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index 6fe9a7eb..8778c92c 100644
--- a/showcase/openfe_showcase.ipynb
+++ b/showcase/openfe_showcase.ipynb
@@ -3,61 +3,49 @@
{
"cell_type": "markdown",
"id": "cb0d308b-8f50-4ac9-a8a3-550d8a2e3feb",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true
- },
+ "metadata": {},
"source": [
- " \n",
+ " \n",
"\n",
"# OpenFE Showcase: Relative Binding Free Energies in the TYK2 System"
]
},
{
"cell_type": "markdown",
- "id": "8eea1a56-3241-4ccf-9d3a-88de6bbf0e0e",
+ "id": "f347f95a-ce31-4d2f-958e-0a6e33374dea",
"metadata": {},
"source": [
- "# Intro\n",
- "\n",
- "Welcome to the Open Free Energy toolkit!\n",
+ "## Introduction\n",
"\n",
- "The OpenFE toolkit provides open-source frameworks for calculating alchemical free energies. This notebook showcases the methods that are available in OpenFE and their usage.\n",
+ "Welcome to the Open Free Energy toolkit! **openfe** is an open-source package for planning, running and analyzing alchemical free energy calculations.\n",
"\n",
- "Throughout this showcase, we will introduce different interchangeable components that users can choose from during the setup of free energy calculations. OpenFE allows you to mix and match different components, such as:\n",
+ "In this showcase we calculate relative binding free energies (RBFEs) for a congeneric series of ligands binding to tyrosine kinase 2 (TYK2), using the Relative Hybrid Topology Protocol. Along the way, we introduce the main building blocks of **openfe** and the choices you can make at each step, such as:\n",
"\n",
"* Atom mappers\n",
"* Scorers (for atom mappings)\n",
- "* Ligand networks\n",
- "\n",
- "This showcase currently focuses on relative binding free energy (RBFE) calculations using a hybrid topology approach. However, OpenFE provides a wide range of protocols for:\n",
- "- [Absolute binding free energy calculations](https://docs.openfree.energy/en/latest/tutorials/abfe_tutorial.html)\n",
- "- [Absolute hydration free energy calculations](https://docs.openfree.energy/en/latest/tutorials/ahfe_tutorial.html)\n",
- "- [Molecular Dynamics (MD) simulations](https://docs.openfree.energy/en/latest/tutorials/md_tutorial.html)\n",
- "- [Relative binding free energy calculations using a Separated Topologies (SepTop) approach](https://docs.openfree.energy/en/latest/tutorials/septop_tutorial.html)\n",
- "\n",
- "If you are planning your own calculations, please also check out our [tutorials](https://docs.openfree.energy/en/stable/tutorials/index.html) which will walk you step-by-step through setup, execution and analysis of different protocols."
+ "* Ligand networks"
]
},
{
"cell_type": "markdown",
- "id": "28f40a29-4ddd-4f25-8c08-154bb3139bb3",
+ "id": "b74dcaf2-5fae-4bff-bd76-e7aa7c2be213",
"metadata": {},
"source": [
- "# Outline\n",
- "\n",
- "0. Setup for Google Colab \n",
- "1. Overview \n",
- "2. Setup \n",
- " 2.1. Loading Ligands and Defining Ligand Atom Mappings \n",
- " 2.2. Creating a ligand network \n",
- " 2.3. Defining ChemicalSystems \n",
- " 2.4. Defining the RBFE simulation settings and protocol \n",
- "3. Running a Relative Ligand Binding Free Energy Calculation \n",
- " 3.1. Using the Python API \n",
- " 3.2. Using the CLI \n",
- "4. Analysis\n",
- "5. Relative Free Energies with the OpenFE CLI\n",
- "6. Useful References for Getting Started"
+ "## Outline\n",
+ "\n",
+ "0. Setup for Google Colab\n",
+ "1. Overview\n",
+ "2. Background: hybrid topology RBFEs\n",
+ "3. The TYK2 inputs\n",
+ "4. Assigning partial charges\n",
+ "5. Atom mappings and scoring\n",
+ "6. Ligand networks\n",
+ "7. Protocol settings\n",
+ "8. Creating transformations\n",
+ "9. Running the calculations\n",
+ "10. Results\n",
+ "11. Analysing results with cinnabar\n",
+ "12. Next steps"
]
},
{
@@ -97,12 +85,12 @@
" \n",
" !pip install -q condacolab\n",
" import condacolab\n",
- " condacolab.install_from_url(\"https://github.com/OpenFreeEnergy/openfe/releases/download/v1.7.0/OpenFEforge-1.7.0-Linux-x86_64.sh\")"
+ " condacolab.install_from_url(\"https://github.com/OpenFreeEnergy/openfe/releases/download/v1.13.0/OpenFEforge-1.13.0-Linux-x86_64.sh\")"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "dlCARZ2_fAI4",
"metadata": {
"colab": {
@@ -141,51 +129,151 @@
},
{
"cell_type": "markdown",
- "id": "0eaea8f6",
- "metadata": {
- "id": "0eaea8f6"
- },
+ "id": "8421015e-4d1e-4206-9d79-db9204e083bd",
+ "metadata": {},
+ "source": [
+ "## 1. Overview\n",
+ "\n",
+ "An **openfe** calculation has three stages:\n",
+ "\n",
+ "1. **Setup**: load the protein and ligands, decide which ligand pairs to compare, and define how each transformation is simulated.\n",
+ "2. **Run**: execute the simulations, usually on GPUs.\n",
+ "3. **Gather**: combine the simulation outputs into free energy estimates and analyze them.\n",
+ "\n",
+ "Each stage can be carried out with the Python API or with the command line interface (CLI). This notebook uses the Python API for planning, and the CLI for running and gathering, which is how most production campaigns are run.\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ "\n",
+ "**Which protocol?** The Relative Hybrid Topology Protocol used here works best for ligands that share a common core, such as this TYK2 series. For chemically diverse ligands, such as scaffold hops, the Separated Topologies Protocol is usually a better choice. See [Choosing a protocol](https://docs.openfree.energy/en/stable/guide/protocols/index.html).\n",
+ "\n",
+ "**Tutorials for the other protocols:** [Separated Topologies](https://docs.openfree.energy/en/stable/tutorials/septop_tutorial.html) · [Absolute binding](https://docs.openfree.energy/en/stable/tutorials/abfe_tutorial.html) · [Absolute hydration](https://docs.openfree.energy/en/stable/tutorials/ahfe_tutorial.html) · [Molecular dynamics](https://docs.openfree.energy/en/stable/tutorials/md_tutorial.html)\n",
+ "\n",
+ "**Prefer the command line?** This notebook uses the Python API to plan the RBFE campaign. For an equivalent workflow using the CLI, see [Relative Free Energies with the OpenFE CLI](https://docs.openfree.energy/en/stable/tutorials/rbfe_cli_tutorial.html).\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7f1e1784-3d94-4b67-a927-0d74d16f6536",
+ "metadata": {},
"source": [
- "# 1. Overview\n",
+ "## 2. Background: hybrid topology RBFEs\n",
+ "\n",
+ "We compute the difference in binding free energy between two ligands, ΔΔG, from two alchemical simulations (solvent and complex legs), each sampled over a series of λ windows with replica exchange. The hybrid topology approach requires the ligands have a common core and atom mapping.\n",
+ "\n",
+ "\n",
+ "More background: thermodynamic cycle, λ schedule and hybrid topologies\n",
+ "\n",
+ "**The thermodynamic cycle**\n",
+ "\n",
+ "Rather than computing each ligand's binding free energy directly, we compute the *difference* in binding free energy between two ligands, ΔΔG, using a thermodynamic cycle:\n",
+ "\n",
+ "\n",
"\n",
- "In this example we show how to set up a network of transformations using\n",
- "the OpenFE toolkit for small chemical modifications of ligands binding to tyrosine kinase 2 (TYK2).\n",
+ "The horizontal legs (binding) are very hard to converge, so instead we simulate the vertical legs, alchemically transforming ligand A into ligand B both in solvent and in the protein-bound complex:\n",
"\n",
- "For convenience, a prepared (capped and protonated) PDB structure of the\n",
- "TYK2 protein is provided under `inputs/tyk2_protein.pdb`. "
+ "$$\\Delta\\Delta G_{\\mathrm{bind}}(A \\rightarrow B) = \\Delta G_{\\mathrm{complex}}(A \\rightarrow B) - \\Delta G_{\\mathrm{solvent}}(A \\rightarrow B)$$\n",
+ "\n",
+ "Every ligand pair therefore needs **two** simulations: a solvent leg and a complex leg.\n",
+ "\n",
+ "**Sampling along the alchemical path**\n",
+ "\n",
+ "The transformation from A to B is controlled by a coupling parameter, λ, which interpolates the interactions of the atoms that differ between the two ligands. The system is simulated at a series of discrete λ values (λ windows), chosen so that neighbouring windows overlap, and the free energy is estimated from all windows with the multistate Bennett acceptance ratio (MBAR).\n",
+ "\n",
+ "\n",
+ "\n",
+ "*Default λ schedule for the Relative Hybrid Topology protocol. Markers indicate the 11 λ windows.*\n",
+ "\n",
+ "For atoms mapped between the two ligands (the common core), nonbonded parameters are interpolated between their values in states A and B. Atoms unique to either ligand instead have their interactions progressively turned off or on, as illustrated above.\n",
+ "\n",
+ "To improve sampling, **openfe** uses Hamiltonian replica exchange (HREX), where swaps between neighbouring windows are attempted at regular intervals. This lets configurations move along the alchemical path and escape local minima.\n",
+ "\n",
+ "\n",
+ "\n",
+ "*HREX sampling across λ windows, reproduced from Mey et al., [Best Practices for Alchemical Free Energy Calculations](https://pmc.ncbi.nlm.nih.gov/articles/PMC8388617/).*\n",
+ "\n",
+ "**The hybrid topology approach**\n",
+ "\n",
+ "In a hybrid topology, both ligands are represented by a single combined molecule. Atoms shared by the two ligands (the core) are interpolated between their parameters in A and B, while atoms unique to either ligand are switched on or off as dummy atoms. Because only the differing atoms are perturbed, these transformations are usually cheaper to converge than perturbing whole molecules.\n",
+ "\n",
+ "\n",
+ "\n",
+ "*Hybrid topology scheme, reproduced from the [alchemistry.org wiki](https://www.alchemistry.org/wiki/Constructing_a_Pathway_of_Intermediate_States).*\n",
+ "\n",
+ "
\n",
+ "\n",
+ "The terminology varies across the literature: the approach called \"hybrid topology\" here is described elsewhere as \"single topology\" or \"dual topology\".\n",
+ "\n",
+ "
\n",
+ "\n",
+ "This approach comes with some requirements:\n",
+ "\n",
+ "* **A common core.** Ligands must share enough of their scaffold for a sensible atom mapping. Transformations with many appearing or disappearing atoms converge poorly.\n",
+ "* **Aligned poses.** The common core must occupy the same position in both ligands' input poses. This is why the TYK2 ligands are provided pre-aligned.\n",
+ "* **Similar binding modes.** Large differences in binding mode between ligands are hard to capture.\n",
+ "* **Dummy atom approximations.** The treatment of dummy atoms can introduce systematic errors (see [Fleck et al., 2021](https://pmc.ncbi.nlm.nih.gov/articles/PMC8280730/)).\n",
+ "\n",
+ "The Relative Hybrid Topology Protocol builds on the hybrid topology implementation\n",
+ "originally developed in [perses](https://github.com/choderalab/perses) by the Chodera\n",
+ "lab and collaborators, and on the multistate sampling tools in\n",
+ "[OpenMMTools](https://github.com/choderalab/openmmtools). We are grateful to their\n",
+ "developers for making this work openly available. If you use this protocol, please\n",
+ "also consider citing perses ([Rufa et al., 2023](https://doi.org/10.5281/zenodo.8350218)).\n",
+ "\n",
+ ""
]
},
{
"cell_type": "markdown",
- "id": "31608e5a-cedf-4840-b809-a7df32b319ea",
+ "id": "56f13e09-6458-4979-8a49-b014f406c343",
"metadata": {},
"source": [
- ""
+ "
\n",
+ "\n",
+ "**Learn more:**\n",
+ "\n",
+ "- **User guide:** [Relative Hybrid Topology Protocol](https://docs.openfree.energy/en/stable/guide/protocols/relativehybridtopology.html) – how the protocol works (the hybrid topology, the λ schedule and sampling), and the analysis it produces.\n",
+ "\n",
+ "
"
]
},
{
"cell_type": "markdown",
- "id": "e9a7f137",
- "metadata": {
- "id": "e9a7f137"
- },
+ "id": "c6891318-635f-4c97-91d4-a5345b96315a",
+ "metadata": {},
"source": [
- "## 1.1. The dataset: Alchemical transformations of TYK2 ligands\n",
+ "## 3. The TYK2 inputs\n",
+ "\n",
+ "The inputs for this showcase are in `inputs/`:\n",
+ "\n",
+ "* `tyk2_protein.pdb`: the prepared (capped and protonated) TYK2 protein.\n",
+ "* `tyk2_ligands.sdf`: ten ligands with their binding poses, aligned on their common core.\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
"\n",
- "Here we explore how OpenFE can be used to build a network of alchemical transformations between the TYK2 ligands.\n",
+ "**openfe** does not prepare structures. Protonation, modelling of missing residues and generation of ligand poses must be done beforehand. See [Preparing input structures](https://docs.openfree.energy/en/stable/guide/setup/preparing_inputs.html) for what **openfe** expects.\n",
"\n",
- "First, we will use rdkit to visualize the TYK2 ligands."
+ "
\n",
+ "\n",
+ "Let's look at the ligands in 2D:"
]
},
{
"cell_type": "code",
"execution_count": 1,
- "id": "711d73bc-519f-4b81-9daf-c7594c7bbf8f",
- "metadata": {},
+ "id": "e9a7f137",
+ "metadata": {
+ "id": "e9a7f137"
+ },
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -196,235 +284,356 @@
}
],
"source": [
+ "# First we do a set of imports\n",
+ "%matplotlib inline\n",
+ "%config InlineBackend.figure_formats = ['svg']\n",
+ "import openfe\n",
"from rdkit import Chem\n",
- "from rdkit.Chem import AllChem\n",
- "from rdkit.Chem import Draw\n",
- "\n",
- "# Extract the contents of the sdf file and visualise it\n",
- "ligands_rdmol = [mol for mol in\n",
- " Chem.SDMolSupplier('inputs/tyk2_ligands.sdf', removeHs=False)]\n",
- "\n",
- "for ligand in ligands_rdmol:\n",
- " AllChem.Compute2DCoords(ligand)\n",
- "\n",
- "Chem.Draw.MolsToGridImage(ligands_rdmol)"
+ "from rdkit.Chem import AllChem, Draw\n",
+ "\n",
+ "# Load the ligands without hydrogens for a clearer 2D depiction\n",
+ "ligand_rdmols_2d = [mol for mol in Chem.SDMolSupplier(\"inputs/tyk2_ligands.sdf\", removeHs=True)]\n",
+ "for mol in ligand_rdmols_2d:\n",
+ " AllChem.Compute2DCoords(mol)\n",
+ "\n",
+ "Draw.MolsToGridImage(\n",
+ " ligand_rdmols_2d,\n",
+ " legends=[mol.GetProp(\"_Name\") for mol in ligand_rdmols_2d],\n",
+ " molsPerRow=5,\n",
+ " subImgSize=(220, 180),\n",
+ ")"
]
},
{
"cell_type": "markdown",
- "id": "0870f84e",
- "metadata": {
- "id": "0870f84e"
- },
+ "id": "e3dfe665-c0c5-444d-aaf0-f9097500bb03",
+ "metadata": {},
"source": [
- "## 1.2. The plan\n",
- "\n",
- "Here is what we will achieve in this notebook and what software toolchains are\n",
- "used along the way.\n",
- " \n",
+ "And one of them in its binding pose (left-click and drag to rotate, scroll to zoom):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ea19d4bc-03b5-496d-85fe-e0a47e335646",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/3dmoljs_load.v0": "
\n
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
\n
\n",
+ "text/html": [
+ "
\n",
+ "
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
\n",
+ "
\n",
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import MDAnalysis as mda\n",
+ "import py3Dmol\n",
+ "from MDAnalysis.lib.distances import capped_distance\n",
"\n",
- "| **Actions** | **Software** |\n",
- "|:------------------------------|:--------------------------------------------------------|\n",
- "| Create OpenFE Molecules | OpenFE RDKit |\n",
- "| Create Network | OpenFE Lomap, Kartograf, NetworkX |\n",
- "| Visualise Network | OpenFE NetworkX, RDKit, Matplotlib |\n",
- "| Create ligand topologies | OpenFE interface - OpenFF tk |\n",
- "| Create hybrid OpenMM topology | OpenFE interface - OpenMMTools (eventually - ex Perses) |\n",
- "| Create Lambda Protocol | OpenFE interface - OpenMMTools (eventually - ex Perses) |\n",
- "| Set up and run RBFE calculations | OpenFE interface - OpenMM + OpenMMTools |\n",
- "| Analyze RBFE calculations | OpenFE interface - PyMBAR + OpenMMTools |"
+ "example_ligand = Chem.MolFromMolBlock(\n",
+ " Chem.MolToMolBlock(next(Chem.SDMolSupplier(\"inputs/tyk2_ligands.sdf\", removeHs=True)))\n",
+ ")\n",
+ "protein_mda = mda.Universe(\"inputs/tyk2_protein.pdb\")\n",
+ "\n",
+ "# Protein residues with any atom within 5 Å of the ligand\n",
+ "pairs = capped_distance(\n",
+ " example_ligand.GetConformer().GetPositions(),\n",
+ " protein_mda.atoms.positions,\n",
+ " max_cutoff=5.0,\n",
+ " return_distances=False,\n",
+ ")\n",
+ "binding_site_resids = sorted(set(protein_mda.atoms[pairs[:, 1]].resids.tolist()))\n",
+ "\n",
+ "view = py3Dmol.view(width=600, height=400)\n",
+ "with open(\"inputs/tyk2_protein.pdb\") as pdb_file:\n",
+ " view.addModel(pdb_file.read(), \"pdb\")\n",
+ "view.addModel(Chem.MolToMolBlock(example_ligand), \"mol\")\n",
+ "\n",
+ "view.setStyle({\"model\": 0}, {\"cartoon\": {\"color\": \"lightgrey\"}})\n",
+ "view.addStyle({\"model\": 0, \"resi\": binding_site_resids}, {\"stick\": {\"colorscheme\": \"greyCarbon\"}})\n",
+ "view.setStyle({\"model\": 1}, {\"stick\": {\"colorscheme\": \"cyanCarbon\"}})\n",
+ "view.zoomTo({\"model\": 1})\n",
+ "view.zoom(0.6)\n",
+ "view.show()"
]
},
{
"cell_type": "markdown",
- "id": "14ade1f0",
- "metadata": {
- "id": "14ade1f0"
- },
+ "id": "e3aec94d-5aa5-4461-9648-3ade70ee2c68",
+ "metadata": {},
"source": [
- "# 2. Setup"
+ "### Creating **openfe** `Components`\n",
+ "\n",
+ "**openfe** represents each part of a system as a `Component`. \n",
+ "\n",
+ "Ligands (and if present cofactors) are `SmallMoleculeComponent`s, created here from RDKit molecules, which keep the 3D coordinates, bond orders and names from the SDF file:"
]
},
{
- "cell_type": "markdown",
- "id": "5962dec2-d9ef-475d-8086-ee15954e81f6",
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e37fe6a4-3ccc-47bd-90c0-e031f909df25",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['lig_ejm_31', 'lig_ejm_42', 'lig_ejm_43', 'lig_ejm_46', 'lig_ejm_47', 'lig_ejm_48', 'lig_ejm_50', 'lig_jmc_23', 'lig_jmc_27', 'lig_jmc_28']\n"
+ ]
+ }
+ ],
"source": [
- "## 2.1. Loading Ligands and Defining Ligand Atom Mappings"
+ "ligands = [\n",
+ " openfe.SmallMoleculeComponent.from_rdkit(mol)\n",
+ " for mol in Chem.SDMolSupplier(\"inputs/tyk2_ligands.sdf\", removeHs=False)\n",
+ "]\n",
+ "print([ligand.name for ligand in ligands])"
]
},
{
"cell_type": "markdown",
- "id": "f734578f",
- "metadata": {
- "id": "f734578f"
- },
+ "id": "d43fbff2-8767-40d6-bfe1-147b03c1d50f",
+ "metadata": {},
"source": [
- "### Creating OpenFE SmallMoleculeComponents\n",
+ "#### Working with OpenFF Molecules\n",
"\n",
- "To keep track of the various inputs being passed through the OpenFE\n",
- "toolkit, OpenFE implements a set of Components which define the proteins,\n",
- "small molecules, and solvent components which a system may contain. Here we\n",
- "use the [SmallMoleculeComponent](https://github.com/OpenFreeEnergy/gufe/blob/main/gufe/components/smallmoleculecomponent.py)\n",
- "which takes in either [RDKit molecules](https://www.rdkit.org/docs/source/rdkit.Chem.rdmolfiles.html)\n",
- "or [OpenFF molecules](https://open-forcefield-toolkit.readthedocs.io/en/0.9.2/api/generated/openff.toolkit.topology.Molecule.html).\n",
- "\n",
- "In the backend, OpenFE treats the RDKit molecules as the central representation\n",
- "of the ligands, and uses the OpenFF toolkit to convert between objects from\n",
- "various toolchains (for example OpenEye's OEMol).\n",
+ "`SmallMoleculeComponent`s can also be created from [OpenFF `Molecule` objects](https://docs.openforcefield.org/projects/toolkit/en/stable/users/molecule_cookbook.html), which is useful if you already prepare or charge your ligands with the OpenFF Toolkit. Here, we load the same ligands using the OpenFF Toolkit:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "50530996-7708-4dc1-8511-c47a2091677e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['lig_ejm_31', 'lig_ejm_42', 'lig_ejm_43', 'lig_ejm_46', 'lig_ejm_47', 'lig_ejm_48', 'lig_ejm_50', 'lig_jmc_23', 'lig_jmc_27', 'lig_jmc_28']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from openff.toolkit import Molecule\n",
"\n",
- "Here we demonstrate how to load the ligands from `inputs/tyk2_ligands.sdf` into a\n",
- "list of OpenFE `SmallMoleculeComponent`s for further processing."
