+OZ35fPPl4+PtWc43n+5v7seX8Wd8uPq+/3r/y6Po/c2HT7cP/4c9XD5/+PLw86MOE2+GePf57S/h7w/fbk5tZSanhVej5rzcnWwlcdWF1upFHCe6WjgHTiLCAycCtAYvZQ1j1aTxSk1bA0icouKE1hKkwIhqLTZOVJ1swQciVoDyRwcPqGZaaBy1UisJXGIu0ZY7XK3VLH3UovBI8EsUgS0rgn/vdC3GNYDwWmi5qyvuAvXdKVzhVCsvcNwFV4HhYTAHZjRqeWCgZEThFnmFWJS8kr1ZTQBZKC2nsqo0K56YGNcjilDRNzuNOU9IrbRImmhF0kAjkfA23JfG46ZqbJk+ZEaYOI+4iQrlVYghj/AbZUHAQ0wy0od8uCTNKPOJzASKNlLVQnRBzYoz60gnwhgnpqhfXlOttbqgipVUI1kIA7GekLdWbLCoqmW0vIq0Aq9PmZqSyQNRin0/a4w85e1QD/kQghYuNZkNaml5WdEMeITnShDaEAM89nEmjp9tXCetORxAiqoPXYHMVALRwgRVaYPq5CVF6+JBw2gUyBY8FEvqkCjqaj7KFZp6SpQ7LgK/CkUaRMBEeYTgCH7zOGvmEFOGjqz46AmNmnInFs2som9CKgxlwQJiqMvt8vT6/Nfnl+d/elk/P728/vH05/3vTy+/fV3LTz+q3RVuLgWlp4puKo0cwOQ8PL9+XYU6TWlKBg8VYvIKjbgAd2FZ5yvS6Y1lFTkvATwH5FcSeGFRdJnbyjKZWWVJsXLiNlbpOjdGsNaIILwQrurAXVit1zmpIH2GRrKa/RFVAbywarepgyQM1wwlxiApSgrclsLS29wWWjL7kq1VNDEHAXihefe5LVQYQ04Jw4uJYMs3UvSYmxLMLG9FUS00kEQCLyzWTnNxYAZXhDYswlNpCdxSXztdq+M995SjT9CZiJHQBYnciNbpWiHv6Xc3dzRNVEEL14HccglP5xqZEXcqEbh6LZNj4k4ogq8HUpnEuNMKIVdztcws7vUC4rVgjok7xYh3mmtmRtypRqLTgW4QIyae4i2SwHBrlMgtRul8MFXwjrkxZgO0hv5VTeRmkTvP58qMuFOOwOKBcqxxwajEW86EAYn883644J65clrBw27clLKU7i2BG486H8wXrYHU4FFx9C5joiVyayp4eiAczOoqBWMwAvsGHE/kRiydj4QjzgYSZgx6UYsncnMVXw+E41hDUEK8Ty2NvyEvRIXFA+FMiDvhKJJzLZxj4k44yl3mwkH1DFMe8x4ji/DAJnALEbyDiaOtRsEap8hqzm9L5JZUEOe6EYxfAqXiPQs8xAO4Oapd5rKZ8Hay0drlSDalYhnCLpl6xWM4kJuj1OVaNt8NilW8Znjxm4fCeAI3g0j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"text/plain": [ "" ] @@ -305,16 +317,16 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:11.917433Z", - "start_time": "2026-10-30T11:41:11.909230Z" + "end_time": "2026-11-04T12:16:21.052976Z", + "start_time": "2026-11-04T12:16:21.000016Z" } }, "cell_type": "code", "source": [ - "# Higher score is better\n", + "# Higher score is better, with scores going from 0 to 1.\n", "edge.annotations" ], - "id": "e9df66ff253b9e4b", + "id": "9056997577f8437f", "outputs": [ { "data": { @@ -335,15 +347,17 @@ "source": [ "# Configure the `Protocols`\n", "\n", - "Before building the network transformations, configure the settings for both the hybrid topology and separated topologies protocols. By default, the protocols use the same small molecule force field, if you want to use a different force field this setting should be changed in both protocols as shown. We also use `_adaptive_settings` to adjust settings for each system. This is particularly useful for the hybrid topology protocol, which adjusts solvent padding based on whether a receptor is present." + "
Warning: Key settings that affect free energy results, such as the model, should be consistent across protocols. In this example, the force field settings are matched between the two protocols.
\n", + "\n", + "Before building the network transformations, configure settings for both the hybrid topology and separated topologies protocols. The example below sets the same small-molecule force field and number of repeats for each protocol. The transformation building functions defined below also use `_adaptive_settings` to adjust settings for each system. For the hybrid topology protocol, this includes adjusting solvent padding based on whether a receptor is present." ], - "id": "ab9906252acf1ca" + "id": "ef25fc3e2d868524" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:13.064971Z", - "start_time": "2026-10-30T11:41:11.928887Z" + "end_time": "2026-11-04T12:16:22.496552Z", + "start_time": "2026-11-04T12:16:21.055750Z" } }, "cell_type": "code", @@ -351,8 +365,10 @@ "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n", "from openfe.protocols.openmm_septop import SepTopProtocol\n", "\n", - "# Configure the small molecule force field to be used by both protocols and the number of repeats per execution of the protocol\n", + "# Set the force field and number of repeats for both protocols\n", "small_molecule_forcefield = \"openff-2.3.0\"\n", + "\n", + "# Get the default setting for each protocol and update the small molecule force field and number of repeats\n", "default_hybrid_settings = RelativeHybridTopologyProtocol.default_settings()\n", "default_hybrid_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", "default_hybrid_settings.protocol_repeats = 1\n", @@ -361,7 +377,7 @@ "default_septop_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", "default_septop_settings.protocol_repeats = 1" ], - "id": "43fc256c3e0ff239", + "id": "ab94a294fc8927a3", "outputs": [ { "name": "stderr", @@ -396,8 +412,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:13.432983Z", - "start_time": "2026-10-30T11:41:13.074892Z" + "end_time": "2026-11-04T12:16:22.884869Z", + "start_time": "2026-11-04T12:16:22.499741Z" } }, "cell_type": "code", @@ -405,7 +421,7 @@ "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocolSettings\n", "from openfe.protocols.openmm_septop import SepTopSettings\n", "\n", - "# load the receptor from the provided PDB file and create the solvent component\n", + "# Load the receptor from the provided PDB file and create the solvent component\n", "protein = openfe.ProteinComponent.from_pdb_file(\"inputs/protein.pdb\")\n", "solvent = openfe.SolventComponent()\n", "\n", @@ -419,17 +435,23 @@ ") -> list[openfe.Transformation]:\n", " \"\"\"Build the complex and solvent leg transformations for a given ligand atom mapping using the hybrid topology protocol.\"\"\"\n", " transformations = []\n", + "\n", + " # For the mapping between the two ligands create the complex and solvent leg transformations\n", " for leg in [\"solvent\", \"complex\"]:\n", + " # Create the ChemicalSystem objects starting with the common components\n", " state_a_dict = {\"ligand\": edge.componentA, \"solvent\": solvent_comp}\n", " state_b_dict = {\"ligand\": edge.componentB, \"solvent\": solvent_comp}\n", + "\n", + " # Add the protein component to the complex leg ChemicalSystem objects\n", " if leg == \"complex\":\n", " state_a_dict[\"protein\"] = protein_comp\n", " state_b_dict[\"protein\"] = protein_comp\n", "\n", + " # Create the ChemicalSystem objects for each end state of the transformation\n", " state_a = openfe.ChemicalSystem(state_a_dict, name=f\"{edge.componentA.name}_{leg}\")\n", " state_b = openfe.ChemicalSystem(state_b_dict, name=f\"{edge.componentB.name}_{leg}\")\n", "\n", - " # adjust the settings based on the systems involved in the transformation\n", + " # Adjust the settings based on the systems involved in the transformation\n", " ts_settings = RelativeHybridTopologyProtocol._adaptive_settings(\n", " stateA=state_a,\n", " stateB=state_b,\n", @@ -437,7 +459,7 @@ " initial_settings=default_settings,\n", " )\n", "\n", - " # create the transformation for this leg\n", + " # Create the transformation for this leg\n", " transformation = openfe.Transformation(\n", " stateA=state_a,\n", " stateB=state_b,\n", @@ -455,21 +477,24 @@ " protein_comp: openfe.ProteinComponent,\n", " solvent_comp: openfe.SolventComponent\n", ") -> list[openfe.Transformation]:\n", - " \"\"\"Build the transformation for a given ligand atom mapping using the separated topologies protocol.\"\"\"\n", + " \"\"\"Build the transformation for a given ligand atom mapping using the separated topologies' protocol.\"\"\"\n", + "\n", + " # Create the ChemicalSystem objects for each end state of the transformation\n", " state_a_dict = {\"ligand\": edge.componentA, \"protein\": protein_comp, \"solvent\": solvent_comp}\n", " state_b_dict = {\"ligand\": edge.componentB, \"protein\": protein_comp, \"solvent\": solvent_comp}\n", "\n", + " # Create the ChemicalSystem objects for each end state of the transformation\n", " state_a = openfe.ChemicalSystem(state_a_dict, name=f\"{edge.componentA.name}_septop\")\n", " state_b = openfe.ChemicalSystem(state_b_dict, name=f\"{edge.componentB.name}_septop\")\n", "\n", - " # adjust the settings based on the systems involved in the transformation\n", + " # Adjust the settings based on the systems involved in the transformation\n", " ts_settings = SepTopProtocol._adaptive_settings(\n", " stateA=state_a,\n", " stateB=state_b,\n", " initial_settings=default_settings,\n", " )\n", "\n", - " # create the transformation for this edge\n", + " # Create the single transformation for this edge which runs the complex and solvent legs together\n", " transformation = openfe.Transformation(\n", " stateA=state_a,\n", " stateB=state_b,\n", @@ -486,39 +511,51 @@ { "metadata": {}, "cell_type": "markdown", - "source": "We can now build the transformations required to execute the planned network's edges. The code below iterates through the `LigandNetwork` and builds transformations according to whether each edge is within or between series. We then write the transformations to disk where they can then be executed using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow).", + "source": [ + "We can now build the transformations required to execute the planned network's edges. The code below iterates through the `LigandNetwork` and builds transformations for each edge, assigning the appropriate protocol as follows:\n", + "\n", + "- **Hybrid Topology Protocol**: For edges within the same series\n", + "- **Separated Topologies Protocol**: For edges between different series\n", + "\n", + "We then write the transformations to disk where they can then be executed using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow)." + ], "id": "c3b0890f5793275c" }, { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:20.392419Z", - "start_time": "2026-10-30T11:41:13.434816Z" + "end_time": "2026-11-04T12:16:30.226415Z", + "start_time": "2026-11-04T12:16:22.894626Z" } }, "cell_type": "code", "source": [ "import pathlib\n", "\n", + "# Create a list to hold all transformations for the network\n", "all_transformations = []\n", + "\n", + "# For each edge in the network, build the appropriate transformation(s) and add them to the list\n", "for edge in network.edges:\n", + " # Check if the edge is within the same series or between different series\n", " if ligand_series_mapping[edge.componentA.name] == ligand_series_mapping[edge.componentB.name]:\n", - " # build the hybrid topology transformations for edges within a series\n", + " # Build the hybrid topology transformations for edges within a series\n", " transformations = build_hybrid_topology_transformation(edge, default_hybrid_settings, protein, solvent)\n", " else:\n", - " # build the separated topologies transformation for edges between series\n", + " # Build the separated topologies transformation for edges between series\n", " transformations = build_septop_transformation(edge, default_septop_settings, protein, solvent)\n", "\n", - " # add the transformations to the network\n", + " # Add the transformations to the network\n", " all_transformations.extend(transformations)\n", "\n", + "# Make an AlchemicalNetwork to hold all transformations for this series\n", "alchemical_network = openfe.AlchemicalNetwork(all_transformations)\n", "\n", - "# first we create the directory\n", + "# First we create the directory\n", "transformation_dir = pathlib.Path(\"transformations\")\n", "transformation_dir.mkdir(exist_ok=True)\n", "\n", - "# then we write out each transformation\n", + "# Then we write out each transformation\n", "for transformation in alchemical_network.edges:\n", " transformation.to_json(transformation_dir / f\"{transformation.name}.json\")" ], @@ -535,12 +572,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:20.402716Z", - "start_time": "2026-10-30T11:41:20.394110Z" + "end_time": "2026-11-04T12:16:30.251085Z", + "start_time": "2026-11-04T12:16:30.236831Z" } }, "cell_type": "code", "source": [ + "# Count the number of transformations for each protocol\n", "edges_per_protocol = {\n", " RelativeHybridTopologyProtocol: 0,\n", " SepTopProtocol: 0\n", @@ -581,8 +619,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:20.428842Z", - "start_time": "2026-10-30T11:41:20.414091Z" + "end_time": "2026-11-04T12:16:30.283076Z", + "start_time": "2026-11-04T12:16:30.267345Z" } }, "cell_type": "code", @@ -593,20 +631,19 @@ "import numpy as np\n", "from openff.units import unit\n", "\n", + "# Define some helper functions to classify the results and combine repeated runs of each transformation into a single estimate and uncertainty for each edge and protocol.\n", "def classify_result(fpath: pathlib.Path) -> dict:\n", " \"\"\"Load a JSON file containing a quickrun result and return a dict containing the relevant information for classifying the result.\n", "\n", " Parameters\n", " ----------\n", - " fpath : os.PathLike | str\n", + " fpath : pathlib.Path\n", " The path to a gufe-serialized JSON.\n", "\n", - "\n", " Returns\n", " -------\n", " dict\n", " A dict containing data from the results JSON.\n", - "\n", " \"\"\"\n", " with open(fpath, \"r\") as f:\n", " result_data = json.load(f)\n", @@ -614,10 +651,13 @@ " def to_kcal(q: dict) -> float:\n", " return unit.Quantity(q[\"magnitude\"], q[\"unit\"]).to(\"kilocalorie_per_mole\").m\n", "\n", - " # grab all the units\n", + " # Grab all the units executed for this transformation\n", " units = [u for u in result_data[\"unit_results\"].values() if u.get(\"outputs\")]\n", + "\n", + " # SepTop units should have SepTop in the source\n", " is_septop = any(\"SepTop\" in u[\"source_key\"] for u in units)\n", - " # get the phase and ligands involved from the file name\n", + "\n", + " # Get the phase from the file name if it was hybrid topology result\n", " edge_name = fpath.stem\n", " if \"solvent\" in fpath.name:\n", " phase = \"solvent\"\n", @@ -626,14 +666,15 @@ " phase = \"complex\"\n", " edge_name = edge_name.removeprefix(\"complex_\")\n", " else:\n", - " # this is the septop result\n", + " # This is the septop result\n", " phase = None\n", + " # Get the ligand names from the edge name\n", " lig_a = edge_name[:6]\n", " lig_b = edge_name[7:]\n", " classified = {\"lig_a\": lig_a, \"lig_b\": lig_b, \"phase\": phase}\n", "\n", " if not is_septop:\n", - " # the top level estimate is the DDG (or the leg DG) of the hybrid topology protocol, in kcal/mol\n", + " # The top level estimate is the DDG (or the leg DG) of the hybrid topology protocol, in kcal/mol\n", " classified.update(\n", " protocol=\"RelativeHybridTopologyProtocol\",\n", " estimate=to_kcal(result_data[\"estimate\"]),\n", @@ -644,18 +685,27 @@ " # For SepTop, combine the individual legs following the approach used by SepTopProtocolResult.\n", " complex_dg = solvent_dg = complex_error = solvent_error = None\n", " complex_corr = solvent_corr = 0.0\n", + "\n", + " # For each unit get the estimate and uncertainty for the complex and solvent legs, as well as any standard state corrections\n", " for u in units:\n", " outputs = u[\"outputs\"]\n", " if \"unit_estimate\" in outputs:\n", + " # Convert the estimate and uncertainty to kcal/mol\n", " dg, error = to_kcal(outputs[\"unit_estimate\"]), to_kcal(outputs[\"unit_estimate_error\"])\n", + "\n", + " # Classify the estimate and uncertainty based on the simulation type (complex or solvent)\n", " if outputs[\"simtype\"] == \"complex\":\n", " complex_dg, complex_error = dg, error\n", " else:\n", " solvent_dg, solvent_error = dg, error\n", + "\n", + " # Check for standard state corrections and convert them to kcal/mol\n", " if \"standard_state_correction_A\" in outputs:\n", " complex_corr = to_kcal(outputs[\"standard_state_correction_A\"]) + to_kcal(outputs[\"standard_state_correction_B\"])\n", " elif \"standard_state_correction\" in outputs:\n", " solvent_corr = to_kcal(outputs[\"standard_state_correction\"])\n", + "\n", + " # Check that we have both a complex and solvent estimate, otherwise raise an error\n", " if complex_dg is None or solvent_dg is None:\n", " raise ValueError(f\"{fpath}: missing a complex or solvent estimate, this is likely a failed simulation.\")\n", "\n", @@ -670,14 +720,18 @@ "\n", "\n", "def septop_ddg(repeats: list[dict]) -> tuple[float, float]:\n", - " \"\"\"Combine the septop results of an edge into a DDG (kcal/mol) and uncertainty, matching ``SepTopProtocolResult``.\n", + " \"\"\"Combine the SepTop results of an edge into a DDG (kcal/mol) and uncertainty, matching ``SepTopProtocolResult``.\n", " \"\"\"\n", " complex_dgs = np.array([r[\"complex_dg\"] for r in repeats])\n", " solvent_dgs = np.array([r[\"solvent_dg\"] for r in repeats])\n", + " # Compute the estimate as the difference between the mean complex and solvent estimates\n", " ddg = complex_dgs.mean() - solvent_dgs.mean()\n", + "\n", + " # If we have more than one repeat compute the uncertainty using the sample standard deviation\n", " if len(repeats) > 1:\n", " error = np.sqrt(np.std(complex_dgs, ddof=1) ** 2 + np.std(solvent_dgs, ddof=1) ** 2)\n", " else:\n", + " # If we only have a single repeat, use the reported uncertainties from that repeat to compute the uncertainty\n", " error = np.sqrt(repeats[0][\"complex_error\"] ** 2 + repeats[0][\"solvent_error\"] ** 2)\n", " return float(ddg), float(error)\n", "\n", @@ -687,6 +741,7 @@ " \"\"\"\n", " complex_dgs = np.array([r[\"estimate\"] for r in complex_repeats])\n", " solvent_dgs = np.array([r[\"estimate\"] for r in solvent_repeats])\n", + " # Compute the estimate as the difference between the mean complex and solvent estimates\n", " ddg = complex_dgs.mean() - solvent_dgs.mean()\n", "\n", " def sample_std(values):\n", @@ -706,8 +761,232 @@ "source": [ "## Downloading the example results\n", "\n", - "First, let's download example results for the transformations created above.\n", - "\n", + "First, let's download example results for the transformations created above, this may take a few minutes." + ], + "id": "22c27a2eff6716fb" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-11-04T13:10:16.656184Z", + "start_time": "2026-11-04T13:08:25.555786Z" + } + }, + "cell_type": "code", + "source": [ + "!wget --show-progress \"https://zenodo.org/records/23947882/files/mixed_results.zip\" -nc\n", + "!unzip -o mixed_results.zip" + ], + "id": "78530a0e74cc84b3", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=76613) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + " pid, fd = os.forkpty()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2026-11-04 13:08:26-- https://zenodo.org/records/23947882/files/mixed_results.zip\n", + "Resolving zenodo.org (zenodo.org)... 188.185.40.132, 188.185.47.192, 188.185.42.113, ...\n", + "Connecting to zenodo.org (zenodo.org)|188.185.40.132|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 59182107 (56M) [application/octet-stream]\n", + "Saving to: ‘mixed_results.zip’\n", + "\n", + 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"source": [ "## Gathering the raw $\\Delta\\Delta$G results\n", "\n", "Now we can classify the results by protocol and collect the $\\Delta\\Delta$G estimates in kcal/mol in a DataFrame. We also combine repeated runs of each transformation into a single estimate and uncertainty for each edge and protocol." @@ -717,29 +996,34 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:24.782669Z", - "start_time": "2026-10-30T11:41:20.436182Z" + "end_time": "2026-11-04T13:10:29.905495Z", + "start_time": "2026-11-04T13:10:20.645237Z" } }, "cell_type": "code", "source": [ "import pandas as pd\n", "\n", - "all_results = pathlib.Path(\"/Users/joshua/Documents/Projects/openfe/free_energy_2026/mixed_networks/bace1_baumann/results_download/mixed_results\")\n", + "all_results = pathlib.Path(\"mixed_results\")\n", "\n", "# Containers for the results of each protocol, grouped by the ligands involved in the transformation\n", "septop_results = defaultdict(list)\n", "hybrid_topology_results = {\"complex\": defaultdict(list), \"solvent\": defaultdict(list)}\n", "\n", + "# Loop through the downloaded results and classify them by protocol, grouping them by the ligands involved in the transformation\n", "for file in all_results.rglob(\"*.json\"):\n", " result_data = classify_result(file)\n", + "\n", " if result_data[\"protocol\"] == \"SepTopProtocol\":\n", " septop_results[(result_data[\"lig_a\"], result_data[\"lig_b\"])].append(result_data)\n", + "\n", " elif result_data[\"protocol\"] == \"RelativeHybridTopologyProtocol\":\n", " hybrid_topology_results[result_data[\"phase\"]][(result_data[\"lig_a\"], result_data[\"lig_b\"])].append(result_data)\n", "\n", "# Create a dataframe to hold the results for each edge and protocol\n", "results_df = []\n", + "\n", + "# Add the SepTop edge results to the dataframe\n", "for edge, repeats in septop_results.items():\n", " ddg, error = septop_ddg(repeats)\n", " results_df.append({\n", @@ -750,6 +1034,7 @@ " \"error (kcal/mol)\": error\n", " })\n", "\n", + "# Add the hybrid topology edge results to the dataframe\n", "for edge, repeats in hybrid_topology_results[\"complex\"].items():\n", " solvent_repeats = hybrid_topology_results[\"solvent\"][edge]\n", " ddg, error = hybrid_topology_ddg(repeats, solvent_repeats)\n", @@ -1095,12 +1380,12 @@ "" ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 13 + "execution_count": 19 }, { "metadata": {}, @@ -1117,15 +1402,15 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:24.991799Z", - "start_time": "2026-10-30T11:41:24.785983Z" + "end_time": "2026-11-04T13:10:32.772408Z", + "start_time": "2026-11-04T13:10:32.697440Z" } }, "cell_type": "code", "source": [ "from cinnabar import FEMap\n", "\n", - "\n", + "# Create the cinnabar FEMap object to hold the relative estimates and run the MLE method\n", "fe_map = FEMap()\n", "\n", "# Add each of the relative estimates to the map, converting the values and uncertainties to kcal/mol\n", @@ -1136,6 +1421,7 @@ " value=row[\"ddg (kcal/mol)\"] * unit.kilocalorie_per_mole,\n", " uncertainty=row[\"error (kcal/mol)\"] * unit.kilocalorie_per_mole,\n", " )\n", + "\n", "# Run the MLE method\n", "fe_map.generate_absolute_values()\n", "\n", @@ -1344,12 +1630,12 @@ "" ] }, - "execution_count": 14, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 14 + "execution_count": 20 }, { "metadata": {}, @@ -1360,15 +1646,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-10-30T11:41:26.783378Z", - "start_time": "2026-10-30T11:41:24.994077Z" + "end_time": "2026-11-04T13:10:36.298760Z", + "start_time": "2026-11-04T13:10:34.274210Z" } }, "cell_type": "code", "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_formats = ['svg']\n", - "import json\n", "from gufe.tokenization import JSON_HANDLER\n", "from cinnabar import plotting\n", "import requests\n", @@ -1376,6 +1661,7 @@ "# Load the experimental values from the provided JSON URL\n", "experimental_data_url = \"https://raw.githubusercontent.com/OpenFreeEnergy/openfe-benchmarks/refs/heads/main/openfe_benchmarks/data/benchmark_systems/scaffold_hopping/bace_baumann/experimental_binding_data.json\"\n", "\n", + "\n", "response = requests.get(experimental_data_url, timeout=30)\n", "response.raise_for_status()\n", "experimental_data = json.loads(response.text, cls=JSON_HANDLER.decoder)\n", @@ -1385,7 +1671,7 @@ " fe_map.add_experimental_measurement(\n", " label=ligand_name,\n", " value=exp_data[\"dg\"],\n", - " uncertainty=0.0 * unit.kilocalorie_per_mole,\n", + " uncertainty=0.0 * unit.kilocalorie_per_mole, # If your experimental data has uncertainties, you can include them here\n", " )\n", "\n", "# Get the shift value to align the MLE estimates with the experimental values\n", @@ -1404,26 +1690,18 @@ ], "id": "4401ca21e5bd73c9", "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/matplotlib_inline/config.py:59: DeprecationWarning: InlineBackend._figure_formats_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n", - " def _figure_formats_changed(self, name, old, new):\n" - ] - }, { "data": { "text/plain": [ "
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"execution_count": 15 + "execution_count": 21 }, { "metadata": {}, @@ -1431,7 +1709,8 @@ "source": [ "# Recap\n", "\n", - "- **openfe** provides a number of free energy protocols, any number of which can be combined into a single alchemical campaign.\n", + "\n", + "- The available **openfe** free-energy protocols can be combined in a single alchemical campaign.\n", "- The protocols should be configured to use the same force field and settings should be adjusted to ensure consistency between protocols.\n", "- Each protocol has different requirements for the `ChemicalSystem` compositions used to represent transformation end states; take care to use the correct systems for each protocol.\n", "- `Transformations` containing any **openfe** protocol can be written to disk for execution using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow).\n", From 373fc29ecb54811ac20a149ebb8ed16c9e98b940 Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Wed, 4 Nov 2026 13:56:43 +0000 Subject: [PATCH 5/9] fix alert box rendering --- mixed_networks/mixed_networks.ipynb | 3341 ++++++++++++++++++++++----- 1 file changed, 2739 insertions(+), 602 deletions(-) diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb index cd5e6e8..ed152f8 100644 --- a/mixed_networks/mixed_networks.ipynb +++ b/mixed_networks/mixed_networks.ipynb @@ -1,8 +1,9 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", + "id": "6d660986567950a0", + "metadata": {}, "source": [ "# Mixed Protocol Networks\n", "\n", @@ -30,18 +31,19 @@ "\n", "First, load the ligands from the provided SDF file and map each ligand name to its series. Convert the ligands to `SmallMoleculeComponents` for use with **openfe**.\n", "\n", - "
Note: Ligands should have partial charges assigned before planning the network. Here, we use pre-charged ligands, but charges can also be generated via the CLI or API.
" - ], - "id": "6d660986567950a0" + "
\n", + "\n", + "**Note:** Ligands should have partial charges assigned before planning the network. Here, we use pre-charged ligands, but charges can also be generated via the [CLI](https://docs.openfree.energy/en/latest/reference/cli/charge_molecules.html) or [API](https://docs.openfree.energy/en/latest/cookbook/user_charges.html).\n", + "\n", + "
" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:08.361601Z", - "start_time": "2026-11-04T12:16:06.307258Z" - } - }, "cell_type": "code", + "execution_count": 1, + "id": "878ed1f41247982c", + "metadata": {}, + "outputs": [], "source": [ "from rdkit import Chem\n", "from gufe import SmallMoleculeComponent\n", @@ -73,14 +75,12 @@ " \"lig_74\": \"amide\",\n", " \"lig_81\": \"amide\",\n", "}" - ], - "id": "878ed1f41247982c", - "outputs": [], - "execution_count": 1 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "ca3f2dbf591ec0dd", + "metadata": {}, "source": [ "# Creating the `LigandNetwork`\n", "\n", @@ -89,35 +89,13 @@ "* **Atom Mapper**: Proposes potential atom mappings (descriptions of the alchemical change) for pairs of ligands. We will use the `KartografAtomMapper`.\n", "* **Scorer**: Given an atom mapping, provides an estimate of the quality of that mapping (higher scores are better). We will use `default_lomap_scorer`.\n", "* **Network Planner**: Creates the actual `LigandNetwork`; different network planners provide different strategies. We will create a `LOMAP` redundant network with the `generate_lomap_network` method.\n" - ], - "id": "ca3f2dbf591ec0dd" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:20.625792Z", - "start_time": "2026-11-04T12:16:08.372122Z" - } - }, "cell_type": "code", - "source": [ - "import openfe\n", - "\n", - "# Build the mapper using the default settings\n", - "mapper = openfe.KartografAtomMapper()\n", - "# Use the default LOMAP scorer to score the proposed atom mappings\n", - "scorer = openfe.lomap_scorers.default_lomap_score\n", - "# Use the LOMAP network planner to generate a redundant network\n", - "network_planner = openfe.ligand_network_planning.generate_lomap_network\n", - "\n", - "# Build the network using the configured mapper, scorer, and network planner\n", - "network = network_planner(\n", - " ligands=ligands,\n", - " mappers=[mapper],\n", - " scorer=scorer\n", - ")" - ], + "execution_count": 2, "id": "c4cd5b264631d89a", + "metadata": {}, "outputs": [ { "name": "stderr", @@ -147,26 +125,51 @@ ] } ], - "execution_count": 2 + "source": [ + "import openfe\n", + "\n", + "# Build the mapper using the default settings\n", + "mapper = openfe.KartografAtomMapper()\n", + "# Use the default LOMAP scorer to score the proposed atom mappings\n", + "scorer = openfe.lomap_scorers.default_lomap_score\n", + "# Use the LOMAP network planner to generate a redundant network\n", + "network_planner = openfe.ligand_network_planning.generate_lomap_network\n", + "\n", + "# Build the network using the configured mapper, scorer, and network planner\n", + "network = network_planner(\n", + " ligands=ligands,\n", + " mappers=[mapper],\n", + " scorer=scorer\n", + ")" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "4047400ea15f2e1a", + "metadata": {}, "source": [ "## Visualize the `LigandNetwork`\n", "\n", "Now we can visualize the `LigandNetwork` while highlighting the three distinct series using colours to match the examples shown above. The `biaryl` series is shown in yellow, the `spirocyclic` series in cyan, and the `amide` series in magenta. Edges between the series are shown in red while edges within the series are shown in black." - ], - "id": "4047400ea15f2e1a" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:20.804763Z", - "start_time": "2026-11-04T12:16:20.639271Z" - } - }, "cell_type": "code", + "execution_count": 3, + "id": "7f92fa5e68ee95ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "import networkx as nx\n", @@ -194,25 +197,12 @@ "nx.draw(network_graph, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors, node_size=900, font_size=10, font_color='black', font_weight='bold')\n", "plt.axis(\"off\")\n", "plt.show()" - ], - "id": "7f92fa5e68ee95ef", - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 3 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "f63d2a755692dbf9", + "metadata": {}, "source": [ "## Inspecting the atom mappings\n", "\n", @@ -221,24 +211,18 @@ "\n", "Now we can compare atom mappings and scores for edges between and within the series. First, let's inspect an edge between the `biaryl` and `spirocyclic` series, connecting ligands `lig_03` and `lig_11`. The mapping and its score are shown below. The low score reflects the large number of alchemical atoms and suggests that calculations for this edge may be challenging to converge with the hybrid topology approach.\n", "\n", - "
Reminder: Shared core (mapped) atoms are shown in black, and atoms unique to each end state are shown in red. Mapped atoms whose elements change during the simulation are shown in blue.
" - ], - "id": "f63d2a755692dbf9" + "
\n", + "\n", + "**Reminder:** Shared core (mapped) atoms are shown in black, and atoms unique to each end state are shown in red. Mapped atoms whose elements change during the simulation are shown in blue.\n", + "\n", + "
" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:20.857338Z", - "start_time": "2026-11-04T12:16:20.808671Z" - } - }, "cell_type": "code", - "source": [ - "# Find an edge between the biaryl and spirocyclic series\n", - "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_03\", \"lig_11\"}][0]\n", - "edge" - ], + "execution_count": 4, "id": "8edec480a7e5ea99", + "metadata": {}, "outputs": [ { "data": { @@ -251,21 +235,17 @@ "output_type": "display_data" } ], - "execution_count": 4 + "source": [ + "# Find an edge between the biaryl and spirocyclic series\n", + "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_03\", \"lig_11\"}][0]\n", + "edge" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:20.885236Z", - "start_time": "2026-11-04T12:16:20.871688Z" - } - }, "cell_type": "code", - "source": [ - "# Higher score is better, with scores going from 0 to 1.\n", - "edge.annotations" - ], + "execution_count": 5, "id": "fd3f483f1e2602b6", + "metadata": {}, "outputs": [ { "data": { @@ -278,28 +258,24 @@ "output_type": "execute_result" } ], - "execution_count": 5 + "source": [ + "# Higher score is better, with scores going from 0 to 1.\n", + "edge.annotations" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We can then inspect an edge within the `spirocyclic` series, connecting two similar ligands `lig_11` and `lig_32`. The mapping and its score are shown below. The higher score reflects the smaller number of alchemical atoms and suggests that calculations for this edge should be easier to converge with the hybrid topology approach.", - "id": "8624cb56880a1253" + "id": "8624cb56880a1253", + "metadata": {}, + "source": [ + "We can then inspect an edge within the `spirocyclic` series, connecting two similar ligands `lig_11` and `lig_32`. The mapping and its score are shown below. The higher score reflects the smaller number of alchemical atoms and suggests that calculations for this edge should be easier to converge with the hybrid topology approach." + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:20.984945Z", - "start_time": "2026-11-04T12:16:20.905745Z" - } - }, "cell_type": "code", - "source": [ - "# Now find an edge within the spirocyclic series\n", - "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_11\", \"lig_32\"}][0]\n", - "edge\n" - ], + "execution_count": 6, "id": "91e92274b502426e", + "metadata": {}, "outputs": [ { "data": { @@ -312,21 +288,17 @@ "output_type": "display_data" } ], - "execution_count": 6 + "source": [ + "# Now find an edge within the spirocyclic series\n", + "edge = [edge for edge in network.edges if {edge.componentA.name, edge.componentB.name} == {\"lig_11\", \"lig_32\"}][0]\n", + "edge\n" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:21.052976Z", - "start_time": "2026-11-04T12:16:21.000016Z" - } - }, "cell_type": "code", - "source": [ - "# Higher score is better, with scores going from 0 to 1.\n", - "edge.annotations" - ], + "execution_count": 7, "id": "9056997577f8437f", + "metadata": {}, "outputs": [ { "data": { @@ -339,45 +311,32 @@ "output_type": "execute_result" } ], - "execution_count": 7 + "source": [ + "# Higher score is better, with scores going from 0 to 1.\n", + "edge.annotations" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "ef25fc3e2d868524", + "metadata": {}, "source": [ "# Configure the `Protocols`\n", "\n", - "
Warning: Key settings that affect free energy results, such as the model, should be consistent across protocols. In this example, the force field settings are matched between the two protocols.
\n", + "
\n", + "\n", + "**Warning:** Key settings that affect free energy results, such as the model, should be consistent across protocols. In this example, the force field settings are matched between the two protocols.\n", + "\n", + "
\n", "\n", "Before building the network transformations, configure settings for both the hybrid topology and separated topologies protocols. The example below sets the same small-molecule force field and number of repeats for each protocol. The transformation building functions defined below also use `_adaptive_settings` to adjust settings for each system. For the hybrid topology protocol, this includes adjusting solvent padding based on whether a receptor is present." - ], - "id": "ef25fc3e2d868524" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:22.496552Z", - "start_time": "2026-11-04T12:16:21.055750Z" - } - }, "cell_type": "code", - "source": [ - "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n", - "from openfe.protocols.openmm_septop import SepTopProtocol\n", - "\n", - "# Set the force field and number of repeats for both protocols\n", - "small_molecule_forcefield = \"openff-2.3.0\"\n", - "\n", - "# Get the default setting for each protocol and update the small molecule force field and number of repeats\n", - "default_hybrid_settings = RelativeHybridTopologyProtocol.default_settings()\n", - "default_hybrid_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", - "default_hybrid_settings.protocol_repeats = 1\n", - "\n", - "default_septop_settings = SepTopProtocol.default_settings()\n", - "default_septop_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", - "default_septop_settings.protocol_repeats = 1" - ], + "execution_count": 8, "id": "ab94a294fc8927a3", + "metadata": {}, "outputs": [ { "name": "stderr", @@ -395,28 +354,41 @@ ] } ], - "execution_count": 8 + "source": [ + "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n", + "from openfe.protocols.openmm_septop import SepTopProtocol\n", + "\n", + "# Set the force field and number of repeats for both protocols\n", + "small_molecule_forcefield = \"openff-2.3.0\"\n", + "\n", + "# Get the default setting for each protocol and update the small molecule force field and number of repeats\n", + "default_hybrid_settings = RelativeHybridTopologyProtocol.default_settings()\n", + "default_hybrid_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", + "default_hybrid_settings.protocol_repeats = 1\n", + "\n", + "default_septop_settings = SepTopProtocol.default_settings()\n", + "default_septop_settings.forcefield_settings.small_molecule_forcefield = small_molecule_forcefield\n", + "default_septop_settings.protocol_repeats = 1" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "2c1e9779b733eb13", + "metadata": {}, "source": [ "# Building the `Transformation`\n", "\n", "Now we can build the transformations for this network, assigning the appropriate protocol for each transformation. Here we will use the hybrid topology protocol for edges within each series and the separated topologies protocol for edges between the series.\n", "\n", "Each protocol has different requirements for the `ChemicalSystem` compositions that represent the transformation's end states. The hybrid topology protocol requires you to construct the `complex` and `solvent` legs as separate `Transformation` objects, while the separated topologies protocol runs both legs within a single `Transformation`. The code below shows how to build transformations for both protocols with the appropriate `ChemicalSystem` objects. For more information about each protocol's requirements, see the [examples documentation](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html#system-composition-examples)." - ], - "id": "2c1e9779b733eb13" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:22.884869Z", - "start_time": "2026-11-04T12:16:22.499741Z" - } - }, "cell_type": "code", + "execution_count": 9, + "id": "4b08f0258bbdd052", + "metadata": {}, + "outputs": [], "source": [ "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocolSettings\n", "from openfe.protocols.openmm_septop import SepTopSettings\n", @@ -503,14 +475,12 @@ " name=f\"{edge.componentA.name}_{edge.componentB.name}\"\n", " )\n", " return [transformation, ]\n" - ], - "id": "4b08f0258bbdd052", - "outputs": [], - "execution_count": 9 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "c3b0890f5793275c", + "metadata": {}, "source": [ "We can now build the transformations required to execute the planned network's edges. The code below iterates through the `LigandNetwork` and builds transformations for each edge, assigning the appropriate protocol as follows:\n", "\n", @@ -518,17 +488,14 @@ "- **Separated Topologies Protocol**: For edges between different series\n", "\n", "We then write the transformations to disk where they can then be executed using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow)." - ], - "id": "c3b0890f5793275c" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:30.226415Z", - "start_time": "2026-11-04T12:16:22.894626Z" - } - }, "cell_type": "code", + "execution_count": 10, + "id": "fcc7a807d6edc1a8", + "metadata": {}, + "outputs": [], "source": [ "import pathlib\n", "\n", @@ -558,37 +525,21 @@ "# Then we write out each transformation\n", "for transformation in alchemical_network.edges:\n", " transformation.to_json(transformation_dir / f\"{transformation.name}.json\")" - ], - "id": "fcc7a807d6edc1a8", - "outputs": [], - "execution_count": 10 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We can also check how many transformations were created for each protocol. The hybrid topology protocol should have 46 transformations (23 edges * 2 legs), while the separated topologies protocol should have 6 transformations (6 connecting edges).", - "id": "8aa9a3a8ad2ce7c5" + "id": "8aa9a3a8ad2ce7c5", + "metadata": {}, + "source": [ + "We can also check how many transformations were created for each protocol. The hybrid topology protocol should have 46 transformations (23 edges * 2 legs), while the separated topologies protocol should have 6 transformations (6 connecting edges)." + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:30.251085Z", - "start_time": "2026-11-04T12:16:30.236831Z" - } - }, "cell_type": "code", - "source": [ - "# Count the number of transformations for each protocol\n", - "edges_per_protocol = {\n", - " RelativeHybridTopologyProtocol: 0,\n", - " SepTopProtocol: 0\n", - "}\n", - "for transformation in alchemical_network.edges:\n", - " edges_per_protocol[transformation.protocol.__class__] += 1\n", - "\n", - "edges_per_protocol" - ], + "execution_count": 11, "id": "d4f5c88c40f92a9", + "metadata": {}, "outputs": [ { "data": { @@ -602,28 +553,40 @@ "output_type": "execute_result" } ], - "execution_count": 11 + "source": [ + "# Count the number of transformations for each protocol\n", + "edges_per_protocol = {\n", + " RelativeHybridTopologyProtocol: 0,\n", + " SepTopProtocol: 0\n", + "}\n", + "for transformation in alchemical_network.edges:\n", + " edges_per_protocol[transformation.protocol.__class__] += 1\n", + "\n", + "edges_per_protocol" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "ab7ecee9702fad84", + "metadata": {}, "source": [ "# Analyzing a mixed protocol network\n", "\n", "After executing each transformation we can analyze the results from a mixed-protocol network using the API. First load results from both protocols and extract the $\\Delta\\Delta$G estimates for each leg, then combine them to estimate the absolute binding free energies for each ligand. We begin by defining helper functions to load the results and group them by the ligands and protocols involved in each transformation.\n", "\n", - "
Warning: Gathering the results of a mixed-protocol network is not currently possible via the CLI.
" - ], - "id": "ab7ecee9702fad84" + "
\n", + "\n", + "**Warning:** Gathering the results of a mixed-protocol network is not currently possible via the CLI.\n", + "\n", + "
" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T12:16:30.283076Z", - "start_time": "2026-11-04T12:16:30.267345Z" - } - }, "cell_type": "code", + "execution_count": 12, + "id": "27d647049e330837", + "metadata": {}, + "outputs": [], "source": [ "from collections import defaultdict\n", "import json\n", @@ -750,40 +713,29 @@ "\n", " error = np.sqrt(sample_std(complex_dgs) ** 2 + sample_std(solvent_dgs) ** 2)\n", " return float(ddg), float(error)" - ], - "id": "27d647049e330837", - "outputs": [], - "execution_count": 12 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "22c27a2eff6716fb", + "metadata": {}, "source": [ "## Downloading the example results\n", "\n", "First, let's download example results for the transformations created above, this may take a few minutes." - ], - "id": "22c27a2eff6716fb" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T13:10:16.656184Z", - "start_time": "2026-11-04T13:08:25.555786Z" - } - }, "cell_type": "code", - "source": [ - "!wget --show-progress \"https://zenodo.org/records/23947882/files/mixed_results.zip\" -nc\n", - "!unzip -o mixed_results.zip" - ], + "execution_count": 13, "id": "78530a0e74cc84b3", + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=76613) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=86606) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] }, @@ -791,16 +743,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2026-11-04 13:08:26-- https://zenodo.org/records/23947882/files/mixed_results.zip\n", - "Resolving zenodo.org (zenodo.org)... 188.185.40.132, 188.185.47.192, 188.185.42.113, ...\n", - "Connecting to zenodo.org (zenodo.org)|188.185.40.132|:443... connected.\n", + "--2026-11-04 13:53:19-- https://zenodo.org/records/23947882/files/mixed_results.zip\n", + "Resolving zenodo.org (zenodo.org)... 137.138.121.78, 188.185.45.63, 188.185.40.132, ...\n", + "Connecting to zenodo.org (zenodo.org)|137.138.121.78|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 59182107 (56M) [application/octet-stream]\n", "Saving to: ‘mixed_results.zip’\n", "\n", - "mixed_results.zip 100%[===================>] 56.44M 853KB/s in 1m 45s \n", + "mixed_results.zip 100%[===================>] 56.44M 755KB/s in 2m 11s \n", "\n", - "2026-11-04 13:10:12 (549 KB/s) - ‘mixed_results.zip’ saved [59182107/59182107]\n", + "2026-11-04 13:55:31 (442 KB/s) - ‘mixed_results.zip’ saved [59182107/59182107]\n", "\n" ] }, @@ -808,314 +760,34 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=76613) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=86606) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Archive: mixed_results.zip\n", - " creating: mixed_results\n", - " inflating: __MACOSX/._mixed_results \n", - 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" inflating: mixed_results/results_0/lig_05_lig_45.json \n", - " inflating: mixed_results/results_0/complex_lig_74_lig_81.json \n", - " inflating: mixed_results/results_0/solvent_lig_11_lig_32.json \n", - " inflating: mixed_results/results_0/complex_lig_11_lig_36.json \n", - " inflating: mixed_results/results_0/lig_02_lig_45.json \n", - " inflating: mixed_results/results_0/complex_lig_41_lig_81.json \n", - " inflating: mixed_results/results_0/solvent_lig_41_lig_69.json \n", - " inflating: mixed_results/results_0/solvent_lig_16_lig_32.json \n", - " inflating: mixed_results/results_0/complex_lig_11_lig_1a.json \n", - " inflating: mixed_results/results_0/complex_lig_41_lig_74.json \n", - " inflating: mixed_results/results_0/complex_lig_11_lig_16.json \n", - " inflating: mixed_results/results_0/complex_lig_02_lig_07.json \n", - " inflating: mixed_results/results_0/solvent_lig_03_lig_05.json \n", - " inflating: mixed_results/results_0/solvent_lig_02_lig_07.json \n", - " inflating: mixed_results/results_0/lig_03_lig_11.json \n", - " inflating: mixed_results/results_0/complex_lig_03_lig_05.json \n", - " inflating: mixed_results/results_0/solvent_lig_11_lig_36.json \n", - " inflating: mixed_results/results_0/solvent_lig_41_lig_81.json \n", - " inflating: mixed_results/results_0/complex_lig_41_lig_69.json \n", - " inflating: mixed_results/results_0/complex_lig_16_lig_32.json \n", - " inflating: mixed_results/results_0/solvent_lig_11_lig_1a.json \n", - " inflating: mixed_results/results_0/solvent_lig_41_lig_74.json \n", - " inflating: mixed_results/results_0/solvent_lig_11_lig_16.json \n" - ] } ], - "execution_count": 18 + "source": [ + "!wget --show-progress \"https://zenodo.org/records/23947882/files/mixed_results.zip\" -nc\n", + "!unzip -qq -o mixed_results.zip" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "20736e7c898ee06a", + "metadata": {}, "source": [ "## Gathering the raw $\\Delta\\Delta$G results\n", "\n", "Now we can classify the results by protocol and collect the $\\Delta\\Delta$G estimates in kcal/mol in a DataFrame. We also combine repeated runs of each transformation into a single estimate and uncertainty for each edge and protocol." - ], - "id": "20736e7c898ee06a" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2026-11-04T13:10:29.905495Z", - "start_time": "2026-11-04T13:10:20.645237Z" - } - }, "cell_type": "code", - "source": [ - "import pandas as pd\n", - "\n", - "all_results = pathlib.Path(\"mixed_results\")\n", - "\n", - "# Containers for the results of each protocol, grouped by the ligands involved in the transformation\n", - "septop_results = defaultdict(list)\n", - "hybrid_topology_results = {\"complex\": defaultdict(list), \"solvent\": defaultdict(list)}\n", - "\n", - "# Loop through the downloaded results and classify them by protocol, grouping them by the ligands involved in the transformation\n", - "for