+ "openff_ligands = Molecule.from_file(\"inputs/tyk2_ligands.sdf\")\n",
+ "ligands_from_openff = [openfe.SmallMoleculeComponent.from_openff(mol) for mol in openff_ligands]\n",
+ "print([ligand.name for ligand in ligands_from_openff])"
]
},
{
"cell_type": "markdown",
- "id": "fbc94d04-5d68-4123-82b2-97a1b8872375",
+ "id": "7485d1bd-ba1c-46cf-af7b-6526970467ff",
"metadata": {},
"source": [
- "Load ligands using RDKit:"
+ "``SmallMoleculeComponents`` can be converted back to RDKit or OpenFF molecules at any point, for example to use other toolkits for analysis:"
]
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "4096ce97",
- "metadata": {
- "id": "4096ce97"
- },
+ "execution_count": 5,
+ "id": "a5f370d0-2551-409c-94a7-8b3b9eb8fcc1",
+ "metadata": {},
"outputs": [],
"source": [
- "import locale\n",
- "locale.getpreferredencoding = lambda _: 'UTF-8' # hack for google colab, not needed for local execution\n",
- "from openfe import SmallMoleculeComponent\n",
- "\n",
- "# Load ligands using RDKit\n",
- "ligands_sdf = Chem.SDMolSupplier('inputs/tyk2_ligands.sdf', removeHs=False)\n",
- "\n",
- "# Now pass these to form a list of Molecules\n",
- "ligand_mols = [SmallMoleculeComponent(sdf) for sdf in ligands_sdf]"
+ "openff_molecule = ligands[0].to_openff()\n",
+ "rdkit_molecule = ligands[0].to_rdkit()"
]
},
{
"cell_type": "markdown",
- "id": "044045af-020c-4926-b295-0c9db5bd6a0c",
+ "id": "2a932388-59ac-4c7f-8462-b257098dc47b",
"metadata": {},
"source": [
- "Load ligands using the OpenFF toolkit:"
+ "The protein is loaded as a `ProteinComponent`, which also holds any crystallographic waters and ions in the PDB file. \n",
+ "\n",
+ "The solvent is a `SolventComponent` which describes the composition of the solvent (here water with 0.15 M NaCl, neutralizing the system) rather than explicit water positions. The size and shape of the box and the water model are not part of the `SolventComponent`, they are set in the protocol's `solvation_settings`, covered in Section 7."
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "b5e5b538-4847-42f5-b29e-6e1f25bbbeed",
+ "execution_count": 6,
+ "id": "589b2b74-5e48-4281-a495-f8e93f71b8a2",
"metadata": {},
"outputs": [],
"source": [
- "from openff.toolkit import Molecule\n",
- "from openfe import SmallMoleculeComponent\n",
+ "from openff.units import unit\n",
+ "\n",
+ "protein = openfe.ProteinComponent.from_pdb_file(\"inputs/tyk2_protein.pdb\")\n",
+ "solvent = openfe.SolventComponent(\n",
+ " positive_ion=\"Na\",\n",
+ " negative_ion=\"Cl\",\n",
+ " neutralize=True,\n",
+ " ion_concentration=0.15 * unit.molar,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02eda65f-2512-431a-b788-fe6e34993a44",
+ "metadata": {},
+ "source": [
+ "
\n",
"\n",
- "# Load ligands using OpenFF toolkit\n",
- "ligands_sdf = Molecule.from_file('inputs/tyk2_ligands.sdf')\n",
+ "**Learn more:**\n",
"\n",
- "# Now pass these to form a list of Molecules\n",
- "ligand_mols = [SmallMoleculeComponent.from_openff(sdf) for sdf in ligands_sdf]"
+ "- **How to:** [Loading molecules and chemical systems](https://docs.openfree.energy/en/stable/cookbook/loading_molecules.html) – how to create components from SDF and PDB files or from RDKit and OpenFF molecules, and combine them into a `ChemicalSystem`.\n",
+ "- **User guide:** [Components, chemical systems and thermodynamic cycles](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html) – which component types exist, and how chemical systems define the end states of each leg of a calculation.\n",
+ "\n",
+ "
"
]
},
{
"cell_type": "markdown",
- "id": "3a8d5433",
- "metadata": {
- "id": "3a8d5433"
- },
+ "id": "22a869a4-f112-4447-a749-ff30df58404f",
+ "metadata": {},
"source": [
- "OpenFE `SmallMoleculeComponent`s have some useful built-in attributes and methods.\n",
+ "## 4. Assigning partial charges\n",
+ "\n",
+ "Partial charges are assigned during setup if they are missing, but we recommend assigning them once, up front. Conventional methods such as AM1-BCC are not perfectly reproducible. Small differences between runs (of the order of 0.03 e) are common, and if a ligand ends up with slightly different charges in its solvent and complex legs, the error enters the thermodynamic cycle ([Osato et al., 2025](https://pmc.ncbi.nlm.nih.gov/articles/PMC12079016/)). Charging once and reusing the charges avoids this.\n",
+ "\n",
+ "Here we use [OpenFF NAGL](https://docs.openforcefield.org/projects/nagl/en/latest/) with the AshGC model, a graph neural network that reproduces AM1BCCELF10 charges in a fraction of the time. It is also the charge model the OpenFF 2.3.0 force field was fitted with ([Wang et al., 2026](https://doi.org/10.1021/acs.jctc.6c00169)), which we use in section 7.\n",
"\n",
- "For example, the molecule's name (as defined by the SDF file) can be accessed:"
+ "You can pre-charge molecules using the CLI command ``openfe charge-molecules``, or use the Python API:"
]
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "faacbebb",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "faacbebb",
- "outputId": "bc0ad1d9-72ad-49d0-e322-61b2a87d5301"
- },
+ "execution_count": 7,
+ "id": "d79ec609-52a2-4c0e-82f7-c4628138e7f7",
+ "metadata": {},
"outputs": [
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "name: lig_ejm_31\n"
+ "Generating charges: 100%|███████████████████████| 10/10 [00:04<00:00, 2.08it/s]\n"
]
- }
- ],
- "source": [
- "print(\"name: \", ligand_mols[0].name)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b24a3ffa",
- "metadata": {
- "id": "b24a3ffa"
- },
- "source": [
- "As previously stated, `SmallMoleculeComponent`s also use the OpenFF backend to allow conversion between different object types. For example, it's possible to obtain an OpenFF Molecule:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "29b7c68a",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
},
- "id": "29b7c68a",
- "outputId": "39e7a77a-d2b8-49d2-8f70-fd8118845a99"
- },
- "outputs": [
{
"data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "**Learn more:**\n",
+ "\n",
+ "- **How to:** [Assigning partial charges](https://docs.openfree.energy/en/stable/cookbook/user_charges.html) – with the Python API.\n",
+ "- **How to:** [Generating partial charges with the CLI](https://docs.openfree.energy/en/stable/tutorials/charge_molecules_cli_tutorial.html) – with `openfe charge-molecules`.\n",
+ "\n",
+ "
"
]
},
{
"cell_type": "markdown",
- "id": "3128085c-db8a-4a70-89ef-0fde956fdb40",
+ "id": "4624345e-5602-4f1d-add4-24340365e486",
"metadata": {},
"source": [
- "In the [hybrid topology RBFE protocol](https://docs.openfree.energy/en/latest/guide/protocols/relativehybridtopology.html) , an atom mapping defines which atoms are mutated during the alchemical transformation.\n",
- "The user can choose between two different atom mappers:\n",
- "1. `KartografAtomMapper`\n",
- " * based on the 3D geometries of the ligands\n",
- "2. `LomapAtomMapper`\n",
- " * based on the maximum common substructure (MCS)\n",
+ "## 5. Atom mappings and scoring\n",
"\n",
- "While we use the defaults here, please note that the various supported arguments of\n",
- "Lomap and Kartograf can be passed to the atom mapper."
+ "### Mapping two ligands\n",
+ "\n",
+ "In a hybrid topology RBFE, an atom mapping defines which atoms are shared by two ligands (and interpolated) and which are unique (and become dummy atoms). **openfe** provides two atom mappers:\n",
+ "\n",
+ "* `KartografAtomMapper`, which maps atoms based on their 3D positions ([Ries et al., 2024](https://doi.org/10.1021/acs.jctc.3c01206)). It is the default in the CLI and, in our hands, performs best, especially for ligands that are well aligned on their common core.\n",
+ "* `LomapAtomMapper`, which maps atoms based on the maximum common substructure (MCS).\n",
+ "\n",
+ "Let's map `lig_ejm_31` to `lig_ejm_47`:"
]
},
{
@@ -432,24 +641,32 @@
"id": "f21bf9e5-6cb8-41c1-b0fd-e70ee2e71852",
"metadata": {},
"source": [
- "**1. `KartografAtomMapper`**\n",
- "\n",
- "The `KartografAtomMapper` is based on the 3D geometries of the ligands."
+ "#### 1. `KartografAtomMapper`"
]
},
{
"cell_type": "code",
- "execution_count": 6,
- "id": "c710a20e-2098-461f-8fa6-5e44b19ad6aa",
+ "execution_count": 8,
+ "id": "4b0efdac-1443-4f90-84a8-24152f1d044a",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "from kartograf import KartografAtomMapper\n",
- "# Build Kartograf Atom Mapper\n",
- "mapper = KartografAtomMapper(atom_map_hydrogens=True)\n",
+ "ligands_by_name = {ligand.name: ligand for ligand in ligands}\n",
"\n",
- "# Get Mapping\n",
- "kartograf_mapping = next(mapper.suggest_mappings(ligand_mols[0], ligand_mols[4]))"
+ "mapper = openfe.KartografAtomMapper()\n",
+ "mapping = next(mapper.suggest_mappings(ligands_by_name[\"lig_ejm_31\"], ligands_by_name[\"lig_ejm_47\"]))\n",
+ "mapping"
]
},
{
@@ -457,59 +674,25 @@
"id": "d0be837c-0cf6-4a17-8e44-f548f7c9a314",
"metadata": {},
"source": [
- "We can also visualize the atom mappings by invoking the individual OpenFE `AtomMapping` objects directly.\n",
+ "In the 2D depiction, atoms unique to one ligand are shown in red,\n",
+ "while mapped atoms that change element are shown in blue (none are\n",
+ "present in this example).\n",
"\n",
- "Unique atoms between each mapping are shown in red, and atoms which are mapped but undergo element changes are shown in blue. Bonds which either involve atoms that are unique or undergo element changes are highlighted in red."
+ "The same mapping can be viewed in 3D. The two ligands are shown left and right, and their overlay in the middle. Atoms with spheres of the same colour are mapped; atoms without a sphere are unique to that ligand."
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 9,
"id": "56117a03-6503-4317-9364-27e343a4911e",
- "metadata": {
- "scrolled": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# We can display the atom mapping in 2D by calling it\n",
- "kartograf_mapping"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d3f10c24-e50c-46da-b8d5-8de1f3efae90",
- "metadata": {},
- "source": [
- "It is also possible to visualize the mapping in 3D using py3dmol:\n",
- "\n",
- "Here, the ``view_3D`` method displays the two end state molecules (left and right), in addition to the hybrid molecule (middle).\n",
- "\n",
- "Atoms that have the same sphere color in both end states are mapped (i.e. will be interpolated between each other), whilst those that do not have a coloured sphere are unmapped (i.e. will be transformed into dummy atoms in the opposite end state)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "bbcfd2e2-a50e-4398-85d1-f80b01597720",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/3dmoljs_load.v0": "
\n
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
\n
\n",
+ "application/3dmoljs_load.v0": "
\n
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
\n
\n",
"text/html": [
- "
\n",
- "
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
\n",
+ "
\n",
+ "
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
\n",
"
\n",
""
]
@@ -622,7 +805,65 @@
{
"data": {
"text/plain": [
- ""
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mapping.view_3d(show_atomIDs=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad520da7-bc63-4e1c-bfcb-4beb37c84627",
+ "metadata": {},
+ "source": [
+ "Under the hood, the mapping is a dictionary from atom indices in ligand A\n",
+ "to atom indices in ligand B. Atoms unique to either ligand are not mapped\n",
+ "and therefore are not included in the dictionary."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "98e127ef-8ae2-4546-bf15-3f86997421d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{0: 0,\n",
+ " 7: 38,\n",
+ " 12: 12,\n",
+ " 19: 19,\n",
+ " 21: 21,\n",
+ " 28: 35,\n",
+ " 29: 36,\n",
+ " 31: 7,\n",
+ " 1: 1,\n",
+ " 2: 6,\n",
+ " 3: 5,\n",
+ " 4: 4,\n",
+ " 5: 3,\n",
+ " 6: 2,\n",
+ " 8: 37,\n",
+ " 9: 9,\n",
+ " 10: 10,\n",
+ " 11: 11,\n",
+ " 13: 13,\n",
+ " 14: 14,\n",
+ " 15: 15,\n",
+ " 16: 16,\n",
+ " 17: 17,\n",
+ " 18: 18,\n",
+ " 20: 20,\n",
+ " 22: 22,\n",
+ " 23: 23,\n",
+ " 30: 8}"
]
},
"execution_count": 10,
@@ -631,8 +872,7 @@
}
],
"source": [
- "# Visualize the mapping in 3D\n",
- "kartograf_mapping.view_3d(show_atomIDs=True)"
+ "mapping.componentA_to_componentB"
]
},
{
@@ -640,52 +880,117 @@
"id": "2cd43a28-1a2e-496f-bf50-a7358e5323b6",
"metadata": {},
"source": [
- "**2. `LomapAtomMapper`**\n",
+ "#### 2. `LomapAtomMapper`\n",
"\n",
"Alternatively, mappings can be generated using the MCS-based `LomapAtomMapper`. For more information on configuring and using this mapper, see the [ligand network generation cookbook](https://docs.openfree.energy/en/stable/cookbook/generate_ligand_network.html)."