file in all_results.rglob(\"*.json\"):\n", - " result_data = classify_result(file)\n", - "\n", - " if result_data[\"protocol\"] == \"SepTopProtocol\":\n", - " septop_results[(result_data[\"lig_a\"], result_data[\"lig_b\"])].append(result_data)\n", - "\n", - " elif result_data[\"protocol\"] == \"RelativeHybridTopologyProtocol\":\n", - " hybrid_topology_results[result_data[\"phase\"]][(result_data[\"lig_a\"], result_data[\"lig_b\"])].append(result_data)\n", - "\n", - "# Create a dataframe to hold the results for each edge and protocol\n", - "results_df = []\n", - "\n", - "# Add the SepTop edge results to the dataframe\n", - "for edge, repeats in septop_results.items():\n", - " ddg, error = septop_ddg(repeats)\n", - " results_df.append({\n", - " \"lig_a\": edge[0],\n", - " \"lig_b\": edge[1],\n", - " \"protocol\": \"SepTopProtocol\",\n", - " \"ddg (kcal/mol)\": ddg,\n", - " \"error (kcal/mol)\": error\n", - " })\n", - "\n", - "# Add the hybrid topology edge results to the dataframe\n", - "for edge, repeats in hybrid_topology_results[\"complex\"].items():\n", - " solvent_repeats = hybrid_topology_results[\"solvent\"][edge]\n", - " ddg, error = hybrid_topology_ddg(repeats, solvent_repeats)\n", - " results_df.append({\n", - " \"lig_a\": edge[0],\n", - " \"lig_b\": edge[1],\n", - " \"protocol\": \"RelativeHybridTopologyProtocol\",\n", - " \"ddg (kcal/mol)\": ddg,\n", - " \"error (kcal/mol)\": error\n", - " })\n", - "\n", - "results_df = pd.DataFrame(results_df)\n", - "results_df" - ], + "execution_count": 14, "id": "804fec03b92d85f", + "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - " lig_a lig_b protocol ddg (kcal/mol) \\\n", - "0 lig_02 lig_41 SepTopProtocol -2.922078 \n", - "1 lig_03 lig_32 SepTopProtocol 0.293982 \n", - "2 lig_02 lig_67 SepTopProtocol -4.698609 \n", - "3 lig_05 lig_45 SepTopProtocol -3.587460 \n", - "4 lig_02 lig_45 SepTopProtocol -1.938648 \n", - "5 lig_03 lig_11 SepTopProtocol 1.266813 \n", - "6 lig_11 lig_32 RelativeHybridTopologyProtocol 1.513028 \n", - "7 lig_04 lig_05 RelativeHybridTopologyProtocol 0.366913 \n", - "8 lig_06 lig_07 RelativeHybridTopologyProtocol 3.081323 \n", - "9 lig_41 lig_67 RelativeHybridTopologyProtocol -0.571720 \n", - "10 lig_02 lig_03 RelativeHybridTopologyProtocol -3.089208 \n", - "11 lig_11 lig_13 RelativeHybridTopologyProtocol -0.544669 \n", - "12 lig_67 lig_69 RelativeHybridTopologyProtocol -0.646350 \n", - "13 lig_13 lig_36 RelativeHybridTopologyProtocol -2.818366 \n", - "14 lig_13 lig_1a RelativeHybridTopologyProtocol 2.736797 \n", - "15 lig_05 lig_07 RelativeHybridTopologyProtocol 0.718161 \n", - "16 lig_02 lig_04 RelativeHybridTopologyProtocol 0.502572 \n", - "17 lig_02 lig_05 RelativeHybridTopologyProtocol -0.937120 \n", - "18 lig_05 lig_06 RelativeHybridTopologyProtocol -0.194521 \n", - "19 lig_74 lig_81 RelativeHybridTopologyProtocol 2.351567 \n", - "20 lig_11 lig_36 RelativeHybridTopologyProtocol -4.127683 \n", - "21 lig_41 lig_81 RelativeHybridTopologyProtocol -0.809334 \n", - "22 lig_11 lig_1a RelativeHybridTopologyProtocol 1.757897 \n", - "23 lig_41 lig_74 RelativeHybridTopologyProtocol -3.055768 \n", - "24 lig_11 lig_16 RelativeHybridTopologyProtocol 0.623395 \n", - "25 lig_02 lig_07 RelativeHybridTopologyProtocol -0.109589 \n", - "26 lig_03 lig_05 RelativeHybridTopologyProtocol 1.946267 \n", - "27 lig_41 lig_69 RelativeHybridTopologyProtocol -2.824896 \n", - "28 lig_16 lig_32 RelativeHybridTopologyProtocol 0.423409 \n", - "\n", - " error (kcal/mol) \n", - "0 1.322566 \n", - "1 1.265479 \n", - "2 0.435410 \n", - "3 1.891973 \n", - "4 2.541345 \n", - "5 0.559891 \n", - "6 0.213193 \n", - "7 3.247126 \n", - "8 4.612958 \n", - "9 0.227340 \n", - "10 1.556545 \n", - "11 0.372752 \n", - "12 0.593911 \n", - "13 0.153809 \n", - "14 0.317653 \n", - "15 0.284047 \n", - "16 1.895183 \n", - "17 0.608549 \n", - "18 4.321454 \n", - "19 0.281969 \n", - "20 0.386216 \n", - "21 0.112824 \n", - "22 0.251879 \n", - "23 0.200733 \n", - "24 0.398826 \n", - "25 0.381654 \n", - "26 0.826207 \n", - "27 0.860073 \n", - "28 0.581679 " - ], "text/html": [ "
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" - ], - "image/svg+xml": "\n\n\n \n \n \n \n 2026-11-04T13:10:36.212669\n image/svg+xml\n \n \n Matplotlib v3.10.6, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 21 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "e6e00203a0fb2af9", + "metadata": {}, "source": [ "# Recap\n", "\n", @@ -1716,27 +3854,26 @@ "- `Transformations` containing any **openfe** protocol can be written to disk for execution using the [openfe quickrun CLI](https://docs.openfree.energy/en/stable/guide/introduction.html#cli-workflow).\n", "- Analysis of a mixed-protocol network is not currently possible via the CLI; however, results can be gathered and combined using the API, as demonstrated above.\n", "\n" - ], - "id": "e6e00203a0fb2af9" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "openfe_dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.12.9" } }, "nbformat": 4, From 6f427e5222cd09ab8755f210c2c6feb07bd84cd6 Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Thu, 5 Nov 2026 10:45:18 +0000 Subject: [PATCH 6/9] hide warnings --- mixed_networks/mixed_networks.ipynb | 1316 +++++++++++++-------------- 1 file changed, 652 insertions(+), 664 deletions(-) diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb index ed152f8..f9f12f0 100644 --- a/mixed_networks/mixed_networks.ipynb +++ b/mixed_networks/mixed_networks.ipynb @@ -31,7 +31,7 @@ "\n", "First, load the ligands from the provided SDF file and map each ligand name to its series. Convert the ligands to `SmallMoleculeComponents` for use with **openfe**.\n", "\n", - "
\n", + "
\n", "\n", "**Note:** Ligands should have partial charges assigned before planning the network. Here, we use pre-charged ligands, but charges can also be generated via the [CLI](https://docs.openfree.energy/en/latest/reference/cli/charge_molecules.html) or [API](https://docs.openfree.energy/en/latest/cookbook/user_charges.html).\n", "\n", @@ -161,7 +161,7 @@ "outputs": [ { "data": { - "image/png": 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", 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EyMhIQkNDee+993j33XeZM2cO5cuXtzoslxg2bBg7d+5ky5YtVociYj07MBrXdeHPCztwBnjY6kC8lxJ+ERERkUJu3rx5tGjRgkOHDrFs2TLGjBlTYEv4M9OrVy/KlSvHTz/9ZHUoItb7EliBZyT8YBr5TQLmWR2Id/Ken+QiIiIikiNJSUk89dRT9O3bl44dO7Jp0yY6dOhgdVguFxAQwODBg/npp5+w293dhlzEgyUD/7M6iEz4Aq9aHYR3UsIvIiIiUggdOnSIrl278tFHH/HBBx/wyy+/UK5cOavDcpthw4YRGRnJypUrrQ5FxDpzgeNWB5EJO7AO2Gh1IN5HCb+IiIhIITNnzhxatGjB0aNHWb58OY8//jg+Pj5Wh+VWHTt2pHr16irrl0JpxYoVLFq0iN3v7Aa/lMdYwSIWsZvd1gaXyh/4wuogvI8SfhEREZFCIjExkSeeeIKbbrqJLl26sGnTJtq1a2d1WPnC19eXYcOGMX36dJKSkqwORyRfDR06lF69evHF31+YNfPAUIbSi1584aYsex/7WMlKNrCB05y+7HmHOcwiFrEoeRFHJx6FGLeEU2gp4RcREREpBCIiIujSpQufffYZH330EbNmzSI4ONjqsPLV8OHDOX36NH/88YfVoYhYrjOd6UEPGtDApdddxSrqUY+61OVBHqQPfahEJYYylHjinedtYxsDGUhb2tIr5c/8xPmwwaXhFHpK+EVERES83OzZs2nZsiUnTpxg5cqVPProo15fwp+ZZs2a0ahRI6ZMmWJ1KCLWSJf9PcADPMMz9KBHhlPiiWcVq9jHPhw42MY2FrGIdazL1i3u5E72spdbuIVNbGIzmwGYxjTGMc553gEOMJzhrGVt2ot9UMLvYkr4RURERLxUYmIijz76KAMGDKBbt25s2rSJa665xuqwLOPj48Pw4cOZPXs2Fy5csDockfyXbpOKzEr6/+ZvalKTjnRkAAO4lmsZzWh60YuHeChbt/iP/wCoSU0AqlCFohTN8BzATdzEQAbij3/GC6zPxfuSy1LCLyIiIuKFDhw4QKdOnRg3bhyffvopM2fOpEyZMlaHZblhw4YRGxvLnDlzrA5FxKMkk8xIRnKCE9zJnWxlK9/yLZvYlKPrvM3bBBLIZCYzjWk8zdPEEEMDGnAf9135xQ5gTe7fg1xKCb+IiIiIlwkPD6dly5acPn2aVatW8dBDDxXKEv7M1KlTh7Zt26qsXwoXR9anLGMZe9gDwChGAdCUprSkZY5u1Yte9KQnxzjG27zNBCZQhCLcyZ1Uo1rWF7h8fz/JBSX8IiIiIl4iISGBhx56iIEDB9KrVy82btxI69atrQ7L4wwfPpzffvuNqKgoq0MRyR/ZSPj3sc95XJWqzuNsJekpkkmmBz2Yz3zu5342sYld7KIkJRnDGN7kzawvkpDt20k2KOEXERER8QL79u2jY8eOfPXVV3z++edMnz6d0qVLWx2WRxo8eDA2m42ZM2daHYqIxwgmbdeOOOKcxxfIfr+LPSl/wMz0A5SiFNdgeof8xm9ZX0TFSC6lhF9ERESkgJsxYwatWrUiOjqa1atXc//996uE/wqqVKlC9+7dVdYvhUc2fhy0pS0BBAA41+0nk+zssp8dZSjjPD7FqUuO0w8qXFaRbN9OskEJv4iIiEgBFR8fzwMPPMDgwYO54YYb2LhxI61atbI6rAJh2LBhLFmyhKNHj1odioj7ZSPhr0ENZyf+F3iBqUzlDu7IMNuflSpU4f/4PwDe5E2mMY3XeI11rCOQQJ7kSee5hznMIhaxnOXOx3awg0WlFrF58+Zs31OuTAm/iIiISAG0Z88eOnTowLfffsu4ceP46aefKFWqlNVhFRhhYWEEBAQwbdo0q0MRcbvOnTvTI7gHDWiQ9hid6UHGx97jPb7gC5rRjN/5nX70owtdAJxb62XlG77hN36jN72ZzGS2s50XeIHd7KYrXZ3nbWADb/M24xlPj5Q/W3y28HbS2/q+dCEfh8ORjRYOIiIiIuIppk2bxl133UXlypWZPn06LVq0sDqkAiksLIzIyEjWrVtndSgibhMVFcWyZcso/npxuq7v6izbz8xZzlKatN4fCSRQk5oc5zjP8Axv8ZZ7g/UBvgDude9tChN/qwMQERERkeyJi4vjscceY/z48QwbNozx48dTsmRJq8MqsIYNG8bgwYPZs2cP9erVszocEZeIjo5m2bJlLFmyhCVLlrB582YcDgf3lr+XnvS84mt/4AeWsYybuZlAAvmO7zjOcZrSlDGMIZlklrAk09cGEJBhBj9XHIA2FnEpzfCLiIiIFAC7d+9m8ODB7Nq1i08//ZQ777xTjfnyKC4ujooVKzJmzBheeuklq8MRyZWzZ89mSPA3bdqEw+GgevXqdOvWjW7duhEaGkrN4jWhKpB85evNZz6zmMVhDlORinSlK7dyK0EEEUss/emf6etKUILZzM7bmwkGjgOBebuMpFHCLyIiIuLhpkyZwj333EO1atWYPn06zZo1szokrzFq1CjWrFnDjh07NIAiBcLZs2dZsWIFixcvdib4drudkJCQjAl+zZqX/pseDswgy6TfEn7AU8CbVgfiXZTwi4iIiHiouLg4HnnkEb7++mtuu+02xo0bR4kSJawOy6v8/vvv9O7dmw0bNmiHA/FI586dY8WKFSxZsoTFixezceNG7HY71apVcyb33bp1o1atWlkPWq0EOuVL2DnnAxwArrI6EO+ihF9ERETEA+3cuZPBgwezd+9exo4dy+23364ZaDdITk6matWqjBo1ivfee8/qcEQ4f/68M8FfsmQJGzZswGazUbVqVWeCHxoaSp06dXL+M8EBXA3sBOxuCD63/IDrgXlWB+J9lPCLiIiIpBcDbAbWAxuATcB5IBEIAIoDzTCNpVoDrTDrTl1o0qRJ3HvvvdSoUYPp06dz9dVXu/YGksGDDz7I7NmzOXToEL6+2rVa8ldMTEyGBH/9+vXYbDaqVKmSIcGvW7euawb95gF9834Zl/LD/MxtYXEcXkgJv4iIiIgDWAJ8DswGbIBvykdma139Uj7bUj73BB4Cbkz3XC5cuHCBhx56iO+++45Ro0bx+eefU7x48dxfULJl1apVdOzYkSVLltC1ax67jItkISYmhlWrVjnX4K9btw6bzUblypUzJPj16tVzX1XPKGAyaT/DrOQDvAS8YnEcXkoJv4iIiBReNuBr4ENgD2bD4tw0s/JLuVZV4GHgEaBIzi7x77//MnjwYA4cOMDnn3/O6NGjcxGI5IbD4aBWrVr07t2bL7/80upwxMvExsZekuAnJydTqVIl5/r70NBQ6tevn3/LdqKBBsB/WFva7wc0BDaizvxuooRfRERECqddwEhgLWaGyVW/EfkAdYFJQNvsveTHH3/k/vvvp2bNmsyYMYPGjRu7KBjJrmeffZavvvqKY8eOERiozENy78KFC6xatcrZZG/t2rUkJydTsWLFDAl+gwYNrO3L8TtwA6772ZdTvpgk/2+guUUxFAJK+EVERKRwsQEfA89hZrbcsT2VX8q1n8KUqV5mtj82NpYHHniAH3/8kTvuuIPPPvuMYsWKuSEgycq2bdto1qwZc+bMoW9fT1vgLJ7swoULrF69OkOCn5SURIUKFZzl+d26daNhw4ae13jzB+B2C+7rg/k5OR/oZcH9CxEl/CIiIlJ4JAK3Aj/n0/18gI6YJlmlMj61fft2Bg0axKFDhxg3bhwjRozIp6Dkcpo2bUrTpk2ZMmWK1aGIB4uLi3Mm+EuWLGHNmjUkJiZSvnx5Z4IfGhpK48aNPS/Bz8w3wN0px/mRGfpilk/NxPOaB3ohJfwiIiJSOCQC/YGF5O+aVT+gKaYpYGmzXvz777/nwQcfpE6dOsyYMYOGDRvmY0ByOW+++SZvvPEGJ0+eVLNEcYqPj8+Q4P/9998kJiZSrly5SxL8ArvLwwxMI78k3FP1lMoPKA2EA+qPmS+U8IuIiIj3swFDgFlY06DKD2gLMbNjuP/J+5k4cSJ33XUXn3zyCUWLFrUgIMnMgQMHqF27NlOmTGHYsGFWhyMWiY+PZ82aNc4me3///TcJCQmULVuWrl27OtfgN2nSpOAm+Jk5iCnvX+yGa6f2SRkCjAXKu+Eekikl/CIiIuL93gRewLrmVIDD18G0ktO4y3YX48ePZ/jw4dYFI5fVoUMHypUrx5w5c6wORfJJQkJChgR/9erVJCQkEBwcnCHBv/rqq70rwc+MA7NzyWNAXLrHcssXM8haDvgKCMtTdJILSvhFRETEu/0DtMS9Zao5cPj7w4SMDrE6DLmMzz77jMcff5zjx49Trlw5q8MRN0hISGDt2rXOJnurV68mPj6eMmXKZEjwmzZt6v0J/uVEAxOAz4C95HzL0tTzW2C2Kh0CqB+pJZTwi4iIiPdKxmyNtw2PSPgdvg58KvvATqCk1dFIZk6cOEHVqlUZN24cd999d9YvEI+XmJjoTPCXLFnCqlWriIuLo3Tp0nTt2tXZRb9p06b4+flZHa5ncWD6j0zFbJ/3Lxl/ll68pWkg0AxoD4wArsmXKOUKlPCLiIiI9/oIeAJLS/kv4Qs8hNkaUDzS9ddfT0JCAkuWLLE6FMmFxMRE1q1b50zwV65c6Uzwu3Tp4myy17x5cyX4OZWAqZraAVzANEMNgqlzp/L9uu/5/dDvZnZfPIYSfhEREfFONuAq4IjVgWSiCHCCS7bqE8/www8/cMcdd3Do0CFCQrT8wtMlJSWxfv165xr8lStXcuHCBUqVKpUhwW/RooUSfDf56quvuP/++4mPj8ffXxm/J1HCLyIiIt5pLtDP6iAuwwezNvYBqwORzJw9e5ZKlSrxxhtv8MQTT1gdjlwkKSmJDRs2ONfgr1y5ktjYWEqWLEnnzp2da/BbtGih5DOfLFy4kOuvv579+/dTq1Ytq8ORdJTwi4iIiFf58ssviYiIoMn8Joz4dwTY4Eu+JIIImtCEEYywOkST8NfDrOX3sTgWydQtt9zCgQMH2LBhg9WhFHrJyckZEvwVK1YQGxtLiRIlMiT4LVu2VIJvkd27d9OgQQP++usvunXrZnU4ko4SfhEREfEqnTp1YuXKlfSnP7OZbR6jEyvJ+JgrHec485hHJJHUoAZ96UtFKjqff4ZnMn/hSGjSswkjRnjAIIRkEB4ezsCBA9m5cycNGjSwOpxCJTk5mY0bN2ZI8GNiYihevHiGBL9Vq1ZK8D1EQkICRYoU4dtvv+WOO+6wOhxJR98hIiIi4vXu4z760pf61Hf5tccznkd5lMpUZiAD2cQmPuZjPudzOtMZgDKUyfCaoxzlMz6DCXDzuZuV8HugPn36UKpUKX766SdeeeWVtCccwH9ALM6GZZQEyloQpJdITk5m06ZNziZ7y5cv5/z58xQvXpxOnTrx/PPP061bN1q1akVAQIDV4UomgoKCqFq1KhEREVaHIhdRwi8iIiLeKd12Uec5TzTRxBKb4ZQd7GAWsyhJSbrSlcMcZhnLKElJnuf5LG+xiEXcx33UpCZb2ELJlL32EkjgGMec5108w/8iLzqPe/Xqlcs3KO5UpEgRwsLCmD1pNi9f/TI+G3xgHebjXCYvKI/ZAvKalM89MVuUySVsNtslCf65c+coVqwYnTp14tlnnyU0NJQ2bdoowS9AatWqpYTfA6mkX0RERLxKdkv65zOfm7kZGzbu4R72sIe97CWCCCpRieMcz/JevejFIhYxjGF0pjMnOclVXMXN3HzJrH6qRBKpQQ1OcIIKfhU4eP4gRYsWddG7F5f5Fw49d4iyv5SlBCUgALP/+JV+c/YB/FLOKwfcC9wN1HB7tB7NZrOxZcsWZxf9ZcuWce7cOYoWLUqnTp2cXfTbtGlDYKBGSQqq2267jYMHD7J8+XKrQ5F0NMMvIiIihU4yyTzEQySRxH3cxxd8QRJJ1KVujq6zghUA/MRP/Md/1KY2r/AKz/Isf/AHV3P1Ja/5mZ85wQkAHrI/RNEAJfseZQdwP7AEqvtXxye1q2JSNl7rwCT7AKeBt4E3gTDgE6Cai2P1UDabja1btzrX4C9btoyzZ89StGhROnTowFNPPUVoaCjXXHONEnwvUqtWLZYuXWp1GHIRJfwiIiJS6OxgB/vZD5hZeoAAAggllAlMyNY1YoklnngASlOaucwlkECOcpQ5zOF+7mcZyy553VjGAlCc4jzgeADiIGUlgFgpGfgAeBGwm4d8kvO4hYIt5fMvwO/AWGAkXrczg91uvyTBj46OpkiRInTo0IEnn3zSmeAHBQVZHa64Sc2aNTly5AgJCQn6e/YgSvhFRESk0Em/vj6YYOfx5crwM1OEIvjiix07DWhAYMqC7aY0ZQ5zWMtabNjww8/5mk1sYjWrAbiLuyhLWYhHCb/V9gJDgE1cuWQ/t5IxTf5GA9OAH4EKbrhPPrHb7Wzbts25Bn/p0qVERUURFBREhw4deOyxx+jWrRtt27ZV4leI1KpVC4fDQWRkJHXr5qxaStxHCb+IiIgUOtXS1VZHEeU8Ps3pbF/DDz+u5mq2shVHuizRnjI97IMPvvhmeE3q7H4AATzO4+ZBVTRbazPQAziLe5L9VKnXXgi0B/6iwKztt9vt/PPPPxkS/DNnzhAUFET79u155JFHnAl+kSJFrA5XLFKzZk0ADhw4oITfgyjhFxERkUKnYcqfnexkIQsZwADiiecv/srRde7iLh7iIXaykzjiKEpRNrIRgP70T1v/DZzhDD/xEwDDGEZ1qgMw/6/51KhXg5o1a1KiRAkXvUPJli1AF+ACaeX37mYDIoAOwN9ASD7dNwfsdjv//vuvs8ne0qVLOX36NIGBgbRv356HHnqI0NBQ2rVrpwRfnKpXr46vr6869XsYJfwiIiJS6Pjhxxd8wY3cyHjGE0MM+9lPSUpyjGMZEvUruZ/72cAGfuAHetCDGtRgIQsJJdQ5m5/qO74jjjh88GEMYwA4ylFuDLvReU758uWpWbOm86NWrVrO46uuuorixYu77j9CYbcP6E7+JvupbMAJoBuwBiibz/e/iMPhuCTB/++//wgMDKRdu3Y88MADzgRfO0rI5QQEBBASEqKE38NoWz4RERHxKpMnTyZyXCT1/65PmC3MPMZkIomkPvUJI8x57iEOMZe5FKMYnejEG7zBD/xAIxrxL/9m+56b2cxKVjrL/DvR6dK4UmKoQAX+j/8DX3D0d3Bs7DEiIiKIiIjgwIEDzuOIiAgOHjxIUlJae/iKFStecUBAyVg22TAz7BvI/2Q/PT9M74DJ+Xtbh8PBjh07nE32li5dyqlTpwgICODaa6+lW7duzgS/WLFi+RucFGhdu3alWrVqTJkyxepQJIUSfhEREfE+k4HbrnzKTnZSgQqUoxwAMcTQkIYc4QhP8RTv8I57Y/QDXgOeu/wpNpuNY8eOZRgESD8ocOjQIZKTk53nV65c+bIDAjVq1FD5daoPgCetDiKd2UB/913e4XCwc+dO5xr8JUuWcPLkSQICAmjbtq0zwW/fvr0SfMmT0aNHs3v3blatWmV1KJJCCb+IiIh4n11AwyufsoY13M7tNKc5wQSzgAUc4AADGMAkJlGMYrzKq8QRl+nrX+EVipDHBHoBcH3uX26z2Thy5MhlBwQiIyOx2dKmsKtUqZJhECD9oECNGjUKx57ou4BmQKLVgaTwBYIxcZVzzSUdDge7du3KkOCfOHECf39/2rZtS2hoKN26daN9+/ZaJiIu9corrzB+/HiOHTuW9cmSL5Twi4iIiPdxAI2A3Vyx83oMMfzBHxzgAGUoQ3va04hGzuc/5mPiic/0tY/xGEHkYcux0sBxyOuYwZUkJydz+PDhyw4IHD58GLs9ZVcBHx+qVq162QGB6tWrExAQ4L5g80sf4A/MVnmewg94EPg4dy93OBzs3r07Q4J//Phx/P39ueaaa5wJfocOHZTgi1v9+OOPjB49mgsXLmiJkYdQwi8iIiLe6QtMEuWJv+n4AU+Au1cNZCUpKYnIyMjLDggcOXKE1F8VfX19qVat2mUHBEJCQvD39/B+0PuBunjmv4nimAGgbGzU4HA42Lt3r7PJ3pIlSzh27Bh+fn7OBD80NJSOHTtq5wfJV0uXLuX60OvZ/vt26lStY3pkFMFUsVS0OLhCSgm/iIiIeKdzQGW4TEW+tXyAvUBtqwO5ssTERA4dOpRhQCD9oMDRo0ed5/r5+RESEnLZAYFq1arh5+dn4bsBnsas37eyUd/l+ABfAndf+pTD4WDfvn3OJntLlizh6NGj+Pn50aZNmwwJfsmSJfM7cinsDgK/AeshcXkivrt98c9sM7gKQFvgGkzTzO6YwU9xKyX8IiIi4r0eAMbjWQmeP9ALmG91IHkXHx9PZGTkJbsLpH59/Phx57n+/v5Ur179kmaCqV9XqVLFbQMCN954IyeOn2D4v8N5PP5x8xg3coITDGc4j/O4W+6bIz6YZSj/gAMH+/fvd87eL168mCNHjuDr60vr1q2dTfY6duxIqVKlrI5cCiM7sBD4DJPsg0nes1oq44v5t24DQjBVWHdgBgPELZTwi4iIiPc6BTQAovGcMu5AYCsmLi8XFxfHoUOHLjsgcPLkSee5AQEB1KhR47IDApUrV8bX1zdXcYSEhHDkyBEe4RE+TlkoH0IIR8j4mKs9y7P8wR8ADGEIYxiT4fl1rONzPucf/gHgRV5kRf8VTNs4jcjISHx9fWnVqpUzwe/UqZMSfLHeL8AjmJl9P/I2oOqb8vF/mCVOpfMcnVzEwxdaiYiIiORBBeArYJDVgaTzJoUi2QcoWrQoDRo0oEGDzN/whQsXOHjw4CUDAlu2bGH27Nn8999/znMDAwO56qqrLjsgUKlSJXx8fK4ckA/OgZ/5zCeRRCpRyUXvNqPv+I63edv5dTvaZXj+Ez5hEpMYylB+5EcATnGKgK0BDB48mG7dutGpUydKl1YGJB7iNPAQ8BMmSYe8V0/ZUz6+wQwk/ECedi6RS2mGX0RERLzbhQvQcBtEtsbSuQ5/oDWwEq1bzaaYmBgOHjx4STPB1K/PnDnjPLdIkSKXHRAYMGAAx44dyzCbn1lJfxJJvM/7/MZvVKISN3ADu9jFn/xJU5ryPd9nK+61rKULXahJTU5ykiiieIAHGMvYtPdGDCUowXGOU4UqAHzl+xV3PX4XvOei/4AirrIAuA1TLeWuJVK+mOT//4CxuHUHk8JEM/wiIiLivQ4cgLAw+O80VPwXzpSwZjs2P6AUMBkl+zlQokQJmjRpQpMmTTJ9/ty5c5kOCKxZs4apU6cSHR192WtvYQtHOEInOjkfe5AH+YqvqE1tnuEZ5jOfr/maRBIzb0KWiROcIIwwHDiYylR60zvz95ZJO34fuw+szdZtRPLPRGB0yrHdjfdJvfb3wB5gLqAelHmmhF9ERES806JFMGQIlCkDf8+FMiWgHWZdf34m/X5AUeBPoE4+3rcQKFWqFE2bNqVp06aZPh8dHc3Bgwfp0b0Hp8+cvuK19rOfb/kWgNd4jT704XquZwYzOMnJK742VRJJ3MItHOEIH/ERLWiRo/cDwI6cv0TEbSYAo/L5nnZMJVQvzM/N4vl8fy+Tu84nIiIiIp7K4YD334frr4drroF166BZM6gBrAKqkX+z7Kkz+8sgN7mf5E2ZMmVo3rw5RYKyrg1exzpsKbXKLWkJgB9+NKNZtu/3Pd+zghX0oQ+P8Ejugo7P3ctEXG4ucLtF97YB64Gb8axdVgogJfwiIiLiPWJjYdgwGDMGnn4a5s2DsmXTnq8J/A10y4dYfIBmwBpIyR/Fg53nvPO4SLrFw0VysJD4AhcA2MMeruEa2tCG05jKgulMpw1tSCTxyhdJykHQIu5yArNm38pubzZgEfChhTF4AZX0i4iIiHfYvx9uvtl8njEDbrkl8/MqY/aP/haztVQiri3xT/3t6jVgDPptyxNk0bwfoB71nMdHOEJtagMQSWS2bzOc4Rl6AgD0pjenOU03ujGGMQQQcMVrnI0/S6OqjQgODqZMmTLZ+px6XLJkyVxvXSji5ADuAWLwjO1Mnwf6Ao2sDqRg0v+CREREpOD7/Xczs1+2LPz9N1x99ZXP9wHuBK7DbDM1B1P3mJfSUX/MwEF74AsgixAkH2Uj4e9MZ5rQhO1s5wd+oDOdWctatrAl27epmPInvdRmfxWoQBvaZHkNv9J+3HvvvURFRREdHU1UVBSHDx9m27Ztzq/Pnz+f6Wt9fX0pXbp0jgYL0n8OCgrK9nsVLzYNs0Wep7ADIzHVWWp6mmNK+EVERKTgcjjg3XfhuefMmv3JkyE4OPuvr4H5xfYAMD7lIxrzG5KNrGe3AjAl2EUxa13vQ4l+AeWLL5OZzAAG8B3f8Sd/UpWqtKEN61mPr4tXwk5nOu/yLsnpykte53W+5Es6/NeBTz/99LKvTU5O5uzZsxkGBa70OTIy0vl1VFQUycmZl7QULVr0kqqB7H5WdYGXSAAexAySecLsPqSt50+/W4Bkm4/D4fCUv0oRERGR7IuJgdtvh59/hhdegFdeAb88Tv/EA78D69J9RGVyXgmgNdAWaAPcgLaP8mBbt24l8dlEKv1eieq26uYxtpJIIpWoRHWqO8+1YWMXuyhHOSpRiba0ZR3rGMQgpjM9x/fewhaSSKIiFalBDefjpzjFQQ5mPNkP+D8o/WRp6tWrhzs4HA4uXLiQ7cGCiz/npbrgSs8FBga65f1KDv0EDLc6iEz4As2BjVYHUvAo4RcREZGCZ+9es17/4EGYMAEGDHDPfRyYbfxiMDNfgUAxTB+AbJSJiwf5FHiMK+4jHk441anONVwDwG/8xo3cCMDv/E4verk/zrmQckuPlJycTHR0dK4GC7KqLshN34Lg4GBKliyJj4++IV2iA6bR6BW+Tyy1FlK+PSWblPCLiIhIwfLbbzB8OFSsCLNmQePGVkckns7hgO+2w51XXm/xD//wAA+wj30EEEAEEdSjHm/yJrdwCzHEEEpopq8tSUkWszhvcfoAx4BKebuMp0pfXZCbwYKYmJhMr+vr60uZMmVy1bdA1QVw4sQJevToAfHw/r736U1vTnCCHvQA4H3MY5bzx+wc8L3VgRQsWsMvIiIiBYPDAW++CS++CDfeCJMmQenSVkclnsrhgM2bYdo08xERAX77wXYVl9uZ+mquZilLOcMZIomkIhWpQhXn80Upypd8melr/fP6a7Uf0BOvTfYBfHx8KF68OMWLFyckJCTHr79cdcHlBgkOHjyY4eusqgtyM1jgDdUFSUlJbN++HYBon2hwQBJJbCflMaJddq8FLOBJnrzs85kNLsQTz0AGcjD5IEyAt8Pepm+/vi6Lydsp4RcRERHPd/48jB4N4eHw8svw0kugBmGSme3bTYI/dSrs2QPlypktGocMgc1XwRO+WTYjK5vy52J++GWr036u2DA7Rshl+fv7U758ecqXL5/j1zocDmJjYy9bPZDZYMGWLVucX+e2uiCrQQOPqy5I+d6oTGW2sQ0gQ4+LvGpPe6YyNcNjr/EaM5gBQAIJl7zmXu5lPvPNF3aI3h3tsngKAyX8IiIi4tl27zbr9Q8fhl9+gZtusjoi8TR79qTN5P/zj6n8CAuDzz6D7t0hIMCc1xJ4FjLJKawXAp5QNe2tfHx8KFGiBCVKlMhVdUFSUtJld0bI7LGDBw9m+Npmy3zPz2LFiuW4qiD9zgjuqi44zWmGMhTIOOt+ghO8yItsYhO1qMVoRvMhH3Kc44xgBE/z9BWvWzrlT6pYYlnIQsAMMlw8u/8pn/IjP9KZzixnuXnwgKveZeGghF9EREQ819y5cOutULUqrF0LDRtaHZF4iogImD7dJPkbN0KJEtC/v1n2cd11kNme8mWA/8Nsv5h5/mUNH+BxtMe4BwsICMhzdcGV+hNcPFiwefNm59exsbGZXje1uiA3SxGyqi7IrKTfho1e9GIb2+hDHx7kQd7lXZawBBs2jnEsx/9tJjGJs5wF4BEeIYi079ulLOUJnqA2tRnLWJrT3DyhhD9HlPCLiIiI57Hb4fXXTfl+//6mE3+pUlZHJVY7cgRmzDBJ/t9/Q5Ei0LcvPPcc9OkDRYtmfY03gJmY3Rc8oRO5P9AUlfN7sfTVBdWr57w8PikpKVs7I6QeR0REZHjuStUFwcHBFC9ePFtxTGOas8x/LGOpRS2qU53a1M7xe0r1BV8ApunlfdznfDySSAYxCIApTKEU6X7+H8/17QolJfwiIiLiWc6dg5Ej4ddf4bXX4PnntV6/MDt5EmbONGvyly835fm9e8PkydCvH5QsmbPrlcF0+e7jhlhzawL6rVwuKyAggAoVKlChQoUcv9bhcBATE3PFwYJDhw6xe/fuLK+1lrUAlKIUtagFQC1qUZKSnOd8jmNbxjK2shUw6/TTl/q/xEuc4hRv8RbXci0RRKS90BOX5Hgw/WgRERERz7Fzp1mvf+yYSfj7qhNzoXTmjNlycdo0+PNP8PGBXr3gu+/Mv48yZfJ2/RuA2zGJtpWl/T7Aq8CVdwsUyTUfHx9KlixJyZIlL1tdcPjwYb7/Puu97mIxSwvSl92nfp2bhP9zPgcgkEAe5dEMz8URB8AEJjCJSSSR5Hzu2T3PMvuW2fz88885vmdhpIRfREREPMMvv8CIEVC9OqxbB/XrWx2R5Kdz58y/gWnTYOFCsNkgNBTGjTMN+HKxdvqKPgX+ATZiTdLvC/SHLHqciXiM+pifyWc4wwUuUIxixBDDGc7k+FrHOMYsZgFwG7dRlaoZnn+f93mBF5xfH+Uo13M9AA/UfIBB7wzK7dsodFQfJyIiItay281a/ZtvNrO4f/+tZL+wiI01CX5YGFSsaJZynD0LH35o1uv/+Sfcfbfrk32AEsDvQCPyv1meL9AL+MmCe4vk0ihGUYIS2LDxPaYi4Cu+wp6LZhjjGU8SSfjgwxjGXPJ8CCFcne5P6mADQEhwCHXq1Mn9GylklPCLiIiIdaKjTVO+//0P3ngDfv4552uypWCJjzfl+kOHmiR/6FCz5eKbb8KhQ2ad/oMPQuXK7o8lGFgOXEP+/lY8APgVyGQjARFPVZGKTGYy5SjHgzxIDWqwiU2UpSwAftkcvUoiia/4CoD+9KchOdx9pUbOTi/sVNIvIiIi1vj3XzOrf+oUzJsHN9xgdUTiLomJ8McfZjZ/9mw4fx6aN4cXX4TBg6F27rt851kZ4C/gReBDTOLvjhJ/f8ya/TfQFnziUSpXrsy2bdvgRqh+yKzzr0xlZ0f+6qSt/b+JmzjKUQ5xiBBC8MGH4hS/5LwrsWNnIQsBLinlv5xqVEuLZ2jOdzoozHwcDofD6iBERESkkAkPh1GjoGZNM9tbt67VEYmrJSfDkiWmu354OERFQcOGZkZ/yBBz7GlWASOACFy3ZZ8P4ADaABMhp5OZIvlmJGaZSfLlT/mYj+lLX+pSFwcOnud53uItggnmX/6lMvlQmXMKcMMqH2+lGX4RERHJPzabWa//xhswaJDpul6ihNVRiavY7bBihZnJnzHDVG/UqQP33WeS/KZNTcd9T9UB08jvA2AscAIzE5+bGX9/TOJUC3gCuAfN6otnuxaYdOVTruZqBjCAKKKIJZZznKM73fmYj6lMZVayknu4J9PXdqITX/Jl3mKsgZL9HNIMv4iIpzsLHADiMb88FsGsO62FOrFIwRIVBbfeCr//Dm+9BWPGeHbyJ9njcMCaNSbJnz4djh41Oy0MGWI+WrcumH/PycAc4DNgccpjfpjZ+sxm/9M/5wvcBDwIdMfM8ot4uv+AqpBuB7wrnPofUURRjWoUo5jz8RhiiCAi09eUpCRXcVXu4/PFbGP5QlYnSnpK+EVEPM1OYC6wHvgbOHiZ84oBLTEj8u2BfqgBlHiuf/4x6/XPnDEl3tddZ3VEkhcOB2zalJbkR0SYJnuDB5skv1078PWiEcn/MNv3bQDWYaoAYjGJUSBQEvPzuA3QOuW4lCWRiuRNNsr6LeMHHIb8WDXgTZTwi4h4giRMx+bPgKWYUWwfsldGGpDy+mBMyeg9QE23RCmSOzNmwO23m9LuWbOsbdAmebN9uxmwmTYN9uwx2+XdcotJ8jt3Bj/VrIsUaGuAdlYHkQl/IAyYZnUgBY8SfhERKzmAqcCjwElyv1Y0lR+mnHQQZvCgYh7jE8kLmw1eeAHefts0avvmGyhe3OqoJKd27zYJ/rRpJuEvUwYGDDB/p927g79aQol4DQeml8V6PG+WfzWeORjh4ZTwi4hY5QRmNv4X0ro4u4ofpsR0PDDYhdcVya4zZ2DYMFi0CN59Fx5/vGCu4y6sIiLSkvxNm0xjxf79TZJ/3XUQGGh1hCLiLjuA5mRrLX++8AXuwzTSlBxTwi8iYoXZwGggBvfs9wxpgwhhwA+YAQCR/LB1q1mvf+6cKf/u2dPqiCQ7jhwxyy+mTjVN+IoWhb59Tbl+nz7maxEpHN4FnsG1kxG54QtUA/4FtKFLrijhF5EcigU2A7uBC5jh3yBMNtkUaIxZVC6X9Q1wd8pxfvwE9gOaAQvRVjbiflOnwv/9H9Svb9br16xpdURyJSdPws8/m5n85cshIABuuMEk+f36actEkcIqGVM+vwXrS/sXA6EWx1CAKeEXkSzYgPnAz5jFU/tI24/Ih7Rp5NQfJYGYxL8DMArTrlicviYt2c9PfkADYAWmuZ+IqyUnw7PPwvvvm633vvoKihXL+nWS/86cgfBwk+T/9Zfppt+rl0ny+/c3a/RFRCIxOwGdxH3ViFn5EHjMont7CSX8InIZJ4Fvgc+BI5j2qDkZ4k09vxXwMGYheSEvBw0HbsG68jg/zJZRS9H2feJap0+btd2LF5uE/5FHtF7f05w9C7/8YpL8hQvBbofQUPP3FhYG5cpZHaGIeKLdQCcgivyf6X8VeCmf7+mFlPCLyEWSgQ+AFzHDufYrn54l35RrVMAMIPTL4/UKqGOYGfYYrF0P5wM8B7xuYQziXTZvNh3bY2LMfuzdulkdkaSKjYU5c0yS/9tvkJAAnTqZJH/gQKiszaxFJBv2Y0rqj+L+mf7UXxvfB55w870KCSX8IpLODmAEsMEN1079CX4r8ClQ1g338FAOzDjH71i/Dg7MX8VatNpC8m7KFLjzTmjUyKzXr1HD6ogkPt4k91Onwty5cOECtG1rkvxBgyAkxOoIRaQg+g94CLOVsKt3Fkrlh5kf+h7o7YbrF1JK+EUkxTfA/Zik3J3Dt36YReRzKDSbqU4ERlodRDp+QD1M70WV9ktuJCfDU0/BRx/ByJHw5Zfq4G6lxET44w+T5P/yC5w/Dy1amDX5gwdD7dpWRygi3iIcuAs4i+t+XfRLudbtwEdAaRddVwAl/CICwDuYvVfyix+mk/8cwMu360oAqmLWvnnST1sf4DPgAasDkQLn1CmTSC5bZhL+Bx/Uen0rJCebngnTppkGfFFRptJi6FDz99OggdURioi3+g+zbd9XmMQ/tYgzp1IT/Z7A03j9r4RWUcIvUuh9ADxpwX19MY39/gC6WHD/fPITMNzqIDLhg5nl35lyLJIdGzea9fpxcWa/9q5drY6ocLHbzdZ506aZrfROnYI6ddKS/Kuv1uCLiOSfOGAG8AmwMeUxP8wER2YDAAGY3ZzB7OZ8J3Af5vcRcRsl/CKF2jRgqIX39wWKYHoGNLQwDjdqj1kvn9feh+6ivW0luyZOhLvvhqZNYeZMqF7d6ogKB4cD1qwx5fozZsDRo6ZXwpAh5qNVKyX5ImK9U5hf5zYA64E9wAVM76JiQDlM76DUj4aYeR9xOyX8IoXWMcxP2/NYW2vuD7QEVmOGhQu+qKgomjZtCknw0cmPGMQgooiiKU0B+AjzmOX8gf7Az1YHIh4tKQmefBI+/RRuvx2++AKKFLE6Ku/mcMCmTSbJnz4dDh6EKlVM072hQ+Haa8HX1+ooRUSkANC4ikih5MB0XLmA9QvLk4F1mC4tViwtcD2bzcaRI0cAiPWJBQfYsHGElMeIdct944jjBm5gL3sBeI/3GMYw5/NhhLGWtWkvSAZmQqt+rfh1zq9uiUkKuJMnTZK5ahV8/jncd59mk93pn39Mkj9tGuzdC+XLwy23mCS/Uyfw845BURERyT9K+EUKpcnAPKuDuMhzQF+8tbS/LGWJJNJ57A53cRdLWer8+uKBhZOc5AhHGM94+tDH+XjQk2rVL5lYtw7CwswM/+LFJuEU19u92yT4U6fCv/9CmTLmv/sXX0C3buCvX9VERCT3VA8mUujYyN+O/NnlAF63OgjXSymgOMtZ2qX8mZdusOUIRxjGMOpRj170YgIT6ExnQgjhWZ7N9m0+5EMmM5nruT7Lc8tSlpB0fyocqJDjtyVe7ocfoHNnqFYNNmxQsu9qBw7AO+9Ay5amm/6775q1+HPmwIkT8O230KuXkn0REckzJfwihc5vkFJa7lmSMU0ET1kdiFuklvQf4Yhz5t2Gjeu4jqlM5Wqu5gM+YCELWclKjnCEKKKyde0/+ZOneIq61GUsY7M8/zEeoza16UIXXvN9jdi/3bPEQAqgxESzzd7tt8OIEbB0qUn6Je+OHDHbGLZrB7Vrw6uvQv36pgHiyZOmKWLfvhAYaHWkIiLiRZTwixQSJ0+e5Pjx48TEfExqc7yTJ+H4cYiJsTS0dOzAd1YHkW9mMIN/+Rcw6+2b0YwP+ACfHOyTF0EEQxiCL778xE+UoMRlz61OdT7hE+Yxj/GMJ5JIXra/TPvJ7UlISMjz+5EC7vhx6NEDvvoKxo+Hr7+GIC33yJMTJ0zvgy5dzK4GzzwDlSvDTz+ZH8DTppny/aJFrY5URES8lBJ+kUKidu3aVKlShfff/xNT1m8mmapUgfffd889HQ6Ijobs5JIXLsDx43aOH/+YmJhz7gnIw2xM2bQ2iCDqUheASlSiPOWzfY03eIPTnOYN3qANba547mQm8zAP04xm9KIX7/AOANtithEeHp7LdyFeYc0aaN3aNIpbssRsvye5c+YMfPMN9OwJVavCo49CiRJmmcTJkzB7tmnCV+Lyg3MiIiKuooRfpBCrVMl8uPr3Trsd/vc/c+3KlaFYMeje3TSgzkxMDFxzjRl8qFLlOG+//bRrA/JQCZiRkAACMjx+8ddXkkQSAB/zMSGE0IpWzuee4ilCCXV+7XvRj/zUbQIB9u7dm+17ipf59lszA33VVWa9focOVkdU8Jw9CxMmQJ8+5gffPfeYx7/80lROzJ8PI0dC6dLWxikiIoWOusGIFDq+mNJ5WL3aJOeZJfxnz0KpUmYHrqgoM0sfFATBwVnf4dlnTQ+q0FBYsAD+/BNuvBG6doWtWzMuCXY4YNQo05w6zbE8vL+Co2HKjgQxxHCGM5SlLLHEcpKT2b7GR3zE6+maHZ7ilDPpf5ZnGcnIy772SLpeDlWrVs1p+FLQJSbCI4+YpPTee+GTT7R+PCdiY02TvalT4bffzH/Pzp3h44/NVnqVKlkdoYiIiGb4RQofu/Mos5L+nTtNZW+ZMlChAtx6K/Tubc4bNuzSq13s1CnTlwpMVXBQkJn0qlXLVLp+8EHG8994A8LD4f/+L/1y4eN5eYMFxq3c6izf/4zPABjLWOesfXYEE5yh434VqmR4rhIm6djDHh7mYfawB4AooniFVwCoVrQat9xyiyvekhQUx46ZLd+++86s1R83Tsl+dsTFmR9YQ4aYH5DDhpkZ/LffhshIWLYMHnhAyb6IiHgMJfwi4mS3m4mpjRvNEtPjx02yvnZt9q+xbJnZthtMkp8q9XjRorTH5s2Dl1+Ghg3h00/TX+Vobt9CgVKKUoQTTi1q8QqvUIlKHOAAVTGz7X4pzRVd4SquohGNuIVbKEtZKlCBrWzlNp/bWHnfSkqr1LjwWLXKjOpFRJhv2DvvtDoiz5aYCHPnml0LKlaEgQNh927zw2v/fvj7b3jsMQgJsTpSERGRS6ikX6TQcGR5xsKFsH27OX7sMbMF9PDh8NRTcDSbOfiRdDv+FS9+6XHq83v2mOqBgADTsLpYsfRXOZu9m3mosmXLEhkZCb2g7M6y5jHKEkmk8zhVZzqzj32c5jTlKEcSSfzADwDUpGaO712RipneJ5BA7kv5k0QSMcQQTLD5Z9Ejd+9TCqDx4+Ghh+Daa2HGDNNkQy6VnAyLF5ty/VmzzLqmxo3ND8MhQ8x2eiIiIgWAEn4RcdqxI+24dm3z2ccHatbMfsLvl25S2uG49Dj1+SlTTJ+A5583OcfxdFX8sbFJnDhxgkoFtCzW19eXkJAQ6ATsBZJNw7wQLp0BfJu36UY32tKWZJJ5judIIIHylGcEI3J+78vcJ70AAkyyn6p1jm8jBU1Cgkn0v/7alJx/+KFK+C9ms8GKFSbJnznTrE+qWxfuv9+UPF19tdURioiI5JgSfhFxSv/7v82WdpycnP1rpC/jP3v20uPU54sXN8tcv/nGfEDa9n1ffx3HrFnXEhERkf0be6L2wLdXPqUHPRjDGLawhVhi8cef/vTnHd6hMpVZzWoGMegyl2/PDGbkLcbqQMEcV5HsOnLElKFv3mzW7N9+u9UReQ6Hw5TkT51qKh6OHYMaNWD0aJPkt2xpRj1FREQKKCX8IuLUsmXa8c6dJiFPSjLl99nVtSuULAnnz5trdO5sHt+1y3zu29d8fvJJ85FekSIm6X/00VK8/npErt+Hx7gFeBCIu/wp13ANS1hCAgmc5SzlKJdh7X4rWvE3f2f62iCCMn0823wBbbfu3VasMI05AgJg+XKz/2Vh53CYRiXTppmPQ4dMV9LBg02Sf+21SvJFRMRrKOEXKTSy/gW2Qwfo2dM01nv1VfjsM5g0CaKjs3+X4sXhvffMLl8ffGDyi3nz4ORJs+z10UezdZXs39CTlQJGA18DWVRJBBFERSpm+nhWJfq55gOoX5t3cjhM5/1HHjHf2DNmmIZzhZXDAf/8k5bk791ruuzfcotZk9+pU8b1SCIiIl5CXfpFColKlSpRqZIfJUqkf8x8pH9s+nS44w44cMBslVe6NLRrZ57L7pLfe+4xfa5CQqB/f7Ne/5FHYOVKM/t/OZUrm3hKlrwq52/QU91Plsm+JfwcMBBQzzbvEx9vvnkfeMCsP1+0qPAm+7t2wWuvmfX3zZrB55+bMqSFC01jki++MF8r2RcRES/l43A4sm7dLSJeYjQwmStloA5HxmrWpCSTuJ88Cc88A2+95d4IHQ5YsOAWypZ9klatWhEQEODeG+aH64DFeFjib4eb3oIv7zDlzOIdIiPNev1t20xH/pEjrY4o/x04kDaTv3mzGWXs39+U6/fqpWaFIiJSqCjhFylUPgMe4Upb9L38sllLf+ONJvH/8EP44QeoVg3WrzdVsKdOZf5aPz/zfF61a1eENWviKV68OB06dKBr16507dqVa665hqCgPK5bt0IE0JgrruXPV74O6LwFtvUwTROefRYefxyKFrU6MsmLZctg0CDzDRweDq0L0fYLhw+bZQtTp8Latebfcr9+Jsnv3Vv/tkVEpNBSwi9SqGwFml/xjNhYk+TPmWO2yqtUCbp3hyeeMFXBkZGX7/tVvTqsW5fXGEuTmHiEDRu2smzZMpYuXcqKFSs4f/48RYoUoX379nTp0oWuXbvSrl07ihaUX+THYcr7reaH6cy/HUiIMuXOY8dC1arw9tsmQVLDsoLF4TB/h48/brpkTpvmmpE3T3fiBPz8s0nyV6yAoCC44QazJr9v34xrlURERAopJfwihc41wEbAbnUgmfADngDeyfBocnIymzdvdg4ALF++nKioKAIDA2nbtq1zAKBDhw6U8NRf8u1Ad2Al1pb2+wBLgc7pHtu9G8aMgV9/NQ0bPvoorXGDeLa4ONMhc8IEk/C/8w74e3E/3tOnTfXC1KmwZAn4+sJ115kkv39/03REREREnJTwixQ6EwFPXdfrA+wFal/xLLvdzrZt25wDAMuWLePUqVP4+/vTunVrunbtSpcuXejUqROlPSkBOAl0AA5iXdL/OZevNPjrL3jsMdi6FYYNMzP+NWrkZ3SSE4cOwYABsGMHfPMNDB9udUTucfYszJ5tKhf++APsdlN2NGSIef/lylkdoYiIiMdSwi9S6MRjWrOftTqQi/gBvYDfcvxKh8PBjh07nMn/0qVLOXbsGL6+vrRo0cI5ANC5c2fKWZ0cHAY6AUfI/6T/HeCpLM6x2eD77+GFF0yi9cQTplujp1ZOFFaLF5t940uUMFtitGhhdUSuFRNj1hVNmwa//Wa6h3bqZJacDBxo1hqJiIhIlpTwixRK7wLPcKXmffnPB1gOdMzzlRwOB3v37s0wAHDo0CEAmjZt6lwC0KVLFypZkTgcw5T378b9Kyt8MX/NY8lZD4Hz582WDB9+CMHB8MYbMGqUti+zmsMBn3wCTz4J3bqZ0narB7FcJS4O5s83Sf7cuebra681Sf6gQaZzqIiIiOSIEn6RQikZuBbYAtgsjgVMVvoQ8LHb7hAREZFhAGDfvn0ANGzY0Jn8d+3alWr5lVTEYGbbx4H5O3BDIu0LVAMmAKG5vEZEhJnhnzbNzCJ/9BGE5vZikicXLsDdd8PkyabnwptvFvz1+omJsHChGbj45Rczs9+ypSnXHzwYatWyOkIREZECTQm/SKH1L6Zjv9Wbw/sBIZh4iuXbXY8cOZJhAGDnzp0A1KlTJ8MAQM2aNd0byAMz4Yu24BsCdhd1x/fDjCE8ALwNuKIaf9Uqs75/7Vq4+WZ47z2oW9cFF5ZsiYgw69V374bvvjMJcUGVnGz6RUybZhrwRUdDkybmPQ0ZAvXrWx2hiIiI11DCL1KovQs8bXEMvsASMraNz38nTpzI0ARw27ZtANSoUYOuXbs6BwHq1q2Lj6u2rTt+HBo0gMF3QM2PTEO9Y6Ql7Dnlh1kicBMwBlesjsjIboeffjIz/idOwEMPwYsvQpkyLr6RZLBokSlrL13arNdv1szqiHLOZoPly02S//PP8N9/UK9eWpJ/9dVWRygiIuKVlPCLFGoO4F7ga6xbzz8RuM2ie1/e6dOnWb58uXMAYPPmzdjtdqpUqZJhAKBRo0a5HwAYNQrmzTOztmXLmmKLeZj19n9hkncfTCJ/cSHGxY9XBO4B7gKq5y6cbLtwAT74wHTxL1oUXn0V7rmn4JeXexqHw/RQeOop6NULpkwx/04KCrsd/v7bJPkzZsCxY3DVVWlJfsuW4KrBMxEREcmUEn6RQs8G/B/wYz7f1wf4Erg7n++bO2fPnmXFihXOAYD169djs9moUKECXbp0cS4BaNq0Kb6+vllfcOVK03X8q6/grrsuff4CpsXChpSP3UAcJsEvCpQDWgKtUz5qYP6T5qejR+H55+HHH6FhQzMIcMMN+RyEl4qNhTvvNGvbn30W/ve/gtEw0eGADRtMkj99utk6sGpVsx5/yBDThE9JvoiISL5Rwi8imKnkMcCHmBJ7d7aO98dkphOAoW68j3vFxMSwatUq5wDAmjVrSEpKIjg4mM6dOzsrAFq0aIH/xTPfNhu0bg2BgWYGNDsDBJ5s40azvn/ZMrj+epP4N2lidVQF1/79Zr3+vn3www9wyy1WR3RlDgds22aS/GnTTNwVKpjO+kOGmIGtgv5vXEREpIBSwi8i6cwHbgdO457u/T6Y6egJQCM3XN86cXFx/P33384BgNWrVxMfH0/JkiXp1KmTcwCgTZs2BHz1lVn/vmYNXHON1aG7hsNh1pePGQMHD5pu8q++ahI/yb6FC816/bJlYfZsz17bvmuXqUCYNg127DDbN4aFmfhDQ7XEQ0RExAMo4ReRi0QBj2FK/P1xTRd/P0zlwBvA47hlCzoPk5CQwLp161i6dClLly5l1apVxMbGUqNoUf5JSmJv8+bEfPQRbdu2JSgoyOpwXSchAT77zJSg+/jACy+YwQ1veo/u4HDAu+/Cc8+ZKonJk00C7WkOHDAJ/tSpsGULlCxpdm0YMsT0GQgMtDpCERERSUcJv4hcxhpM97ipmBL/nJX5Oxy++PjYSUwsRmDgg8B9QE1XB1lgJCUlsXHjRoo++CC1tmyhWVAQETExBAUF0a5dO2cFQPv27SlWLP+2J3SbU6fg5Zdh/Hizl/q775oyda3fvlRMDNxxh2ls98IL8MornrVe//Bhsx5/6lRYtw6KFYN+/UySf8MNUKSI1RGKiIjIZSjhF5EsnAK+B34C/iFtxj8g5bODtG5xSSmfiwNtefPN4yxdWoHff1+aX8F6tjVroF07+OILbHffzZYtW5wVAMuXL+fMmTMEBARwzTXXOAcAOnbsSMmSJa2OPPe2b4cnnoDff4euXU3X+VatrI7Kc+zdawZCIiJgwgRz7AmOHzfb502bBitWmAqNPn1Mkt+3LxQvbnWEIiIikg1K+EUkBxKB7Zi28bswbeMTgSJASaApZo1+HcCXCRMmMGrUKA4cOEDNmjWtCdlT2GymQ7ndbmZJL5rBtdvtbN++3TkAsGzZMk6ePImfnx+tWrVybgXYqVMnyhTEfe8XLIDHH4edO812hG+8Ybq3F2a//QbDh0PFiqb/QePG1sbz338QHm6S/CVLTKO96683SX7//lCqlLXxiYiISI4p4RcRt4mJiaFy5co8/fTTvPjii1aHY63x4+Hee2HVKmjfPsvTHQ4Hu3btcg4ALF26lKNHj+Lj40Pz5s2dAwCdO3emfPny+fAGXCA52WxD+NJLEB8PTz9tZv8tX8JwAjOAdQFTpRIElACaAKVdfzuHA956y5Tv33gjTJoEpd1wn+yIjjbNAadNg0WLzIBU9+6m8d6AAaZ5oIiIiBRYSvhFxK1uv/12li9fzp49e/AprOu3T5+G+vXhppvg++9zdQmHw8H+/fszDAAcPHgQgCZNmjgHALp06ULlypVdGb3rRUfD66/Dp59CpUrw9tswbFg+bt22E/gZWIfpVXHiCufWBNoBbTHbSFbJ263Pn4fRo81M+ssvm8GP/N6yLiYGfv3VJPkLFkBSEnTubJL8gQNNxYGIiIh4BSX8IuJWS5YsoVu3bixfvpxOnTpZHY417r3XNDzbtcskuC5y8OBBli1b5hwA2Lt3LwD169fPMABQvXp1l93TpfbuhaeeMuXsbdvCRx9Bhw5uulkS8CvwGbAUs1OEg+w1o0w91wcYADwAdCWtd0U27d5tZs0jI82s/k035ez1eREXB/Pnm3+H8+aZr9u1M+X6gwZBtWr5F4uIiIjkGyX8IuJWdrudOnXq0KNHD7755hurw8l/69ebZPbjj+Hhh916q6NHjzoHAJYtW8a///4LQK1atZwDAF27dqVmzZqeVW2xZIlZ379pk0lA33kHrrrKhTeYA9wDHMMk77Y8XCt1q8qrgR8wPSuyYe5cuPVW07dg1ixo2DAPMWRTQgIsXGhm8n/5xczst2pl/hsPHgyFva+GiIhIIaCEX0Tc7pVXXuHDDz/k+PHj3rHlXHbZ7WbGOi4ONmwAf/98vf3JkydZvny5swJg27ZtOBwOQkJCMgwA1KtXz/oBAJvNdKl/7jmIijIDAM8+a/Z5z7UzwMPAZMCXnG4teWWps/7PAi9i1v1nwm43yxdeftk0vpswwb3N75KS4K+/TJIfHg5nz8LVV5skf8gQqFfPffcWERERj6OEX0Tcbv/+/dSpU4dJkyZx6623Wh1O/vn2W7jzTli2zKyRttiZM2dYsWKFcwBg06ZN2O12KleuTJcuXZwDAI0aNcI3v9eVp4qJMTP8779vGtm9/jrcfnsu9qVfDAwGosjbjH5WfID6wC9Ag4xPnTsHI0ea9fKvvgrPP++e9fo2m/k3Nm0azJxpuu3Xq2fW5A8ZAk2auP6eIiIiUiAo4ReRfNG1a1eCgoJYuHCh1aHkj6go06ivd2+YONHqaDJ17tw5Vq5c6RwAWL9+PcnJyZQvX57OnTs7BwCaNm2KX44T7jw6dMjM8E+ZAs2bw4cfmu7x2TIbGISZ0XflrP7l+AGlgEVAK/PQzp1w881w7BhMnmz2rnclux3+/tusyZ8xA44fN8sgUpP8Fi3A6qoNERERsZwSfhHJF9999x133nknBw8e9Nwmcq704IOmfHvXLqiSx87u+SQ2NpbVq1c7BwDWrFlDYmIiZcqUoVOnTs4BgJYtW+KfX8sT1qyBxx6D1atNk7v33jMDKZf1K6axniPlI7/4AcWAFfDLARgxAqpXN+v1rxhvDjgcZmnI1Kkwfbpp/le1qlmPP3So6RWhJF9ERETSUcIvIvni/PnzVK5cmeeff57nnnvO6nDca/NmaN3aJKePP251NLkWFxfH2rVrnQMAq1evJi4ujhIlStCxY0fnAECbNm0IDAx0XyAOhylXf/ppOHrUDKa89BIEB1904kqgG6apngX/a3P4wYVAaBgHbcPghx/y2IMA8963bTNJ/rRpsH+/2TbvllvMTH6nTvm/rZ+IiIgUGEr4RSTfjBgxgrVr17Jz507rm8S5i91u1uufPWu6zgcEWB2RyyQmJrJ+/XrnAMDKlSuJiYmhaNGitG/f3jkAcO2111KkSBHXBxAXZ7bue+stCAyEV14xWx4GBACxQGPgMPlTxn8ZScDh2lBzD/jkIRHfudMk+FOnmuPgYBg40CT5oaH53gBSRERECiYl/CKSb/7880969uzJqlWraN++vdXhuMeECTBqlOmU3q2b1dG4VXJyMps2bXIOACxfvpyzZ88SGBjItdde6xwAaN++PcWLF3fdjY8fhxdegO++gwYNTIO/PgvA5wssTfYz+Aq4K2cv2b/fJPnTpsGWLaY6YMAAk+T37GkGOURERERyQAm/iOQbu91OzZo16dOnD19++aXV4bje2bMmAQ0NNTOzhYzNZmPbtm3OAYBly5Zx+vRp/P39adOmjXMAoGPHjpRyxdZ0mzebJRO2xbA075dzrWLADqDGlU+LjDTr8adNg3XroFgx06tgyBDT8NEdlRIiIiJSaCjhF5F89cILLzB27FiOHTtG0aJFrQ7HtR59FL75xpRgh4RYHY3l7HY7O3bscA4ALF26lBMnTuDr60vLli2dAwCdO3cm+JL1+NnkcMDZOlDygOmb5zH8gRHAd5c+dfy46aw/bRqsXAlBQdCnj2m8d+ON4MpqCBERESnUlPCLSL7as2cP9evXZ+rUqQwZMsTqcFxn2zZo2RLefBOeesrqaDySw+Fgz549GQYADh8+jI+PD02bNnUOAHTp0oUKFSpk86rrgLbuDDsPAoHjQDD89x/MnGmS/KVLwc8PrrvOJPk33QSuqHgQERERuYgSfhHJd6kl3b/99pvVobiGw2HK+E+cgK1btdY6mxwOBxERERkGAA4cOABA48aN6dKli3MQoMpltzYcDUzGdOb3MA4fWDcEXoqCRYvMv5MePUy5/oABULas1RGKiIiIl1PCLyL57uuvv+bee+8lMjKSqlWrWh1O3k2ZArfeCgsXQq9eVkdToEVGRrJs2TLnAMDu3bsBqFevXoYBgC5duvDff//xxBNxvPqqadRXs6aZSH/iCXj1VQvfRCo7cBC4owsMHmq67FesaHVUIiIiUogo4Rdxq2RgJ7AB2AacBxKAAKAo0ABoDTRP+bpwOHv2LJUrV+bVV1/lqYJe/n7uHDRsCB06wM8/Wx2N1zl27BjLly93DgBs374dAF9fX+x2O08/DW+/bc4tXx5OnybDY67y889mR8Bdu0xfvZ494X//g2rV0s7ZswfGjYMFC0yxR9WqZvznmWdWULFiR9cGJCIiIpINSvhFXO4C8BOmWddGID7l8dT92FO/5XwwAwIOwBeT/A/DbOVVOb+Ctczw4cPZvHkz27dvx8fHx+pwcu/JJ+GLL0yjvhpZdGSXPDt16hQrVqxg+PDhxMfH8+ST8N575rnYWFM1Hxjo2lUVX30F99xjxnV+/dXsuHjvveave9OmtMr8Dh3MeV26mFhuvdWs8KhfvzJbt0YQFBTkuqBEREREssHX6gBEvMce4FGgEnAn8DdpyT5AUspHcspHEmnJvx2zhdcrQAgwGFieDzFbZ9SoUezYsYP169dbHUru/fsvfPIJPP+8kv18UqFCBQYMGEDxlE72vun+L9akCVSuDG+8kfbY6dMm8a5YEZo1g+eeg/79oUQJuOGGrO8XF2cqBgAefhjq1TNJfa1acOgQvPNO2rmrVsGoUea5q69Oe93u3cdZutTj9g0UERGRQkAJv0ieJQH/AxoDnwMxKY/bc3EtO2ADZgFdMDP+p10Qo+fp2bMnVatW5YcffrA6lNxxOOChh8zC8SeftDqaQit9cUhMjJlZT0hIe2zYMNNioUEDMztfpoz5HBtrkvmsrFwJ0dHmuH79tMdTj+fPv/xrT6f71o2Pj7/8iSIiIiJuooRfJE+2Ytbgv0zazL0rpF5nBlAfMwDgXfz8/Bg5ciQ//fQTCekztIJixgxT2/3pp2YfdclnWa9GW7cO/vjDHL/wghmbGTMGLtvwPxMREWnHJUteepz++fTOnYOxY81x6dI+dO3aNfs3FREREXERJfwiuTYBk+z/S3aSj9yxAVFAGPAguasa8FyjRo0iKiqKOXPmWB1KzsTEwOOPm9rw7NSFixtk/T23ZUvaccOG5rOPT8aZ+izvku426asJUo8z64KTmAhhYbB7N/j7w8SJxShdunT2byoiIiLiIkr4RXJlLDAKMxNvc/O9UjOKL4AReOR+47nUsGFDrr32Wn788UerQ8mZN94w9doffWR1JHIFtnTfmn5+ace+Ofg/X0hI2vH585cep38ewG6HESPgzz8hIMAUgvTrV4CbUoqIiEiB5m91ACIFz7fAQxbc14Hp/h+YEoN3jNeNGjWKhx56iBMnTlCpUiWrw8narl3wwQemUV+tWlZHU6A5HA5iYmKIiorizJkzREVFOT/Sf53ZcXR0VJbXb9w47fjAgbTkfN++7MfYsaPZhu/CBfO67t0zXuO66zKe//DDMH26qQD4/nu4+WaAItm/oYiIiIgLKeEXyZElmG3zrOIAfgDqAc9ZGIfrDB06lEcffZTJkyfz+OOPWx3OlTkcJqMLCYGnnrI6Go8RFxeX44Q99SM5OfOKldKlS1O2bFmCg4MJDg6mbNmy1KpVy3n8v/+9RkxM7BXj6twZrr0W1qyBDz+ENm1MMn7oUPbfW6lSprP/Cy/A+PEwaBAsWQJ79kBwcMZ/Bv/7H3z+uTn+8EOzO4ARnP0bioiIiLiQj8OR2QpEEblUDNAIOIr1a+n9gY1AU4vjcI3BgwezY8cOtm7dio+PB5c/h4fDwIGmzXu/flZH41KJiYk5TthTjy/XdLFEiRIZEvbsHpcuXRq/9DX4mShfvjynT5/m6afh7bdTHzMrLdI/dugQ3H47LF5snu/TxyTrq1ZBjx6waFH2/vt8+il8/DEcPGjW5XfvDu+/b7YCTBUQAKnjFym7Bqbw5913P+H+++/P3s1EREREXEQJv0i23Q+Mx/pkH0zC3wRYBwRYHEvezZ8/nxtvvJENGzbQqlUrq8PJ3IUL0KiR2WB97tyMHdw8hM1mIzo6OscJe1RUFLGxmc+WFylSJMcJe3BwMGXKlCEwMNBt7zU2NhaH4x4CA6cSGGhLecwUYQQGmo/0kpNNog5Qrx7s3Qt3321m7XMiKclcJ7O//tT7Z+QDvElQ0BMEBBT871UREREpWFTSL5Ity4BxVgeRTjJmS8D38IbS/uuuu47KlSvz448/em7C/9ZbcPy46cbmxmTfbrdz/vz5HCfsZ86c4dy5c5le09/f/5LEPCQkhKZNm142YU/9KFq0qNvea14UL14caIfDMSXdY5ee9/nnULYs9O1rEvVPPjHJfvHiZnVGTl0pZ8/s/mYZTge8YWBORERECh7N8ItkSy9gMe7vyJ9TpYFjgGcmZTkxZswYvv/+e44ePerWmeFc2bvX1G4/9ZRZqJ0Fh8NBbGxsrsrjo6OjsdsvrSLx9fWlTJky2Z5hT/918eLFPXupRA7t27eP8PBw/vlnAj/++M8Vzz16FF580RRlnDpl1uSHhsKrr0Lz5nDyJNSunflrK1XKWYO/zAUAp4GSeb2QiIiISI4p4RfJ0h4gBxt357sfMFsEFmz//PMPTZs2ZdasWdxsWptbLj4+nqgzZygxdCgBu3fz19ixnLpMIn/x10lJSZles1SpUjlO2IODgylVqhS+OdlPzos4HA62b99OeHg44eHhbNmyhaJFi9K79/V8++0GypQ5go9P1kttEhIgKOjia5tS/Mz4+poO/bnnD9yK+R4VERERyX9K+EUuY+PGjZw8eZKyZSfQtu10wMbGjWZGsGxZaNvW6gjBbM3XAthgcRyu0aZNG0JCQpg9e7bLrpmUlJSr7vFnzpwhPj6evsAcIAyYlXLN4sWL5zhhL1u2LKVLl8bfXyupssPhcLBhwwZmzpxJeHg4u3fvpmTJkvTr14+wsDB69+6dUtb/DdbunJGVNYBH/LAQERGRQkgJv8hl9O3bl3nz5tG1qx9LlthSHoN586BrV7M1l6udOwdbt5rjZs1M+XF6f/5pmoZdaixly15DW88Yhci1sWPH8thjj3H06FEqVKjgfNxms3H27NkcJ+xRUVHExMRkeq+goKAsE/YKJUrQ/7nnSLjqKk788APBKc973JIDL2Gz2Vi1apVzJv/QoUOUK1eO/v37M3DgQHr06EHQxVP0xAKVMbtoeBJfzC4amzCN+0RERETyn6aaRLKUtm6/dWvT7btZM9ff