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 11,
"id": "01384f2a-99ce-400d-a988-82fd393c4ee5",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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n6NChn+St7RjV1dXw74dsQRSADCHxVFRUODs7AwBoNJqHhwefzz9z5oyioiIAwMbGJisrq53j1NTU+Pr6wgpPMFzNw8MjPT0dj2Rzc3NraGgg6SpiYmLGjx+/aNGisWPHhoeHkySlGbDMDX6AikBQAFEKW1xc7OXlhWfrGhoabt26NTMzE/cDQV8rSVfx+PHjcePGfffdd+PGjXv06BFJUpqhrq4OAPgK4tq+NkPI4/EwDBMIBOSJaPtM+Pnz57CGta6ubnh4eG1trYuLC64GXC63PSIyMzPDwsIwDGOz2ZcvX75y5cqIESPgIHDVCSPZiLme1mloaCgrK8vLyzt37lxERATZ4jAMs7e3BwAQFfKD+IJgs9kSiaS+vp7ssKxmEKKwaWlpd+7cwTCMyWTevn377Nmz0AsKTy6grzUkJITkS8E4HE5lZWV2dvaZM2ciIyPJFodhGCzZSMYel2K+KkN4//79PXv2fPjwISQkBKYoEEhCQoKPj09ycrKfn19GRkbLL0DvCjRUDg4Oubm5cXFx5ubmHVADkUh0+vRpDMP8/f3hCziBefPmaWpqamlpZWZmEnJRbbB7924AwK+//nrz5k0AgPSRIXksWLAAABAaGkqBLMTnQ15e3vPnz319fZ8/f75jxw5CFrIvX748fvx4YmLi8ePHc3JyWn5BpsL26dOnAworEAhOnz4tkUhOnDiBK+yLFy+mTZumra3do0cPmRMgls2bNwMAjh8/fvnyZQCAq6sr2RIxDJs6dSoA4O7duxTIIpWvqtbotGnTdHR0TE1NDQwM8Pw8orCzs9PS0nr27JmOjg7s85ecnLx+/XpYz/PGjRu///67p6enWCzeuXPnixcvbt++7ejoyGKx7O3tk5KS8FP39gAnn5qayufzk5KS8AlcunSJw+GIRCIyav42A7ZDKigooLIfG8WV/rszWVlZISEhN2/eDA0N3bNnT9dOpnfv3mpqalZWVlZWVjAmpfNjjho1Sl5e/tGjRzo6OtnZ2QCAuLi4H3/8EX565cqV/fv3Q4XdsWMHrrC5ubnDhw9PTEz8JIWF1vTly5cCgSA1NRW+6eTkdO7cuZqaGnl5eRiDQyqwDGlBQQGVSkRxLx3y+KoM4b59+wQCQX5+fn5+PuH9XdPT0xMTE/v161dTU1NfXw8AKC0tvXv37qlTp+CnFhYWa9eujYyM3Lx585w5czw9PUUikYeHR0xMDNwXtp/c3Fw5OTl9ff0tW7Z8//335eXly5YtW758uYaGhoaGRkNDQ21tLbFX1xL8Fkd69VUyYMAADMMUFRUXLlwIA6+6kNTU1ICAAE1NzVevXmlraxNSNjo7OzszM9PMzEwgEJSVlQEACgsLg4ODg4ODoUQHBwcXF5fIyMgffvhh7ty5uMLGxsZ+qt3KysqSk5OztLT09PRcs2ZNUVHRihUrNm3aZGRkJC8vX1ZWJhAIOn9FbYMvWKlUoq9HYbt6S/rlgcc0PnjwYNu2baNHjy4uLt63b9/NmzcxDIuOjjYyMgIA6Onp3bt3jxCJHA4HAKCmpoZhGCxPQ0akaDPevn0LALC2tpZIJDB9mMC0p9YIDQ0FAMyfP59sQa0RERGxffv2nJycn376qavmQA0ZGRmhoaGXLl3asGHDyRMnONGPq/wPVx3/veFZlEREWC3NLoTL5cJWDHhA2Y0bN7y8vBwcHGpqarZu3QpTY589e9azZ08AQI8ePYg6CC8sLAQAGBoaYhgG7VPnS7J9lOfPnwMAHB0dBQIBg8FgMBgE1kRtjYsXLwIApFMYv1BQ94lPRtrpKi8vf/DgwR9//BFvKKioqFhZWTl27NgrV64Q1fkdbgTr6upqampMTU3T0tIKCgpgEiF54AtMGo1mYmKSnZ1dVFREtkuWSjesTBwdHR8+fNinTx+Y6/IVY2lpaWZmpqSkNGPkiNKf11cEV4ImLgCArqJC19Tu6R8o37tPV8+xUygrK8MFnPR+V1lZ+Zdfftm1axes7QIA4HK5JSUlY8aMuXr1KlEKa2hoCDeCfD6/V69e+fn5BQUF8PSRPHDdkZeXNzAwKC4uLi0txVP+SeKr2RF+Va5RAACHwzl79uxvv/0WExNDjcRx48YxGIzY2Fj4xxEjRjx58uTx48dEKRUEv+EoMxU9evRQUVGprq5uaGigzDtK8Rkh/q+Gv46JidHQ0GCz2W/fvmWxWNRMo6tQUlLC+LymLWuTsrLDCsoAhgEMiygsv5eRXbTGRdJIQI9MSRNXVFEG2nHmFxkZ+fPPPx88eJDse3v+/PlsNhs/yZs+ffr9+/efPHlCoMIyGIyePXtiGFZUVETZLW1sbMxgMIqKikQiEWVCKV65RkVFyXzdeb4qQ8jlcqdOnaqlpTVhwgR/f/9z585RI/ePP/548+YN/kcnJyfCQ3W65AAALicLCwspE2poaKigoFBeXs7n88mWBQBYv359s9czZszYs2ePqampj48PsUuZz5P6O9fFnBqOQFgu/KcjZpVIXC0USbiNnJBLnRmZlxRf8N303HHW+XPH534zuPLoQYzfaqfl8+fPX7lyZd26dY6OjrNnzy4vL++M6I/i6+v75MkT/I9Tp04lT2EpMxVwIygWi0tKSigTampqSqPRYOIv2bIAAHisk/Tr2NjYCxcuNDU1BQUFPXr0qGMjf1WGMCwsbPr06QsXLnRwcDh//vyff/5Jqjhra2sYWmZgYHD//n2YA0cSzUJXKD4Jp0wonU7Hl9Jky2ptAvLy8nJyctra2jCl+uum4WmkpIkLACgRiBMb+YmN/AK+CACA8XiNTx92eNjGR/dy1rvFvHsHREKM11Rd3xB98VzRygWYQBAeHh4dHc1kMkNCQqqqquD3/f39T5w40a9fv3Hjxq1evfrKlSuEXF0zbGxspk2bBgDo27fv7du3YQ4cSeB7MioVttlymYIdoZKSUo8ePfh8PgxH6hKGDRvGZrOLioqSkpKysrI6NshXZQjZbDasigQAUFVVhWfF5IkzNDQcNGjQn3/+qaGh8eDBA1I3EF0Yw0mxMlN5gSKRaN6/NDY2UiDxcwNrqIcvigSitw28tw28AsE/W0MJt4OuUUl9Xfnen6saG/8qqwOABgBg84UhJVUCdm71hRMWFhZv3rzp0aMHnU7HH1tcLhfWKAEAmJmZwWATwunTp4+1tfXRo0eNjIxSUlJIVVjqbRLoUoWlRlZDQwOusCLRPzcqn88fOXJkQkLC1KlTYdW3DvBVGUIjIyN8JyEUCul0OqxeTSrKysr19fVk3wdde4tTn0pIjSw5Obmb/9LlKQRdgsLAITQGAwAwXE1xrYHmWgPN0epKAABApylYDOrYmI3PHgJZ60+M11T/91UdHR0+n6+iomJiYoJvI1RVVWFoNAAgNzcX3m8kQaPRSktLqVFYil04XXKAQuXDQU1NDVdYObl/Ij0LCgoaGhrmzp0rEAjgjr8DfFWGcMGCBSEhIYmJiXV1dbt27Vq8eDEFQqm556T1CjrlYSI/ZUIByavatLS0y5cvR0VFUaBXIpHI29s7NzeXPBFfCpoL3YCcfMv36YqKWotXdWxMYR4LultTufz1rPL1rPLfi2r++ai6+vGjR7a2tunp6Tk5OXjfhk2bNm3YsIHFYkVHR1+6dAmvpksG1Dy4cZWB4ihOw6XgGpOTky9cuBAVFUXBw6GpqWnz5s15eXkyP7W2tnZxcVFSUpo1a9bw4cM7JuKrSp/Q0dG5ceOGv79/ZWXlhAkTli1bRoFQavQKlwKd8hUVFeXl5bBENQVCKbjGQYMGPX/+vHfv3mQvLAoKChYvXhwTE/Po0SMbGxv8fenX3QeFvgN0Nv4yytfHuokL35mipcpQUlKfs0hpSAf/Quja2jQFBSAQDVFRPGWuDwBIbuSHVNYDABiKit8tWgS/Blu0Q1auXGlsbHzixAktLa3bt2/jdavJgBpfJa4yurq6qqqqtbW19fX1uPuXJJr5jUi9Rmtr6+fPn2tpaZGtsBkZGQsXLnz//n1qaqqTkxP+vvTrzvNV7QgBAElJST169PDz8yssLLSxsbl//z7ZEqnRKxMTE3wjSJnfAxekrq6uqanZ2NhYU1NDkqyUlBQjI6P8/Pzs7Gw5ObnQ0FA/Pz8ul0uslPDwcBsbm5iYGBMTk99++y0gIAD/SPp1t0Jr8cpB/hfMbeyAnByNweg1eIj5vIXcl88k3A4emqo6TQB0WUGYDIaq41iZP9m0adNff/21efPmHj16bNmy5enTpx0T3R6odOHAxwKMvqZAYXHra2BgABOam5qaSJKVlJSkq6tbWFiYnp6upKR06dIlPz8/wg/aAwMD7e3t379/b2FhcezYsZMnT+IfSb8mgK7N5yccW1tbAEBcXNzGjRsBAIQ3wJSJlpYWAKCqqopUKbD4Z0lJydy5cwEAN27cIFUchmF1dXUAABUVFQzDYDX9d+/ekSeutLQUbuJx77+ent7OnTuLioo6P7hQKPTy8qLT6QCAyZMnl5aWdn7Mzwc+n//y5Us+n5+Xl0dAASCxuGjd4tKtGzr4c4mkYNGMOLt+W3pq59ia5diaPRpk7N1Ll+U0WFCYL/MXsF7S+/fvV6xYAQC4ePFih+fejtlJFBUVaTRaOztLdBiYs8/hcCZPngwAePDgAaniMAwrLS0FAPTo0QPDMFjWsf09pDoA/izCFVZXV3fbtm35+bL/lT+J+vr6pUuXwmHd3NzILmv1te0Iv+KCe9Qfv6urq2tpacFSVWQLTU9PnzRpUmBgoLKy8u+//w4bf1dUVBw8eLBXr16zZs2STn7/VNhs9pgxY/bu3Uun0728vB48eAArFH815OfnKyoq3rhxIzw8nMlkdnY4Ol1/35Gm+Jd1t6598m8l4ooD28Wcmt5TnTf21qerqNIUFc11tZdbDeh5Llje2FT2jyQSAACdTsdfdO4C2oJGoxkbG2Pkp+jgG0HKAkf19fWVlJQqKyu5XC7ZZ5Px8fGOjo63bt3S0NAIDAy8c+eOo6NjVVWVj4+PmZnZrFmzOpzSBwBIT08fOXLk5cuXlZWVz549GxgYiFcCIomvzRB+xRH/XRs4SqpQ3AEycODAN2/ebNmyZfTo0dHR0bDxN51Oj4iIcHJyGj58eGBg4KeWU//7779tbGxevXrVq1ev6Ohob29vUp+zXUK/fv1ycnK0tbU7HCzQDDl9Q/19RysPeQuyMj7hZxJx+d5fuHGxPc8G6x/0Nb35tMfWfboe2wwPnza7+0LRcnCrv6PQEALKjwkpU1ho4wHJRTAwDPPz83NycmKxWMOHD09KSnJ1dR0yZMizZ8+gwjIYjIiIiMmTJ9vZ2Z09e/ZT3bPwaZCWlmZlZRUXF7d27VrCL6ElXflEePXqVXV1NZfLffbsGVHNIr7iiP8uTCWcO3fu9evXAQnXWFdX5+rqunz5ci6X6+bmlpCQMGTIEADAt99+26tXr/Dw8GPHjrHZbNj4++3bt8uXL+/Vq5e3t3dlZWWzoTAMCwsL+/vvv/F3eDze5s2bFyxYUFNTM2/evHfv3o0aNYrY+X8mpKamNjU1aWhoZGRkpD17XL7LM3/ehAKXqVV+v4mrm/9FtRMVx3GaC93Kdm7GeO16kGFCYekvG3jvk43Ph8mb9AYAyBkYqc+cr7lohfIIR9mnhv9CsSHsksBRKpMZpkyZEh4eDkh4SlRWVs6cOdPT01MoFMJOHdAHO2fOHKiwR48ezc/P9/Ly0tPTS0xMXLduXZ8+fbZt29Zy/y2RSC5fvhwREYG/0+xpEB8fj/c3Jh1SHa9tUFFRkZ6e7uPj8+uvv75//14sFhMyLOyx4uLiwmazAQDQB0I2Bw4cAABs27aNVCmHDh0CAGzZsgWWUR05ciSbza6vry8tLSWvGcXMmTOl7xYdHR1fX1+8nH8nSUhIgP1u1NXVr169ir9fVVU1YMAAKFFVVXX9+vUZGRk8Hi8gIACaSQCAoqKim5tbswvncrlHjhyBrzMzM2FcopKSEjVHxZ8DNcEX00ZaZtiZwUUOETAAACAASURBVMO5VPt+2U6Dm1KTOjaaRCQsXLmgfN/Wj3+ziVv8P7eCxTNF1R05KYe3QU5OzqJFiwAAwcHBHRik/ezYsQMAsG/fPlKleHt7AwB27dr18OFDAMCECRPYbHZTU1NpaanMzt6EMGHCBGmF1dXVJVBhnz17BneczTp1lJSU4F0HlJWV165dm5qaChUWjw1WUFBwcXF59eqV9IBCoRBX2Pj4eHgbaGhoSD8NqKErg2Xi4uKioqJ++OEHf3//1NRUQsaEJ0kjR44UCoVycnIMBoOQbtdtExgYCABYsmQJqVJCQkKgjYerPGNjY39//8TExOTk5MOHDxN1r+OIRCI8usTa2trV1RWucAEA2traP//8c15eXocHh83BFRQUAAB2dnbZ2dktvxAVFTVz5ky8JIKjo2NoaKhIJHrx4oWLiwteHBJ/H8Owp0+fPn/+HMOwgIAAeKhgYWGRlNRBM/DF0ZT8ljV6oIeR1glzfWgIl+iphwwwzB1vLW7s4O0hLC1mjRtWF/F3G98R19cVrvy2wG22mFPTMSmwMwOLxVq4cCEA4Nq1ax0bp53AHqJr164lVcqFCxcAAMuXL4d9vPv373/48OGMjIzExMRff/0V9h8mEOlwMFtb2yVLluCtJ3R0dH755Rc2m93hwSUSiY+PD1S6MWPGwOKizWhNMeH7eECNnZ1dQEAALPsVERERFxf30acBBXSZIczLy9u+fXt0dPSNGzf8/PyICrmERgLWq4T3QWee1+0ERnuPGTOGVCmxsbH6+vrLli0TiUTQxsfGxiYmJtbX1+/Zs4fYmNWCgoIxY8YAABgMhpeXV3h4uK+v74cPH+7cuTNp0iR4Q9Pp9JkzZ0ZFRX3q4NC7AgCg0WgeHh5tPxGysrI8PDzwsi/9+vXz9fWtr6/PycnZunUrjNcFAJibmx88ePDIkSPBwcH4oQIFwWafFcUbl+fY9WlpCFmOAzlhlzs8bGP0Y5aTlSA3R+anYk5tgducorWLOmxrMQzr3bs3VNVvv/0WABAWFtbhodoDdMdNmzaNVClRUVE6OjqrV6+GSQVKSkpPnz7NyMjgcDg7duwg9s5ks9mwOoGcnJyXl9ft27ePHDnCYrEIUdiysrIpU6bgCtv21qKlYvr4+FRXV7NYrK1bt+ro6MD3jYyMdu7c6e/vHxQU1P6nAXl05Y5QIhI2xj6rCTrLCbssYOcSMiY0EnQ6nc/nwwOhFy9eEDJyG+Tk5AAAzMzMyBaEA1Mpfvzxx2vXrt27d+/YsWME6tXDhw9hUKWBgQFsXurv7x8SEnLq1Cn4hbdv37q7uyspKcF7etiwYWfOnGlnMPrr16/h8l9TUzM0NLSdU+JwOL6+vvBxCZ0nHh4eubm5tbW1R48ehacUAAB1dXU4c1VV1UuXLnXs8r9cWBNsc2zNPIy0ZmirehhpeRhpDVFRDBlgmGNrVrrdozMjV/y+J3/hVEkTl/c+ue5OWMOTB6LaagzDRJXl+QunFnuskvCaOjM+fpw2f/58QH5qUEpKCgDAysqKVCnSaGpqQoWFkb1HjhwhMHnj5s2b2traAABTU9OYmBgMw/z8/AIDA3EVaKawNjY27VfYR48ewU7j+vr67c8AqaurO3PmjKWlJa6Y7u7u6enp9fX1/v7++MGHiooKNJk6Ojq3b9/u2OUTQpcZQl5Gat5UB9aYIcxRFixHK+Zoy7KdmyVELAegUuXm5sLWEBS4m3k8Ho1Gk5eXJ+qksw24XK6Hhwe8dXD/A1GtqKW9K5MmTSopKYHvV1ZWXrt2rdmSorS0VLpXkb6+/tatWwsKClobHDpA5OXlAQAjRozoQM9usVgsc4UrEolwP6qJiYmZmRlRnvYvi9yJdtAQ7jTRCR/YM3xgzxnaqtAQlu36oTMjS/j8/G8ns8ZaM50GsZwGscYMZo6yLPfZw547rnTbRkmnbz881hHmpd28ebOTA7YNrAuhrq5OqhQIl8t1d3en0WhaWlo0Gm3SpEl37tyRSCSEDM7j8Tw8PODxwZw5c3C3UHl5eWBgYHx8vPSXP1VhpZ8GEyZMKC4u/tTpicVi6QMOOp0OL1/6fWNj44EDB3bgaUAsXWMIhcWFLKfBe0117w3sCX04P/bUjrXrV/rz950fHLoIoqOjf/rpJwCAj49P58f8KHAjAu+VtLS0qKgoou51aVJTU+GhtIqKypQpU/CKTebm5seOHeNwOJ0ZvJl3Rdqol5SUpKeny/wVn88PDQ0dOXIknAk8En/58mWzr5WXl8N6uO3xrnwUGKUNDxXglhQ+OletWgUA2Lt3b2cG/3Ip/en7HDvzlq5RpuOguvDrnRlZWFrCchoCx/z//+z65M+fIBERsAiDe47i4uLZs2cDACjYHGhoaAAAampqMAxLS0t78+YNGVKSk5PhrkhFRWXixIkqKirwjrW0tDx58mQnz/U/fPgA6wIqKir6+vpKP3BKSkpaS6WXqbCxsbHNvpafnw9rmLV8GnSA5OTk1atX41vSQYMGwU0/LKBx+PDhzgxOCF1jCEu2b2La91uqpx48wBAq1XRt1UgrY6bjQF56SicHh4FnV65c8fPzAwBs2NDRAhmfAszfev36NYZhrq6uAIABAwYQGK+FYVhAQABUJEtLSxgt2cz/oKam5u7unpaW1oHB//77b+hd6dWrV0utaA/QOMk8En/y5Al80unp6d27d68Dg8ukrKwMX+EeO3YMwzB/f38AwPr164kS8WXBz8pgjrb0NNI69a8hXKanEWphlDd1hITP68zIpds9cuz7NTeEtmbM0ZaCfALO4OE6srS0FB4XhYeHd37MtoG1bFJSUjAMgxE68I4lMLYOV9iBAwdCQbW1tTI9/B0Y/Pr169CpaGZmBh87n4pMhYWXf/v2behwMjExiY6O7sDgMoGXDxM8zp07h2HYH3/8AQDw9PQkSkSH6RpDyPpmSI6t2VI99aNmeo8GGT8aZDxWQznSyjjHvl/1Wb9ODv7zzz/DjSBMKZs9ezaGYeHh4du3b8/Kyrpw4cKFCxeIuIj/ABX47NmzGIYdOnSoZ8+e8PaCNYfa8D+0B5heAwd0c3NrZlxb8z+0c0va1NQEfa0AgLlz51ZXV3dmqrm5uT/99BN+VN6rV6/JkydD78rEiRM74F35KDBKu7a2FsOw27dvAwCcnZ0Jl/KlUP8wgjXakjniH6PFGj0wd/yw+ocRH/9lm7AcrVpawRxbM+ZIi9or5zs/bT09PQBAeXn5jBkzAAB379599+5dYGBgRUXF1atXnzx50nkRzYBlz0JCQjAM27lzJx7EYWxsfPDgwYqKis4MzuFwpBW2sbFR+tPWPPwdUFiYI9uZqTZTWFNT07Fjx8InyaxZsyorKzszuEwEAkFISAg8oQwNDYVXQbiUT6VrDCHTvi80hLO0VWEXNHMl+Ugr4xxbs4rfvTs5OGxMv2HDhvj4eACAjY0NfN/X15fH4xUXFwcEBHT6Cv7D06dPtbS0zMzM5OXloZ9BJBLBmkPN7vUODP727VvYbVhdXf3y5bZi/zIzMz08PHD3S//+/T+6Jc3MzBw2bJhM70pnaGpqCggIgItuExMTWNgMZjiQSmJiIgBg6NChZAv6nBEWF1Yd9ylc5VK0fmnt1YtVZ3zzpo+CsS0dRCzOsTOXaQhzbM2q/vy983PW1dUFAFRWVkL/+f379xsbG1NSUh48eCCdG0oUsG1C7969FRQU3Nzc8KS3trNU20lCQgKusFeuXGnjm4mJie7u7srKyriH/6MxLBkZGXCSxGbHQoWF2esmJiYwVpyCiIdXr14BAOzt7ckW9FG6xhDmOTtCQ9jcNTrainO9rVunPdy8eRMuZ2DbT1iC1sfH5/Tp08nJye7u7gSmn4tEoj179sDUGVNTUzyHZvTo0deuXRMKhdD/ACNEAAC2trZnzpxpampXiB2MLlFUVIQ/bGf9XGn/AwBAU1PTw8MD5pDU1NTgx+klJSVXrlyByXYDBgwgPNkuOjo6JCRk586dAIBx48YRO3hrVFRUAAC0tbWpEfdlIBYXb1hWvHkV1olVDmuirewdoZMV52ZI5+cI3fLV1dUwTD8yMrK6uvrEiRNCofDBgwcdc/3JRCwW79+/H/oDe/bsCbc+NBpt6tSp9+7dk0gkMpPh2hmMJp0PZ2tr2858OGkPPzw+2Lp1K6xbXV1djXto4AoeLnOtrKwIDwd78uTJ9evXf/nlFwDA1KlTiR28NQoLCwEAhoaG1Ihrg64xhDVB55ijB7Y0hCzHgZ1aumIYhmEJCQlweSWRSODxbGNjY21tLSznVl1dTdS5XWlpKXSw0Gi0rVu3ikSi4uJiLy8vuLwFABgZGXl5eVVUVJSUlMAiYfB9AwODrVu3ykxKxamtrXVxccG9K58abC0QCIKDg/EjcTk5uYULF+7cuXPAgAHQDM+ePTsoKIhGo3377bfQr0ggjY2N58+fP3369Js3bwAAw4cPxzBsxIgRenp6PF6nDqs+CnxS1NXVkSrly0JUXZU31aE2+GKHRyjz/jHHrk9LQ8hyHCiq6azCYv9mF9TW1kKHYVRU1P3790NCQlJTU0+fPi1dxKQzlJWVTZ06FUhFbLXMUvXx8ampqYHJcNA8AwD69Onj4+PTdqqutMK6u7t/6n3O4/EuXboEm+cAABQUFJYsWbJt27bBgwfDc7spU6ZcvHgRyDoc6TwcDicgIODMmTPR0dHQ/ItEoiFDhmhra5PqyBGLxQoKCjQajezHwkfpGkMoEQmLVrmEW5vHDzWFGnXdwijFwaLu7q3OD15eXg4A0NXVjYmJgRlmHz586PywzYiKioJ9cfX19SMjI6U/kvYz4G6WlJQU6H4ZOnQofq+7uLjIXO2+efMGT7brZJWNhIQEmEJkZWW1b98+FxcXLy8vDMNmz55dWFhIUrBcTExMQECAp6dncXEx/CvCMMzMzAwAkJMjOy+bKGCKUmsxrt0WbsJr5uiBvIyObCPqwm+wHK1YY4eyRln8xxAON+fcJqYEDIx/fvXq1bhx4wAAjx49ImRYaaQjtu7fvy/9EcxShfcnAEBDQ8Pd3T0jIwMGo+HFw9oIRpNW2PZnx8oEj2GxsbHZvXu3i4vLb7/9hmHYlClTKioqCNwcS/PkyZPLly//+OOPubm50LmF/RvK28n4ho8C/9qZTCapUj5Kl+URSoTCqtPHcsdZM0f0z7HvV7DYucjdtfzAdgJGlkiUlZWNFBjyDAZ0cbR9tPapSKfXTJw4EU+2w8nNzW1oaEhISLh8+bLMmkOPHz+ePXs2Xll45syZ0pPHk+3s7e2Juj+KiorevHmzf//+W7dujRs37sOHD9AQEjJ4axQWFuIrvqampm+++QYA8PTpU1KFwi1Fs6UJAsOwqlNH2bPHihs+ofCCuKG+dNvG3El2jbHPxA315Xu3MkcOYDlZMUdZ5M5wzJ0+quokMad3qqqqPeQYdDodOi1v3SJgQYwjXSxw/PjxLdtbFhUV1dXVxcXFnTp1SjqGBQadiUSi27dvT5w4EX9/0aJF+G9JUti8vLy4uLjdu3ffv38f9nmAhpCQwVujsLBQIBAwGAwGgyEUCkeMGAEAaJkKRSzUPBY+Stc35hXX18HYbkEhmzVmSP09Gbm0VVVVeH7ohQsXysrK2h5zg92QFNs+8/v+E6bMYDBcXFwIKTHTRrIdJD8/f/fu3U1NTUwmE+bHZGdnS9cc6tu3L3S/MJlM6H7ZuHEj/G15efn06dMBQcl2Ldm/f//t27eTkpKcnZ0pMIQQuFLOyclZsmQJACAwMJBUcStXrgQA/PXXX6RK+SIRi4u+X9L+dru8tBT27LFF7q6iiv9XN4lAIChkiyrLMQzjZ2eyHK24r2WoFXS1wXOsq1evftT3Pk1fK36o6RRTI6gjysrK69at61giUDMKCgrgoxYGgLR09OXm5u7cuZPL5ebk5Pzxxx8YhiUlJUnHsFhYWPj6+jY2Nn748AEGo/3wwz/VCZopLOHlwXbv3h0ZGfn69ev58+dTYAghMOI9Pz8flrsju+7r4sWLKXgsfJSuN4TS1EfdYzkNEuQ1X1Xx+XyxWHzkyJG4uDgvL682TqElfF7FIW/WN4Pr79/GOldbqCUtSxnJ5PTp0xiGPX78WNpBB90v0CqAf2sOQfdLeXk5hmFPnjyBt6Cent7du3c7NsO2gYYQwzAPDw89PT1oCBMSEv766y8mkwmfAoQDa5Y+efJk27ZtAICDBw+SIQVnz549AADo/kU0Q1RVkTvZnvP3x3o7SCS1Vy8wR1lWnT6GtRk6WBsSkDvJTtpSQurq6kQi0R9//PHu3buP1BsSCatOH2OOHlh79QKGYS9evJBZab29V/hf7ty5Aw/sTUxMYDV2mUCFjYqKkm4KAWNY8LrVWlpaHh4ebDa7srISKuzTp0+hwvbo0YMkhYWGEMMwd3d3PT09aAjfvHlz8eLFrKwswoNpITCwIDY29ocffgDkZ7tT81j4KJ+XIcQwrPzgjoIls5rVWhOLxRcvXiwoKPjjjz/27NnT2hGCII+Zv2h6weKZzZJ8YW0hPLcP1hb6pP0QLGUEfy5dykjG/MvLd+zYkZOTI7NwMJ5CJJ3zd+vWLTz0dNy4cS1dN0Th7+8P/+o4HM6wYcNKS0vh+/BBAP9POHAjGBAQcOLECQDA998TUDyoDc6dOwcAWLlyJalSvlwaY58xR1nyP7R6hiqqqS72WJU3fWRTYlx7Biz56fsid1dM/B9bJZFIgoKCWCzWvn37tm/f3tqqUVhSVLhyQf6CSfzsTOn3s7OzPTw88Kbk0InySRmuzYoF4re6jPmXlMCzMZmpDrAOC3QCwW3lzJkzIyMjvby8oMKOHTuWPIU9evQo9GNVVVUNGTIE/xsgVWFhyE9wcPDRo0cBAB4enapS+1GoeSx8lM/OEEr4vALXGRV//KdVWFZW1vHjx2HJ17y8PJkqURd+g+VkVXHIu7WCpTweLzQ01MHBAd7TMvtjyeTDhw+EJ9slJSWtWLECpkbgOrZ3714Kku2eP3/u5+eHF/errq4+cOBATU3NgQMHyOjVsH37dgDAgQMH7ty5AwCYMWMG4SKkefDgAXz2kSrli6bS91f2vPFN799V+f1WvHlV+W+7uAn/aAH37Zu8aSNLPFe3P3hbXMdhz/qm+q/j0m+mpaWdPn0aHi9lZmbKdI02PI1kjbMu2/2DmNvY8lOsRQwL7kT56JTYbDYsuE9IeTBITEzMwoUL8TosbfhaCefp06d+fn54E6WqqipcYQkPH8UwbMuWLQCAQ4cOwV7c8+bNI1yENNQ8Fj7KZ2cIMQwTsHNZ3wxueNreeAcJr6l8/zbWmCH1Ue1yULTRH6slYWFhMLa7w6WMZJKTk1NVVZWWlrZy5Up1dXUGg0F2xX0cWFAK731aXFyckJBQUlKSkJBAbC8nyMmTJwEA69atS0pKAgAMGTKEcBHSpKenw3MdUqV80UhEwrzpo3Ls+zFh1TS7PkxHq5If1tQEnWvDHXr9+vXw8PC0tLTg4GDoG8ThvU9mjrTgxrc3qkLC51cc8mY5DZIZENAMmU6UNgondb5YoEzYbHZtbW1ycvKyZcvU1dXl5OQo65YwZ84cAMDff//TErKwsBBX2E7WgZLJsWPHAACbNm2CuU92dnaEi5Dm3bt3FDwWPsrnaAgxDKsLv84aZy0s+njkLp+Zle8ypcBtjqAw/5NENOuP1bNnTy8vL5gaCHdLqampBw4cgHlpixcvJjY1LSQk5Pnz54cOHQoODoaJFiRlMrTkxx9/BAD8/jsBBUHaQ3h4OFzxVVVVwbMWUsXV19cDAFRUVEiV8kVTdzOYOdqyeWr8cHOWk1VT8luZP5FIJPX19X/88cfFixdDQkJabkRqAs7kTR/ZnpxCQR6rwHVGgavzp3Zee/funbu7O144Cdbyra6uXrhwISzdl5qaevToURjA2fnaY824cOHCq1evfHx8AgICYIoOgXU52mbjxo0AAALryLTNjRs3AABz586Vzn0iD/hY0NTUJFXKR6GDzxL1mQtUx0ws274JE4na+Fp9xN9Fy+Yq2482vnBd3tj0k0TAJNm8vLzjx48PGDCguLh47969586dy8zM9PT0BADU1taWl5efOHHir7/+unLlCt7qgRBgNqGamlppaSlsLlhQUEDg+G0A21RRLC4/P19HR0dNTa22trauro48cWpqalpaWlwuFyoYojkYVnX8EMbjNX9bIsGE4taUiEajJSUl2djYLF68eNKkScHBwc2+oOW2VtFycPmeHwGGtSG84dH9wmVzFPpZGF+8Lt/L7JMmbm1tfebMmdzc3P379xsZGWVlZXl6ep44cSI2Nhb2mSkvLy8uLj58+LC/vz9ek5ooYKq7qqpqUVERTCCGPcApoKsU1tDQUFFRsaKioqmpiTxx8LHA4XBIfSx8lM/UEAIAemzbL2lsqDl9DGCYIDuz6U2sICsDVzNJY0PZDo8q34MGv5/o8bMXTcp3/0moq6tv3LgxIyMjIiLC2dl59erVvXv3Njc3DwsLg19YsWLF6tWribkkKVJTU1kslpGRkbq6OqxG/3UbQigObztHqkRYXo6yC/yyEBYXSvj8f//0H6NFU1TgpSTK/JVAIEhLS+NwONnZ2Xfv3oWB9f+BRtP3Pixgfqi9ch4AICor4WekispL8c8xPq/y8N6K/Vv1dv2mv+8oTVGpY/PX19fftWtXXl7elStXvvnmm2XLlllYWCgrKz98+BB+YfPmzRs2bOjY4G3w/v17JpOpr6+vq6tL8Q1GsThcYWGzQAzDioqKSJUIQ3O7VmE7aD8ogK6sYvD7iSK3OXW3r2E8HqDRAMBoahoG+4/RlVXKtm+SM+xpEnxPTs+AAFl0urOzs7Ozs1AoBADs3bt3woQJBw4c6PzIrQEP6iCwTejXqlfSKz5TU9MPHz4UFBRYWVmRJ9HU1DQlJaWgoACGOCH+g0hIo9P+NYC0/3yEYZhAIPNHCgoK33//PXwNa6m3hK6hZXDAt3j9Uk5YkLiynMaQx0QCebO+BnuPADm5sm0baYqKJlcj5I17df4iFBQUFi9evHjx4sbGRgDAb7/9NnPmTG9v786P3Bow+BkCS9pSv0WjRpyBgYGiomJlZWVTU5OpqSmLxcrPz4dlxEmiV69emZmZBQUFrd1aFPD57ggBAJL6OkwiFldXSbiNksYGSWOjuKykZINb0apv1WbM7XkqiBAr2BINDY0tW7b4+PjAP1ZVVQUHB9fX18fHx8NafMRC8Y1O8Y4QSK34qLHBFP99flnI9TTFxBJZn2BAIla07NSTiKagCAAmKszHeDxJYz3G5wuyMgqXzS1aOkt5+Cjj89cJsYIt0dPTW7lyJew/CgCora29d+8en89PTk6Oi4sjXNzXrbA0Gs3ExATDsMLCQmpEfw4K+1kbwrLdWzChsNmbmFBI19DUcd8M6AzyRC9evBgvjdbY2Dhp0qTQ0NAnT55kZ2dXVlYSK4viG11fXx+6/rlcLjUS8Qsk9UqvX78ODzOot/RfEnSaQn/L5ntBAABdTsFysHxv886MXbbTo/mhPoZhAj5dW7fHz160f3uwkIG7u3ttbS18XVVVZWVlde3atcjIyPj4eMIPnyi+wYyNjeXk5EpKSoQtHoYkgVsm8lauGIZdvHjx81HYz9cQCvOYklrZ8Q5YE1fAyiZDKIPB2LdvH3zt7+8P63WZmprevn17+vTpCgoKGhoahNuPLlnxAQDIdv3j4OpE0tKvqanJ3d3dxcUFpkBBKUwmk1gpXwGikqLiNd9h9XVKloPoyir4+zQ6g8agGxzw7czggtwccVWFzGAZSU21sIiU9b6ioiKsJUSn04OCgtzc3AAAffv2ffz48bhx41RUVHR0dBoaGogVCu9nynYwDAbDyMhIIpHAME4KIHvlyuFwvvvuu1WrVsGwRCjlw4cPxEr5JD5fQyiqKAP0Vo4wGXRxZTkZQul0+vjx49+8eaOsrOzu7g4Pme7fvy8UCjkcjqmpKY/Hw1v9EUXPnj3l5ORKS0sFrZzQEE5X+XbI0Kv09HR7e/tz584pKytbWlryeLy7d+/q6en9/fffs2bNevToEYGyvmgan0YWLHaWN+1tciXcOOiW7o+7FS2sGFo68r3MtFatl+9tzgm+1JnxRaXFrSksTV5eVFrSmcFbQ05ObuzYsbGxsXp6euvXr4fB2JGRkU1NTVwuV1tbm8/n4yWliMLExIRGoxUVFYnFYmJHbg2KFRa39GTIjYuLs7GxCQsL09DQmDhxYl1d3bVr1wwMDK5fvz558uTw8HCszcBjkvh8g2Xk9I0wsWxXACYSM/QNyROtr6/P4/Hw5/WMGTPgC7whC7HAFV9BQUFxcTFeR4NUuipwlHC5gYGB69ev53K50BVGo9GGDx+elpamoKBAp9MjIiIiIiJGjBjh6en57bffypPpmvucwQT8Kj+f+vDrejsOqE2bA9/UmLdIY94i/DvqM+YXLp2lZGOvOm5ys58XFxc/ePBAT0+PwWDExcV5eHjg2bfSyOnqYZhswyARi+R66BF0NTLQ0dGprKzE76upU6fCvoOWlpZkiFNUVNTX1y8rKysrKyPcysqkV69eL1++pF5h582bB4hTWAzD/vzzz59//lkoFNrb24eEhFRVVQ0bNiw3N1dZWVleXv7Ro0ePHj0aOnSoh4fHkiVL8BrRFPD57gjle/dhqGvK/Iih20OhD4lRTMbGxnQ6ncoVX1dFSFMpDi4wx48fP378+M6PWVdX5+rqunz5ci6X6+bmFh8fn5iY6ODgkJaWZmlp+ezZs9jYWNgMOS4ubvHixb169fL29obxft0KIZtVtGI+712CyZUI3Aq2RL53nx4/7Snf+7OopLnDvGfPnq6urvn5+TNmzDAwMJBpBQEACv0t6fKKUm/8/7qeoandydPHtsF3MJRtJrpEYal34fTq1WvatGl4a6rOUFFR2Ebo9wAAIABJREFU4ezs7OnpKRKJPDw8Xrx4ER4e7uTklJuba2dnFx0d/ezZMx8fHxjyvWbNGkNDw82bN1MXQdN1ufxt8U9N+pEWTIf+Obb/aY3NdOjflJpE9gRgziw1jYowDFu0aBEAQGbZXzI4deoUAGDt2rXUiMvIyAAA9O/fn6gB4+Pj+/btCwDQ0NC4evVqXV0dHt0O+3cnJyefOHECwzDYDHnIkCHwU9gkmbKaIF0OLMBbfmB7awV4m1G2+4fC5fMk/y03WFVVtXv37ry8vMzMzGfPnsn+JWxY4TAgZ0S/5jVrHPpz35JeNQmaZ2oaFWEYNn/+fABAJ3vwtp8///wTAPC///2PGnGpqakAACsrK6IGlO7UERERUVlZOXPmTCDVvio9Pf3kyZNYK1XOo6KiiJpJa3yOO0JRWUmxu2tDZLjxpb+NL9yQ792HrqxMV9ekKyvLGfQENDqQyIz/JpKve8VH8dWx2Wx5efmSkpJ169Z18kgcwzA/Pz9HR0cmk2lnZ/f27VsLCws7OztY+icoKCgwMFBVVXXo0KEw6FdRUXHZsmUpKSmwwKxIJAoKCrK2tnZycgoLCxO1WbfoS0HS2FB1/He2syPLcVD+vPE1gWcxoRAmsFce9tbzOqy381eagkJ7huqx/YC4vq7mrJ/0m3w+f8yYMRwOR1VVdezYsS1/Ja6tKdm8ujboXM/TV4z8Lsjp9KCpqtE1NOkqqnRNTRqDztDSJuZSW+frTmmgWFxWVpaiomJ+fv66devgKrbDiMVib2/vSZMmFRcXjx079t27d3p6evb29hEREZqamteuXfPz81NQUBg4cCBsFQJ7IcTGxiYkJLi5ucEDjsmTJw8fPvzs2bO8FhWRCINsS/upNDyLyh1vU7b7B3Hj/9ekFxSyuQmvBAV5GIZV/3U8b8ZoMYfIQoItWbBgAfh6V3zFxcVnzpwhpFNx24hEIm9vbzwRBQDAYDDmzZvXsYbU0LsCpBaSAQEBsIHqoEGD3r9/j38zKSlp7969TU1NzUbAmyHDycAye2SUGqcMYXlp3vSRj2z+32vy2LYve/7E/AWTCtxmyyzAW1tbe+7cucuXLz98+DAoKKhZ4dA22u3KhJvwOm/ayJIf1vx/wwqxiPc+uT7qHi8tBROLK4/sz3eZImnqYBPQdjJr1ixAdGv7Nvjjjz8AAJ6entSIKywsPHPmTBs9UIlCKBRu3boV7wcJAKDT6TDorANdd2DpZmmF9fX1hUf1I0aMwBvgYBgWExOzdevWlgpbUlICDzjgZAwMDLZu3UpG36vPyBBKRMLKP33wFp2tIhYXrV9a8vN6UicD43pJ6lXbkps3bwIAZs2aRYEsNpu9ZMkS+DohIWHz5s0kCSorK5syZQpUg40bNz5+/HjNmjV4429YOrKxUXYLnpY8f/4cVmjT1dUNDw+vra3Fq/O4ubl9UrNlDodz7Ngx6FwFAKirq2/atCk7O3v+/PnwCzU1NatXr/7kC+4KCtcsZI7oN0BZAVrBTJveg1QUmPb98hdNl4hkN1TBMEwgEBw7duyHH344c+ZMS3di7TXZ7XabI5HUXDzFHDmg5uIprPWnpEQoLFwxv/zA9k+5rE/mf//7HwDgzz//JFUKTmhoKAAAv2FIhclkLl++HL5+8+bNTz/9RJKg/Px8JycnuFrdsWNHZGTkhg0bmlU5b3/jp0ePHsEDJn19/QcPHpSXl0+bNg03igKBoP0T43K5Z8+ehc0JAABKSkqrVq1KSUmZPn06/EJ5efnSpUs/+YKl+Fxco6KSouLVC7nRj00Cb2m6rmzrq3S6wQFfXvJbTlgQefOh2BdBpa9SLBbDFg0AAJFIBItUEc7jx4+tra0fPnyor69///59BwcH2LI8Ly8PNv5OTk5et26dmZnZtm3b2r5wDMP8/PwmTZpUVFTk4OAQHx9vYGBga2sbGhqqoaEREhISGBiI29f2oKGh4enpmZWVFRUVNXPmzIaGhuPHj9+6dSs7+5/kVJFIxGKxOnX9lCAsYAvSU1sWpsfEImEeC7SSf11fX3/gwIHp06fr6+v36NEjKyur2Rc0Fy5THuFYtmMzkLQaLCaurirZuJxz/XLPsyFaK74HtBYZ+v9Ck5Mz+O14w6P7DfdvtfvKPpmv2FcprbBCoZDwtEjInTt3hg0bFhMTY2Ji8vTp0wEDBtBotF9//RUqrKmpKaxybmxsvHnzZjab3cZQ0A80ZcqU0tLSCRMmvHv3TkFBwdraGsYe371718/P75NCuJWVldeuXZuamgoPOIRC4YULF6Kjo/HESrFYXFZW1qnr74wV7QC8tJSq08fK92+rDb4kqvpnKdrw+D5r7NCy3T+033/CjX/JHD2Ql/n+41/tEFSu+DAMKy8vBwDo6upSIIvFYtna2gYFBQUFBXl7e69Zs4bY8aWbg0+YMAG2yElMTDx//nx0dDT8DjwSh91TAQDy8vLwYKDlaFCXAAB0On3nzp0CgcDX11dBQQEAYG9vz2QyOz/hlJSUdevWVVdXm5ubP3ny5MmTJ7du3Ro/fnznRyaD69ev+/v7p6SknDx5MuzgXtaYITm2ZgbyDBddNfjfIBWFHFsz1jeDW2tDz+FwEhISMjMzGxoapP3J0vzTbvfccUEhuzY0sObiqfqHEXgHXe6bmNzJ9iU/rhNzZHTclUl91D2Wk5Ugj4B/L5lcvnwZALBo0SKSxm8GLEZhaGhIgaysrCx7e/vLly9fvnx5z549hDdzFwgEuDt08uTJpaWlGIYlJSWdP38ePz0RCAShoaGOjo5QYel0emsxLAUFBXhX5F9//VUgEHh5ecHDkXHjxhHi1czJyfnpp584HE6fPn1u3bp169atS5cuTZ48uTNjUpdHiAmF5bs8G2OfYnwBkIhpikrVx3/X/dlLkJ1ZH35db8dBtWmz2z+a8vBRWotXlW3dYHIlgq6qRvhsKV5g9ujRY+XKlSYmJhKJBJoQUmEwGHALpaio+NEvfxIFBQWurq6xsbFycnK7d+/es2cPvByxWCwQCPBlIDwSd3Fxefv2rZ+fX3BwcFhYWFhYmJ2dnYeHh6urK/5NOp2emZmpp6cXGBhoZ2c3d+7ce/fuQe/K4cOHFdoXA9I2Q4YMOX36NACAz+fDeDmSdsmEMHr06GPHjkVFRXl6eh7avNEOAACAOoO+1VgHACDCsNXMMgAAwDBaK/+4Ghoadnbwd61X0FbXMDj4Z+Hqb2vOHwc0Oibg05VVAZ2uf+AoPz21NvCs7safP+K5+S9qk6Y3xb8s3brBJPAWTYHguw5QXu3F0NBw8eLFsEITBdDpdKiqhNzw0rDZ7EWLFr1+/VpOTm7nzp24wgqFQj6fjzta4FIVV9iQkBCYpGtra7tu3To3Nzf8myoqKkVFRSYmJsHBwWZmZpMmTYqOjmYwGF5eXrt375YOF+gwffv2PXz4sEQiEYlEsGMBh8Pp7KCdt8/tpMzr5w+jLJy1VeFhxqshpkv01Jn2fdnzJwgK2R0YUCISFq76tnTbRsKnilG74sMwbN26dfhrd3d3UmWxWKzZs2fD169fv27PjrC0tPTq1asfzTq4desWjGI3NTX9pEic4uJiLy8vXV1deE8aGhp6eXnhZ1fx8fGlpaXNIrDbP3j7wdtkV1RUSO8IIyIiKAgsaicxMTG7du2ClW+P+fgwR1nm2Jo1OyPMsTXLHW8ts9F8+6n43SvHoX+zXIic4eZ5Mxxb22u2jYTPL3B1rjjk3ZlZtUZubi688cgYvCWbNm2S+ZoMsrKycNdUTExMe3aEeXl5V69eTUlJaftrN27cgF0be/XqJdMf0xowhkVP758iCTCGBU82S0pKqqysjIqKMjAwgJ8+fPiw/YO3E7FYbG1tjc9HekcYFhb2+PHjTxqNojNCUXFhQ1S4qIlXwP/nPEOIYcUCESYWAwx0rCY9jSFn8OvxprjY+vDrhE4WAAAMDQ3l5eXLysr4/9+8jURevnwp8/VnApvNzsnJaSPzgc/nb968ed68edXV1bNnz3737h08dW8nRkZG3t7eRUVFAQEBgwcPLi0t3bt3r4mJybJly1JTU21sbE6dOiUdgQ1jR6mBzWY3NDR8Pv8oZmZm69evd3FxMTIy2rBli+Z3y2nKKkby/yy0aQAYysvRlZV1PbaBTrgWRGWl9bdCZJwySiR0bR2FAR0psURTUDDw8a8Pv974NLLDE2sNY2NjBoNRXFxMTUrMs2fPZL7+TCguLmYymS1Pf3F4PN7mzZsXLFhQW1s7d+7cd+/e4al77cHQ0NDb27ugoCAgIMDa2rqsrOz33383NzdfuHDh69evBw8efPz48alTp5aVlU2aNOndu3cwdpQasrOz5eTkEhNld9ZsFSJt9H958ODBnTt3CgoKrly5knnhNMvJKm1Y775K8kH9DYP6G/r20RuvqZxja8Z06CdubG8kUksaY56wnKz4zCwMw8R1HGFpMSYWETJ/2C+XkFOoj2JlZZX5L4MHDyZVlkgkKi8vh6/5fH57kgfOnz+fn59/+PBhmZ/m5uY6ODgAAOTl5X18fDoQZi2NRCKJjIycMWMGdNHQaDS4EYTeFZGImH9cmbx79w6+EAqF+OFZdnb2zZs3oSuGPNEdRywq8/qJOXpgjr05zF5njrasOnW0k6PW3Q5lOVk13w7C/0b0k/Cah7m3n/oHd1hjhwqLCjAMkwiF4naHDX8UGFQM68uQzaBBg1j/gjsSSEIoFOIKy+Px2qOwZ8+eLSoqai3oPTMz09raGgCgqKjo6+vb+Rk+efJk7ty5+JkO3AjKy8sfOnSIVK3JycmBL4RCYV5eHnydnZ39999/f2rAP4lnhPb29hcvXpRIJGKxWJ1B44slAACeBMvmCQAAHNG/SfEMOYzXBFRUOyZFxXG8xjzXil93iWuqREX5NHk5TAI0Frjqbvi5w12wIaNHj+7Tpw81rU/4fP7FixfhawnJ5QIYDAbu01BQUGitYpY08+bNe/z4Maym0YwbN26sWbOmtra2d+/eISEhI0eO7OT0aDTalClTpkyZwmQyz507d/LkSQUFBXV19evXr1dWVkZHRxNSoU0m8OkAAJCTk8MPz/r165eQkDB48GBa64GRXQmdoe99WPO7ZY2P7gtLi+V7m6tPnSXfu08nR5U01GNC2VsrGoMh4XIZHVUutamzuK+iy/dvlXC5/Iz3NBqN/n/t3WlcU1fCBvCTEMjGEpBFNhcQkGUQq9bi3losauuKVXGp0xbFFRXnrdL6a0dLW2o7MlK12LdOVcSXSnFFcaGoRVsV3FjaSliUVUEIhIQQSO774ToZRhECJMR4n/+neEnOOdCePPeec+85lpZW898VvbOUZdKjr6OhQ4c6OTn1zvyuXC6Pi4vrhYoIIRwOR9NhuVyuNvP6ISEh6enpISEhT/9o//79K1eubGxs9PT0TEpK0snm1fS6icXFxfHx8bt37+bz+dbW1idOnCgoKMjIyKBvdtMHzRNQHA6Hvm4hhAwaNOj27duaWXBt6Tij27h9+/bRo0ebm5ulUum2yHVFY/3yAvr7C7j0eeUlPxf6irBorF8HDyFpo+H4j3de9krw6EuXfNXfNfkv/UvnT9FyWal2HTp0SDNH9d1333XpMbVuaHtSqe8rQtqdO3ekUin9f6o271coFFlZWb///rtarT548ODdu3ebmprWrFlD/180a9asujq9LHGQkpJCCJkwYcL9+/dXrVp1+vRpfdQCT5BdvlA01q/dK8Ki0T497bCnj+a87Jky2IkuMMu/36kAt/Kw+T0Zy0lJSamtffxE/4EDBxQKRU9a2Km2HVbfV4S027dvNzY23r1797ffftPm/XK5/Nq1a/n5+Wq1ev/+/QUFBXK5fOnSpXSHDQkJkUi0veO3S+gOO3HixMLCwoiIiNTUVH3UonP6miOkKCo9PV2pVIrF4pSUlPkR69gia8J+8pyazeNZzl3cwUNInVJJ6mpiPn4oazxQ/Xj7zRJFa0plbcv94rr933a72JSUFM0GvAkJCfQGki8MtVpNL2bf0tLy+++/a3Y0fZaioqKxY8empKTExcVt3bo1Kyvr+vXrI0eO3LFjB4/Hi42N1cy66xy9gUBFRYWpqWlAQAB9VyfoGy9gOFG1c0XI4vIsZs3rSYdVN9TXREdVNMq+e/D4Tr8ChTKxvFqRf1vyf/u7XWxCQoKmw8bHx+txLS5DUKvVmZmZ5eXlra2t2mw1LBaLx40bd+LEiZ07d3766ad0h3355Zf37NnD5/NjY2MPHz5sZdX+lgY95OXlRQgpLy/n8/l+fn7G0mH1NTTKYrHWrVtHv/bx8SGEKLf/b9l7c2LcHy8Pb2dqEuXmZObpaxMW0ZOKZOmn2z2ubmpqSD5k8/6anhTea/7xj39oXm/fvl3f1bHZbHpLKVdX15aWlk4zLDo6evPmzfQqVmq1esOGDVVVVTk5OYMHD05KSqI3gdMTzVIDDg4O7u7umkcPQX9a7hVVbVxl0teZVJVTSiWlGatnsdiWln1WbOhJ4Y3pp9vdIYJqktcf2isK7cIjGQYUExPT7ms9adth1Wq1paVlx+/funXrli1bJk+eTAihKGrt2rVVVVV5eXne3t5JSUmaNej1QfMci6Ojo6+vr2ZQ9znXe88Rmrl79jt81nzn17JL5yiFgm9rN3zuO1bz3unhxEBLSaG6qYkQclPWvLzoISFE0qr24JkSQtQ1PVprYPXq1ebm5oSQvLy8npSjjbYbnehk05NOFRYWSiSS1NRUFxeXhoaGjrtWTk7Ol19+Sb+mp8RHjhyZlJQ0depUzQpMeiIUCq2trevq6h49ejRhwgS91gWEEOnJlJqYzRbT3u6zNqql7F7Nti1N2b+xWBSbL+SPGie7cF55r4jr1f7Th9pQFvxJyeWEkBuNCk2H9eKbEkJUD3q0ee/KlSuFQiEhpIfrRGuDzpinX+vPjRs3Hjx4sH//fmdnZ4lE0vHJa25uruaUkZ7bDgwMPHz48JQpU7q0BlM3mJubi0QiiUTy6NEjIzpt7dWNeTl2DvaffKnbMk1sbFmmpqS5ZaiQu9PNnhCS3dh8rLaREMLq7g04tLi4OHpcrne+f1UqlUKh4PF4NTU19G1XevXXv3bh1FsoFMpkMs1zfrQ5c+boulHtc3V1raurKy0t1ay9C/pAb1jReC7V7uNt5q9PIYSYDRzktGs/oSh1UxNbICCE1O76uoerWJhYiwiHQ0jLS+a8uIF2hJDrjYrUOhkhhMXr0Xf0zp07PTw8CCFjx47tSTlaUqvVSqWSy+XW1tY+0TX0ITIyUvs3m5uby2SyJ8KS3kigF7i6ukokktLS0l74s+jK87LWaLcJxk1s/5rShCMcN7HXm9N9p0+fPnLkyLfffnvmzJmMjAxDN+e/BAcH01sYEkJ6f7q0lzeNYiZlUUHZounNd393SUylU/A/WCz2v6/7bcLXcvo6V3+6qdsVCUa/xjJtb3kUExNB4LhuF9v7jh8/npKSsnv37pSUlMzMTEM3578EBwfv2rWLfi2Xy3u5dmPssEYfhGbunsKgqeZ8wVDh47uKrTlsHyGXzefbrP6g28XOnDlTczqzYMECfY8nEELom4zpBzYKCwv1XV2XrF27tqWlJTg4eNq0aYmJifb29r3wB9Ho5a3mGEh6MqV88Qz+iFHOe5NNnV07eivbxP7vXzdduyw99mP36uL6/EUwcrQ5n+fJe7yKnoUJ241nyubybNZ0v8NOnjxZcwE0e/Zsna8d+LSJEycSQlxdXdlsdgePrhvE+vXrlUrl5MmTp0+f/uOPPzo4OKDDdqxXh0b1xG7z52web+nxZMIxIa0qd6G5m5vI9KMYpYXV/aIigUBA7wbSJfPnz9e8DgsL02l725eenn7t2rWwsDCxWDxjxoxeqFF7XC6X3oDNIHp53dcXlbpRWp98UH4pnWpWcH39reb/1WzgILWssTo6qunaZYeYnYLRE7Qph+PQ137LP6r+ZwXXx9/MY3A3WuLweRxr8/qIzAyKUpOWFh/bPt7OZvZf7zF17kevSduNb+33339f85reQ03frl69mpubGxoaWlRU9Bx22G3bthmqdmPssCyKavceLuPT+rBKkf2bWqEwGzjo19qGrOzsqVOnyuXy9PT0Dz7o/pkmGFxCQsKiRYvmz5+fmJho6LYYK2VRQXnY3NamJnazghBCTEwoU1Pr0Pdk505y7B3to2M5dl2bln60/TP5rxdd9h/t9sSesqig6fqvlKLJzM0j/VFD5q+/xsTE7Nu3TyKRRET06E5yMCy6w4aGhh48eNDQbdGW0Q+NanDs+5pPnmE5cx4vYPirr73G5XIHDBhQU1PT7vIK2vj1119jY2OvX7/+wQcfdPqkHeiPMZ5gPlcoZXPlioW3Kh5+eLeMPpJR2/h1UaXkh9384YFO3yZ0NQUJITar/4ctNK/Z9vdut8rMzcNq7mLRO8sEY197ZfTo1tbW/Px8Hq/7q0FduXIlNjb25s2bH374oQ62I4DuMsah0RcnCNvaunVrXV1dfX19RUWFZhmergoMDPT29uZyuSNGjFCpnrlDKeibMc69P1caz59Wy2SEPLV0H0W1VJYTdnd2xnm83e7PZ6SndLDd7qVLl/h8PpfL7dOnD72xTjeMGjWK3sR8yJAh+l6nEDpgjB32xRka1bkzZ85cvHhx5cqVe/bsmTRpkmZTSuhl9KQRm81WKBQ62c/shdfU1HTo0KG+ffvy+fzc3NzgqkJ22rE78ua/l9ZOEQkJIWKFso+pyQYnaxMLywEXbne7Iln66YefbHBJOMG2FDXn3aJUaq7PX7pxfakTFy5cuHz58rJly77//vvg4GDNyrHQy4yxwyIIn+nhw4eNjY12dnbV1dVCobAXnu2DZ3FycqqsrCwrK6N3GIBOURS1fft2lUrl6+tb9kP8xMI7d+TNcZWSpQ5WhJCbsuYGlbrnQUgIqfnso+a7vzffzWOZcQlhkeZmwZjxdp98pY/tsjvWtsOam5vb29v3cgNAg+6wpaWlvbZ3cQ+9CHeN6om9vT3dlywsLAzdFqZzdXWl+xWCUBtqtfrAgQP0I0BHjx4dMGIkq7KIyJttOSYjzHmEkEYVlS1TEBbLzKdny+NRlLL83u1bNyobmyZY8QkhWY0KcvrUqLL7LgeOszi9+vWCDvv80HRYYwnCF3OOEF4wxjj9bkASiUQgEBQUFNy/f9/FxSUoItJEICDkqSXvuTyb8HU9qUj2y8/Nd26WSOV35I/3r76raPmzQd5SWiI9frgnJYNRM7ppQlwRghHAjaNdYmNj88QCeI67DgSEzfUTWRBlMyFkvLVwvL3IZt2HPP+XelKRNPWIWi4jhFCEqChCCKFnWqimJumJZMtZ8zv8NLywjO7MFUEIRsDV1dXBwQHz2d1m5u7Z/9jF+h/3yy+lq5uaeP4BVvPfNXP37GGxqocV9ItTdbKCJiUh5L6ydW4fC0KI6lF1DwsH4+Xm5tavXz9O746N9wRuloHnXXFxcX19Pb2Vdn5+PpvNphdDB4N7+HGkNPXImTrZn03KNY4iQkhijZSiyAI7C/7wV5ziDxm6gWAAYrFYIpEMHz6cEJKTk8Nms319u79dSe/AHCE8765fv37y5En69c8//3zp0iXDtgc0LKa9zW5vZRmWQGA5K7T32wPPgxs3bqSlpdGvL168ePnyZcO2RxsIQgDoJv6wkcLX3vASWYyyeLwizFAh9yUbC57/S+aT3jRs2wC0ZzRjuMBkiYmJ2dnZhJCioqKVK1caujnwH/affPXSAHfJ3l2EzSaE+HBbSwJfe7QozFqhuHjx4qhRozrdTh1ePImJiVlZWYSQ4uJio+iwCEIwAqGhoR999BEh5JtvvjF0W+C/sdnW764UvbOspbiQUJRp/4EtxSUpKSleRUU2NjaHDx9+7733DN1E6G1G12ERhADQUywTjtkgL/q1hYWFo6Mjm80uLS0VCoWGbRiANhCE8Lzr16+fqenjTVw9PT2N6J5sBlKr1bt37/b393d0dFQoFFgJiIEGDhyo2VHS29vbKDosHp8AAABGw12jAADAaAhCMA4NDQ2HDx/+/PPPDd0QAOicRCLZs2fPpk2bDN0QrRjB6C0AIcTS0vKNN97AhqsARkEkEs2ZM+fcuXOGbohWcEUIRuOnn36aNm2aoVsBAFpJSUkxlg6Lm2XAaFRXV9vZ2Rm6FQCgFSPqsAhCAABgNAyNAgAAoyEIAQCA0RCEAADAaAhCAABgNAQhAAAwGoIQAAAYDUEIAACMhiAEAABGQxACAACjIQgBAIDREIQAAMBoCEIAAGA0BCEAADAaghAAABgNQQgAAIyGIAQAAEZDEAIAAKMhCAEAgNEQhAAAwGgIQgAAYDQEIQAAMBqCEAAAGA1BCAAAjIYgBAAARkMQAgAAoyEIAQCA0RCEAADAaAhCAABgNAQhAAAwGoIQAAAYDUEIAACMhiAEAABGQxACAACjIQgBAIDREIQAAMBoCEIAAGA0BCEAADAaghAAABgNQQgAAIyGIAQAAEZDEAIAAKMhCAEAgNEQhAAAwGgIQgAAYDQEIQAAMBqCEAAAGA1BCAAAjIYgBAAARkMQAgAAoyEIAQCA0RCEAADAaAhCAABgNAQhAAAwGoIQAAAYDUEIAACMhiAEAABGQxACAACjIQgBAIDREIQAAMBoCEIAAGA0BCEAADAaghAAABgNQQgAAIyGIAQAAEZDEAIAAKMhCAEAgNEQhAAAwGgIQgAAYDQEoS4VFBQ8fPjQ0K0AYKji4uLy8nJDtwKMD4uiKEO34cVhb2+/YMGC7du3FxQUZGdnh4SEcDgcfVR069at0tLSt956S4dltrS0pKWlicViW1vbCRMmuLq6tv1pc3Nzamqqm5tbQECADisF0KGhQ4d6enomJSVVVFRcunRpypQplpaW+qjo3r17165de/PNN/l8vs4LV6lUR44coSjK19fXx8dfCrfBAAAKE0lEQVSHEJKZmVlZWfnE20QiUVBQkM5rZygKdMfOzm7t2rUURe3YsYMQIpVK9VTR2rVrHRwcdFhgbm6uu7u7UCj08fGxsrLicrnx8fH0jyQSSWxsrLOzMyFk+vTpOqwUQLcCAgLefvttiqKOHTtGCMnNzdVTRfv27SOElJWV6aPwbdu28Xg8QsiWLVvoI2+//bb1f+NwOMOGDdNH7cyEoVG9CA8Pl0ql5ubmeio/Ojr6zz//1GGB7777rp+fX1VVVV5eXmVl5euvv75q1ara2lpCyKxZs3766ad169a5ubnpsEYA/Zk6dapUKvX29tZT+fPmzautrXV0dNR5ySUlJZ988smHH37Y9mBSUlJtG2KxmMfjzZgxQ+e1M5ZeBu6gpqZGLBaPHj2azX58qlFdXX3lyhWpVOrk5CQQCGxtbQcNGtRpOZWVlVeuXFEoFEOHDqUHSWhlZWVVVVXjxo1TKpWXLl0KDAxUKBQZGRlqtXr8+PEODg4qleqXX36prKz09fX19/fvtKJTp05xOBw6ufl8/syZM1NTUwsKCkaOHHn27FkTExNCyIEDB7r55wDoXfX19Xl5ecOGDRMIBPSRhoaGzMzM2tpaW1tbW1tbMzMzbfpFfX39L7/8UldX5+Hh8corr2iOP3r0KC8vb+zYsVwuNzMz093dXSQSZWRk1NXVDR8+3MvLixBy8+bNP/74w9nZecyYMZrvgY5RFLV06dIBAwZERkZu3rz5WW/bvXu3Wq0ODw/XpkzQiqEvSV8ozxoa/eKLL7hcrqWlpbe3Nz2psHHjxk5Li4uLMzMzs7e3d3NzY7FYmzZt0vxIMzRaVVVFCImIiLCysvLx8RGJRObm5idPnhwxYoSTk5O7uzshJCoqqqu/SEhIiJWVlUQiaXtwyJAhGBqF59mzhkYTEhIsLS35fL63tzc9axgSEtJpaadOnRKJRFZWVl5eXmw2e9q0aWq1mv5R26HR/v37L1682NXV1d3d3dHRkc1mx8fHL1iwQCQS+fj4sNnsSZMmqVQqbdr//fffs1iszMzMlpYW0mZotC2FQuHo6Lh8+XIt/yagDQyN6l1ycvLGjRtXrlxZW1ubn58vl8u5XG6nn8rOzo6IiAgPD6+oqCgsLIyKivryyy8LCgraffPZs2evXbuWl5dXXFwsFAqnT5++YsWKsrIysVi8YsWKmJiYuro6bZqalpa2cePGwMDAq1evJicnW1lZde1XBXj+ZGVlLVmyJDg4uKamJj8/v76+vu3gyrPU1tbOmzcvMDCwoqLijz/+SExMPH78+IkTJ9p987Fjx/bt2ycWi+/fvz9+/Pjw8HB7e3t6omHv3r1nz57NyMjotMaqqqoNGzaEhYWNHj26g7cdPHiwqqpq9erVnRYI2kMQ6t2uXbvc3NxiYmLoAUYtHT16lM1mR0ZGNjQ01NXVhYaGqlSqtLS0dt+8atUqT09PQohIJBo2bJivr++SJUtYLBYhJCgoSKVSFRcXa1PpvXv38vLyHjx4QFFUdXW19q0FeG7t2bOHx+PFx8drhknprtGx8+fPNzQ0REZGNjc319XVTZo0ydraOjU1td03z5gx49VXXyWEcDic8ePHUxT12Wef0ee7r7/+OiFELBZ3WuPq1atNTEyio6M7fts///nPt956S3/Tn8yEOUK9y8vLe/XVV7v6HEVhYWFra2v//v3bHiwtLe30g09cbpqZmRFC6JGWTi1btmzZsmVqtToyMnLhwoVazi8CPM/y8vK8vLxEIlGXPlVYWEj+HWMaz+qAbZOV7nEadH9UKpUdV3fy5Mnk5OSEhARbW9sO3paWlnbnzp24uLiOS4OuQhDqnUAg0HKq/IlP9e3bNz8/v+1BbcZUe47NZoeFhcXGxp4/fx5BCMZOIBC0trZ241OEELFYbGNjozmop8eCCSGbNm0ihKxevbrtmOfnn39+/Pjx69eva458/fXXw4YNGzdunJ6awVgIQr0bNGhQVlaWWq2m4/DevXvaXJ8NHjx47969jx490ubm0p7Lyspyd3e3tram//ngwQPy7+8CAKM2aNCgxMREqVRqYWFBCJHJZBUVFZ0OLQ4ePJgQkpOT0ztPKURFRUmlUs0/1Wr18uXLg4KCQkNDNQdzcnLS09MTExN7oT1MgyDUuyVLlixcuHD9+vUREREPHjxYs2aNWq3u9FOLFi364osv5s2bt2PHjv79+xcWFp46derTTz+lz0k1saoTra2tCxcupCjqq6++cnd3Lygo+Nvf/mZtbT1z5kxCSE1Nza1btwghUqm0urr6/PnzZmZmOCcFY7F48eI9e/a89957W7ZsUSqVUVFR2tw7NnHiRD8/vzVr1piZmQ0ZMqSysvL48ePh4eFOTk6EELoL67APzp8/v+0/W1tbly9fPnz48Llz52oObtu2zdnZefbs2bqqFDRws4wucTgc+o4YNpttYmJCzxwsWLDgq6++SkpKcnNzCw4Onjx5sjYjnA4ODufOnTM1NR0zZoyLi8ubb75ZVVVVX19P/7SkpMTFxUWHzT5z5oyPj8/s2bN9fX1nzpxpZ2d3/vx5BwcHQsjVq1eDgoKCgoKKioquXLkSFBQ0Z84cXVUNoEPtdsDAwMCEhITs7Gxvb+/AwEBHR0cPDw9tikpLS/P3958+fbqLi8uYMWNu377d0NBA/7SkpITL5drb2+v112mrvLw8KSlp3bp1pqamvVYpc2Ct0d4jkUjoBxLMzMw2bdq0ZcuW8vLysrKyp9/Zp08fzYhoY2OjQqFoO4VeUVHh4eGxefPmjRs3dqkBN27caHdU1sfHhx41ampqevDggY2NjZ5WaAQwIKlUyufzORzO4MGDX3rppcTExNra2nYfSRIKhX5+fvTrpqYmqVRqZ2enuSNGqVT6+/sPGTIkKSmpSw0oKCigV2t6Qr9+/fSxSA10gUGfYmQiuuP98MMPFEV9/PHH7f5HmTt3bgclzJ4929PTUyaTdbVqOzu7dqu7cOFC938fAKMik8kEAsFHH31EUdShQ4fa7RH+/v4dlBAdHW1hYVFUVNTVqqdPn95udZ999ln3fx/QBcwR6t2RI0f27ds3b968/v37SySS6OhoKyurN954gxCyYcOGZcuWPf2RDpa0l8vlEonk5MmT3biTJTc3V6VSPX28T58+XS0KwFjcunVr/fr1ixYt8vT0VCgU33zzTUtLy6xZswghM2bMqKioePojHQ8/5ufnJycnDxw4sKst+de//qVQKJ4+jgEYg8PQqN7l5OTs2rXrwoUL9+7dE4lEAQEBW7duHTZsmKHbBcAIJSUlu3btSktLoyf2fHx8oqKi6DNRABqCEAAAGA13jQIAAKMhCAEAgNEQhAAAwGgIQgAAYDQEIQAAMBqCEAAAGA1BCAAAjIYgBAAARkMQAgAAoyEIAQCA0RCEAADAaAhCAABgNAQhAAAwGoIQAAAY7f8Bb4HCtT0TVksAAAIlelRYdHJka2l0UEtMIHJka2l0IDIwMjUuMDMuNgAAeJx7v2/tPQYgEABiJgYIUABiRSBuYGRU0ADSjIxsCSCamYVYGqZPUAFkFiObAkSYA0IzsUNoZkLGs0NoZlzGY5iDZg8b2Hq4ckI01LUwLjcDI9DpGUyMTAlMzBlMTPJAkxOY5RhYWDOYWDgVWNkSWDkY2NgZOLk0mDi5Fbh5GLh5FXiFEnj5Mpj4+BP4ZBn4BTKY+GUYBAQTBIUymARFFISEGUREGUTEFMTENZjEJBgkJBkkpBgkpBlEQGHPxsjEzMLKxsbHLyAoxCvexQh0BQMsTk69+ezwqvXCfnBkPTvu8J+9BcwOKDrkMP35KzsQe1r2NIeL5u32ILb1xniH3fPUweJ7ZmQ58J5bCVYvHrzQ4Yf3NzD7xrFlDu/bog6A2KeLZB2YOfnA7BOL2R1aHzuBzemr+Gl/RVLAAcSO/p5pv30i2z4Qe8OpLnvpKefB5jysebPv4rp3YLtK7yjun5n7C6x3QgvDgUOlymC9Tat8D3QvvwIWZzjsdMBQVx+sXjr76n6RxFtgMyXn7N+vFfUVbGbll/YDy/dYgNk7GpoOyEzgBbvN6PfmA01s//aC2J/zdh6oebgObKZ2360DSSpNYPWTJjAcjM74AmZryX8/wGvjCLZL9+/eA0oWED8K5nAfiMpuBLstcLeQ/ckIMzD72vcJDrf/KIHZHqwvHe7/XQM2XwwAIuefIQBjVMAAAAL8elRYdE1PTCByZGtpdCAyMDI1LjAzLjYAAHicfVXLbtswELz7K/gDEbhPksfYDtqiiA20aa85BUWK9Nb/R2ephFIQorINSKvhvmZ2/fL86/Hp959HoUOK69v56/PfNC4+H2DP//m21tJPyTkf7lPcpOPdpy+XdHq4Pb5ZTtcfl4fvSTiJ4Aw+77G3D9f7Nwulz6kszuxm6YZw13ItKS+5X9tRTqfkC6tX0QBmIW06AUoH5uJq8VqtllYnOAVOl5KzI0v4M5dGE5wBJ4s2o8Z4TS4+xXnHOeooHAkKu+cZsABoS1aSJgFsrIS7j8CK3tjCRKUAiBxItfoE2NLpJfGiiCeUbhhnmgTgA5LADpCE9yVyK05cZ10kSld0pWltKBsejazMYhOnC8wtqkFsNAjEkM2QgnrQ6KqCNDrXVPKMawpu4Eqdaosy1J3KjESyFenCXHIkbK5lWnnwg9bkXDP448VMs/MMWVAR2s0kBg0jTyfxGUFUw6eASyHr4iiUy4xzaoEMV0XWNikUz7PwnNEmQLU5NNcFQl5pRhJTZKqYimqhJfDJhAmaQTm8Kuaioq5VIxD0LFeOCbox0KNQXKeUNcuMfFaoBNDSFF+0StzZpshOlC+VW10nPRN6MdMJe6RaMeHGrUNbbjqHloAWlM3Wp51qLXkaP4YJmYbuIz7qZ8hvCm0BxZCUqiATPVNEn4WXoCovVgTEQ1JVQdWs+6AcE6qLSWaTEB8UXWeSEu4LkSuUb9FQw5qbKf/ucn63SNfVerxezttqjQ9vC1TxK9uajEfZtqGGaVt6hEcZh70DbFthFHjaNhVeJt/2EYWH/dZRuBixKRLz1HY7hOCz7jYFxyEaAaBD6paREUWKcXCUgAlfLaMKTLL2o1uk+moZpWAytTsb2XNvnYHd3Zxpdz8q4MgYFh6eORqKgDSq4GgZAm7Ruedc4W+ndYoUeC9pioC8EVdfLaNSKJQjII9KO91hGXVJJ98QYSet1bJpQF4tI+dQ1V5D8fz21477wz9qgYgh8by5EQAAA7V6VFh0U01JTEVTIHJka2l0IDIwMjUuMDMuNgAAeJx1VsuK3FYQ/ZVAILRBLepd994hq9l45XhvvBga4wScjBlm6Y/3KfW4JToSdGtapXPqdU+V5tP7zxf+73L6cPr0/vO7x9Off+FSP5dLfS+n63WDuMjl9Pjt9mRzlTIzfv7243SWmbNZTjaTmfr0AAtRoz7J7G4UsOgsrK4TzxGsVwuZsk8ENlMyTDaLNe/TmeYUDumLjbIJT/ApzF1g8jnEjAvGYrR48zm7ZYd/jZCyxNykN5/OPBNHGkxtpsoBlk7dFJZEIPElidaSrp66JscSUIQbTJV0qlZAk3CRxWY9+uK+c7RKH0+DW5/qb6Al0wPhmXa9pqpsKPcB6TQrE7wKV93oHyMmeBkszQrTrfXqnzg7HCmiOZdrDo3OZYlwz3KjElFeLIj12qiO4hwNRvQlQzGQYUELExy4YzNYAh2PtrSEkB8siaxQ4zU9tKkwlGHVI/OWvRVGWhWK0F4l2ZxEUYdLHogIgyuJ1/mjFW3pTahIAtI9oBW0xlOrfJmbdc7p3fT0+vzvx5fn74Pmj08vr/88fXv8++nl65eZ/vjd4oGETTQMRSYlclQAi/Ph+fULMhq8SzPLRk0zm6MvmQbcjcU+5I51vtKgSW0G+aarm0cCuNJi6G4wD+rpWcqjSKgOuJWVw/aDuXdGO5BiGItSALhWRsN3gykLNO9kOKiGPgdwK4tH7LKiYcSg4aZFRGeAW1ky8ihF6aIQUsMcsyQDuNJ0tH0aN0giopOhkxBzA3Cl2ej7Rx29oSEgQ6VdXAPAleaDDyTyfx5vNYLbfZHsETcykRx8oBPFJGFxUM0yFo+1Qq5Hjtt9pUDEoOGwo3eMekYBV14ffK+VN55gBHtNZHPF3HABV54OvhfLW6LWSAkrCKOKk8S8FnIlws++XlQUw05CXYngpBVw5cnge8W86axjgjomDjOkWIxZwBsP9R0oxq2pCpl4aRoLrJBrPBp8r5lfUlN3DFBDa+qF0nshb0QbcqAZlwy0Rg2dVeoYHNloBgviXjLnX5ohTowQztC4YWm2gt6IuDuQDMa2svSOmD25dtBGMjHkQDGOZYu3iJtRa928gDca9tPBcsGrDsdWssbudwhuga4dHbK/XvCGwKxjwyTejFYTLxvB0JB7vRyXt90wQ/b1slPeRi+K6PeCOT6/jWB0yIFe9s5vIxilofeKeWMCik8NEXY2co5Crg3F0t/fMvg/AfOD8Y1KtmZXt0um//gJsLJ5hL1SPlQAAAKGelRYdHJka2l0UEtMMSByZGtpdCAyMDI1LjAzLjYAAHice79v7T0GIBAAYiYGCFAHYk0gbmBkVNAA0oyMbAkgmpmFWBqmT1BBAUSzKUCEOSA0EzuEZiZkPDuEZsZlPIY5aPawga1nwUrB9FBKQ70I43IzMAL9m8HEyJTAxJzBxKQGdE4CsyoDC2sGEwunAitbAisHAxs7AyeXBhMntwI3DwM3rwKvUAIvXwYTH38CnwoDv0AGE78yg4BggqBQBpOgiIKQMIOIKIOImIKYuAaTmASDhDSDhCSDhBKDpBSDpAKDpCKDlDSDlByDlDyDtAyDtCyDCDPQKWyMTMwsrGxsfPwCgkK8LJJS0hLi9xiBTmSAxXLbgc8Odzgv7Adx9mw77qC9ohnMDrc+5PAg4o0diN1nOM3hZXm7PYgd7BbvsPGmBlhc1jbLQXfxSrD6CI2FDvdYvoHZexcvc1iYGHUAxA5VkHX48J8XzD5gxO5w7qcT2JzX5T/sl8oKOIDY98Iz7OP+se0DsZslu+z1550Hm3Pnzft9U9jfge2aflF5v4fDL7Den+yMB/4GKIP1HlTxO+DuegUs/l3X+YCUuQ5Y/QmTa/vNsm+DzTR2P7DfcOZXsJkJqh0H5q+2BLN/rWw6IHkK4jZN/S0Hvi5gBqvnuLD5QEbXerCZvXtvHbh4oQmsflfI9wP/XK3B5vd/5Dv47LkpWPznUoaDLboQ849ueXKga7M72Eztt8IHpY8ag9kHLZQOLg04C1ajait7sELsL9icz185DwYu2gi2S67nyoFj22eD2Ylq+w4YtvKB9R7+x31APKIR7N/uhQL2dX5mYLbGtgkOatKQcNh98oXD66K1YL1iALAZv2/zHd+YAAADjHpUWHRNT0wxIHJka2l0IDIwMjUuMDMuNgAAeJx9VstuYzcM3ecr9AMWxJdELSfJoC2KsYE27XZWg2KK6a7/jx7qOroOQNSOAZv3iI9zSCo/vv/19dvf/3zV8VTi9dvrr9//LfvFr0+wt//5m3OWP6W19vSlxJfy/PmnX67l5e3T87vl5fbH9e33IrMo4QzeH7Gf3m5f3i1Ufi6jdhpsVC5UO5s0nKptvc6jXF5Kr6zUbASwsdtoCVAWsPU5VPFYO+Gd4BQ4rX2K6SzwZy46EpwBJ1Xde4/H1NWnJLi+cN3m9BYJChtPToADQKtNULQFcDI31gTo4MYqt4Hg5YIcCMiZAGd5+VG4qlmbQHJl8qFZ0QR1gCRiHVHEADdpbKJyAytTiTrIr5DH09jE5QrzbMMmFARBQi6ZMCSoB0QPn00PrbmRZcjQ5hLaeW+HiDpbxiXZgewyJ41IGBx4JiOFPqCmoR2QM1dDReQZcqAi0B1kQ2nkidboKUkePqU2nd3bag6nkbI0AwlXQ9T7qg39a1lJ3EAToNaYoFE0CNSULFOmyFQhjlrn8Iox4pFRyhxetTYXRr+vHjHtmU4cE3QxcApNx5IUuWpGAFrntqA0SQ1UySB0TIZcQvXqYtDqGGDCqGTQpdRALbOtSSNn9zR8jNLF6xTBtK/6Yz2kRfkBbdqM28EqWEsFmEHVqKSjYeShL3ETyWZellYTa8kU/QlWvaG7MwGElqwxH+7WVzMgmXT4hA+3zgNtEv0/VSjLVWKmUBZGCWqGAD5GzyZfNJC9dmXCTBH075buCLFAGmYfa8RWq3D3tK2lBxT7BOsGlaC9lFtLmRpAtmoY5G6YPle0VNYoaHQsM63GzHCPOQVS0trnujvAES6NKB2j37MsP19fP9w5xy30fLu+nrdQvPm8axSfcd4o8VPOi0PDdN4PhJ8yz3sgAHZuewq8n0sdD0s/VzeFh8cFrXCxY1Mk1sv2ji4g+NzusFQ5DhE9LE9all0ORYpxcJeAZXhYdhVYerqOnpH8btmlYInpcraz50WdQd2HlaTL/WmJjGGR81QQGtt6VxG9EwHP6Lxydvh7WAsUKfDj+FME5FM4v1t2pRhmjoC8K11yh2XXJUt8dOXOR/hu2Tlj0JZFzlPBM5Lis0/ssMiOLv1uOaOvnMdjLD8ssiuVebfsunTlDDY2P0qHRT50+WNPx+/3/8rw/ek/yRLZv/jdefcAAARhelRYdFNNSUxFUzEgcmRraXQgMjAyNS4wMy42AAB4nH1WTWscRxD9K4FAkGA11HdVj8hJF58c340PQhgn4ERB6Ogfn1ez8s6wmslhh5ne97qrq1697s8fvjzxP083H28+f/hy+3Dz+x+3D/L23s93r+c/+/d0c35uqE/ydPPw/fLP5ik9zHj95cfNnUxcaXKyifC8xzdxMJ9kcufqEZ1EaIwTT+GlsYyQjagTTRzFOTBkk7h5nO5oSnFJX8aoVPyEOYXdmulTKOUCYyk3O4/xYMP8mmyMkZhKPex0xxOJLJBhwrXMJFGFoUIM5NKYgcl7vZzYkvKE+FhIFUNjEnNrXlENXnhDdeQSKJMnhhjBaI2eKjCpLLySNMUObZiyLLOnDBq96ZIqW+YKEfeOvDJziTwQKAaEwzsmTJlqo5czbNhlGXMSznPoSD6G8G8UFu8Fw0bHQNOgdMTcuVIubAfpyLc4URLsBsVjsQQrg0OsIcOYA+UTlA8QnawqcqmV1dAeCR+jehpUR0ZPYyj2uU7VpfOJNN4ixErSI0LZ+8B8LNjifSC1THEuUnUiE4XMrnfH59oQipHnTUFU0RCkNbQz5o5S3dsUQ5cUEsTlPeAi2An0Z9WTYPuhYyAaBAPS6NRAHSMcoALITrenx9fnvz+9PP870/Tp8eX1r8fvD38+vnz7OtFvv1rcI98ozFAKRn4laADYnI/Pr18Ry8y7NDN2CHc4ZyS2NoC7sNhnuWLdnWmpSU7kgYx74AXAlRaz7i7mUgqxIg9hhoYT4FZWzra/mLsSCgt5Eztq5QBeaEKz7y6mYorWhW6ZaGQO4FYWz7HLQutlN3zG6LxLALeyZM6DEKMMqQ9R61avAnCl6Vz7NPZMHYz1VKKVCuBKs3nslxoyReYzA1Yj5CMAXEttM19L5O4n0cs8kXoJTzjCAl0XzJn3VUKhGdgaeHBIL5ZGrsSa+Voox8SNUrBhvpbKz1jH0mqIMlXgf5QNXZkx87Vc3oiOJihNtFgMVuNq5JoemnlfMHvEjWSUZ77WzP/kdSsbNN+1bo7TsxGO4vNaOcfEjXRUZ77WzlussAjrzoXAM6j3uNEOPmXfXvrcKTicwuoDndi4lYbPfeEw3JYZnQ6nh/OaNXDl6SwHBmMFc+m+IBgU1mzgyuNZ9h1GRXE6AJ8D54+VNHDl4XNfNCg4Thf4EmwU7RijgRcevq4l87PvsQRVixTHJOoRjVzXo1kOJMMK14VbaJViCopGXohI04FgYKG4oSgqh+USJ3cjLzzY8IHTUJs0SxIMQxGuW0MvRNT9QC8s8BpcEHDjwVVIYYGyEQyc/kAvuAjgHPQhKCBMrhp4oeWs13q5tL0pWiggGCcI1RbomtFZ942GzQaKAKNw3IsorIEXGs16YDPvt6dbl5l1Xy8729t6DD6PPOZd/XTrMbMeWcz7+ulGMIo0HRxNEjg2FVePcmgcIm/kmlDMs28xSAdOJdW+SzrOmAW4Lhg//gMhTQWRVlcuVwAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "from openfe.setup import LomapAtomMapper\n",
- "mapper = LomapAtomMapper()\n",
- "lomap_mapping = next(mapper.suggest_mappings(ligand_mols[0], ligand_mols[4]))"
+ "mapper = openfe.LomapAtomMapper()\n",
+ "lomap_mapping = next(mapper.suggest_mappings(ligands_by_name[\"lig_ejm_31\"], ligands_by_name[\"lig_ejm_47\"]))\n",
+ "lomap_mapping"
]
},
{
"cell_type": "markdown",
- "id": "de4592a7",
- "metadata": {
- "id": "de4592a7"
- },
+ "id": "3751c772-506e-4e77-9485-64d1d5441e7e",
+ "metadata": {},
"source": [
- "## 2.2. Creating a ligand network\n",
+ "### Scoring a mapping\n",
"\n",
- "A `LigandNetwork` is a set of `SmallMoleculeComponent`s that are connected by `AtomMapping`s of two small molecules. \n",
+ "To decide which ligand pairs to simulate, each mapping needs to be given a score between 0 (hardest) and 1 (easiest). The default LOMAP score applies empirical rules that penalize transformations known to be difficult, such as those with many unique atoms or ring changes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "f7c2e776-2b6a-42e1-ab54-ff2f3cf58ec2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.33287108369807955"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "openfe.lomap_scorers.default_lomap_score(mapping)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc772ed2-f27d-4447-a498-1e8e5c6bc0d2",
+ "metadata": {},
+ "source": [
+ "This pair scores only about 0.33: replacing the methyl group of `lig_ejm_31` with the cyclobutyl group of `lig_ejm_47` means 4 atoms disappear and 11 appear. For comparison, `lig_ejm_31` to `lig_ejm_50` differs only by a small substituent change and scores about 0.9:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "f3174968-f9c9-4d28-9e62-6b7ce5fd4c0d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9048374180359595"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "easier_mapping = next(mapper.suggest_mappings(ligands_by_name[\"lig_ejm_31\"], ligands_by_name[\"lig_ejm_50\"]))\n",
+ "openfe.lomap_scorers.default_lomap_score(easier_mapping)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48456e21-f5f9-411b-94e3-c7a66cfbfde6",
+ "metadata": {},
+ "source": [
+ "## 6. Ligand networks\n",
"\n",
- "The user can choose between multiple different network topologies:\n",
- "* Minimial spanning tree (MST)\n",
- "* LOMAP network\n",
- "* Radial (star) network\n",
- "* Loading in networks from external software (FEP+ or Orion)\n",
- "* Loading in a user-defined network\n",
+ "Simulating every possible ligand pair would be expensive, and some pairs would not map well. Instead, a network planner uses the mapper and scorer to select a set of transformations that connects all ligands. The result is a `LigandNetwork`, whose nodes are ligands and whose edges are atom mappings.\n",
"\n",
- "In this section, we will create and visualize the MST, LOMAP, and radial networks for the TYK2 dataset.\n",
+ "Here we compare three common network types:\n",
"\n",
- "Here, we will be using the `KartografAtomMapper` as the atom mapper for all networks."