5dVX4c03oUoV8/WxY2b/75dfTjtn3DhIn78mJcFffwE8SNeuXVnijlEIF3E4HFk2ozt27Bg2m41OnTpRokQJ53Nnz54ls7FJPz+/SxL2qlWr0qRJkyzXumerGd2rr8KZMxRbtozg+p68rKPgSkpKYvHixYSHhzN79mxOnDhB1apVGTBgAAMHDqRz585ZVEUUBx4F3sA0yPMUduB5lOyLiIiIlZTwi+RA//7Qvr0p6U/PZoN166BIEbNz25EjsHs3+PlBr15ZX/eLL+CVV6BuXdi2Dex2s/vbK69AxYpw333mvJ9/zvi6qVNTE35o1KhRXt9elhwOBxcuXMjWGvaLj6Ojo7HZLm166OPjk6EZXYUKFTh58iTdunXLcq17iRIl3NeM7sABs5n7E0+Akn2XiouL448//mDmzJn8+uuvREdHU6tWLUaMGMHAgQNp27ZtDvsVPA9MB/bhGY01/YE+wC1WByIiIiKFnEr6RS4jraTfhyVLHCmPXVrSf+gQXH897NwJDRqY/b8rVDCJeFAQxMdnfa9atSAiAh55BD7+2Dz2wANmIKBmTZN7ZqZTJ1i50jQX27VrD3Xr1s3We0tISMhxwp56nJiYmOk1S5Ysmav92kuXLp0hufv111/p378/mzdvpnnz5tl6P25x882wYQPs2AElSlgXh5c4f/488+fPJzw8nHnz5hEbG0vjxo0ZOHAgYWFhNG/ePI+DN2uBdlg/y+8DlAJ2YpYaiIiIiFhHM/wiWbpyAvHggybZ79ABli0z6/BzMtl+5IhJ9gGqVUt7PPU4IsKU96eW+qfassUk+wADB8L+/dtZu3ZttpL3uLi4TGMpVqzYJU3n6tevn2XyXqZMGZc1o7vhhhuoUKECP/74Ix9++KFLrpljv/0Gv/wC06Yp2c+D06dPM2fOHMLDw1m4cCEJCQm0bt2a559/ngEDBtCwYUMX3q0t8DTwDtYm/Q7gC5Tsi4iIiCdQwi+SB6dPw5w55njYMFPCHxxsKgG+/TZ71/jvv7TjIkUyPz516tKEf+zYtOOnnoKbbrqZY8cgMDDwksT8qquuomXLllnOtl/aEC3/BQQEcOuttzJ58mTeeecdAgIC8jeAhAR4+GHo3h0GDcrfe3uB48ePM2vWLMLDw1m8eDF2u52OHTvy1ltvERYWxlVXXeXGu/8P+BeYi1lDb4XngeEW3VtEREQkIyX8Inmwf3/acdWqmR9nJX1in36Je/rj9OcAREfDlCnmuHt3aNMGNmxYTqlSLSlWrJj71rXnk9GjR/Pxxx+zYMEC+vXrl783/+ADU1bxyy9QwP875peIiAhmzZrFzJkzWbVqFb6+vnTv3p2xY8fSv39/qlw8WuU2/pi1/P2BP8j/pP9RzKCDiIiIiGdQwi+SB8HBacfpq+QvXMj+NWrVMmv9ExLgzJm0x1OPg4LMOv70vvsu7R5PP20+V6lSDdOxvOBr3rw5LVq04Mcff8zfhP/QIXj9ddNMoXHj/LtvAbRz507Cw8OZOXMmGzduJCgoiOuuu47vv/+efv36Ufbizpb5Jgj4FRiBSf7dzRczsPByyocGiURERMRz5KQNsohcpFattLX2GzemPb52bfavERho+sMBbNqU9vjmzebzgAHmnFQOh9meD6BFC7juutRnsrHNXAEyatQofv31V06fPp1/N338cShTJuNeiAKYHRo2b97Miy++SJMmTWjUqBFvvvkmdevWZerUqZw6dYpff/2VUaNGWZjspwoEpgJfYr4v3DW27QdUAOYBr6BkX0RERDyNZvgtZ8f8kqhfFAsiPz945x247Tb48kuzrV5kZMbEPTvefx9WrzY7ALz9ttmWb8ECqFED3nsv47kLFsDeveY4dXYfAoByeXw3nmX48OGMGTOGqVOn8sADD7j/hn/8ATNnwuTJULKk++9XANjtdtasWcPMmTMJDw/nwIEDBAcHc9NNN/HWW2/Rq1cvihb11IEmH+Ae4HrgdmAJJkF3xbZ9/kAyporgI6CMC64pIiIi4nrali/fHAXWAOuBdcBG4ByQhPnFNBC4CrOtVGugTcpHYGYXEzeKjo5m48aNvPXWW5w5s4rQ0At88IF57uWXYc0aaNYM3n037TXz5sHEiRAQYLbK27PHLAUvUwaiorJ336goM2iwfr35uk0buPfejMsGwNz3r7+gVCn46Scz6ACtgA15e+MeqH///hw9epR169a590aJieYvtVIls99iIV67n5yczLJlywgPD2fWrFkcPXqUihUrMmDAAAYOHEhoaGj+N1LMMwcwA/gEWEVawp5TfphB2n7AY0Coi+ITERERcQ8l/G5lA34HxgILML90+qc8frn/7OmfLwvcB9wN1HB3sIXS6dOn2bhxIxs2bHB+3p/Sia948eKMGVONl1/efcVrnDtnJoTT54ihobB0KfTpYwYD3CsAuBOzFZh3mTVrFmFhYfzzzz80adLEfTd691147jlTmtG0qfvu46ESEhL4888/mTlzJr/88gunT5+mRo0ahIWFERYWRocOHfAzI0teYBswDpgCnAUgKQn8/S8e5/HFDMamVgRUw1QK3A1Uz69gRURERPJECb9b2DFrR98GIslbGWnqjFLflOupkVhunTx5MkNiv3HjRg4ePAhAyZIladWqFa1ataJ169a0atWK+vXr4+e3F7jyXuFLlphc8bbboEIFk+D/+CNUrmxm4hs1Msl/+qZ+6XXtCnmviv4G+L+8XsTjJCYmUrVqVe644w7eTV9S4UqHD0PDhnDnnfDxx+65hweKjY1lwYIFzJw5k7lz53L+/Hnq16/PwIEDCQsLo3Xr1gV+t4crcwCHiIpaxLhxdzJ6dHuqVi0FxAPFgJLA1ZhKq9ZARetCFREREcklJfwutxcYhSkbdSU/zGzT68ATqP3ClR09evSSmfsjR44AUKZMGWdSn5rg16lTB1/fzHpY2jFLLQ5f8X5btsCECWZtfcmS0LYtjBiRVo4/ahScOJH5a3/80VSS55bDAZs3h9Oixc1emaA9/PDDzJgxg8jISPz93fDvfuhQWLwYdu+G0qVdf30PEh0dzdy5cwkPD2fBggXExcXRvHlzZ5LfuHFjr/w3dCUrV66kU6dObN26laaFsLpDREREvJsSfpdxAJ8BT2Fm83OzPjQ7fDDrtSeR1cxzYeBwOIiMjMyQ3G/cuJHjx48DUK5cOVq3bu1M8Fu3bk3NmjVzmNS8AzxH/u/pnTWbzYclS4Lo2TOeevXqMWLECG677TZq1apldWgus2HDBtq0acP8+fO54YYbXHvxxYuhe3f44QczKuOFTp06xezZswkPD+fPP/8kKSmJdu3aOcv169SpY3WIlpo0aRIjRozg/PnzlChRwupwRERERFxKCb9L2DBr7b/Op/v5Y7aa+h1on0/3tJ7D4SAiIuKSsvz//vsPgEqVKmVI7Fu1akX16tVdMGP5H1AV02DR89hsc1mypAgTJ07k559/JjY2ls6dOzNixAgGDRpEmTJlrA4xTxwOB82aNaNx48ZMmzbNdRdOSjL7GpYpA8uXQ6YVHgXT4cOHmTVrFjNnzmT58uUAdO3albCwMAYMGEC11L0khddee43PP/+cE5crwREREREpwJTw55kdGI2Zcc/P/5R+mGZtfwCd8vG++cNut7Nv375LZu6jUlreV6tWLUNi37p1a6pUqeLGcuQRmH293VW5kRs+mOZhBzANxsy67NmzZzNhwgQWLVpEQEAAN910EyNHjuT6668vgN3VjQ8++IDnn3+eY8eOEZxh24L/gF3ABSARCMKsvW4CZDFb++GHMGYMbNhgEv8Cbu/evYSHhxMeHs6aNWsICAigZ8+eDBw4kJtuuokKFSpYHaJHGj16NLt27WL16tVWhyIiIiLickr488QBPIh13dF9MTP9y4GWFsWQdzabjT179mSYud+0aRPnzp0DoEaNGpfM3FfKy6L3XNmDaeCVmM/3zcpPwNBMnzl69ChTpkxhwoQJbNu2jQoVKjBs2DBGjBhR4BqyHT9+nJCQECZOfJFhwwIwW1uuAY5d5hU+QG3MNpfXAoOBdP9mjh2DBg1g5EgYO9atsbuLw+Fg+/btzJw5k/DwcLZu3UrRokW54YYbCAsLo2/fvpT28p4ErtClSxdCQkKYMmWK1aGIiIiIuJwS/jyZCgyzOAY/zCzvdkxnac+WnJzMzp07M8zcb9q0idjYWABq166dIbFv1aoV5cuXtzjqVB8Bj1sdRAo/zF7g4Zjk9sq2bNnCxIkTmTx5MsePH6dRo0bO9f7Vq3v6FmPJwFy2bLmT5s1PY967g+z1VEg91xcYBDwAdIARI2HBAtOoL0PFgGdzOBysX7/emeTv2bOHUqVK0a9fP8LCwujduzfFinn+zwFPUr16dUaNGsXrr79udSgiIiIiLqeEP9dOYJrmnSV/S/kz4ws8jElIPUdSUhL//vtvhpn7LVu2EJeyP129evUyzNy3bNnyonJtT2PDLJ9Yj7Wl/T5AaWAnGWatsyE5OZk///yTCRMmMGvWLOLj4wkNDWXkyJEMHDiQkiVLuiPgPJgD3AscxW73xdc3L40T/YFkiKkLnfbCQ9/A/3n+VoY2m42VK1c6y/UjIyMpX748/fv3Z+DAgXTv3p2goCCrwyyQ4uPjKVasGN988w133HGH1eGIiIiIuJwS/lxxAAOAuZgk0BP4AEuBzpbcPSEhgX/++SdDcr9t2zYSEhLw8fGhYcOGGWbuW7ZsSalSpSyJNW/2YPbkjsXarv0/AwPzdIXz588zc+ZMJk6cyOLFiylSpAgDBgxgxIgR9OzZ0z1b4GVbFGYQaxJmQMuF/62TMd8uvi+CzwtAoOuu7SKJiYksXryY8PBwZs+ezcmTJ6latSphYWEMHDiQTp06Wfz34x127dpFw4YNWbx4MaGhoVaHIyIiIuJySvhzZT5wo9VBXMQPs2Z5J6kN3NwlLi6OrVu3ZijL/+eff0hKSsLX15fGjRtnmLlv3ry5l213tQrogVnPb0XS/xmmd4TrHDp0iMmTJzNx4kR27NhB5cqVufXWWxkxYgTNmzd36b2ytgSz5v4M7h1Q8wEaA7OBum68T/bExcWxcOFCZs6cyZw5c4iOjqZ27doMHDiQsLAw2rZti68X7STgCX777Tf69OnDwYMHqVGjhtXhiIiIiLicEv5c6QUsxnNm99NbiInPNWJjY9myZUuGmft///0Xm82Gv78/V199dYaZ+2bNmhWSNcRLgD6YpD8//x18BDzqtqs7HA42bNjAxIkTmTJlCv/99x/NmjVjxIgRDB8+nKpVq7rt3sZszFp7O/kzmOIHlAH+Aprlw/0yOnfuHPPnzyc8PJz58+cTGxtLkyZNnEl+s2bNClRzxYLmiy++4NFHHyUuLg4/Pz+rwxERERFxOSX8ObYXqGd1EJfhj0lCf8nVq8+dO8fmzZszzNzv3LkTu91OYGAgzZo1czbSa926NVdffTVFihRx6TsoWNZjGuedwr1Jvz8mMf0KGOnG+2SUlJTE77//zoQJE/j1119JSkqiZ8+ejBw5kptvvpnixYu7+I5zgJsxS2bye4vLEsBKzHZ+7nX69Gl+/fVXwsPDWbhwIYmJibRp04awsDDCwsJo0KCB22MQ48knn+SXX35hz549VociIiIi4hZK+LNp8uTJzJkzhzJldvDll9sBG5Mnw5w5UKYMfPml1RGm8gEOYjr3X150dLRzb/vU5H737t0AFClShObNm2coy2/cuDGBgZ631tl6Z4EngG9x+Vpzpw7Aj1hZdh4dHc2MGTOYOHEiy5cvp0SJEgwcOJARI0YQGhrqgtnRVUAoZoG9FT+S/ICywCagmsuvfuzYMWbPns3MmTNZsmQJdrudjh07MnDgQAYMGMBVV13l8ntK1sLCwoiNjeX333+3OhQRERERt1DCn01PPvkkH3zwAZUq+XD8uCPlMfjgA6hUCY4fd+39bDaYMQOWLIGEBGjSBG6/HcqVu/TcyEiYMgV27YKyZeGGG+6mR4/xzudPnz6dIbHfsGED+/fvB6BYsWK0bNkyQ1l+o0aN1BAsx34HbsfsC+9H3mb8U0u4g4B3MOv1PWft9oEDB5g0aRITJkxg7969hISEONf7N2mSmxnyWMxa+iNYu0zGH9Ob4Teys9VhViIiIggPD2fmzJmsXr0aPz8/unXrxsCBA+nfvz+VK1fO8z0kb1q0aEH79u0ZN26c1aGIiIiIuIUS/mxKS/jTkvvNm2HnTihaFPr3d929EhPhhhvgr79g5Eho3BheeslUEixZAo0apZ07Y4Y5p3JluO8+KFHChzlzypKU1IJSpUqxceNGDh48CEDJkiVp2bJlhpn7+vXra+2qyyQA4ZimeqtxbgOXbann1wQeAkZjZp09k8PhYM2aNUyYMIGpU6cSFRVFq1atGDlyJMOGDaNixYrZvNLDwOdYu+tBet9hBm9ybufOncycOZPw8HA2btxIUFAQ119/PWFhYfTr14+yZT3377OwcTgclClThueff56nnnrK6nBERERE3EIJfzZllvBfrqR/+3b45hszM9+mDdSrB59/bp777DOoUOHK93rnHXjmGahdG/bsAV9feOghGDsW2reHVavMebt3Q7NmEBBgBh6qpVQiHzwIzZqVok2bNhlm7uvWrasu3/lmGzABU6q+CYhL95wPGcvW/YGGQDtMw7qeeNKMfnYkJCQwf/58Jk6cyNy5c7Hb7fTu3ZsRI0Zw0003UbRo0cu8cimmlN+TlAB2ACFZnulwONi8ebNzJn/Hjh2UKFGCG2+8kbCwMPr06eNlO1R4j9OnT1O+fHlmzJjBLbfcYnU4IiIiIm6huu082LQJpk0zJf2pCf+mTdCxI8TFwcMPm3L7p5+G//4zz7/9dtYJ/6RJ5nOrVibZB2jd2nxevRr27zeDAV9+aQYV6tUzAwwREVCxoqk2iI4+gI+PZhOt0xR4L+XYDuzGDAKcB+Ixe78XBRqknBtkQYyuExQUxIABAxgwYACnT59m+vTpTJgwgaFDh1KqVCkGDRrEyJEj6dSpU7pBJwdmdt9dvQ9yKx54DdMk8VJ2u52///6b8PBwwsPDOXDgAMHBwfTv35933nmHXr16FfJmlgVD6rKm2rVrWxyJiIiIiPso4XexZ54xyX7XrvDJJ+ax/fthwoTsvd7hgB07zHH69frpj7dvNwn/2rXm63/+gfLlYdAg+N//4L334JNP/sdDD32U9zckLuCLmcFvaHUg+aJcuXLcd9993HfffezZs4eJEycyceJEvv32W6666ipGjBjBiBEjqF8/CthqdbiZSAYmAu9ituyD5ORkli5dSnh4OLNmzeLYsWNUqlSJAQMGMHDgQLp27UpAQICFMUtOKeEXERGRwqBg1Q17OIcDli0zxx07pj2e/jgrCQmmYR9A+qX16Y8vXDCfo6PTHvv6a7j/fnjqKRPHmDGfc+7cuRzFL+Jq9erV47XXXmPfvn0sW7aMXr168dlnn9GgQQPmz78Ru91TfwQlkJz8HfPmzeOOO+6gUqVK9OzZk3nz5jF06FCWL1/OkSNHGDduHD179lSyXwAdOHCAMmXKUKZMGatDEREREXEbzfC70PnzEB9vjtMv2y1ZMvvXKFIEihUzSX1CQtrjqdeFtNn+1N9TfX2hTh1zXL+++ZyQkMTzzz9P/fr1KVKkiPOjaNGiGb6+3HNq5Ceu5OvrS+fOnencuTN//vknSUmJrFlzmj59zPN16sDp0/DYY/Dyy9bGCmC3Ozh06En69nXQoEED7r33XsLCwmjVqhU+Pnnv4C/W279/v2b3RURExOsp4XehUqXMtnhnzsCpU2mP53TLvnbtTIf+I0fSHjt61HwOCDBr+wGuuQZWrgS7HZKTzXOJiWmv+fHHH0lKSiI+/WhBNvn7+2c5SOCqxy9+LiAgQEmVFzt37hwXLsRlGNA6e9Z8xMVd/nW5NXs2fPSR2bayWDHo2RNefRWqVEk75+GHM1t246Bq1Qrs2LFD/x69kBJ+ERERKQyU8LvYrbeaTvzz5sFbb5kk/Oefc3aNRx81Cf/q1abZX/nyZjcAMFvwpe7sdffd8MUXJslftgx69EhbUtCyZX02bNiJj48PDoeDhIQE4uPjM3zExcVd8lhWz138+JkzZ7I8327PWUM2Hx8fywYbgoKCtJNBvklr1rd/vxm4cnWvu2+/hTvvhAYNYPFi83314IOwcKFpsBkcbM67cMEMODz/PDz5ZNrrfXzeVrLvpfbv38+gQYOsDkNERETErZTwu9grr5hZ940bzUx8iRKQfkey7OQO/frB+++b5KNNGzMT+fffcPPNaY0AARo1gilT4K67YPBg08n/zz9Nw8AffpjmTFTSJ9D5zeFwkJyc7JbBhpiYGP77778rnp+UlJTjmAMDAy0ZbChSpAj+/oXpWzJtIKhly0tL+s+cMV//8QdUrgx9+5oGlX/9BZ06wdy5V756XByMGWOOH37YfL80amS+tyIi4N13zaBcekWKpC2VgQDM9nzibZKTkzl06BC1atWyOhQRERERtypM2UWe3HbbbbRp46Bo0Q/TPWYS8vQJfdmysGYNrFhh1t03bw7h4bB0qVlrn77b/pU88QTcfru5VkICNGlitt+72MCBcMMNphrgwgUzINCoUQjQIk/v11V8fHwICAggICCAkjlpZuAiNpvtkuoGVww2xMfHc+7cuSzPzyk/Pz/LBhsCAwMtm83OrKR/+HD4/Xdo396U28+YAbNmmediYrK+5qpVEBVljlN7W6QeR0SkVeGk9+GH5nuoUiVo1y6Jl15aRo0aeXpr4oEiIyOx2Wwq6RcRERGvp4Q/m1q0aEGLFuWAD9M9Zj7SO34c/v0Xunc3X8fHw8SJ5rhv34zN/LJStqxJ5rNSrJgp5zf8gHbZv4mX8/Pzo1ixYhQrVizf7+1wOEhMTHTLYEN0dPQVz8/NUgogXwYbbLbkLONYv94k+wAvvQR168LTT8Onn2a/J8aBA2nHpUpdehwRkfZYhQom2e/d2+yScd99ZjnArFnr2Lw5kurVq2fvplIgaEs+ERERKSyU8OdICFAOOH3ZM4oWNSXDjzwCNWvC5s2m+d5NN8EPP5hz3n7bPJ6ZZ581VQF5c01eLyAu4OPjQ1BQEEFBQZQuXTrf73/xUgpXDTZcuHCB06dPX/H8xPTdIy+SnRYJ6b8/GjVKe12DBtlP+B2OtOP0hQupx+nHQy6e6f/qK2jcGM6ccfDll1/yxhtvZO+mUiDs378fX19faqh8Q0RERLycEv4c8QFuA8YCtkzPKF0a5s+HPXvMTH9gIDRrBtWqpZ3Tvr0ZDMhMpUp5jdEODM7rRcQL+Pv7U6JECUrkpKzERex2e4alFOkHDrp3D+Xs2fNXfH1yuiKA9DtE5mS3yJCQtOPz6W6Xuhwg/fMXa9DADAw4HBCRvhRAvMKBAwcICQkhMDDQ6lBERERE3EoJf47dB3yS5Vn16mW+5h5MUz338AeuA2q66wYi2eLr60vRokUpmr7BRQp//4AsX9+kSdpxRERacr5vX/Zj6NDBVNzExZldAFKX2aReo2fPy782MjKtQiDkSiMDUiBpSz4REREpLLT/WI41AEIxa+U9TTLwoNVBiGQh68aAnTvDNSkrUz7+2DSunDwZDh7M/l1Kl4ZnnjHH48ebWf65c2H3btOJ/6mnzHM2G/Tvb7a0jI+Hc+fM7gAApUr5cs8992T/plIgKOEXERGRwkIJf648yuVK+q3jC9QCrrc6EBGXmD4dunSBmTOhalX47TezHAYgu7sXvvQSfPABnDgBwcEwYICZ2V+2DGf3fT8/uPtuePNNqF7dnLdsGQwe7MOaNX2VGHohJfwiIiJSWPg4HOlbW0n2OIC+wELMrLqnWIypPhDxXOfOncNuv5siRWZQpIg95THTRK9IEfORXkICBAWZ4/r1TX+Mu+4yjfVyIi7O9NTIqg9A2v18MbtyPJKzG4lHO3fuHKVLl2by5MkMHz7c6nBERERE3Eoz/LniA3wNFCU75cnu5wvcj5J9KQhKlSpFmTLtMyT2pUqZMvv0j40bBzNmmJL7hASz+8WePWYbyoceyvl9ixbNXtO/1MEF0wCzdc5vJB7tQMp+jZrhFxERkcJACX+uVQU+x8z2W8kPqAa8Y3EcIjnRAZNQX16/fmbNfY0aJsl/9VW48UZYvhyaNoWTJ80gQWYfDRq4IsZAoKUrLiQeZP/+/YASfhERESkc1KU/T24DlgHfYk3i74tJSmYC+b/1mkjutQEaATu53PdOSAj8+KM5jo01Sb9PuoKaChVMB//M+OZ5KNMfGA4Uz+uFxMPs37+f/2/vvsOjKvM+jN+ThB5ApKMoCfpiRbAAoigWUMBeUbD3VXTtHVSsrLruKrqsYKOJ2EWlKbaACEhzrZAoYkFEmvQk8/5xiCBSApnJmXJ/rmuuGc6cOc8vBE2+52nVqlWjbt26YZciSZIUdwb+MokA/wGWAMMp39CfQfDtexM4oBzblWIhAlxJMBVly6ptJHdHIkFvfnwUUtralFwKCgrIyckhEkmE6ViSJEnx5ZD+MssEBgNnl3ObVYAxwGHl2K4US90J/h0nmgyCofzeSEtFrtAvSZLSiYE/JrKAp4A+QAXiO3AiAuwBfAwcEsd2pHjLJujlT7T/DRUDN4ddhOLEwC9JktJJov2mncQygOuBGcRnoa/MtY87gCnAXnFoQypvPYEcgn/biSATOA44JexCFAfFxcUUFBQY+CVJUtow8MfcbsAEgv276689VpYwU/LZwwmCfk+CUQRSKqgCDGRLK/aXjwjBqIN+JMZ2m4q1n376idWrV5OTkxN2KZIkSeXCwB8XmcDVwFzgZf489H5LYT2Tdd+WGmuv8w0wGtgntmVKCeFA4FrCD9lR4HGgQch1KF7ckk+SJKUbV+mPqyzgxLWPbwl6/qcAnwBTgd/XOzcDaAy0AfZb+ziQxFzUTIq1e4EvgLcJr7f/VoKt+JSqSgJ/kyZNwi1EkiSpnBj4y02TtY8z1jsWBVYSfBscpq90VoFga8tjgXGUd+h/7bUcOnfuSQX/M0xp+fn5NGzYkKpVq4ZdiiRJUrlwSH+oIgQ9+KYMKfhv4U3g+HJqL/jf36xZZ3Lqqd9zzjnnUlRUVE5tKwwFBQXO35ckSWnFwC8pgVQCXgQeBSoTv0FImUAdYAS77DKYIUOGMmzYMC6//HKi0Wic2lTY3JJPkiSlGwO/pASTAVwBfEawpkXJsVgouYHQHfgS6ALAKaecQv/+/enXrx833XSToT9FGfglSVK6cQ6/pATVFHgfeB54BJhE8L+swm24VibBugBHA9cAh/3ljPPOO48lS5bw97//nZo1a3LLLbdsY91KRCtWrOCnn34y8EuSpLRi4JeUwDIIVs4/E5gGPAEMBZaufb8CsGYjn8lg3Y2BBsD5wMXAzptt7aqrrmLRokXceuut1KxZk8svvzwGX4MSwbfffgvgHH5JkpRWDPySkkQLoB/wHyCfYIvLKcBXBFtcriJY+K8GsDfrtrdsuFWt9OzZk8WLF3PFFVdQo0YNzjrrrFh9AQpRyZZ89vBLkqR0YuCXlGQiBMP9mwKnxf7qkQgPPfQQS5Ys4bzzziM7O5sTTzwx5u2ofOXn51OxYkUaNWoUdimSJEnlxkX7JGkDkUiEfv36cdJJJ9G1a1fGjh0bdkkqo/z8fHJycsjI8MeeJElKH/7mI0kbkZmZyaBBgzjiiCM4/vjjmTBhQtglqQwKCgqcvy9JktKOgV+SNqFixYq8+OKL7LfffnTu3Jnp06eHXZK2kVvySZKkdGTgl6TNqFq1Km+88Qa5ubl07NiRr7/+OuyStJWi0aiBX5IkpSUDvyRtQc2aNRk1ahS1a9fmyCOPZM6cOWGXpK0wf/58li1bZuCXJElpx8AvSaVQp04dxowZQ2ZmJkceeSTz5s0LuySVUkFBAeCWfJIkKf0Y+CWplHbYYQfGjh3L77//TseOHVm4cGHYJakU8vPzAVy0T5IkpR0DvyRthaZNmzJmzBjmzp1Lly5d+P3338MuSVuQn59P7dq1qVGjRtilSJIklSsDvyRtpT333JORI0fy2WefceKJJ7Jy5cqwS9JmuGCfJElKVwZ+SdoGBxxwAG+88QYfffQRZ5xxBoWFhWGXpE0oKCgw8EuSpLRk4JekbXTooYfy4osvMmLECM4//3yKi4vDLkkbkZ+f7/x9SZKUlgz8klQGXbp0YdCgQQwaNIgrr7ySaDQadklaz+rVq/n+++/t4ZckSWkpK+wCJCnZnX766SxZsoSLL76YmjVrcs8994RdktaaM2cOxcXFBn5JkpSWDPySFAMXXXQRixcv5vrrr6dmzZrccMMNYZckgvn7gIFfkiSlJQO/JMXIddddx+LFi7nxxhupWbMml1xySdglpb38/HwyMzNp3Lhx2KVIkiSVOwO/JMXQXXfdxeLFi7nsssuoUaMGZ5xxRtglpbX8/Hx22mknsrL8cSdJktKPvwFJUgxFIhEeeeQRlixZwllnnUV2djbHHnts2GWlrfz8fIfzS5KktOUq/ZIUYxkZGfTv35/jjz+eU089lXHjxoVdUtoqKCgw8EuSpLRl4JekOMjKymLIkCEccsghHHfccUycODHsktJSfn4+OTk5YZchSZIUCgO/JMVJpUqVeOWVV2jevDmdOnVi5syZYZeUVhYuXMjChQvt4ZckSWnLwC9JcVStWjXefPNNdt55Zzp27MisWbPCLiltuCWfJElKdwZ+SYqz7bbbjlGjRlGzZk2OPPJI5s6dG3ZJacHAL0mS0p2BX5LKQb169RgzZgzRaJQOHTowf/78sEtKefn5+VSvXp3tt98+7FIkSZJCYeCXpHLSuHFjxo4dy8KFCznqqKNYvHhx2CWltJIt+SKRSNilSJIkhcLAL0nlaNddd2X06NEUFBRwzDHHsHz58rBLSlklgV+SJCldGfglqZw1b96ct99+m6lTp3LSSSexatWqsEtKSQUFBQZ+SZKU1gz8khSCNm3a8NprrzFu3Di6detGYWFh2CWllKKiIr799ltycnLCLkWSJCk0Bn5JCskRRxzBCy+8wKuvvsrFF19McXFx2CWljB9++IE1a9bYwy9JktKagV+SQnT88cfz7LPP8swzz3DNNdcQjUbDLikl5OfnA27JJ0mS0ltW2AVIUrrr1q0bixcv5vLLL6dmzZrceeedYZeU9AoKCohEIuy8885hlyJJkhQaA78kJYC//e1vLFmyhJtvvpmaNWtyzTXXhF1SUsvPz6dRo0ZUrlw57FIkSZJCY+CXpARx0003sXjxYq699lpq1qzJBRdcEHZJScst+SRJkgz8kpRQ7r33XhYvXsxFF11E9erVOe2008IuKSnl5+fTrFmzsMuQJEkKlYFfkhJIJBLhscceY8mSJXTr1o3s7Gw6d+4cdllJp6CggE6dOoVdhiRJUqhcpV+SEkxGRgZPP/00Xbp04eSTT+aDDz4Iu6SksmzZMubNm0dOTk7YpUiSJIXKwC9JCahChQo8//zztG3blmOOOYbJkyeHXVLSKCgoANyST5IkycAvSQmqcuXKvPbaa+yxxx4cffTRfP7552GXlBTy8/MBA78kSZKBX5ISWHZ2Nm+99RaNGjWiQ4cOf/Rea9MKCgqoXLkyDRo0CLsUSZKkUBn4JSnBbb/99owePZqqVaty5JFH8uOPP4ZdUkLLz88nJyeHSCQSdimSJEmhMvBLUhJo0KABY8eOZfXq1XTo0IEFCxaEXVLCys/Pdzi/JEkSBn5JSho777wzY8eOZf78+Rx99NEsWbIk7JISkoFfkiQpYOCXpCTSrFkzRo0axTfffMOxxx7LihUrwi4poUSjUQoKCgz8kiRJGPglKem0bNmSN998k8mTJ3PKKaewevXqsEtKGPPmzWPFihXk5OSEXYokSVLoDPySlIQOOuggXnnlFcaOHcvZZ59NUVFR2CUlBLfkkyRJWsfAL0lJqmPHjgwdOpThw4dz6aWXEo1Gwy4pdCWB3x5+SZIkA78kJbWTTjqJp556iv79+3P99denfegvKCigXr16ZGdnh12KJElS6LLCLkCSVDbnnHMOS5Ys4corr2S77bbjtttuC7uk0LhCvyRJ0joGfklKAT169GDx4sXcfvvt1KhRgyuvvDLskkKRn5/vcH5JkqS1DPySlCJuvfVWFi9ezFVXXUWNGjU499xzwy6p3OXn59OuXbuwy5AkSUoIBn5JShGRSIQ+ffqwePFiLrjgAmrUqMFJJ50UdlnlZtWqVfzwww8O6ZckSVrLRfskKYVEIhGeeOIJTj31VLp27cro0aPDLqncfPfdd0SjUQO/JEnSWgZ+SUoxmZmZDBw4kI4dO3LCCSeQl5cXdknlwi35JEmS/szAL0kpqEKFCgwfPpxWrVrRuXNnpk6dGnZJcZefn09WVhY77rhj2KVIkiQlBAO/JKWoKlWq8Prrr9OsWTM6duzIl19+GXZJcVVQUECTJk3IzMwMuxRJkqSEYOCXpBRWo0YN3n77berVq0eHDh347rvvwi4pbvLz852/L0mStB4DvySluNq1azNmzBgqVqzIkUceyc8//xx2SXGRn5/v/H1JkqT1GPglKQ00atSIsWPHsnz5cjp06MBvv/0WdkkxFY1G7eGXJEnagIFfktJETk4OY8aM4aeffqJz584sXbo07JJiZuHChSxZssTAL0mStB4DvySlkT322INRo0bx+eefc8IJJ7By5cqwS4qJki35DPySJEnrGPglKc3st99+jBgxgvHjx3P66aezZs2asEsqs5LA7xx+SZKkdQz8kpSGDjnkEF5++WXefvttzjvvPIqLi8MuqUzy8/PZbrvtqFWrVtilSJIkJQwDvySlqU6dOjF48GCGDh3K5ZdfTjQaDbukbVZQUOBwfkmSpA1khV2AJCk8p556KkuWLOHCCy9ku+2247777gu7pG3iCv2SJEl/ZeCXpDR3wQUXsGTJEq655hpq1qzJTTfdFHZJWy0/P5+TTz457DIkSZISioFfksTVV1/N4sWLufnmm6lRowZ/+9vfwi6p1AoLC/nuu+/s4ZckSdqAgV+SBECvXr1YtGgRl19+OTVq1KB79+5hl1Qqc+fOpaioyMAvSZK0AQO/JAmASCTCww8/zJIlSzj33HOpXr06xx9/fNhlbVHJlnwGfkmSpD9zlX5J0h8yMjJ48sknOfHEEznttNN45513wi5pi/Lz84lEIuy0005hlyJJkpRQDPySpD/JzMxk8ODBHH744Rx//PFMmDAh7JI2Kz8/n8aNG1OxYsWwS5EkSUooBn5J0l9UrFiRl156iZYtW9K5c2dmzJgRdkmbVFBQ4HB+SZKkjTDwS5I2qmrVqowYMYLc3Fw6duzIN998E3ZJG5Wfn2/glyRJ2ggDvyRpk2rWrMnIkSOpVasWRx55JHPmzAm7pL/Iz88nJycn7DIkSZISjoFfkrRZdevWZcyYMUQiETp06MAvv/wSdkl/WLp0Kb/++qs9/JIkSRth4JckbdGOO+7I2LFjWbJkCUcddRSLFi0KuyQgmL8PbsknSZK0MQZ+SVKp7LLLLowZM4bvvvuOLl26sGzZsrBLIj8/HzDwS5IkbYyBX5JUanvttRcjR45kxowZnHjiiaxatSrUevLz86latSp169YNtQ5JkqREZOCXJG2VVq1a8cYbb/DBBx9wxhlnUFhYGFotJSv0RyKR0GqQJElKVAZ+SdJWa9++PS+++CJvvPEGF1xwAcXFxaHUUVBQ4HB+SZKkTTDwS5K2yTHHHMNzzz3HwIEDueqqq4hGo+VeQ0kPvyRJkv4qK+wCJEnJ64wzzmDp0qVccsklbLfddvTu3bvc2i4uLqagoICcnJxya1OSJCmZGPglSWVy8cUXs3jxYm644QZq1qzJddddVy7t/vTTT6xatcoefkmSpE0w8EuSyuz6669n8eLFXH/99dSoUYOLL7447m0WFBQAbsknSZK0KQZ+SVJM9O7dm8WLF3PppZdSo0YNunbtGtf28vPzAWjSpElc25EkSUpWBn5JUkxEIhH+9a9/sWTJEs466yyqV69Oly5d4tZefn4+DRo0oGrVqnFrQ5IkKZm5Sr8kKWYyMjIYMGAAxxxzDKeccgrvvfde3NpyhX5JkqTNM/BLkmIqKyuL559/noMPPphjjz2WSZMmxaWdgoICA78kSdJmRKJhbJwsSUp5y5Yto0OHDnz11Ve8//777LXXXlvx6WJgNjBl7eMTYC6wcu37lZg4cS5FRfvRtm0PYD9gV7yPLUmStI6BX5IUN4sWLaJ9+/bMmzePjz76iKZNm27hE/OAAUBf4Me1xyoAa/5yZjQK0WgmGRlFa4/UB/4GXAQ0jEn9kiRJyczAL0mKq3nz5nHIIYewevVqPvzwQ3bccceNnPU/4G7gRYLe/eJtbC0DiAAnArcCLbbxOpIkScnPwC9Jirs5c+Zw8MEHU61aNT744APq1q279p1C4AHgjvX+HAuZa59vXfuoGKPrSpIkJQ8DvySpXHz99de0a9eOHXfckXfffZeaNb8HugMzgHj9KIoAewCDsLdfkiSlGwO/JKncTJ8+nfbt23PeeTvw0EOziEQKgaItfq5sMgmG+r8MHBPntiRJkhKHgV+SVK6++OJBdtnlejIzIaPcFtWPEIT+54FTyqtRSZKkUBn4JUnlaBxwFNFoIZFIef/4iRD09r8BHF3ObUuSJJU/A78kqZz8DOwGLGXbV+EvqwygCvAF0DikGiRJkspHuQ2mlCSlsyhwMfA74YV91ra9CriA+C0UKEmSlBgM/JKkcjCUYCh9vBfoK41CYAzwdNiFSJIkxZVD+iVJcbYY2BlYQmL1qlcDvgXqhFyHJElSfNjDL0mKs+dIvLAPsAJ4KuwiJEmS4sYefklSHEWB/wNmk3iBH4KF+woIVu+XJElKLVlhFyBJSj2ff/45zz33HPAd118/i9q14fPP4bnngvevvx5q1w61xLW+B0YBncMuRJIkKebs4Zckxdyrr77KiSeeCMA332Syyy5FvPoqrD3EN9/ALrvEts3Zs+HNN2HBAthpJzjhhL/eVIhGYdw4+PRTWLoUdtghQocOR5OT81Zsi5EkSUoAzuGXJMVZsDL/nnvCffcFjzoxXifv8cdht93giSdg2TK44YbghsIHH6w7Z/FiaNUK+vWDJUtgwgS45JIou+76NoMGDYptQZIkSQnAIf2SpHKxZg0sWhS8Llpvd75Vq2DIEJg7F/bYAw49FB58MHiva1do0WLz150xA668MrjmK68EwX+33eCii+D00yE/H6pUgcxMeP55aNo0+Fw0CrvvDl99BQ88cC/du3eP9ZcsSZIUKgO/JKlcfP01PPBA8PrCC4Ph9qtXQ7t2MGkS7LtvcEOgVy/43/+C8/baa8uB/+mng7Bfp04Q9AEOPjh4/vlneOMNOO00yM4OHiWKi6GwMHhdr16lWH2ZkiRJCcPAL0kKzX/+E4T9SpVgzBjYfnto1Aiuu67015g2LXiuV2/dsbp1//z+aaet+/NjjwW9/nl5wbz/Ll2gb99OZfkyJEmSEpJz+CVJoXnzzeB5v/2CsA/QsePWXWPp0uA5a71b2BUr/vX9EtWrB4/KlYM/f/QRTJ06a+salSRJSgL28EuSQvPTT8FzrVrrjm233dZdo2bN4Hn16nXHVq366/slzjln3evDDw9W7T/vvNfp0mUNFSpU2LrGJUmSEpg9/JKk0OywQ/C8cOG6YwsWbN019t03eJ43b92x9V+XvL8xrVsHz4sWrWLe+h+SJElKAQZ+SVJoTjwxeJ4yZV3QLxnmX1oXXAAVKgQ3DaZODY69+27w3LhxMEcf4JNPgq35SkSjwXB+gIYNq9OwYcNt+yIkSZISlEP6JUmhOf/8YKu8ceOC7fhat14X2gEikS1fY7fd4L//hcsuC24gHHkkDBsG9evDiy8GCwICzJkDZ54J++wDO+4YLNo3ZUqwSODQodeSmZkZny9SkiQpJJFoNBoNuwhJUmr55ptveOmlp4H7uPTSYF7+N9/ASy8F75ccg2BLvddfh++/D8J7/frrtuJ7803o3Ll0bf70E4waBb/9FvTsd+r05234AH7/Hd57D2bNCkYFNGsWbAtYqdJsILesX7YkSVJCMfBLkuKoLvDrZs/46CM4+OB1f+7dG3r2DG4IfPcd1KgR1wKB6sBioBTDCSRJkpKIgV+SFEfHAG8DxZs849RTg975li2hoCDo1a9XD4YMgSOOCIb7jxq18c8efvjWb+P3ZxHgUGBcWS4iSZKUkJzDL0mKo1OBza/CN3x4MJd+0iTYdVe44gpo3x4qVw7er1Rp01v1VakSixpPj8VFJEmSEo49/JKkOFoBNACWhF3IJlQFfiYY1i9JkpRa3JZPkhRHVYCLgURcAT8LOA/DviRJSlX28EuS4mw2sCuQiD9uPgP2DLsISZKkuLCHX5IUZ02B60isHzkZQA8M+5IkKZXZwy9JKgcrgb2BAqAo5FoygR2Az4FqIdciSZIUP4nU3SJJSlmVgYFsbnu+8lMEPIdhX5IkpToDvySpnLQB7g27CKAncGjYRUiSJMWdQ/olSeUoCtwAPBhS+38DHgMiIbUvSZJUfuzhlySVowjQB7g5hLavBh7FsC9JktKFgV+SVM4iBEP7/0Mwtz8rfk0VZ8AqYOaFwEP4Y0+SJKUTf/ORJIXkEuB/BHP74yRyAFxzJBz6Esz5Pn7tSJIkJSDn8EuSQlYMPAH0AhYQ3Ive1tX8Sz67HcHifFfCb4uhZUto3Bjeew+y4jiiQJIkKYHYwy9JClkGcDnwI/A8cODa4xFKN9w/i3U/zg4ABgE/E8zZz4Ttt4ehQ+Hjj+GOO2JZuCRJUkKzh1+SlID+B4wDpgATgG/4S69/MfBbbajTBdgPaA803/Ql77sPbr0VRo+GI4+MR9GSJEkJxcAvSUoCy4FfgZUEW/tVgSNOgx2bwbPPlu4SxcVw1FEwcyZMnw7168evXEmSpATgkH5JUhKoCuwE/B/QLHjdaFeYPbv0l8jIgIEDIRqFs88ObgBIkiSlMAO/JCk55eZCfv7WfaZBAxg0KBjW/49/xKcuSZKkBGHglyQlp9xc+OknWL586z7XoQPcdFMwn3/ChPjUJkmSlAAM/JKk5JSbGzx/++3Wf/auu6BVKzjjDFi4MKZlSZIkJQoDvyQpOTVtGjxv7bB+gAoVgq36Fi+GCy8M5vVLkiSlGAO/JCk5NWgAlStvW+AH2HlnGDAAXn4ZnngitrVJkiQlAAO/JCk5ZWRATs62B36Ak06Cyy+Ha64JtuqTJElKIZFo1HGMkqQkdcwxQfB//fVtv8bKldCmTfA8eTJkZ8euPkmSpBDZwy9JSl65uTB7dtmuUbkyDBsGc+dCjx6xqUuSJCkBGPglSckrNzcY0l/WwWrNmsHjj8Mzz8CgQTEpTZIkKWwGfklS8srNDYbi//xz2a919tlw1llw6aXw9ddlv54kSVLIDPySpOSVmxs8l2XhvvU9/jjssAOcfjqsWhWba0qSJIXEwC9JSl45OcFzrAJ/dnYwn//zz+H662NzTUmSpJAY+CVJyataNWjQIHaBH6BFC3joIXj0UXj11dhdV5IkqZwZ+CVJya1k4b5YuvxyOPFEOP98mDMntteWJEkqJwZ+SVJyi0fgj0RgwACoXh3OPBMKC2N7fUmSpHJg4JckJbd4BH6AWrVg6FD4+GPo1Sv215ckSYozA78kKbnl5sKPP8KKFbG/dtu20Ls33HcfjB0b++tLkiTFkYFfkpTcSrbmKyiIz/VvvBGOOAK6d4d58+LThiRJUhwY+CVJya0k8MdjWD9ARgYMHAjRKJx9NhQXx6cdSZKkGDPwS5KSW8OGUKlS/AI/BFv/DRoEo0fDP/4Rv3YkSZJiyMAvSUpuGRmQkxPfwA/QoQPcdBPceitMmBDftiRJkmIgEo1Go2EXIUlSmRxzTBD8X389vu2sWQOHHgo//ADTpgUr+UuSJCUoe/glSckvXlvzbahChWCrviVL4MILg3n9kiRJCcrAL0lKfiWBvzwC+M47w4AB8PLL8MQT8W9PkiRpGxn4JUnJLzcXVqwov23zTjoJLr8crrkGpk8vnzYlSZK2koFfkpT8Srbmmz27/Np88EHYbTc4/XT4/ffya1eSJKmUDPySpOSXkxM8l8c8/hKVK8OwYTB3LlxxRfm1K0mSVEoGfklS8qtWDerXL9/AD9CsGTz+ODz7LAwcWL5tS5IkbYGBX5KUGsprpf4NnX02nHUWXHYZfP11+bcvSZK0CQZ+SVJqCCvwQ9DLv8MOwXz+lSvDqUGSJGkDBn5JUmoIM/BnZwfz+T//HG64IZwaJEmSNmDglySlhqZN4ccfg+35wtCiBTz0EDz6KLz6ajg1SJIkrcfAL0lKDSVb8337bXg1XH45nHginH8+zJkTXh2SJEkY+CVJqaIk8Ic1rB8gEoEBA6B6dTjjDCgsDK8WSZKU9gz8kqTU0LAhVKoEs2eHW0etWjB0KEycCL16hVuLJElKawZ+SVJqyMiAnJxwe/hLtG0LvXvDfffB2LFhVyNJktJUJBqNRsMuQpKkmOjSBTIz4fXXw64EiovhqKNg5kyYPh3q1w+7IkmSlGbs4ZckpY4wt+bbUEYGDBwI0SicfXZwA0CSJKkcGfglSamjJPAnyuC1Bg1g0CAYPRr69Am7GkmSlGYM/JKk1JGbCytWwLx5YVeyTocOcNNNcNttMGFC2NVIkqQ0YuCXJKWOpk2D50QZ1l/irrugVSvo2hUWLgy7GkmSlCYM/JKk1JGTEzwnWuCvUCHYqm/JErjwwsSZciBJklKagV+SlDqqVQtWw589O+xK/mrnnWHAAHj5ZXjiibCrkSRJacDAL0lKLYm0Uv+GTjoJLr8crrkGpk0LuxpJkpTiItGo4wolSSmke3f47jv48MOwK9m4lSuhTZvgefJkyM4OuyJJkpSi7OGXJKWWRO7hB6hcGYYNg7lz4Yorwq5GkiSlMAO/JCm15ObCjz8G2/MlqmbN4PHH4dlnYeDAsKuRJEkpysAvSUotubnB87ffhlrGFp19dvC47DL4+uuwq5EkSSnIwC9JSi0lgT+Rh/WX6NsXdtgBTj89mNMvSZIUQwZ+SVJqadQIKlVKjsCfnR3M5//iC7j++rCrkSRJKcbAL0lKLRkZkJOTHIEfoEULeOgheOwxePXVsKuRJEkpxMAvSUo9ubkwe3bYVZTe3/4GJ54I558Pc+aEXY0kSUoRBn5JUupJ9K35NhSJwIABUL06nHEGrFkTdkWSJCkFGPglSamnJPBHo2FXUnq1asHQoTBxItxxR9jVSJKkFGDglySlntxcWLEC5s0Lu5Kt07Yt9O4N990HY8eGXY0kSUpyBn5JUupJpq35NnTjjXDEEdC9e/LdsJAkSQnFwC9JSj05OcFzMgb+jAwYODCYjnDWWVBcHHZFkiQpSRn4JUmpJzsb6tVLzsAP0KABDBoUDOvv0yfsaiRJUpIy8EuSUlPTpskb+AE6dICbboLbboPx48OuRpIkJaFINJpMSxhLklRK3bsHe9p/8EHYlWy7NWvg0EPhhx9g2rRgJX9JkqRSsodfkpSacnNh9uywqyibChWCrfqWLIELLkiubQYlSVLoDPySpNSUmws//hhsz5fMdt4ZnnoKXnkFnngi7GokSVISMfBLklJTydZ8334bahkxceKJcPnlcM01wdB+SZKkUnAOvyQpNc2dC40bw4gR0KVL2NWU3cqV0KZNMGJhypRgJ4JSKAK+An4EVgJRoApQH9gdyIpTuZIkKXz+nJckpaZGjaBixeReqX99lSvDsGGw335wxRXwzDMbPa0QeAt4B/gYmEEQ9DemIrA30AZoDxy39pgkSUoNDumXJKWmjAzIyUmdwA/QrBk8/jg8+ywMHPint34G7gYaA8cDTwCfsOmwD7AamAL0A04FdgB6AnNjX7kkSQqBQ/olSamrc+dgpfvXXgu7ktg65xx46SWYMoXlzZpxK/AowXD94jJeOnPtdS4E/gHUKOP1JElSeOzhlySlrqZNU6uHv0TfvrDDDnx0//3sWVzMvwnm6pc17LPedQYQzPEfG4NrSpKkcBj4JUmpKzc3CPwpNpitKDubGz74gEMGDOD7aDQmQf8vbRBME+gAXAasiUMbkiQpvgz8kqTUlZsLy5fDvHlhVxIza4DuwIP16xPNyKAoMzNubZXcSOgHnAisiFtLkiQpHgz8kqTUlZsbPKfIsP4ioBswjGCefXmJAm8DJxEs9CdJkpKDgV+SlLpycoLnFAn8VwIvUr5hv0QxMBo4L6T2JUnS1jPwS5JSV3Y21KuXEoH/TeBxwg3bxcAQghEGkiQp8Rn4JUmprWThviS2EDifxPihHSFYxC91VkWQJCl1JcLvDpIkxU8KBP6/AwuIzbZ7ZRUFlgKX4NB+SZISnYFfkpTakjzwzwCeI1iwL1EUAa8BeWEXIkmSNsvAL0lKbU2bwg8/wMqVYVeyTR4HssIuYiOygL5hFyFJkjbLwC9JSm0lW/MVFIRbxzZYDDwLFIZdyEYUEuwY4Fx+SZISl4FfkpTaSgJ/Eg3rHzlyJNnZ2dTLzmbl11+XHAx2HcjOhpJjISsG+oddhCRJ2iQDvyQptTVqBBUrJlXgLywsZNmyZaxetgyKi0sOwrJlwaM4xsv3ffghnHwyNGkCtWvD3nvD9dcHbZUoLoYRI+CEE2CHHaBhQ4oPPpgBzz5LNOryfZIkJSIDvyQptWVkQE5OUgX+jerUCZYuDR7NmsXuuvPmQZ8+cNVV8Mkn8MwzMGsWPPggXHjhuvNefx2GDAnOmzIF/vlP+PhjCs49l5533BG7eiRJUswY+CVJqS/JV+oHYMwYaNAgeHzzzbrj774LBxwA9etD+/YwcOC6of+DB2/5uvXrwxtvwCGHQL16cOyxsN9+wXsff7zuvBNOCAL/YYcFNXTtCocfDkC/J5+M2ZcpSZJiJxEX/pUkKbZyc+H998OuomxKhvTDuiH9+fnQuTOsWgUPPACnnQZnnbXuvDVrtr6dTz6BGTOC12ecsflzFywAYHmS7oAgSVKqs4dfkpT6Snr4U22u+UMPBWG/YcNgzn2TJnDrrdt2rcMPh0qVoHXrYNrA6afDnXdu+vyRI+HTTwHY5bjjtq1NSZIUVwZ+SVLqy82F5cvhl1/CriS2pk8Pnps1g0gkeL3bbtt2rTffhLlz4cUXoVo1GDZs0z38U6cGNwTWtnfoP/+5bW1KkqS4MvBLklJf06bBc7LP499QUVHwnJm57ljGNv5or1IF6tYNVus///zg2Esvwfz5fz5v9uxgAcElS2DPPcl6/32yatXatjYlSVJcGfglSakvJyd4nj073DpibY89gueCgnXHZs0q+3UrVVr3ev0tAOfNg44dg+cmTWD0aCL16lGl7C1KkqQ4MPBLklJfdnawAn2q9fBffXXQo5+fHwzBX7482E5va/zrX/Doo/Djj8EaBxMnBlvzAXTpEqziD0GPfqdOQVt168Lo0dCoEUWA/fuSJCUmA78kKT2kwtZ8G9prL3j+edhxRzjzTGjZEg4+eN37WaXYjKdrV/juOzjooKBnv21b2H57uO02GD583XlvvBHM3QdYtChoKzub4uxsbsvOZvHixTH90iRJUtlFotFUW7JYkqSN6NYNvv8ePvgg7Eq2qKioiEUrVlAfKKpWLViQr6gIVqwITig5tr7CwiDgf/QRtGsXHBs/Hg48sPQNr1kTrAewsXUACgthE9vvzQEaZ2eXvh1JklQu7OGXJKWHJOrhz8zMpHZ2NntkZ68L9pmZwdSE9Y8BXHBB0PMeicCcOXDLLcHx9u2hTZuta7hChU0v+peVta799R47ZWcb9iVJSlAGfklSesjNhR9+2GQvdSI6FNjioPyuXeGKK6BGDdh552BrvZtughEjgpsADz+80aBOdjb06lWm+rLW1ihJkhKTQ/olSenh/feDXu8vvtj2verL2TSgZWlP3tgWfRAM01+1auOfqVgxeJTB+8AhZbqCJEmKl1Ks5iNJUgrIzQ2e8/OTJvC3AFoDk4DizZ/616BfokKF4BFjEaAZ0C7mV5YkSbHikH5JUnpo1CjozU6SefwlelCKsB+SKwmCvyRJSkwGfklSesjMhJwcmD077Eq2yilAY2AT/fehyABqA93DLkSSJG2WgV+SlD6SaKX+EpWA54CisAtZTzHwFFA97EIkSdJmGfglSekjCQM/QHvgbyTID+3CQvb49FM6FRaGXYkkSdqChPjdQZKkclES+JNwg5oHgB0Jd2h/ZjRKtVWr+LJjRw477DDmzp0bYjWSJGlLDPySpPSRmwvLl8Mvv4RdyVbLBkYCNQgn9GcAlSMRPqxWjQ9ff51vv/2Wli1bMnLkyBCqkSRJpWHglySlj/W35ktCuwNjCcJ/eYb+TKAyMApoCbRt25Zp06bRqlUrOnXqxC233EKhQ/wlSUo4Bn5JUvrIyQmekzTwA+wL5BGskp9RHP8N+zIJRhV8ABy03vHatWvzxhtv8MADD9CnTx8OO+wwfvjhh7jXI0mSSs/AL0lKH9WrQ926SR34AfYEXps1i8grrwAQiUMbJdc8GvgfsN9GzsnIyOCGG27g/fffp6CggBYtWjBq1Kg4VCNJkraFgV+SlF5yc2H27LCrKJPVq1dzRdeuNL3lFgatXMl2xHaIfybBtIHngDeAhls4/6CDDmLatGkccMABHH300dx6660O8ZckKQEY+CVJ6aVp06Tv4e/VqxfTp09nyJAhdKtcma+B24F6a9/flvBf8gtBLeBG4GvgLEo/eqBOnTqMGDGC+++/nwceeIAjjjiCH3/8cRsqkSRJsWLglySll5Kt+ZLUe++9xwMPPEDv3r3Zb79goH0doBcwF3gRaAdkrT0/AlTYyHWyWBfmM4EDgSHAT8A9QINtqC0jI4Mbb7yR9957j9mzZ9OiRQtGjx69DVeSJEmxEIlGk3AzYkmSttXTT8P558OKFVC5ctjVbJWFCxeyzz77kJubyzvvvENm5qb78lcDnwFT1j5+ApYDxUA1oD7BvPz9gL0JVuGPpfnz53P22WczatQobrnlFu644w6ysrK2/EFJkhQzBn5JUnp5/31o3x6++AJ22y3sakotGo3StWtXRo8ezYwZM2jcuHHYJW1RcXExffr04bbbbuPggw9myJAhNGrUKOyyJElKGw7plySll9zc4DnJhvUPHDiQF154gX79+iVF2IdgiP9NN93EuHHj+Oabb2jRogVjxowJuyxJktKGgV+SlF4aNYKKFZMq8M+ePZvLL7+cc845h9NOOy3scrZau3btmDZtGvvuuy9HHXUUPXv2pKioKOyyJElKeQZ+SVJ6ycyEJk2SJvAXFhbSvXt36tWrx7///e+wy9lmdevW5a233uLuu+/mnnvu4cgjj+Snn34KuyxJklKagV+SlH6SaKX+u+++m0mTJjFo0CBq1KgRdjllkpGRwS233MK4ceP46quvaNGiBWPHjg27LEmSUpaBX5KUfnJzYfbssKvYovHjx9O7d2969uzJgQceGHY5MXPIIYcwbdo0WrRoQceOHenVq5dD/CVJigNX6ZckpZ+HH4bbb4fff4dIZMvnh2DJkiXss88+NGrUiPfffz8lt7QrLi7mvvvuo2fPnhx66KEMHjyYhg0bhl2WJEkpwx5+SVL6yc2F5cvhl1/CrmSTrrjiChYsWMCgQYNSMuxDMMT/1ltv5d133+XLL7+kZcuWvPPOO2GXJUlSyjDwS5LST4Jvzff8888zcOBA+vbtS05OTtjlxN2hhx7K1KlT2XvvvenQoQN33HGHQ/wlSYoBA78kKf2UhOgEDPxz5szh0ksvpWvXrnTv3j3scspN/fr1GTlyJHfddRe9e/emY8eO/Pzzz2GXJUlSUjPwS5LST/XqULduwgX+oqIizjrrLGrWrMkTTzxBJEHXF4iXzMxMbrvtNt555x0+//xzWrRowbvvvht2WZIkJS0DvyQpPSXg1nx9+vThww8/ZODAgWy33XZhlxOa9u3bM23aNPbaay+OPPJI7rzzTof4S5K0DQz8kqT0lGCBf9KkSfTs2ZObb76ZQw45JOxyQle/fn1GjRrFHXfcwZ133slRRx3FvHnzwi5LkqSk4rZ8kqT0dNtt8Oyz8P33YVfC77//zr777kvNmjUZP348FSpUCLukhPLuu+9y5plnEolEGDJkCIcddljYJUmSlBTs4ZckpafcXPjhB1i5MuxKuPrqq/nhhx8YMmSIYX8jDj/8cKZNm8aee+7JkUceSe/evR3iL0lSKRj4JUnpqWlTiEbh229DLePll1+mf//+/Pvf/2bXXXcNtZZE1qBBA0aNGkXPnj3p1asXRx99tEP8JUnaAof0S5LS0/ffw047wZtvQufOoZTwww8/0Lx5c9q3b8+LL76Ydqvyb6t33nmHbt26EYlEGDp0KO3btw+7JEmSEpI9/JKk9NSoEVSsGNrCfcXFxZxzzjlUrlyZ//73v4b9rXDEEUcwbdo0dt99d4444gjuvvtuh/hLkrQRBn5JUnrKzIQmTUIL/I888gjvvPMOzz33HLVr1w6lhmTWoEEDxowZw+23307Pnj3p1KkTv/zyS9hlSZKUUBzSL0lKX506QaVK8Oqr5drstGnTaN26NT169ODBBx8s17ZT0TvvvMOZZ55JZmYmQ4cO5dBDDw27JEmSEoI9/JKk9JWbW+49/MuXL+fMM89k991355577inXtlNVyRD/Zs2acfjhh3PPPfdQXFwcdlmSJIXOwC9JSl8lgb8cB7vdcMMNFBQUMGTIECpVqlRu7aa6hg0bMnbsWG677TZuv/12h/hLkoSBX5KUznJzYdkyKKdgOGLECPr27ctDDz3EHnvsUS5tppPMzEzuvPNORo0axdSpU2nZsiUffPBB2GVJkhQaA78kKX3l5gbP5TCsf968eZx//vl06dKFyy67LO7tpbMOHTowbdo0dt11Vw477DDuvfdeh/hLktKSgV+SlL7KKfBHo1HOO+88IpEITz31lFvwlYNGjRoxduxYbrnlFm677TY6d+7M/Pnzwy5LkqRyZeCXJKWv6tWhbt24B/6+ffvy9ttv88wzz1CvXr24tqV1srKy6N27NyNHjuTTTz+lRYsWfPjhh2GXJUlSuTHwS5LSW5xX6v/f//7HddddR48ePejUqVPc2tGmdezYkWnTprHLLrtw2GGHcd999znEX5KUFiLRaDkuTSxJUqI580z44Qd4//2YX3rVqlW0atWKoqIiJk2aRJUqVWLehkqvsLCQO+64g3vvvZejjjqKgQMHUqdOnbDLkiQpbuzhlySltzj28N9yyy18+eWXDBkyxLCfALKysrj77rt5++23mTx5Mi1atOCjjz6Ka5tFwJfAh8AYYCyQB3wL2OMiSYo3A78kKb3l5gY9/CtXxvSyY8aM4eGHH+aBBx6gefPmMb22yuaoo45i2rRp5Obm0r59ex544IGYDfFfA7wCXA20BbKB3YFDgI5AB+BgIAeoBRwJ3AKMwxsAkqTYc0i/JCm9vfceHHYYfPklNGsWk0v++uuvNG/enL322ouRI0eSkeH99URUWFhIr169uPfee+nUqRPPPffcNg/xnws8CTwBzAcqEIT/LYkAmUAhsAvQAzgb2G6bqpAk6c/8DUSSlN5KtuabPTsml4tGo1x44YWsXr2aZ555xrCfwLKysrjnnnt4++23mTRpEi1atCAvL2+rrrEYuAjYGbiHIOxD6cI+BL36hWtfzwb+DjQEem/FNSRJ2hR/C5EkpbcddoAKFWI2j79///689tpr9O/fn0aNGsXkmoqvo48+mqlTp5KTk8Ohhx5Knz59SjXEfxSwG/A0UEwwX78somsfK4FewH7AjDJeU5KU3gz8kqT0lpkJOTkxCfxfffUVf//737n44os54YQTyl6bys2OO+7IuHHjuP7667nxxhs57rjjWLBgwUbPXQ1cDBwN/ELZg/7GRIEvCEL/Azi/X5K0bZzDL0lKW1GC1dIn33MPU5s147dTTmElwd3wKsBOBIFrX2D7LVxr9erVtG3blqVLl/Lpp59SrVq1eJauOHr77bc566yzqFKlCsOGDaNt27Z/vLccOJFgtf3YLPNXOj2AR7CnRpK0dQz8kqS0UgS8BfQj2CptydrjWWvWEKlQ4Y+e1Ah/Hqa9I3Ac8Ddgz41c9+abb+bBBx/k448/Zr/99otb/Sofc+fOpWvXrnz88cfcd999XHvttazOyKAz8AHx6dXfksuAvgT/NiVJKg0DvyQpLfxKsIr6Y8CPBCujb21oyyJYYO1ggh7Xk9de57333uPwww/nvvvu48Ybb4xd0QrVmjVr6NmzJ/fffz+du3Qh46WXeKtSpXLt2d/QnUDPENuXJCUXA78kKaVFgWEEvaNLiM0w7JKbBfsDjy5ezKl77cUuu+zC2LFjyczMjEELSiRvvfUWp40cybJ//zvsUogA44E2YRciSUoKBn5JUsqaB1wKvEoQlGL9Ay8rGqWoqIhKvXvzxYUX0qRx4xi3oETwPbBbcTHLAULeZjETaALMJFhnQpKkzTHwS5JS0kSgE0GvftznW0ejtItEeAOoGe+2VK6iwFHAu4Qzb39jMoBrgT5hFyJJSngGfklSyhkHdAbWUH4hLRPYC3gHqF1ObSr+RhLcOEo0EaAA2DnsQiRJCc3dXSRJKeVjgrC/mvLtkS0CPgM6AkvLsV3F12MEizUmmgzgv2EXIUlKePbwS5JSxk/AHgSBO6zh15nA0cAbuH1asvsWyCX2az/EyvYEO05UCrsQSVLCsodfkpQSosBFhBv2Wdv2m8DAEGtQ2QwYMIA6deqwR506RFatKjkIdeoEj5JjIfsNeCnsIiRJCc3AL0lKCYMIgnYiLKwWAa4g6H1V8lmxYgULFixgxYIFFJcMhFyxAhYsCB6xHhz5zTdw9dWw447BDYVWrTZ97ujRcNxx0KQJ7LEHvf72N37++efY1iNJShkGfklS0psHXE7iDKGPAsuBi8MuRLFz4YUwf37wqFw5dtctKoJjjoHGjeGAA4IbCr/9tvFzH34YjjoKFi6EwYNhxAgWtWlD7969Y1ePJCmlGPglSUnvcYKAnUhzrUuG9s8IuxDFxuDBsNtuwWP9If2vvQb77w+5uXDiifDUU+uG/uflbfm6mZnw5ZdwzTVQczObOn79NdxwA1SsCK++CgcdBLm5/Hr22dzz2GNl/vIkSakpEReelSSp1NYQBP5EGMq/oSzgibUPJbmSIf2wbkj/xx/DSSdBcTH07x8E/5NPXnfemjWlu3akFGNTBg8ORgPUqAFduwY3AGrUgI4dyevViy41amz91yRJSnn28EuSklJ+fj4jR47krjFj+HXdQRg5EsaMCbO0PxQCzwBLQq5DcXLPPUHYb9UKLrgA9tkn6KmPh88/D54XLgxGD7zzTjCq4OGHufa443DTJUnSxhj4JUlJaciQIXTq1Il7jz2WzHUHoVMnOPbY+DS6alUQtEaOhAkTSnXeygkTGBKfahS2adOC5z32WHds/dextP40gmuvhV12gb//HYCv3n+fjz/+OD7tSpKSmkP6JUlJrXj9PzRtGixqVrFibBtZswZ69YKBA2HZsqCXdb/9YPLkUp33weTJXBrbirQZhYWFrFixguXLl//leWPHNnyevOH3dVNKhuxnZq47tv7rWGrQ4K+v1zuWn5/PgQceGJ+2JUlJy8AvSUodrVtDrVobD12ffQZLl8Jee8Hq1TBpUnD8oIOgevXNX3fFCsjOhk8/hYsuChZq24rz7HuF4uJiVqxYUargXZpQvrn31pR27jxQuXJlqlSpQtWqValatSpVqlRhyZJSTsLYfXeYNw+++27dsfz8rfybKaXDDoMnnwxeL1wYbOG3cOEfbzdp0iQ+7UqSkpqBX5KUOoYMgdtvh0qVYOXK4NjKlcFCam+9FfSI1q8PO+8Mr78evD91KrRosfnr1qgBt9yy5fY3cV4BwTz+RFtWLRqNsnLlym0K1VsbyletPyR9CypWrPhHCN/wueRRp06djb63qc9t7JzKlSuTkfHX2Y2PPfYYPXr02HKh11wD770H778f3EDafXfo23crvgNb4eSToXlzmDEDnnsOHnggGEkC7HbwwbRt2zY+7UqSkpqBX5KU2h5+OAj7tWvDzJmw/fZwxBHlXsY04JBSnBeNRlmzZk1Merq39N7KlStLvdhbZmbmFsN1zZo1txi8txTKq1SpQlZWkvx6cuyx0KcP3H13MLpkt93gmGPWjR4p7dfRpAn8/nvwACgoCBbmg+CGwl57BdNUxowJ5u337Qv//newjd+VVzK4d28ipVnpX5KUdpLkJ6okSdvo6aeD56OPXheiunULglQ5urNvX+p99FGpQnlxcfGWLwhEIpEthus6depsdfDe2LEKFSrE+W8ocVx44YW07dqV/QAqVy45GGyHt/4xgOuvD3r6V6+GKlVg6NB17+XklK7BKVPWbfW3oVq11r2uVy8YxVJcHEwfqVaNqkCL0rUiSUpDBn5JUuqKRuHbb4PXjRqtO77+63Iy55df4JdfqFKlCttttx2NGjXaquHnG3uvYsWK9uzGQeXKlWlZuTI1WG9LxcqV/xz0IVg5v0cPuPXWYJpIfj7cd1/w3hlnwA47lK7B2rW3rsCMDKhWDYB9ccslSdKmGfglSakrEgmGPS9YEPSIlli+vNxLuevOOzmj3FvVtooABwOjgKJNnVSpErRtG+wM8f33wb+rnBy44w64+ebgnOuug2ee2fjnH3oIzjlnm2vMAtpt86clSenAwC9JSm3t2sGrrwYr55f45JNyL6NKubeosroIeGtLJ517bvBYsSK4wbThKIA774Sbbtr4Z7Ozy1RfIXBema4gSUp1Bn5JUmq76y4YNQrGjw9W0N9pp033uG5KNBpcA+CXX4LnJUtg5Mjg9aGHBvO3N3PeHGDFoYdSpYrRP1kcAzQAfi7NyZv6vlar9sfw+1jKBA4Ddo35lSVJqcTAL0lKSk2bNmWfo45iesWK6x8Mhlevf2zvvWHCBHj0UZg1K+hV7dMHzlvbN1qaAB6NwiOPBK9r1AjagHXHWrZcF/g3cl7kkUcYAZzesqWBP4lkAZcDvYDSLaNYfoqAK8IuQpKU8CLR0u7HI0lSgskHmm7ppNWrobAQqlZdd+ymm4J9zOvVgx9+KP32adtob2BGXFtQvMwj+De2LOxC1pMJ5ABfYM+NJGnz/DkhSUpaOfDnldQ3ZtkyOPhgOPts2HXXYAu0Rx4JFlzr1y8I+9Onw08/bfzzLVpAgwbbXGMW0HqbP62w1Qf+DVwQdiHrKQYG4S9xkqQts4dfkpTUTgdeJljAbJN++ikI9zNnBlua7b03dO8OubnB+/ffD++9t/HP3nxzMEe/DIYDp5TpCgpTFOgEvMMW/p2VgwzgOuCBkOuQJCUHA78kKal9AJQtjsdXXeAHoELYhahM5gK7A8sJbz5/FtAEmAlU3vypkiQBwY1iSZKSVjugGcG+6YkmA/gbhv1UsCPwOsH8+TD+rWUCNYG3MexLkkrPwC9JSmoR4Mqwi9iMi8IuQDFzGPAKQfguz1+gsgjWqhgH7FKO7UqSkp+BX5KU9M4jGOqcmUCz1DIIbkTsEHYhiqkuwEigEuWzaF4mwbSQCQS7PUiStDUM/JKkpFcF+Ndvv1EUdiFrZQI7AfeEXYji4giCbRZbxbGNkmkDpxLM2W8Wx7YkSanLwC9JSnojRozggt12o8p//kMkAXr5i4GBQNWwC1Hc7AJ8CDxC7Hv7M4FaBLtPDAVqx/DakqT0YuCXJCWt5cuX87e//Y1jjz2WVq1a8b+TTqJZJBL6/uQ3AAeHXIPiLwO4CvgcuITgBk+EbVvUL3Ptcx3gVuBr4MQY1ChJSm9uyydJSkpTpkyhW7duzJkzh4ceeohLL72USCTCj8CBwI+U/57pEaA78AzeUU9HS4EhQF+CYfgQ/DuIwF+mm2StPRZde85BQA/gBNzVQZIUOwZ+SVJSKSoq4h//+Ae33347e++9N4MHD2b33Xf/0znfAe2B7/lr0Iqn7sDTlM9ibkpsi4GpwBTgU2AesIwg3FclWONhv7WP5gTrUEiSFGsGfklS0pgzZw5nn302H3zwATfccAN33XUXFStW3Oi5PwPHAZMJelHjJVJcTDQjg5Pz83khN9eefUmSlDD8vUSSlBSGDh1K8+bNyc/P59133+X+++/fZNgHaACMB+4nGCIdj173DGDHSIQ2N9/MuAMO4Ifvv49DK5IkSdvGwC9JSmiLFy+me/funHnmmXTq1Inp06fTvn37Un02i2ABvelAy/WOlVUmwbzsS4HPIxFGXHcd2dnZnH766axZsyYGLUiSJJWdgV+SlLA+/PBD9tlnH9544w0GDhzIkCFDqFWr1lZfZ3dgIvAucDzBD79M2Kot/EpWUa8JXAfMJlicLRuoXbs2w4YNY9KkSdx8881bXZ8kSVI8GPglSQlnzZo13HrrrbRv357GjRszffp0unfvTiSyLRueBSLAYcCLwBzgTqDRZ5+RuXjxX87bUA5wGvAcwdoA9689tr42bdrwj3/8g4ceeojXXnttm+uUJEmKFRftkyQllK+//ppu3boxbdo07rzzTm688UYyMzO3/MFtsPvuu3No+/bc+sQTTAV+A1YQ9OZXBnYmmApQo5TXi0ajnHzyybz77rt8+umn5ObmxqVuSZKk0jDwS5ISQjQa5cknn+Tqq69mhx12YPDgwRxwwAFxa2/BggXUqVOH5557jrPOOitm1120aBH77bcftWrVIi8vj0qVKsXs2pIkSVvDIf2SpNDNnz+fE088kUsuuYTu3bszderUuIZ9gAkTJgBw0EEHxfS62223HcOHD2fmzJlce+21Mb22JEnS1jDwS5JCNXLkSJo3b85HH33Eq6++Sr9+/ahWrVrc283Ly6NBgwbk5Gw4G7/s9t13Xx555BH69u3LCy+8EPPrS5IklYaBX5IUihUrVnDllVfSqVMn9tlnH2bOnMnxxx9fbu3n5eVx0EEHlWkhwM259NJL6dq1KxdeeCFff/11XNqQJEnaHAO/JKncTZs2jf3335///ve//Pvf/+att96iYcOG5db+6tWrmTRpEm3bto1bG5FIhP/+9780bNiQU089lRUrVsStLUmSpI0x8EuSyk1xcTEPPvggrVu3JisriylTptCjRw8yMsr3x9HUqVNZuXJlzOfvb6h69eoMHz6cr7/+miuvvDKubUmSJG3IwC9JKhdz586lQ4cOXH/99fTo0YNPPvmEPffcM5Ra8vLyqFy5Mi1btox7W82bN6dv377079+fgQMHxr09SZKkEm7LJ0mKuxdffJGLL76YqlWr8uyzz3LEEUeEWs/JJ5/Mr7/+yvvvv18u7UWjUc477zyGDx/OpEmT2GOPPcqlXUmSlN7s4Zckxc3SpUs577zzOPXUUzniiCOYMWNG6GE/Go0yfvz4uM7f31AkEqFv377k5ORwyimnsGzZsnJrW5IkpS8DvyQpLsaPH0+LFi148cUXefrpp3nhhRfYfvvtwy6LgoICfv7557jP399QtWrVGD58OHPmzOGyyy7DAXaSJCneDPySpJgqLCykV69etGvXjvr16zNt2jTOPffcuG1/t7Xy8vIAyrWHv8Tuu+9Ov379GDhwIAMGDCj39iVJUnox8EuSYmbWrFkcfPDB3HPPPfTs2ZMPPviApk2bhl3Wn+Tl5bH77ruHNtqgW7duXHzxxfTo0YPp06eHUoMkSUoPLtonSSqzaDTK008/zZVXXkn9+vUZPHgwbdq0CbusjWrevDmtW7fmySefDK2GlStXcuCBB7Js2TImT55MjRo1QqtFkiSlLnv4JUllsmDBAk455RQuuOACTjvtNKZNm5awYX/RokV89tlnoQznX1/lypUZPnw4P//8MxdeeKHz+SVJUlwY+CVJ22zs2LE0b96ccePG8eKLL/LUU09RvXr1sMvapI8//phoNFruC/ZtzC677MJTTz3F8OHDefzxx8MuR5IkpSADvyRpq61cuZJrrrmGDh06sMceezBz5kxOPvnksMvaory8POrWrcuuu+4adikAnHLKKfTo0YNrrrmGyZMnh12OJElKMc7hlyRtlc8++4wzzzyTr776ivvvv5+rrrqKjIzkuH98+OGHU6NGDV599dWwS/nDqlWraNeuHfPnz+fTTz+lVq1aYZckSZJSRHL8hiZJCl1xcTH/+te/2H///YlGo0yaNImrr746acJ+YWEhEydODH3+/oYqVarECy+8wKJFizjvvPOczy9JkmImOX5LkySF6qeffqJTp078/e9/59JLL2XSpEk0b9487LK2yvTp01m+fHlCzN/fUJMmTXj22Wd57bXX+Oc//xl2OZIkKUUY+CVJm/Xqq6+y9957M2PGDEaOHMkjjzxC5cqVwy5rq+Xl5VGxYkX222+/sEvZqOOOO47rrruOG2+8kQkTJoRdjiRJSgHO4ZckbdTvv//O1VdfTf/+/TnhhBN48sknqVOnTthlbbPTTz+duXPnkpeXF3Ypm7RmzRrat2/PnDlzmDp1alL/fUuSpPDZwy9J+otPPvmEli1bMmTIEJ588klefvnlpA+f48ePT7j5+xuqUKECw4YNY8WKFZx99tkUFxeHXZIkSUpiBn5J0h8KCwvp3bs3bdu2pVatWkybNo0LL7yQSCQSdmllMmfOHObOnZuQ8/c3tOOOOzJo0CBGjhzJAw88EHY5kiQpiRn4JUkAFBQU0L59e+644w5uvvlm8vLyEma/+rIqGcaf6D38JY4++mhuueUWbrvtNt5///2wy5EkSUnKOfySlOai0SgDBw7kiiuuoHbt2gwcOJCDDz447LJi6oorrmD06NF8