+ "* A **radial network** (also called a star map), which connects every ligand to one central ligand. It suits exploring analogues of a single reference compound, but every edge has to go through that ligand, however poorly some pairs map, and the network has no cycles.\n",
+ "* A **minimal spanning tree** (MST), which uses the fewest, best-scored edges needed to connect every ligand. This is the CLI default.\n",
+ "* A **LOMAP network**, built with the rules of the [LOMAP](https://pmc.ncbi.nlm.nih.gov/articles/PMC3837551/) planner: starting from the scored candidate edges, it removes edges as long as every ligand stays part of a cycle and no two ligands end up more than six edges apart. The cycles give a consistency check on the results, and keep the network connected if an edge fails."
]
},
{
"cell_type": "code",
- "execution_count": 11,
- "id": "5b89da93",
+ "execution_count": 14,
+ "id": "de4592a7",
"metadata": {
- "id": "5b89da93",
+ "id": "de4592a7",
"scrolled": true
},
"outputs": [
@@ -2043,34 +2348,6 @@
"INFO:\tCalculate Mapping\n",
"INFO:\tFind Maximal overlapping connected sets of mapped atoms\n",
"INFO:\tFiltering bond breaks\n",
- "INFO:\tTrying to remove edge 0-2 with similarity 0.8187307530779818\n",
- "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
- "INFO:\tRemoved edge 0-2\n",
- "INFO:\tTrying to remove edge 2-6 with similarity 0.8607079764250578\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 0-1 with similarity 0.9048374180359595\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 0-6 with similarity 0.9048374180359595\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 1-2 with similarity 0.9048374180359595\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 1-6 with similarity 0.951229424500714\n",
- "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
- "INFO:\tRemoved edge 1-6\n",
- "INFO:\tTrying to remove edge 8-3 with similarity 0.9048374180359595\n",
- "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
- "INFO:\tRemoved edge 8-3\n",
- "INFO:\tTrying to remove edge 9-3 with similarity 0.9048374180359595\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 3-7 with similarity 0.9048374180359595\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 8-7 with similarity 0.951229424500714\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 8-9 with similarity 0.951229424500714\n",
- "INFO:\tRejecting edge deletion on cycle covering\n",
- "INFO:\tTrying to remove edge 9-7 with similarity 0.951229424500714\n",
- "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
- "INFO:\tRemoved edge 9-7\n",
"INFO:\t#################################\n",
"INFO:\tMap Heavy Atoms \n",
"INFO:\t#################################\n",
@@ -2205,7 +2482,44 @@
"INFO:\tBuild Distance Matrix\n",
"INFO:\tCalculate Mapping\n",
"INFO:\tFind Maximal overlapping connected sets of mapped atoms\n",
- "INFO:\tFiltering bond breaks\n"
+ "INFO:\tFiltering bond breaks\n",
+ "INFO:\tTrying to remove edge 0-2 with similarity 0.8187307530779818\n",
+ "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
+ "INFO:\tRemoved edge 0-2\n",
+ "INFO:\tTrying to remove edge 2-6 with similarity 0.8607079764250578\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 0-1 with similarity 0.9048374180359595\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 0-6 with similarity 0.9048374180359595\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 1-2 with similarity 0.9048374180359595\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 1-6 with similarity 0.951229424500714\n",
+ "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
+ "INFO:\tRemoved edge 1-6\n",
+ "INFO:\tTrying to remove edge 8-3 with similarity 0.9048374180359595\n",
+ "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
+ "INFO:\tRemoved edge 8-3\n",
+ "INFO:\tTrying to remove edge 9-3 with similarity 0.9048374180359595\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 3-7 with similarity 0.9048374180359595\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 8-7 with similarity 0.951229424500714\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 8-9 with similarity 0.951229424500714\n",
+ "INFO:\tRejecting edge deletion on cycle covering\n",
+ "INFO:\tTrying to remove edge 9-7 with similarity 0.951229424500714\n",
+ "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n",
+ "INFO:\tRemoved edge 9-7\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Radial network: 9 edges\n",
+ "MST: 9 edges\n",
+ "LOMAP network: 14 edges\n"
]
}
],
@@ -2217,28 +2531,32 @@
"from openfe.setup.ligand_network_planning import generate_lomap_network\n",
"from openfe.setup import KartografAtomMapper\n",
"\n",
+ "# Create a radial, choosing the first ligand as central ligand\n",
+ "radial_network = generate_radial_network(\n",
+ " ligands=ligands[1:],\n",
+ " central_ligand=ligands[0],\n",
+ " mappers=[KartografAtomMapper(),])\n",
+ "\n",
"# Create an MST network\n",
"mst_network = generate_minimal_spanning_network(\n",
- " ligands=ligand_mols,\n",
+ " ligands=ligands,\n",
" scorer=openfe.lomap_scorers.default_lomap_score,\n",
" mappers=[KartografAtomMapper(),])\n",
"\n",
"# Create a LOMAP network\n",
"lomap_network = generate_lomap_network(\n",
- " ligands=ligand_mols,\n",
+ " ligands=ligands,\n",
" scorer=openfe.lomap_scorers.default_lomap_score,\n",
" mappers=[KartografAtomMapper(),])\n",
"\n",
- "# Create a radial, choosing the first ligand as central ligand\n",
- "radial_network = generate_radial_network(\n",
- " ligands=ligand_mols[1:],\n",
- " central_ligand=ligand_mols[0],\n",
- " mappers=[KartografAtomMapper(),])"
+ "print(f\"Radial network: {len(radial_network.edges)} edges\")\n",
+ "print(f\"MST: {len(mst_network.edges)} edges\")\n",
+ "print(f\"LOMAP network: {len(lomap_network.edges)} edges\")"
]
},
{
"cell_type": "markdown",
- "id": "41cd9718-0c2e-4b59-86ee-8dc429915de1",
+ "id": "7f5b9679-3351-4918-8fdd-59ae027ac64e",
"metadata": {},
"source": [
"We can plot out the different networks to visualize their structure and to see how ligands are being tranformed."
@@ -2246,26 +2564,970 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "id": "f4e3ac10-236b-4ff4-a69e-600706288164",
+ "execution_count": 15,
+ "id": "9d733974-5ee4-4f7d-8482-9ba23961b6f2",
"metadata": {},
"outputs": [
{
"data": {
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",
- "text/plain": [
- "
"
]
},
- "execution_count": 14,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "# Visualize the radial network\n",
- "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
- "\n",
- "plot_atommapping_network(radial_network)"
+ "lomap_figure = konnektor.draw_ligand_network(lomap_network, title=\"TYK2: LOMAP network\", node_size=5000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "6613fa33-f273-48b6-a0f4-5f8e7a8c2ef2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "lig_ejm_31 – lig_ejm_48: 0.30\n",
+ "lig_ejm_31 – lig_ejm_47: 0.33\n",
+ "lig_ejm_31 – lig_ejm_46: 0.37\n",
+ "lig_ejm_42 – lig_ejm_43: 0.90\n",
+ "lig_ejm_31 – lig_ejm_50: 0.90\n",
+ "lig_ejm_46 – lig_jmc_28: 0.90\n",
+ "lig_jmc_27 – lig_jmc_28: 0.95\n",
+ "lig_ejm_42 – lig_ejm_50: 0.95\n",
+ "lig_jmc_23 – lig_jmc_28: 0.95\n"
+ ]
+ }
+ ],
+ "source": [
+ "for edge in sorted(mst_network.edges, key=lambda edge: edge.annotations[\"score\"]):\n",
+ " print(f\"{edge.componentA.name} – {edge.componentB.name}: {edge.annotations['score']:.2f}\")"
]
},
{
@@ -2334,14 +5557,20 @@
"id": "2a280ffb-0a71-48ad-b4ec-9374f4460c80",
"metadata": {},
"source": [
- "Edges along the network can be accessed to recover the individual molecules involved in a given alchemical tranformation and the atom mapping between the two ligands. \n",
- "\n",
- "**Note: as can be seen in the example below, transformations are defined within OpenFE as going from componentA to componentB**"
+ "Sorting the MST edges by score shows three edges that score much lower than the rest. `lig_ejm_47` and `lig_ejm_48` are each connected only through one of them. Because a minimal spanning tree always includes each ligand's best-scoring edge, this means neither ligand has a high-scoring partner anywhere in the series, so in all three networks they are connected only through lower-scoring edges. Edges like these are worth checking closely once the results are in."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67b5f25b-f459-4fe9-9657-50d4ca679b90",
+ "metadata": {},
+ "source": [
+ "Each edge holds the two ligands and the mapping between them. Transformations are always defined as going from `componentA` to `componentB`:"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 19,
"id": "ad80c243-82d7-4599-a4a6-5d651c662f24",
"metadata": {},
"outputs": [
@@ -2349,40 +5578,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "molecule A smiles: CC(=O)Nc1cc(NC(=O)c2c(Cl)cccc2Cl)ccn1\n",
- "molecule B smiles: O=C(Nc1ccnc(NC(=O)C2CCC2)c1)c1c(Cl)cccc1Cl\n",
- "map between molecule A and B: {0: 0, 7: 38, 12: 12, 19: 19, 21: 21, 28: 35, 29: 36, 31: 7, 1: 1, 2: 6, 3: 5, 4: 4, 5: 3, 6: 2, 8: 37, 9: 9, 10: 10, 11: 11, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 20: 20, 22: 22, 23: 23, 30: 8}\n"
+ "A: lig_jmc_27 O=C(Nc1ccnc(NC(=O)[C@H]2C[C@H]2Cl)c1)c1c(Cl)cccc1Cl\n",
+ "B: lig_jmc_28 C[C@@H]1C[C@@H]1C(=O)Nc1cc(NC(=O)c2c(Cl)cccc2Cl)ccn1\n"
]
- }
- ],
- "source": [
- "mst_edges = [edge for edge in mst_network.edges]\n",
- "\n",
- "# Pick an edge\n",
- "edge = mst_edges[1]\n",
- "\n",
- "# Print the smiles of the molecules and the mapping\n",
- "print(\"molecule A smiles: \", edge.componentA.smiles)\n",
- "print(\"molecule B smiles: \", edge.componentB.smiles)\n",
- "print(\"map between molecule A and B: \", edge.componentA_to_componentB)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "9f92262f",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 317
},
- "id": "9f92262f",
- "outputId": "d8dc47c7-c487-4f78-9e19-c388ef6a7c51"
- },
- "outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -2392,50 +5594,20 @@
}
],
"source": [
- "# We can display the atom mapping of an edge by calling it\n",
+ "edge = next(iter(mst_network.edges))\n",
+ "# mappings can also be saved to file if required\n",
+ "edge.draw_to_file('tyk2_edge.png')\n",
+ "\n",
+ "print(\"A:\", edge.componentA.name, edge.componentA.smiles)\n",
+ "print(\"B:\", edge.componentB.name, edge.componentB.smiles)\n",
"edge"
]
},
{
- "cell_type": "code",
- "execution_count": 17,
- "id": "66dd7d32",
+ "cell_type": "markdown",
+ "id": "8e119e3e",
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 317
- },
- "id": "66dd7d32",
- "outputId": "13a421f2-ce17-49c6-aac0-e3c157d59a91"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from IPython.display import Image\n",
- "\n",
- "# mappings can also be saved to file if required\n",
- "edge.draw_to_file('tyk2_edge.png')\n",
- "\n",
- "# load it back for visualisation\n",
- "Image(\"tyk2_edge.png\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8e119e3e",
- "metadata": {
- "id": "8e119e3e"
+ "id": "8e119e3e"
},
"source": [
"### Storing the ligand network\n",
@@ -2447,272 +5619,88 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 20,
"id": "0be4c1f4",
"metadata": {
"id": "0be4c1f4"
},
"outputs": [],
"source": [
- "# Convert to graphml\n",
- "with open(\"network_store.graphml\", \"w\") as writer:\n",
- " writer.write(mst_network.to_graphml())"
+ "with open(\"tyk2_mst_network.graphml\", \"w\") as graphml_file:\n",
+ " graphml_file.write(mst_network.to_graphml())"
]
},
{
"cell_type": "markdown",
- "id": "feb4d54a-01a8-4bbe-ac13-1a15cdb79254",
+ "id": "eb657e1f-5897-4c1d-b37a-2c7db3b8f0b2",
"metadata": {},
"source": [
- "## 2.3. Defining the Chemical Systems"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cc80f3fb",
- "metadata": {
- "id": "cc80f3fb"
- },
- "source": [
- "`ChemicalSystems` are OpenFE containers which define the various components\n",
- "in a system of interest. You can consider `ChemicalSystems` to be the nodes\n",
- "along an alchemical network which are connected by edges which carry out\n",
- "calculations along Alchemical states to get free energies.\n",
+ "
\n",
"\n",
- "`ChemicalSystems` take in:\n",
+ "**Learn more:**\n",
"\n",
- "1. A dictionary of the chemical components (e.g. `SmallMoleculeComponent`,\n",
- " `ProteinComponent`, `SolventComponent`) defining the system.\n",
+ "- **User guide:** [Creating atom mappings](https://docs.openfree.energy/en/stable/guide/setup/creating_atom_mappings_and_scores.html) – how atom mappers and scorers work, and how to choose between them.\n",
+ "- **How to:** [Plan a ligand network](https://docs.openfree.energy/en/stable/cookbook/generate_ligand_network.html) – generating networks with the available planners.\n",
+ "- **How to:** [Define a ligand network explicitly](https://docs.openfree.energy/en/stable/cookbook/hand_write_ligand_network.html) – choosing the edges yourself.\n",
+ "- **How to:** [Visualise a ligand network](https://docs.openfree.energy/en/stable/cookbook/ligandnetwork_vis.html) – inspecting networks and their atom mappings in 2D and 3D.\n",
"\n",
- "2. An identifier name (optional), for the `ChemicalSystem`. This is used as part\n",
- " of the hash identifier of the `ChemicalSystem`, and can help distinguish between\n",
- " otherwise comparable systems."
+ "
"
]
},
{
"cell_type": "markdown",
- "id": "f5506f1a",
- "metadata": {
- "id": "f5506f1a"
- },
- "source": [
- "In the case of a relative ligand binding free energy calculation for `lig_ejm_31` -> `lig_ejm_47`,\n",
- "four `ChemicalSystems` must be defined:\n",
- "\n",
- "1. `lig_ejm_31` in complex with TYK2 in a box of water\n",
- "\n",
- "\n",
- "2. `lig_ejm_47` in complex with TYK2 in a box of water\n",
- "\n",
- "\n",
- "3. `lig_ejm_31` in a box of water\n",
- "\n",
- "\n",
- "4. `lig_ejm_47` in a box of water\n",
- "\n",
- "\n",
- "Here we will be passing the previously defined `SmallMoleculeComponents` for `lig_ejm_31`\n",
- "and `lig_ejm_47`. We will also pass a `ProteinComponent` generated from the PDB file\n",
- "present under `inputs/tyk2_protein.pdb`. Finally, instead of passing\n",
- "in a specific box of water, we will define a `SolventComponent` which will contain\n",
- "the necessary information for OpenMM's `Modeller` class to add water and 0.15 M NaCl\n",
- "around the solute when creating the OpenMM simulation objects."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "0b6c9ec3",
- "metadata": {
- "id": "0b6c9ec3"
- },
- "outputs": [],
+ "id": "a77db32c-8f4b-4bac-ae9e-be4ad0ecbd6e",
+ "metadata": {},
"source": [
- "# First let's define the Protein and Solvent Components which we will be using\n",
- "from openfe import SolventComponent, ProteinComponent\n",
- "from openff.units import unit\n",
+ "## 7. Protocol settings\n",
"\n",
- "protein = ProteinComponent.from_pdb_file('inputs/tyk2_protein.pdb')\n",
- "\n",
- "# Note: the distance from the solute to add water is not defined here but in the\n",
- "# the relevant RBFE solver method\n",
- "solvent = SolventComponent(positive_ion='Na', negative_ion='Cl',\n",
- " neutralize=True, ion_concentration=0.15*unit.molar)"
+ "A `Protocol` defines *how* a free energy difference is computed: the force fields, sampling method, simulation lengths and so on. Its behaviour is controlled by a settings object, and `default_settings()` gives the recommended defaults. The settings are grouped by what they control:"
]
},
{
"cell_type": "code",
- "execution_count": 20,
- "id": "8c6d6504",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 317
- },
- "id": "8c6d6504",
- "outputId": "47026705-938a-4e62-f1af-66e9c50334aa"
- },
+ "execution_count": 21,
+ "id": "fc60d250-bbcd-4ec0-99fe-812a24634c12",
+ "metadata": {},
"outputs": [
{
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "forcefield_settings : OpenMMSystemGeneratorFFSettings\n",
+ "thermo_settings : ThermoSettings\n",
+ "protocol_repeats : int\n",
+ "solvation_settings : OpenMMSolvationSettings\n",
+ "partial_charge_settings : OpenFFPartialChargeSettings\n",
+ "lambda_settings : LambdaSettings\n",
+ "alchemical_settings : AlchemicalSettings\n",
+ "simulation_settings : MultiStateSimulationSettings\n",
+ "engine_settings : OpenMMEngineSettings\n",
+ "integrator_settings : IntegratorSettings\n",
+ "output_settings : MultiStateOutputSettings\n"
+ ]
}
],
"source": [
- "# Extract the relevant edge for the lig_ejm_31 -> lig_ejm_47 transform in the radial graph\n",
- "ejm_31_to_ejm_47 = [edge for edge in mst_network.edges if edge.componentB.name == \"lig_ejm_47\"][0]\n",
- "\n",
- "ejm_31_to_ejm_47"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "23b778d6",
- "metadata": {
- "id": "23b778d6"
- },
- "outputs": [],
- "source": [
- "# Let's create the four ChemicalSystems\n",
- "from openfe import ChemicalSystem\n",
- "\n",
- "ejm_31_complex = ChemicalSystem({'ligand': ejm_31_to_ejm_47.componentA,\n",
- " 'solvent': solvent,\n",
- " 'protein': protein,},\n",
- " name=ejm_31_to_ejm_47.componentA.name)\n",
- "ejm_31_solvent = ChemicalSystem({'ligand': ejm_31_to_ejm_47.componentA,\n",
- " 'solvent': solvent,},\n",
- " name=ejm_31_to_ejm_47.componentA.name)\n",
- "\n",
- "ejm_47_complex = ChemicalSystem({'ligand': ejm_31_to_ejm_47.componentB,\n",
- " 'solvent': solvent,\n",
- " 'protein': protein,},\n",
- " name=ejm_31_to_ejm_47.componentB.name)\n",
- "ejm_47_solvent = ChemicalSystem({'ligand': ejm_31_to_ejm_47.componentB,\n",
- " 'solvent': solvent,},\n",
- " name=ejm_31_to_ejm_47.componentB.name)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1702be9c-a30d-43dc-a291-fefab56a4433",
- "metadata": {},
- "source": [
- "## 2.4. Defining the RBFE simulation settings and protocol"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d795f993",
- "metadata": {
- "id": "d795f993"
- },
- "source": [
- "Now that we have a set of atom mappings defined, we know which atoms should\n",
- "undergo alchemical transformations to capture the free energy cost of\n",
- "transforming from one ligand to another.\n",
- "\n",
- "To simulate this transformation, we use the equilibrium RBFE protocol\n",
- "implemented in OpenFE. This uses OpenMM to run a Perses-like relative\n",
- "ligand binding free energy calculation using a single topology approach.\n",
- "\n",
- "To achieve this simulation, the following steps need to happen:\n",
- "\n",
- "1. Create OpenMM systems of both end states\n",
- "\n",
- "\n",
- "2. Create a hybrid topology based on these defined end states\n",
- "\n",
- "\n",
- "3. Set an appropriate Lambda schedule\n",
- "\n",
- "\n",
- "4. Set a MultiState reporter to write out appropriate coordinates and energies\n",
- "\n",
- "\n",
- "5. Create an OpenMM sampler (in this case we will be using a replica exchange sampler)\n",
- "\n",
- "\n",
- "6. Carry out the necessary simulation steps (minimization, equilibration, and production)\n",
- "\n",
- "\n",
- "The `RelativeHybridTopologyProtocol` class in `openfe.protocols.openmm_rfe`\n",
- "implements a means to achieve all the above with minimal intervention.\n",
- "\n",
- "Here we work through its usage for the `lig_ejm_31` -> `lig_ejm_47` binding free energy\n",
- "test case. As this involves both a relative binding free energy in solvent\n",
- "and complex phases, `RelativeHybridTopologyProtocol` will\n",
- "be used to build two separate `ProtocolDAG` (directed-acyclic-graph) classes, one for each phase.\n",
- "These `DAG`s (which contain the necessary individual simulations), are then executed to yield\n",
- "the desired free energy results.\n",
- "\n",
- "**Note: the underlying components used for the creation of OpenMM hybrid\n",
- "topologies and samplers is still in flux, originating mostly from Perses.\n",
- "Please consider these to be in beta.**"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fdfc694f",
- "metadata": {
- "id": "fdfc694f"
- },
- "source": [
- "### Defining the RBFE simulation settings\n",
- "\n",
- "There are various parameters which can be set to determine\n",
- "how the RBFE simulation will take place. To allow for maximum user flexibility, these are defined as a series of settings objects which control the following:\n",
- "\n",
- "1. `protocol_repeats`: The number of completely independent repeats of the entire sampling process.\n",
- "\n",
- "2. `simulation_settings`: Parameters controling the simulation plan and the alchemical sampler, including the number of minimization steps, lengths of equilibration and production runs, the sampler method (e.g. Hamiltonian replica exchange, `repex`), and the time interval at which to perform an analysis of the free energies.\n",
- "\n",
- "3. `output_settings`: Simulation output control settings, including the frequency to write a checkpoint file, the selection string for which part of the system to write coordinates for, and the paths to the trajectory and output structure storage files.\n",
- "\n",
- "4. `alchemical_settings`: Parameters controlling the creation of the hybrid topology system. This includes various parameters ranging from softcore parameters, through to whether or not to apply an explicit charge correction for systems with net charge changes.\n",
- "\n",
- "5. `engine_settings`: Parameters determining how the OpenMM engine will execute the simulation. This controls the compute platform which will be used to carry out the simulation.\n",
- "\n",
- "6. `integrator_settings`: Parameters controlling the `LangevinSplittingDynamicsMove` integrator used for simulation.\n",
- "\n",
- "7. `lambda_settings`: Lambda protocol settings, including number of lambda windows and lambda functions.\n",
- "\n",
- "8. `forcefield_settings`: Parameters to set up the force field with OpenMM Force Fields, including the general force fields, the small molecule force field, the nonbonded method, and the nonbonded cutoff.\n",
- "\n",
- "9. `thermo_settings`: Settings for thermodynamic parameters, such as the temperature and the pressure of the system.\n",
- "\n",
- "10. `solvation_settings`: Settings for solvating the system, including the solvent model and the solvent padding.\n",
+ "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n",
"\n",
- "11. `partial_charge_settings`: Settings for assigning partial charges to small molecules, including the partial charge method (e.g. `am1bcc`) and the OpenFF toolkit backend (e.g. `ambertools` or `openeye`)."