/fXXYZdSaoWFhXTo0IGvvvqKqVOnUr9+/bBLkiRJScYefklKYwsXLqRr166cc845nHDCCUybNi3lwj4kx/z9DWVlZTFkyBCKi4vp1q0bRUVFYZckSZKSjIFfktLUuHHjaN68OaNHj+b555/nueeeo2bNmmGXFXNLly5l+vTpSTF/f0MNGzZk6NChjBs3jt69e4ddjiRJSjIGfklKM6tWreKGG27giCOOYJdddmHGjBmcfvrpYZcVNxMnTqS4uDgpAz/AYYcdxh133MFdd93FmDFjwi5HkiQlEQO/JKWRL774gjZt2vDII4/wwAMP8M4779C4ceOwy4qrvLw8atWqxW677RZ2Kdvs1ltvpUOHDnTr1o0ff/wx7HIkSVKSMPBLUhqIRqP07duXfffdl1WrVjFx4kSuv/56MjJS/8fA+PHjOfDAA5P6a83IyGDQoEFUrFiRrl27UlhYGHZJkiQpCSTvbz+SpFKZN28exxxzDFdccQUXXHABkydPpmXLlmGXVS6KioqYMGFC0g7nX1/dunV5/vnnGT9+PLfffnvY5UiSpCRg4JekFDZixAj23ntvJk+ezIgRI3jssceoWrVq2GWVm88++4ylS5emROAHOPjgg7n33nu5//77efPNN8MuR5IkJTgDvySloOXLl3PZZZdx7LHH0rp1a2bOnEmXLl3CLqvc5eXlkZWVxQEHHBB2KTFz3XXXccwxx3D22WczZ86csMuRJEkJzMAvSSlmypQp7Lvvvjz77LM88cQTvP7669SrVy/sskIxfvx4WrZsmVKjGjIyMnj22WfJzs7m9NNPZ/Xq1WGXJEmSEpSBX5JSRFFREffffz9t2rShWrVqfPrpp1x66aVEIpGwSwtNXl5eygznX9/222/PCy+8wJQpU7jpppvCLkeSJCUoA78kpYA5c+Zw+OGHc8stt3DdddcxYcKEpN6GLhZ+/PFHvv3225QM/ACtW7fmwQcf5J///CevvPJK2OVIkqQEZOCXpCQ3dOhQmjdvTkFBAePGjeO+++6jYsWKYZcVury8PADatm0bciXx06NHD04++WTOO+88Zs+eHXY5kiQpwRj4JSlJLV68mO7du3PmmWfSuXNnZsyYwaGHHhp2WQlj/PjxNGnShEaNGoVdStxEIhEGDBhA7dq1Oe2001i5cmXYJUmSpARi4JekJPThhx+yzz778MYbbzBo0CCGDBnCdtttF3ZZCSVV5+9vqGbNmgwfPpz//e9/XHvttWGXI0mSEoiBX5KSyOrVq7n11ltp3749O+20E9OnT6dbt25hl5Vwli9fztSpU9Mi8APsu+++/Otf/+Lxxx/n+eefD7scSZKUIAz8kpQkvvrqK9q2bUufPn3o3bs348aNo0mTJmGXlZA++eQTCgsL0ybwA1x88cWcccYZXHTRRXz11VdhlyNJkhKAgV+SElw0GqVfv37su+++LF26lAkTJnDLLbeQmZkZdmkJa/z48dSoUYM999wz7FLKTSQSoV+/fuywww6ceuqpLF++POySJElSyAz8kpTA5s+fzwknnMCll15K9+7d+fTTT9l///3DLivh5eXl0aZNm7S7KVK9enWGDx/OrFmzuPLKK8MuR5IkhczAL0kJ6u2332bvvfdm/PjxvPbaa/Tr149q1aqFXVbCKy4uZvz48Wk1nH99e++9N48//jgDBgzg2WefDbscSZIUIgO/JCWYFStW0KNHDzp37kzLli2ZOXMmxx13XNhlJY0vvviCRYsWpW3gBzj33HM599xzueyyy/jss8/CLkeSJIXEwC9JCWTatGnsv//+9O/fn0cffZS33nqLBg0ahF1WUsnLyyMjI4PWrVuHXUqo+vbtS9OmTTn11FP5/fffwy5HkiSFwMAvSQmguLiYBx98kFatWlGhQgUmT57MFVdcQSQSCbu0pDN+/Hj22WcfsrOzwy4lVFWrVmX48OF8//33XHrppUSj0bBLkiRJ5czALyntrQG+BCYD49c+fw6sKqf2586dS4cOHbj++uu56qqrmDhxYlqtLh9reXl5aT2cf3277bYb//3vfxk8eDD9+/cPuxxJklTOssIuQJLK20LgFWASMBH4jCD0bygL2ANoDRwAnADUjXEtw4cP55JLLqFq1aq88847HH744TFuIb3MmzePWbNmGfjXc+aZZ/Lhhx/So0cPDjjgAFq0aBF2SZIkqZxEoo7xk5QmpgB9gSHAaoJAv7Ggv6EsoAjIBE4HLgfaAGUZbL9kyRKuvPJKnn32WU499VT+85//sP3225fhigJ45ZVXOOmkk5gzZw6NGzcOu5yEsXLlStq2bcvSpUuZPHkyNWvWDLskSZJUDhzSLynlzQQOBPYHBhIM1Y9SurAPULj2/EJgGNAWaEkw9H9bjB8/nhYtWvDSSy/xzDPPMGzYMMN+jIwfP54dd9zRsL+BypUr88ILL/DLL79w4YUXOp9fkqQ0YeCXlLLWAPcA+xIM34cgtJdFyec/Ixjqfyuln+u/Zs0aevbsSbt27WjQoAHTp0/nnHPOcWG+GHL+/qbtsssuPPXUU7z44ov07ds37HIkSVI5MPBLSkmzCObd304Q0otifP0ioBi4D9gH+N+W6pk1i3bt2nHvvffSq1cvPvjgA3Jzc2NcVXpbuXIlU6ZMMfBvxsknn8xVV13FNddcw6RJk7b8AUmSlNQM/JJSzgyCOfb/IxiKH09RgpsLbQkWAPzL+9EoAwYMoEWLFvz666989NFH9OzZk6ws10yNtcmTJ7N69WoD/xb06dOHli1bcuqpp7Jw4cKwy5EkSXFk4JeUUj4DDgEWUfbh+6VVBCwDDgM+We/4ggULOOWUU7jwwgvp2rUrU6dOpU2bNuVUVfoZP3481apVo3nz5mGXktAqVqzIsGHDWLJkCeecc47z+SVJSmEGfkkpYw5wOPA7sR/CvyVFBCv/dwS+BsaMGUPz5s157733eOmll+jfvz/Vq1cv56rSS15eHq1bt3b0RCk0adKE5557jjfeeIOHHnoo7HIkSVKcGPglpYQocA6wkPIP+yWKgN+jUdrNmUPHo49mzz33ZObMmZx00kkhVZQ+otEo48ePdzj/VjjmmGO44YYbuOmmm8jLywu7HEmSFAeRqGP5JKWA/wCXhV1EiWiUYz76iNcOOoiMDO+rloevvvqK3XbbjZEjR3LUUUeFXU7SWLNmDYcddhjffvstU6dOpW7dumGXJEmSYsjfRCUlvW+Ba8IuYn2RCKPateMrw365GT9+PJFIxDUStlKFChV4/vnnWbVqFWeddRbFxcVhlyRJkmLI30YlJb1bgDVhF7GBKHBd2EWkkby8PPbaay9q1qwZdilJZ8cdd2Tw4MGMHj2a++67L+xyJElSDBn4JSW1X4DhlN+K/KVVCLxNMPpA8ZeXl+f8/TLo2LEjt912Gz179uS9994LuxxJkhQjBn5JSW0AkKiDkDMI1hZQfC1YsIAvv/zSwF9GvXr14tBDD+WMM87g559/DrscSZIUAwZ+SUnpuOOOo0mTJtzTo8e6wH/ccdCkCfToEWJl6xQB/YCVYReS4iZMmABA27ZtQ64kuWVmZjJkyBCi0ShnnnkmRUVh7XchSZJixcAvKSn9+OOPfPfddyybP3/9g/Ddd7D+sVhYvRr69YPOnaFZM2jRAs46CyZP3vi5ffvCEUfAbrux6OijuWvwYNwQJX7y8vJo0KABOTk5YZeS9Bo0aMDQoUN5//33ufPOO8MuR5IklZGBX1LqeOMNKCiARx+N7XVvvz24mdCzJwweDHXqwKBBcMgh8PXX685bvRqOOgquuAL22QcGDybj1lt5b/x4XnnlldjWpD+UzN+PRCJhl5ISDjvsMO666y7uvvtuRo8eHXY5kiSpDLLCLkCSyiJCsCI+AJdcAjNmwLHHrgv9hYVw993w0ktQsyYcfjisWgXDhsGOO8JHH225kXvvhczMdX++6SZ45x1YsQImTID/+7/g+AMPwHvvBe0//PAf9e3Urh0nuN1ZXKxevZpJkyZx7733hl1KSrn55pv58MMP6datG9OmTWOHHXYIuyRJkrQN7OGXlNT+NFB+Y0P6b7gB7rwTliwJAnnt2vDgg8F5c+eWrpH1w35REYwYEbzOzoZ27da99/TTwfOvvwbHd9+dok6dePfNN8nI8H+38TB16lRWrlzp/P0Yy8jIYNCgQVSqVImuXbuyZk2ibXwpSZJKw99AJaWu+fOD+fQA110HBx0EV10VzMHfWu+/HywIuN128K9/BTcORoyA3Nzg/eXLg+kEEPT6n3deMMpg/HjmH3MMg4cMicEXpA3l5eVRuXJlWrZsGXYpKadOnToMGzaMCRMmcNttt5X6c8uBCcBjQC/gRuAm4A6gPzAVWB3zaiVJ0sYY+CWlrpkzg3n1AHvvve74Xntt/bVatQqG67/9drAg34IFcPzx8PnnwfsrVqw7d7fd4Pzz4cgjg50DgLvvvnvbvgZtVl5eHq1ataJixYphl5KSDjroIO6//3769OnDiJKRLRsxA7gC2A2oDrQFrgTuA/4JPAzcC1wE7AtUA1oCtwDfxrF+SZLSnYFfUupaud6GeBUqbPx1aVWpEvTwH3xwsGAfwOLFwer9EPT8l4TORo3WfW7t69mzZm19m9qsaDT6x4J9ip9rr72WY489lrPPPpvvvvvuj+OrgaHAgcA+BFtQfgV/bJMZBdZs8ChRCEwD+gC5QBfg7fU+K0mSYsPALyl17bbbutdz5qx7XTL0flvVrAklK8KX9OxnZgZTBiBYL6DE4sUA7LjjjmVrM40sAt4hCIOnEwTC7YFsoCZQHzgIOH/xYuYdeSSNO3Y0KMZRJBLh2WefpUaNGpx22mmsXr2aicCewJnAJ2vPK9yGaxcR3BgYDXQG2gHeGpMkKXYM/JJSV24udOkSvH7ySVizBqZOhQ8+KP01Vq+Giy+Gb74J/lxUBL17QzQahP6uXdede9ttkJEB06bBV18FwX/kSACuueaa2HxNKSoKfAh0BeoCRxIM934JKAAWAsuAJcAvwHhgYPXqMGgQf2vfnl0Iho4vDKH2dFCrVi1eeOEFPv38c9p++CEHEnxfIDa98iU3Cz4B9gL+HaPrSpKU7gz8klJb//5wyCEwbhzUqRMs2nfEEcF766++vykVKkCbNnDiibD99lCjRrDa/wEHwOuvB9v8lTj8cHj5ZcjJCdYMaNgQiovZuX9/rrjiivh8fUkuCjwH7AEcQhDwS8Jf0drHphSt9/37FrgWaAicD/wQ+1LTXoNWrdh+zhymtG9PlM1/b7ZVIbAKuIrgps+SzZ8uSZK2IBKNRqNbPk2SEstPP/3E3atW0a9aNYrq1i05CKtWQbVqUHKsxIIFULVqMBe/c+dg8b3DD4d33il9oytWwG+/BTcOKlXa/LkLFgQ3CypXptqgQRz15pu0bt2a1q1bs//++1OtWrWt+4JT0PcE4XwsEGGDLRbLIAuoTNBLfO7aa6tsvgEOBeZHoxRGyudvNBNoDowBapdLi5IkpR4Dv6Sk9TJw8pZOev314CbACScEAXzkSDjmGCguDnrjTzghvkVGo3R+8UWWP/44kyZNYtmyZWRkZLD33nv/cQOgdevW7L777mRkpMegqyjwNMEq7qvYtrnfW1JyA+Fogq3gdohDG+liDtAGmE98vlebk0mwIOB7BKv/S5KkrWPgl5S0fgC2uBTezz/DjTcGwR+CRfRatIDbbw+G6QM0axbcFNiYr77acm/+FnxBsF1ZYWEhn3/+ORMnTuTjjz9m4sSJfP7550SjUWrUqMEBBxxA69atadOmDa1bt6ZevXplajcRFQNXE/S+l4csgt7hccDu5dRmKllOELi/pfzDfolM4AhgJI7WkCRpaxn4JSW1jsC7lGI+cVERzJsXbJ9Xteqf3/vuu2ARvo3Zeed1K/JvpQxgf2DiZs5ZsmQJkydP/uMGwMSJE5k3bx4ATZo0+dMNgJYtW1K5cuVtqiURFAMXAwPKud1Mgt7hD4C9y7ntZHcN8C8SYwG9/wIXhV2EJElJxsAvKam9ARwXdhGbMRDovhXnR6NR5syZ86cbAFOmTGHVqlVUqFCBFi1a/GkqwC677EKknOZUl9W1wMMhtZ0J1AI+BpqGVEOy+YhgIcVE+SWhKsFomZ3CLkSSpCRi4JeU1IoIAsCPYReyEdsBPxEsIFcWq1evZsaMGX/cAPj444/5Zu02gbVr16ZVq1Z/3ABo1aoV22+/fRlbjL3hwGkh15BFMKx/ClAh5FoS3QpgT4L5+/FYjX9bZAHtgdE4tF+SpNIy8EtKeo8QzAtPJBHgDqBnnK7/22+/8cknn/xpPYCFC4Nd6P/v//7vT1MBmjdvToUK4UXcXwjWMFhE+L3FEeAu4LaQ60h0TwEXhF3EJowHDgy7CEmSkoSBX1LSKyRYRXw64S0str5MYFdgGlC25f5KLxqNMmvWrD9NBZg2bRqFhYVUrlyZfffd948bAK1bt2annXYqt6kAJwOvkVg9xVMItnzTX0WBFsBnJMbc/fVlAacDg8IuRJKkJGHgl5QSviBYTXxN2IUQLNY3kWDBvjCtWLGCqVOn/nEDYOLEiXz77bcA1K9f/083AA444ACqV4/9xmdvAV1iftWyyQRaAp/g0PAN/e9//2PqmjWcVa8eNGpUchDWrIH1j4Uoi2AKT92wC5EkKQkY+CWljD7AjSHXEAFuBu4JuY5NmTdv3p/WApg0aRJLly4lEomw5557/mlBwD333JPMzMwytXcE8D6J07u/volAq7CLSDANGjRg3rx5RK69luiDD5YcDHa4uPZaKDkWS4sWwdobUey0E2xhDYoM4H7g+thXIklSyskKuwBJipXrCIb1DyWcueIZBL3Zd4bQdmnVr1+f4447juOOC/Y2KCoq4ssvv/zTWgBPP/00xcXFZGdns//++/9pPYCGDRuWuq2vCbZMTERZQF8M/Jvyp/9+9torCP077BDbRr75Bvr0gVdfhV9/DY49/TSce+4Wa3sHA78kSaVhD7+klLKGYDX41yjf0J8BHE6wTWBZV+UP2++//87kyZP/NBLgp59+AqBx48Z/ugGw7777UrVq1T99ftiwYYwbN45Pa9dm6j33BOsqDBsG48ZB7dpwT2KMf6hAsItC7bALSSD1GzTglw178zc1pL+4GGbNCo5vtx0UFMDixZCdDbvssuXGhgyBZcvg0EOhWbPg2MYC/1dfwYoVwevMTKhbl+0bNOBXnJIhSdKWGPglpZxC4HxgYDm2eQLwPOW3SF95ikajzJ07909rAUyePJkVK1aQmZnJPvvs86epAI8++iiPP/44kZ13JloyVPuKK6BvX9h553XDt2OluBjeegvefhuKiqB5c/jb3/563sKF8NJL8MUXwZ933ZX7jz+eG7di1EKq275BAxZuGPg3NqR/+nQ4+WSYPTvo+d91V1i+HD75BA46CD76qPSNzp0LjRsHrzcW+E8/PQj9AL/9Bt9/D82a8crTT3PCga7XL0nS5jikX1LKyQKeBQ4i2K5vDfFZvT+LdfOJryRYDC4VRSIRGjduTOPGjTnllFMAWLNmDZ999tkfNwDGjRvHE088AUDFihWBDUZYdO0aDA2P9cKAP/wA7dvD//0ffPYZzJkDXbr8NfCPGwdXXw0dOgS91EOGwMMP0/Omm+g4bhwtW7aMbV1JqlSLXhYVwZlnBmG/e3cYODAI+O3axaeoYcP+/OcHHoCbbuKi00+ny+zZoW45KUlSojPwS0pJEeAS4GjgPGAcQTiPxTZjJdfZH3iOYAu+dFOhQgVatmxJy5YtufTSSwFYtGgRkyZN4rbbbuOTTz758wd++AGmTQuG9K9v9mx47jkoLIRWraB+fXjmmeC9Pn2gRo3NF1KtGowaBbm50KZNEPg3ZtddYeJEqLR2DEa3btCwIasXL+aZZ54x8K9Vqv8+3n8fPv88eH3xxcHzwQfDHnusOx5ra9bAd98FUwB23BGAX7//nlmzZrH77rvHp01JklKAgV9SStuZYIGvocDDBPuvZ7FtPf4ln9uDYOTAOaRur/622G677ejQoQOvvfbaXwP/hx9Cv37BkP6SOfzTpwdB8fff4YILYOzYYMj92vUCuOOOLQf+7bYLHluyNiT+YcGCP1423GmnLX8+BUSjUdasWcPq1as3+SgqLkXk/+abda/X/7vbaaf4BP5bb4VHHglC/667BjeH1vrxxx8N/JIkbYaBX1LKiwBnrn1MAR4nuAGwdhkwKrDxoczrH69IsBjg5UBrXCwsJm64IQj77dtD//7Bsblzg1Xb42HFimBY/9KlwaiAChXglls44sory3TZoqKizYboWD9WrVq1TZ9bs6YUA/a3dIMFgkX5Sqxeve71ypVb/5e3JaNGwb33Bq8//DC4QbTe9AGXIZIkafMM/JLSyn7AAKAf8CXBDYDJwAxgKbCKINxnA3sTDNvfj6BX35nCMVRYGMyrhyDwl2jfPn6BPzMTWrQIFu+bOTPo5e/fn6sWLGCn+fO3OYAXFRWVubSsrCwqVqxIxYoVqVSp0h+vN/fIzs4u1XnrP7Z07YM7dWKLsX3//SESgWg0WDdh112Def3/+1+Z/x7+Ytasda9btw6ep0yJfTuSJKUoA7+ktJQF7LX2cU7ItaSl334LhmgD1Ky57nhpepi3VcWKsHa9AS6/HHJy4IcfmDliBFWbNv0j9FatWjUm4bm0jwoVKpCRkRG/r3srZJWmjmbN4JxzgrUWbr89WIjxtddg0aKta2z+/GBth19+WXdszpxgrYeaNYPvT/v2wUiMNWvgvvuCGzZ9+mxdO5IkpTEDvySp/NWtC1WqBMPs15tPz/z55dN+jRrBQn+//cb2lSszduzY8mk3we26115MbdAg2GqvxF57BVvzrX/sP/+BJk2CIff//jccc0wQzseMgcqVS9fY6NHwj38Er/fZJ3h++eXgccghwXX33BPefDN4/eabwcJ9/frBbbexB1A91rs+SJKUYgz8kqTyF4kE+7gPGgRvvw133hkce+21+LT36qvQqdO6Vfq//x6++AKAfdzL/Q/vjB3L9hse3NTNkF69ggcEPfB33hm8Lu2OB926BY8t6dAheKynzjHHEIcJBJIkpRwDvyQpHPffDxMmBHOy27ULhm6vvwhcpBRLIy5aBDfdFLwuKAieZ85cN3T/sccgKys4ds01QW91dnbQM71sGRx1FI8++GBMv6xkVgtoDHy/pRP/+c+gt/3444PvW79+8OOPwaiJ668P5vTPnLnxz2ZlBd+HbRQBWm3zpyVJSi+RqEvcSpJi6KOPPmLAZ5/xTPXq63pwP/ooWOBt/WMAy5fDyJFBD/F++8EbbwTBPDMzCOQlPfKbsmwZDBy46fcvuii4FgTrBkyaFOwEULMmtGxJbtOmzC7bl5tyLgKeYQtbVxYXB3/vr74a/H3WqxcM6b/kkmC6xKJFf16McX21aq1bsHEbZAD/AK7Z5itIkpQ+DPySpJj7HNhzSyf99BP8/PO6IeBr1gRztz/+GI49Fl5/Pa41ZgKnA4Pj2kry+ZRgZ4pEVRH4EagddiGSJCUBh/RLkmKuGVAFWLG5kzIzg9XyK1SAnXaCiRPhm2/g8MNhwIDgnIcfhq+/3vjnr7kG/u//trnGKIkdbMOyL8Hfy1SgOORaNpQFnIlhX5Kk0rKHX5IUF+cBg9jC0PBoNBhm/8UXwfD9li2Dbd9KvP56MDd8Y447Dho12ub6IsC3wE7bfIXU9RyJu13lJ8ABYRchSVKSMPBLkuJiEom7uFoW0AmI76SB5LUKaA7MBopCrqVEFtAReDPsQiRJSiIZYRcgSUpNBwAtSMwfNIXAFWEXkcAqAQNJnCH9EYIpIk+GXYgkSUkmEX8PkySliKtJnNBYIgPIBY4Mu5AE1wq4kSBshy0K9AW2fQKHJEnpySH9kqS4KQYOJhjev9m5/OVsLHBE2EUkgZUEC/h9TXjfvwzgWOAVEuPmgyRJycQefklS3GQAzxJsgZcIMoBLMOyXVmVgDEHPehjb+mQCrYEhGPYlSdoWBn5JUlztCtwfdhEEP/AaAv8Iu5Ak0wh4H9iB8g39GQTrQLwNVC3HdiVJSiUGfklS3F0JHEd4P3QygIrAcKB6SDUksybABGBPyq+nvTPwDlCznNqTJCkVGfglSXGXAQwDDqX8f/BkrH28BhxYzm2nkoYEazHcQTDUPh7TNLKAasAAgi0T7dmXJKlsXLRPklRuVgAnAyMJVl6Pt0ygAvAGrsofS9OBs4CZBH/HRWW8XhbBooAdCMJ+4zJeT5IkBezhlySVmyoEPe3XEgwNj+difhlADvARhv1Y2wf4FHgVOGztsW2Z359B8G/gJIJ1AkZh2JckKZbs4ZckhWIC0B34lmD7vljJXHu964E7CVaaV3x9AzwJvEvQ67967fFMglAfXfsoGQlQFdgXOBq4AGhQnsVKkpRGDPySpNCsAHoDjwK/E4TDbQ3/JcPC2wCPEGznpvK3BvicYATAfILvcQbB6I5GwP7ALjjEUJKk8mDglySFbhkwFPgX8BnrhvsXbuYz659TGTgHuIxguLkkSZIM/JKkBBIFphEM95+y9vkr/trrXwtoRbBP+34E88jdvk2SJOnPDPySpIS2GlhOMDS8AkFvfjXKbz94SZKkZGXglyRJkiQpBblmjiRJkiRJKcjAL0mSJElSCjLwS5IkSZKUggz8kiRJkiSlIAO/JEmSJEkpyMAvSZIkSVIKMvBLkiRJkpSCDPySJEmSJKUgA78kSZIkSSnIwC9JkiRJUgoy8EuSJEmSlIIM/JIkSZIkpSADvyRJkiRJKcjAL0mSJElSCjLwS5IkSZKUggz8kiRJkiSlIAO/JEmSJEkpyMAvSZIkSVIKMvBLkiRJkpSCDPySJEmSJKUgA78kSZIkSSnIwC9JkiRJUgoy8EuSJEmSlIIM/JIkSZIkpSADvyRJkiRJKcjAL0mSJElSCjLwS5IkSZKUggz8kiRJkiSlIAO/JEmSJEkpyMAvSZIkSVIKMvBLkiRJkpSCDPySJEmSJKUgA78kSZIkSSnIwC9JkiRJUgoy8EuSJEmSlIIM/JIkSZIkpSADvyRJkiRJKcjAL0mSJElSCjLwS5IkSZKUggz8kiRJkiSlIAO/JEmSJEkpyMAvSZIkSVIKMvBLkiRJkpSCDPySJEmSJKUgA78kSZIkSSnIwC9JkiRJUgoy8EuSJEmSlIIM/JIkSZIkpSADvyRJkiRJKcjAL0mSJElSCjLwS5IkSZKUggz8kiRJkiSlIAO/JEmSJEkpyMAvSZIkSVIKMvBLkiRJkpSCDPySJEmSJKWg/wcC56wZtrkf3wAAAABJRU5ErkJggg==", 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" ] @@ -335,25 +335,24 @@ { "cell_type": "code", "execution_count": 8, + "id": "b7db82e0", + "metadata": { + "nbsphinx": "hidden" + }, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "# Silence a third-party Biopython deprecation warning raised on import\n", + "warnings.filterwarnings(\"ignore\", message=\"The Bio.Application modules\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "ab94a294fc8927a3", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/Bio/Application/__init__.py:39: BiopythonDeprecationWarning: The Bio.Application modules and modules relying on it have been deprecated.\n", - "\n", - "Due to the on going maintenance burden of keeping command line application\n", - "wrappers up to date, we have decided to deprecate and eventually remove these\n", - "modules.\n", - "\n", - "We instead now recommend building your command line and invoking it directly\n", - "with the subprocess module.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n", "from openfe.protocols.openmm_septop import SepTopProtocol\n", @@ -385,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "4b08f0258bbdd052", "metadata": {}, "outputs": [], @@ -492,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "fcc7a807d6edc1a8", "metadata": {}, "outputs": [], @@ -537,20 +536,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "d4f5c88c40f92a9", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{openfe.protocols.openmm_rfe.hybridtop_protocols.RelativeHybridTopologyProtocol: 46,\n", - " openfe.protocols.openmm_septop.equil_septop_method.SepTopProtocol: 6}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "RelativeHybridTopologyProtocol: 46\n", + "SepTopProtocol: 6\n" + ] } ], "source": [ @@ -562,7 +558,8 @@ "for transformation in alchemical_network.edges:\n", " edges_per_protocol[transformation.protocol.__class__] += 1\n", "\n", - "edges_per_protocol" + "for protocol, count in edges_per_protocol.items():\n", + " print(f\"{protocol.__name__}: {count}\")" ] }, { @@ -583,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "27d647049e330837", "metadata": {}, "outputs": [], @@ -727,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "78530a0e74cc84b3", "metadata": {}, "outputs": [ @@ -735,7 +732,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=86606) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=3050) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] }, @@ -743,16 +740,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2026-11-04 13:53:19-- https://zenodo.org/records/23947882/files/mixed_results.zip\n", - "Resolving zenodo.org (zenodo.org)... 137.138.121.78, 188.185.45.63, 188.185.40.132, ...\n", - "Connecting to zenodo.org (zenodo.org)|137.138.121.78|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 59182107 (56M) [application/octet-stream]\n", - "Saving to: ‘mixed_results.zip’\n", - "\n", - "mixed_results.zip 100%[===================>] 56.44M 755KB/s in 2m 11s \n", - "\n", - "2026-11-04 13:55:31 (442 KB/s) - ‘mixed_results.zip’ saved [59182107/59182107]\n", + "File ‘mixed_results.zip’ already there; not retrieving.\n", "\n" ] }, @@ -760,7 +748,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=86606) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=3050) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] } @@ -782,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "804fec03b92d85f", "metadata": {}, "outputs": [ @@ -1115,7 +1103,7 @@ "28 0.581679 " ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1183,7 +1171,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "58d1c1b26eead798", "metadata": {}, "outputs": [ @@ -1386,7 +1374,7 @@ "17 lig_81 -2.059012 0.561859 MLE True" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1424,7 +1412,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "4401ca21e5bd73c9", "metadata": {}, "outputs": [ @@ -1432,7 +1420,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/matplotlib_inline/config.py:59: DeprecationWarning: InlineBackend._figure_formats_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/matplotlib_inline/config.py:63: DeprecationWarning: InlineBackend._figure_formats_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n", " def _figure_formats_changed(self, name, old, new):\n" ] }, @@ -1442,16 +1430,16 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " 2026-11-04T13:55:40.827908\n", + " 2026-11-04T17:54:59.627026\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.10.6, https://matplotlib.org/\n", + " Matplotlib v3.11.2, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -1462,209 +1450,209 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2264,34 +2252,34 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" From 158f13d6bf68601d40aedb95301d60bbac7cd47f Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Thu, 5 Nov 2026 12:01:30 +0000 Subject: [PATCH 7/9] small clean up --- mixed_networks/mixed_networks.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb index f9f12f0..b74f26c 100644 --- a/mixed_networks/mixed_networks.ipynb +++ b/mixed_networks/mixed_networks.ipynb @@ -16,7 +16,7 @@ "\n", "## Overview\n", "\n", - "In this tutorial, we will demonstrate how to create, execute and analyze a mixed protocol network to estimate absolute binding free energies for `three` distinct ligand scaffold series bound to $\\beta$-secretase 1 (BACE1) taken from [Baumann et al.](https://doi.org/10.1021/acs.jctc.3c00282). The dataset contains a total of 18 ligands with 6 ligands in each of the three unique scaffold series grouped as `biaryl`, `spirocyclic` and `amide`, examples of each are shown below:\n", + "In this tutorial, we will demonstrate how to create, execute and analyze a mixed protocol network to estimate absolute binding free energies for `three` distinct ligand scaffold series bound to $\\beta$-secretase 1 (BACE1) taken from [Baumann et al.](https://doi.org/10.1021/acs.jctc.3c00282). The dataset contains a total of 18 ligands with six ligands in each of the three unique scaffold series grouped as `biaryl`, `spirocyclic` and `amide`, examples of each are shown below:\n", "\n", "\n", "\n", From 8f0c8d776565ee2a9d85d23b0d41f8a83a125466 Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Fri, 6 Nov 2026 12:00:02 +0000 Subject: [PATCH 8/9] pr feedback, rework name finding --- mixed_networks/mixed_networks.ipynb | 457 ++++++++++++++++------------ 1 file changed, 267 insertions(+), 190 deletions(-) diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb index b74f26c..7f7bcce 100644 --- a/mixed_networks/mixed_networks.ipynb +++ b/mixed_networks/mixed_networks.ipynb @@ -39,11 +39,10 @@ ] }, { - "cell_type": "code", - "execution_count": 1, - "id": "878ed1f41247982c", "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "from rdkit import Chem\n", "from gufe import SmallMoleculeComponent\n", @@ -54,6 +53,12 @@ "supplier = Chem.SDMolSupplier(ligands_file, removeHs=False)\n", "ligands = [SmallMoleculeComponent.from_rdkit(mol) for mol in supplier if mol is not None]\n", "\n", + "# Names must uniquely identify the input ligands when matching result filenames\n", + "ligand_names = [ligand.name for ligand in ligands]\n", + "if any(not name for name in ligand_names) or len(set(ligand_names)) != len(ligand_names):\n", + " raise ValueError(\"Input ligands must have unique, nonempty names for this tutorial.\")\n", + "ligand_names = set(ligand_names)\n", + "\n", "# Define a mapping between the ligand names and their respective series\n", "ligand_series_mapping = {\n", " \"lig_02\": \"biaryl\",\n", @@ -75,7 +80,8 @@ " \"lig_74\": \"amide\",\n", " \"lig_81\": \"amide\",\n", "}" - ] + ], + "id": "69b62a6ddc1cb0cf" }, { "cell_type": "markdown", @@ -88,7 +94,13 @@ "\n", "* **Atom Mapper**: Proposes potential atom mappings (descriptions of the alchemical change) for pairs of ligands. We will use the `KartografAtomMapper`.\n", "* **Scorer**: Given an atom mapping, provides an estimate of the quality of that mapping (higher scores are better). We will use `default_lomap_scorer`.\n", - "* **Network Planner**: Creates the actual `LigandNetwork`; different network planners provide different strategies. We will create a `LOMAP` redundant network with the `generate_lomap_network` method.\n" + "* **Network Planner**: Creates the actual `LigandNetwork`; different network planners provide different strategies. We will create a `LOMAP` redundant network with the `generate_lomap_network` method.\n", + "\n", + "
\n", + "\n", + "**Note:** The **default_lomap_scorer** is designed to score edges for a hybrid topology based protocol any may not be appropriate for scoring edges for the separated topologies approach.\n", + "\n", + "
" ] }, { @@ -161,7 +173,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -379,7 +391,7 @@ "\n", "Now we can build the transformations for this network, assigning the appropriate protocol for each transformation. Here we will use the hybrid topology protocol for edges within each series and the separated topologies protocol for edges between the series.\n", "\n", - "Each protocol has different requirements for the `ChemicalSystem` compositions that represent the transformation's end states. The hybrid topology protocol requires you to construct the `complex` and `solvent` legs as separate `Transformation` objects, while the separated topologies protocol runs both legs within a single `Transformation`. The code below shows how to build transformations for both protocols with the appropriate `ChemicalSystem` objects. For more information about each protocol's requirements, see the [examples documentation](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html#system-composition-examples)." + "Each protocol has different requirements for the `ChemicalSystem` compositions that represent the transformation's end states. The hybrid topology protocol requires you to construct the `complex` and `solvent` legs as separate `Transformation` objects, while the separated topologies protocol runs both legs within a single `Transformation`. The code below shows how to build transformations for both protocols with the appropriate `ChemicalSystem` objects. For more information about each protocol's requirements, see the [system composition examples documentation](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html#system-composition-examples)." ] }, { @@ -562,6 +574,68 @@ " print(f\"{protocol.__name__}: {count}\")" ] }, + { + "cell_type": "markdown", + "id": "269228a41c437f2", + "metadata": {}, + "source": [ + "Alternatively we could have used the mapping score generated using the `default_lomap_scorer` to automatically assign the protocol for each edge. For example, we could have used a threshold of `0.5` to assign edges as follows:\n", + "\n", + "- **Hybrid Topology Protocol**: For edges with a score greater than or equal to `0.5`, as these edges are likely to be easier to converge with the hybrid topology approach\n", + "- **Separated Topologies Protocol**: For edges with a score less than `0.5`, as these edges are likely to be more challenging to converge with the hybrid topology approach\n", + "\n", + "The code below shows how to build the transformations for the network using this approach, however this network is not used in the analysis which follows." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7a6e2d565131928c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RelativeHybridTopologyProtocol: 20\n", + "SepTopProtocol: 19\n" + ] + } + ], + "source": [ + "# Create a list to hold the transformations for the network using the mapping score to assign protocols\n", + "lomap_split_transformations = []\n", + "\n", + "# For each edge in the network, build the appropriate transformation(s) based on the mapping score\n", + "for edge in network.edges:\n", + " # Get the mapping score for this edge\n", + " score = edge.annotations[\"score\"]\n", + "\n", + " # For high scoring edges, build the hybrid topology transformations\n", + " if score >= 0.5:\n", + " transformations = build_hybrid_topology_transformation(edge, default_hybrid_settings, protein, solvent)\n", + " # For low scoring edges, build the separated topologies transformation\n", + " else:\n", + " transformations = build_septop_transformation(edge, default_septop_settings, protein, solvent)\n", + "\n", + " # Add the transformations to the list\n", + " lomap_split_transformations.extend(transformations)\n", + "\n", + "# Make an AlchemicalNetwork to hold all transformations for this series\n", + "lomap_split_network = openfe.AlchemicalNetwork(lomap_split_transformations)\n", + "\n", + "# Count the number of transformations for each protocol in the LOMAP split network\n", + "lomap_split_edges_per_protocol = {\n", + " RelativeHybridTopologyProtocol: 0,\n", + " SepTopProtocol: 0\n", + "}\n", + "for transformation in lomap_split_network.edges:\n", + " lomap_split_edges_per_protocol[transformation.protocol.__class__] += 1\n", + "\n", + "for protocol, count in lomap_split_edges_per_protocol.items():\n", + " print(f\"{protocol.__name__}: {count}\")" + ] + }, { "cell_type": "markdown", "id": "ab7ecee9702fad84", @@ -573,14 +647,14 @@ "\n", "
\n", "\n", - "**Warning:** Gathering the results of a mixed-protocol network is not currently possible via the CLI.\n", + "**Warning:** Gathering the results of a mixed-protocol network is not currently possible via the CLI. Matching ligand names from filenames is a temporary solution only required for benchmarking and will be changed in the future.\n", "\n", "
" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "27d647049e330837", "metadata": {}, "outputs": [], @@ -592,13 +666,15 @@ "from openff.units import unit\n", "\n", "# Define some helper functions to classify the results and combine repeated runs of each transformation into a single estimate and uncertainty for each edge and protocol.\n", - "def classify_result(fpath: pathlib.Path) -> dict:\n", + "def classify_result(fpath: pathlib.Path, ligand_names: set[str]) -> dict:\n", " \"\"\"Load a JSON file containing a quickrun result and return a dict containing the relevant information for classifying the result.\n", "\n", " Parameters\n", " ----------\n", " fpath : pathlib.Path\n", " The path to a gufe-serialized JSON.\n", + " ligand_names : set[str]\n", + " The expected ligand names from the input molecules.\n", "\n", " Returns\n", " -------\n", @@ -619,18 +695,21 @@ "\n", " # Get the phase from the file name if it was hybrid topology result\n", " edge_name = fpath.stem\n", - " if \"solvent\" in fpath.name:\n", - " phase = \"solvent\"\n", - " edge_name = edge_name.removeprefix(\"solvent_\")\n", - " elif \"complex\" in fpath.name:\n", - " phase = \"complex\"\n", - " edge_name = edge_name.removeprefix(\"complex_\")\n", - " else:\n", - " # This is the septop result\n", - " phase = None\n", - " # Get the ligand names from the edge name\n", - " lig_a = edge_name[:6]\n", - " lig_b = edge_name[7:]\n", + " phase = None\n", + " if not is_septop:\n", + " if edge_name.startswith(\"solvent_\"):\n", + " phase = \"solvent\"\n", + " elif edge_name.startswith(\"complex_\"):\n", + " phase = \"complex\"\n", + " else:\n", + " raise ValueError(f\"{fpath}: hybrid topology filename must start with 'complex_' or 'solvent_'.\")\n", + " edge_name = edge_name.removeprefix(f\"{phase}_\")\n", + "\n", + " # Match the entire edge name to determine the ligands in the edge\n", + " matches = [(a, b) for a in ligand_names for b in ligand_names if a != b and edge_name == f\"{a}_{b}\"]\n", + " if len(matches) != 1:\n", + " raise ValueError(f\"{fpath}: expected exactly one matching ligand pair, found {sorted(matches)}.\")\n", + " lig_a, lig_b = matches[0]\n", " classified = {\"lig_a\": lig_a, \"lig_b\": lig_b, \"phase\": phase}\n", "\n", " if not is_septop:\n", @@ -724,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "78530a0e74cc84b3", "metadata": {}, "outputs": [ @@ -732,7 +811,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=3050) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=36441) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] }, @@ -748,7 +827,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=3050) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=36441) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] } @@ -770,7 +849,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "804fec03b92d85f", "metadata": {}, "outputs": [ @@ -1103,7 +1182,7 @@ "28 0.581679 " ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1119,7 +1198,7 @@ "\n", "# Loop through the downloaded results and classify them by protocol, grouping them by the ligands involved in the transformation\n", "for file in all_results.rglob(\"*.json\"):\n", - " result_data = classify_result(file)\n", + " result_data = classify_result(file, ligand_names)\n", "\n", " if result_data[\"protocol\"] == \"SepTopProtocol\":\n", " septop_results[(result_data[\"lig_a\"], result_data[\"lig_b\"])].append(result_data)\n", @@ -1171,7 +1250,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "58d1c1b26eead798", "metadata": {}, "outputs": [ @@ -1374,7 +1453,7 @@ "17 lig_81 -2.059012 0.561859 MLE True" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1412,7 +1491,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "4401ca21e5bd73c9", "metadata": {}, "outputs": [ @@ -1435,7 +1514,7 @@ " \n", " \n", " \n", - " 2026-11-04T17:54:59.627026\n", + " 2026-11-06T11:57:04.393642\n", " image/svg+xml\n", " \n", " \n", @@ -1468,7 +1547,7 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " 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style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1695,7 +1774,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1736,7 +1815,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1774,7 +1853,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1829,7 +1908,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2252,12 +2331,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2272,7 +2351,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2321,7 +2400,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2336,7 +2415,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2351,7 +2430,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2366,7 +2445,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2412,7 +2491,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2426,7 +2505,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2452,7 +2531,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2568,11 +2647,11 @@ " \n", " \n", + "\" clip-path=\"url(#pba30bf7eff)\" style=\"fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2660,7 +2739,7 @@ "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2726,7 +2805,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3223,7 +3302,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", @@ -3658,34 +3756,13 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3706,7 +3783,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3716,7 +3793,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3776,7 +3853,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", From 9dfa1e2cd6229dc6934a6e19f33b15df32e92d3d Mon Sep 17 00:00:00 2001 From: Josh Horton Date: Fri, 6 Nov 2026 12:35:21 +0000 Subject: [PATCH 9/9] render figures as svg --- mixed_networks/mixed_networks.ipynb | 1135 ++++++++++++++++++++++----- 1 file changed, 959 insertions(+), 176 deletions(-) diff --git a/mixed_networks/mixed_networks.ipynb b/mixed_networks/mixed_networks.ipynb index 7f7bcce..befeee2 100644 --- a/mixed_networks/mixed_networks.ipynb +++ b/mixed_networks/mixed_networks.ipynb @@ -39,10 +39,11 @@ ] }, { - "metadata": {}, "cell_type": "code", + "execution_count": 1, + "id": "69b62a6ddc1cb0cf", + "metadata": {}, "outputs": [], - "execution_count": null, "source": [ "from rdkit import Chem\n", "from gufe import SmallMoleculeComponent\n", @@ -80,8 +81,7 @@ " \"lig_74\": \"amide\",\n", " \"lig_81\": \"amide\",\n", "}" - ], - "id": "69b62a6ddc1cb0cf" + ] }, { "cell_type": "markdown", @@ -171,9 +171,801 @@ "id": "7f92fa5e68ee95ef", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/matplotlib_inline/config.py:63: DeprecationWarning: InlineBackend._figure_formats_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n", + " def _figure_formats_changed(self, name, old, new):\n" + ] + }, { "data": { - "image/png": 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" ] @@ -184,6 +976,7 @@ ], "source": [ "%matplotlib inline\n", + "%config InlineBackend.figure_formats = ['svg']\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "\n", @@ -811,7 +1604,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=36441) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=38357) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] }, @@ -827,7 +1620,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=36441) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", + "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=38357) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n", " pid, fd = os.forkpty()\n" ] } @@ -1495,14 +2288,6 @@ "id": "4401ca21e5bd73c9", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joshua/mambaforge/envs/openfe_dev/lib/python3.12/site-packages/matplotlib_inline/config.py:63: DeprecationWarning: InlineBackend._figure_formats_changed is deprecated in traitlets 4.1: use @observe and @unobserve instead.\n", - " def _figure_formats_changed(self, name, old, new):\n" - ] - }, { "data": { "image/svg+xml": [ @@ -1514,7 +2299,7 @@ " \n", " \n", " \n", - " 2026-11-06T11:57:04.393642\n", + " 2026-11-06T12:34:17.999473\n", " image/svg+xml\n", " \n", " \n", @@ -1547,7 +2332,7 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke: #808080; stroke-width: 2\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1774,7 +2559,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1815,7 +2600,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1853,7 +2638,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1908,7 +2693,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2331,12 +3116,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2351,7 +3136,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2400,7 +3185,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2415,7 +3200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2430,7 +3215,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2445,7 +3230,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2491,7 +3276,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2505,7 +3290,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2531,7 +3316,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2647,11 +3432,11 @@ " \n", " \n", + "\" clip-path=\"url(#pbd5db456af)\" style=\"fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2739,7 +3524,7 @@ "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2805,7 +3590,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3302,7 +4087,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", @@ -3751,17 +4517,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3782,8 +4548,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3793,7 +4559,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -3837,7 +4622,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3853,7 +4638,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3868,8 +4653,6 @@ } ], "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_formats = ['svg']\n", "from gufe.tokenization import JSON_HANDLER\n", "from cinnabar import plotting\n", "import requests\n",