+ "settings = RelativeHybridTopologyProtocol.default_settings()\n",
+ "for name in type(settings).model_fields:\n",
+ " print(f\"{name:25s}: {type(getattr(settings, name)).__name__}\")"
]
},
{
"cell_type": "markdown",
- "id": "d7c8ab9e-0185-40d7-a441-7a4cd948dda9",
+ "id": "b65d0967-cc26-4354-aa49-bf28ab04f3c9",
"metadata": {},
"source": [
- "The `RelativeHybridTopologyProtocol` class can directly populate the above set of default\n",
- "settings through its `default_settings` method.\n",
- "Parameters can be overriden after creation.\n",
- "In this case, we'll reduce the equilibration length to 0.01 * nanosecond\n",
- "and the production to 0.05 * nanosecond in order to reduce the costs of\n",
- "running this notebook (in practice, values of 1 and 5 nanoseconds,\n",
- "respectively, would be most appropriate).\n",
- "\n",
- "**Note: by default the settings use a solvent padding of 1.5 nm, this is appropriate for solvent simulations, but for complex simulations this leads to excess water being in the box, slowing down your simulation. To deal with this, we will create two sets of settings, and two sets of protocols, one for each leg of the transformations.**"
+ "Each group can be inspected in the same way, for example the simulation lengths and sampler:"
]
},
{
"cell_type": "code",
"execution_count": 22,
- "id": "309c059b-85c7-4911-a417-69889a474fe1",
+ "id": "33bc37fd-fc21-47da-9637-d4798aa9c80b",
"metadata": {},
"outputs": [
{
@@ -2720,10 +5708,10 @@
"output_type": "stream",
"text": [
"{'early_termination_target_error': {'unit': 'kilocalorie_per_mole', 'val': 0.0},\n",
- " 'equilibration_length': {'unit': 'nanosecond', 'val': 0.009999999999999998},\n",
+ " 'equilibration_length': {'unit': 'nanosecond', 'val': 1.0},\n",
" 'minimization_steps': 5000,\n",
" 'n_replicas': 11,\n",
- " 'production_length': {'unit': 'nanosecond', 'val': 0.04999999999999999},\n",
+ " 'production_length': {'unit': 'nanosecond', 'val': 5.0},\n",
" 'real_time_analysis_interval': {'unit': 'picosecond', 'val': 250.0},\n",
" 'real_time_analysis_minimum_time': {'unit': 'picosecond', 'val': 500.0},\n",
" 'sampler_method': 'repex',\n",
@@ -2734,500 +5722,394 @@
}
],
"source": [
- "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n",
- "from openff.units import unit\n",
- "\n",
- "# Create the solvent settings\n",
- "solvent_rbfe_settings = RelativeHybridTopologyProtocol.default_settings()\n",
- "solvent_rbfe_settings.simulation_settings.equilibration_length = 10 * unit.picosecond # Reduce equilibration length to 10 picoseconds\n",
- "solvent_rbfe_settings.simulation_settings.production_length = 50 * unit.picosecond # Reduce prodution length to 50 picoseconds\n",
- "solvent_rbfe_settings.engine_settings.compute_platform = None # default is to look for CUDA, none allows falling back to OpenCL or CPU\n",
- "\n",
- "# Create the complex settings\n",
- "complex_rbfe_settings = RelativeHybridTopologyProtocol.default_settings()\n",
- "complex_rbfe_settings.simulation_settings.equilibration_length = 10 * unit.picosecond # Reduce equilibration length to 10 picoseconds\n",
- "complex_rbfe_settings.simulation_settings.production_length = 50 * unit.picosecond # Reduce prodution length to 50 picoseconds\n",
- "complex_rbfe_settings.solvation_settings.solvent_padding = 1 * unit.nanometer\n",
- "complex_rbfe_settings.engine_settings.compute_platform = None # default is to look for CUDA, none allows falling back to OpenCL or CPU\n",
- "\n",
- "# For context, let's display the complex simulation settings!\n",
- "complex_rbfe_settings.simulation_settings"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ab0eaea9",
- "metadata": {
- "id": "ab0eaea9"
- },
- "source": [
- "### Creating the RFE Protocol\n",
- "\n",
- "With the Settings inspected and adjusted, we can provide these to a Protocol.\n",
- "This Protocol defines the procedure to estimate a free energy difference between two chemical systems,\n",
- "with the details of the two end states yet to be defined.\n",
- "\n",
- "In this case, since we need to run both solvent and complex simulations with different settings, we will create two Protocols, one for each leg."
+ "settings.simulation_settings"
]
},
{
"cell_type": "code",
"execution_count": 23,
- "id": "d1829ab6",
- "metadata": {
- "id": "d1829ab6"
- },
- "outputs": [],
- "source": [
- "# Create RBFE Protocol classes\n",
- "solvent_rbfe_protocol = RelativeHybridTopologyProtocol(\n",
- " settings=solvent_rbfe_settings\n",
- ")\n",
- "\n",
- "complex_rbfe_protocol = RelativeHybridTopologyProtocol(\n",
- " settings=complex_rbfe_settings\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "075cad59-5b65-42cf-a900-1df5a2de67f1",
+ "id": "b5bb93da-de81-411f-a7fc-faf700028c92",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'box_shape': 'dodecahedron',\n",
+ " 'box_size': None,\n",
+ " 'box_vectors': None,\n",
+ " 'number_of_solvent_molecules': None,\n",
+ " 'solvent_model': 'tip3p',\n",
+ " 'solvent_padding': {'unit': 'nanometer', 'val': 1.5}}\n"
+ ]
+ }
+ ],
"source": [
- "# 3. Running a Relative Ligand Binding Free Energy Calculation"
+ "settings.solvation_settings"
]
},
{
"cell_type": "markdown",
- "id": "06d9d8a4-d570-42ea-9936-222b5e1728b2",
+ "id": "db2ddcbb-5c4e-4de9-a6cd-18380bc386e4",
"metadata": {},
"source": [
- "## 3.0 Creating the Transformations\n",
- "\n",
- "Once we have the ChemicalSystems and the Protocol, we can create the Transformation.\n",
- "\n",
- "The `Transformation` requires as input:\n",
- "\n",
- "* the two `ChemicalSystem` objects defining either end of the alchemical transformation (`stateA` and `stateB`)\n",
- "* a mapping between the two systems\n",
- "* the protocol\n",
- "* a name (optional)\n",
+ "The defaults are a good starting point for most systems. The settings most often changed are the simulation length (`simulation_settings.production_length`), the number of independent repeats (`protocol_repeats`), the number of λ windows (`lambda_settings.lambda_windows`), the solvent padding and box shape (`solvation_settings.solvent_padding` and `solvation_settings.box_shape`) and the force fields (`forcefield_settings`). See [Choose and configure a protocol](https://docs.openfree.energy/en/stable/cookbook/choose_protocol.html) for how to change them, and the [settings reference](https://docs.openfree.energy/en/stable/reference/api/openmm_rfe.html) for every option.\n",
"\n",
- "As previously detailed, we create two sets of transformations for the complex and the solvent legs of the thermodynamic cycle."
+ "Here we change one setting for the whole campaign: the small molecule force field, to OpenFF 2.3.0, to match the AshGC charges assigned in section 4."
]
},
{
"cell_type": "code",
"execution_count": 24,
- "id": "853d6eef-e716-4284-b02f-8b06f2beb916",
+ "id": "2b9518ba-3879-4750-af4b-23bd8fde848f",
"metadata": {},
"outputs": [],
"source": [
- "transformation_complex = openfe.Transformation(\n",
- " stateA=ejm_31_complex,\n",
- " stateB=ejm_47_complex,\n",
- " mapping=ejm_31_to_ejm_47,\n",
- " protocol=complex_rbfe_protocol, # use complex protocol created above\n",
- " name=f\"{ejm_31_complex.name}_{ejm_47_complex.name}_complex\"\n",
- " )\n",
- "transformation_solvent = openfe.Transformation(\n",
- " stateA=ejm_31_solvent,\n",
- " stateB=ejm_47_solvent,\n",
- " mapping=ejm_31_to_ejm_47,\n",
- " protocol=solvent_rbfe_protocol, # use solvent protocol created above\n",
- " name=f\"{ejm_31_solvent.name}_{ejm_47_solvent.name}_solvent\"\n",
- " )"
+ "base_settings = RelativeHybridTopologyProtocol.default_settings()\n",
+ "base_settings.forcefield_settings.small_molecule_forcefield = \"openff-2.3.0\""
]
},
{
"cell_type": "markdown",
- "id": "026ad9fc-635f-44d2-937c-bbc58edf64da",
+ "id": "15d5d1e1-bbee-4f8e-b14d-e4bee5492d7e",
"metadata": {},
"source": [
- "## 3.1. Using the Python API"
+ "### Adapting settings to each transformation\n",
+ "\n",
+ "Some settings should depend on the transformation itself. `RelativeHybridTopologyProtocol._adaptive_settings` applies the same recommendations the CLI uses, starting from our base settings:\n",
+ "\n",
+ "* If both end states contain a protein, the solvent padding is reduced from 1.5 nm to 1 nm. The protein already fills much of the box, so the extra water would only slow the simulation down.\n",
+ "* If the ligands differ in net charge, explicit charge correction is switched on, and the protocol uses 22 λ windows with 20 ns of production per window.\n",
+ "\n",
+ "Because these choices depend on the end states and the mapping, the adaptive settings are applied when the transformations are created, in the next section. The leading underscore means the method is not yet part of the public API, so its name may change in a future release."
]
},
{
"cell_type": "markdown",
- "id": "7fd71a0e-7bf6-4529-961b-be8dfa7bbbce",
+ "id": "fba25530-aa1a-4bf3-b227-97ed84fb972f",
"metadata": {},
"source": [
- "### Creating the `ProtocolDAG`"
+ "
\n",
+ "\n",
+ "**Learn more:**\n",
+ "\n",
+ "- **How to:** [Choose and configure a protocol](https://docs.openfree.energy/en/stable/cookbook/choose_protocol.html) – picking a protocol and changing its settings.\n",
+ "- **Reference:** [Relative Hybrid Topology Protocol settings](https://docs.openfree.energy/en/stable/reference/api/openmm_rfe.html) – every setting and its default value.\n",
+ "\n",
+ "
"
]
},
{
"cell_type": "markdown",
- "id": "0e2e7d25-5bb6-4344-963d-8cf5298982b4",
+ "id": "7ca0b0a6-bef7-4614-87a2-623c00dc9f94",
"metadata": {},
"source": [
- "With the `Transformation` defined, we can move onto creating the `ProtocolDAG`.\n",
+ "## 8. Creating transformations\n",
"\n",
- "The `Transformation.create()` method creates a directed-acyclic-graph (DAG) of computational tasks necessary for creating an estimate of the free energy difference between the two chemical systems."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "77e72e9a-bac0-4059-b0bf-96c15c9b2696",
- "metadata": {},
- "outputs": [],
- "source": [
- "complex_dag = transformation_complex.create()\n",
+ "### One transformation per leg and edge\n",
"\n",
- "solvent_dag = transformation_solvent.create()"
+ "Each end state of a transformation is a `ChemicalSystem`: a container for the `Components` present in that state. For the complex leg, this is the ligand, the protein and the solvent; for the solvent leg, it is the ligand and the solvent."
]
},
{
"cell_type": "markdown",
- "id": "051fb438-1ab0-4d3f-9615-4b2caff9a9b2",
+ "id": "305c9e86-ed8a-4583-9256-af9d2bfc3d38",
"metadata": {},
"source": [
- "The individual pieces of computational work are called Units. In this particular Protocol, the Units defined are three independent repeats of the alchemical transformation.\n",
+ "A `Transformation` combines two `ChemicalSystem`s, the mapping between them, and the protocol. For each leg of each edge in the network, we create the two end states, adapt the settings to them, and create the transformation.\n",
"\n",
- "For other Protocols, for example non-equilibrium sampling routines, there might be dependencies between the tasks."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7f5c142d",
- "metadata": {
- "id": "7f5c142d"
- },
- "source": [
- "### Simulating the RelativeLigandTransforms\n",
+ "We also set `validate=True` to check that the end states and their mapping are compatible with the protocol. For the Relative Hybrid Topology Protocol, this includes checking that the end states differ by exactly one small molecule, that the mapping contains at least four heavy atoms and does not break or form any bonds, and that each `Component` passes its own validation.\n",
"\n",
- "Individual Units can then be executed by calling the `.execute()` method.\n",
+ "Validation is optional and disabled by default, but enabling it is good practice to catch incompatible inputs before running simulations.\n",
"\n",
- "In the first instance we do a dry-run (which does everything but\n",
- "starting the simulation) to make sure that the\n",
- "hybrid OpenMM system can be constructed without any issues.\n",
- "Note: A successful call to `.run()` will return an empty Dictionary."
+ "We use the MST network generated in section 6 to demonstrate how to create transformations. The precomputed results in section 10 come from a separately planned CLI campaign and include some different ligand pairs."
]
},
{
"cell_type": "code",
- "execution_count": 26,
- "id": "981cde0c",
+ "execution_count": 25,
+ "id": "0b6c9ec3",
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "981cde0c",
- "outputId": "812389bc-3730-416b-8154-79e0e1fb4346"
+ "id": "0b6c9ec3"
},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "INFO:\tStarting system setup unit\n",
- "INFO:\tParameterizing systems\n",
- "INFO:\tRequested to generate parameters for residue \n",
- "INFO:\tGenerating a residue template for [H][c]1[n][c]([N]([H])[C](=[O])[C]([H])([H])[H])[c]([H])[c]([N]([H])[C](=[O])[c]2[c]([Cl])[c]([H])[c]([H])[c]([H])[c]2[Cl])[c]1[H] using openff-2.2.1\n",
- "INFO:\tPreset charges applied to atom index 0\n",
- "INFO:\tPreset charges applied to atom index 1\n",
- "INFO:\tPreset charges applied to atom index 2\n",
- "INFO:\tPreset charges applied to atom index 3\n",
- "INFO:\tPreset charges applied to atom index 4\n",
- "INFO:\tPreset charges applied to atom index 5\n",
- "INFO:\tPreset charges applied to atom index 6\n",
- "INFO:\tPreset charges applied to atom index 7\n",
- "INFO:\tPreset charges applied to atom index 8\n",
- "INFO:\tPreset charges applied to atom index 9\n",
- "INFO:\tPreset charges applied to atom index 10\n",
- "INFO:\tPreset charges applied to atom index 11\n",
- "INFO:\tPreset charges applied to atom index 12\n",
- "INFO:\tPreset charges applied to atom index 13\n",
- "INFO:\tPreset charges applied to atom index 14\n",
- "INFO:\tPreset charges applied to atom index 15\n",
- "INFO:\tPreset charges applied to atom index 16\n",
- "INFO:\tPreset charges applied to atom index 17\n",
- "INFO:\tPreset charges applied to atom index 18\n",
- "INFO:\tPreset charges applied to atom index 19\n",
- "INFO:\tPreset charges applied to atom index 20\n",
- "INFO:\tPreset charges applied to atom index 21\n",
- "INFO:\tPreset charges applied to atom index 22\n",
- "INFO:\tPreset charges applied to atom index 23\n",
- "INFO:\tPreset charges applied to atom index 24\n",
- "INFO:\tPreset charges applied to atom index 25\n",
- "INFO:\tPreset charges applied to atom index 26\n",
- "INFO:\tPreset charges applied to atom index 27\n",
- "INFO:\tPreset charges applied to atom index 28\n",
- "INFO:\tPreset charges applied to atom index 29\n",
- "INFO:\tPreset charges applied to atom index 30\n",
- "INFO:\tPreset charges applied to atom index 31\n",
- "INFO:\tRequested to generate parameters for residue \n",
- "INFO:\tGenerating a residue template for [H][c]1[n][c]([N]([H])[C](=[O])[C]2([H])[C]([H])([H])[C]([H])([H])[C]2([H])[H])[c]([H])[c]([N]([H])[C](=[O])[c]2[c]([Cl])[c]([H])[c]([H])[c]([H])[c]2[Cl])[c]1[H] using openff-2.2.1\n",
- "INFO:\tPreset charges applied to atom index 0\n",
- "INFO:\tPreset charges applied to atom index 1\n",
- "INFO:\tPreset charges applied to atom index 2\n",
- "INFO:\tPreset charges applied to atom index 3\n",
- "INFO:\tPreset charges applied to atom index 4\n",
- "INFO:\tPreset charges applied to atom index 5\n",
- "INFO:\tPreset charges applied to atom index 6\n",
- "INFO:\tPreset charges applied to atom index 7\n",
- "INFO:\tPreset charges applied to atom index 8\n",
- "INFO:\tPreset charges applied to atom index 9\n",
- "INFO:\tPreset charges applied to atom index 10\n",
- "INFO:\tPreset charges applied to atom index 11\n",
- "INFO:\tPreset charges applied to atom index 12\n",
- "INFO:\tPreset charges applied to atom index 13\n",
- "INFO:\tPreset charges applied to atom index 14\n",
- "INFO:\tPreset charges applied to atom index 15\n",
- "INFO:\tPreset charges applied to atom index 16\n",
- "INFO:\tPreset charges applied to atom index 17\n",
- "INFO:\tPreset charges applied to atom index 18\n",
- "INFO:\tPreset charges applied to atom index 19\n",
- "INFO:\tPreset charges applied to atom index 20\n",
- "INFO:\tPreset charges applied to atom index 21\n",
- "INFO:\tPreset charges applied to atom index 22\n",
- "INFO:\tPreset charges applied to atom index 23\n",
- "INFO:\tPreset charges applied to atom index 24\n",
- "INFO:\tPreset charges applied to atom index 25\n",
- "INFO:\tPreset charges applied to atom index 26\n",
- "INFO:\tPreset charges applied to atom index 27\n",
- "INFO:\tPreset charges applied to atom index 28\n",
- "INFO:\tPreset charges applied to atom index 29\n",
- "INFO:\tPreset charges applied to atom index 30\n",
- "INFO:\tPreset charges applied to atom index 31\n",
- "INFO:\tPreset charges applied to atom index 32\n",
- "INFO:\tPreset charges applied to atom index 33\n",
- "INFO:\tPreset charges applied to atom index 34\n",
- "INFO:\tPreset charges applied to atom index 35\n",
- "INFO:\tPreset charges applied to atom index 36\n",
- "INFO:\tPreset charges applied to atom index 37\n",
- "INFO:\tPreset charges applied to atom index 38\n",
- "INFO:\tCreating hybrid system\n",
- "INFO:\tSetting force field terms\n",
- "INFO:\tAdding forces\n",
- "INFO:\tNo CMAPTorsionForce found. Skipping adding force.\n",
- "INFO:\tHybrid system created\n",
- "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/site-packages/mdtraj/core/topology.py:97: UserWarning: atom_indices are not monotonically increasing\n",
- " warnings.warn(\"atom_indices are not monotonically increasing\")\n"
+ "18 validated transformations\n"
]
- },
- {
- "data": {
- "text/plain": [
- "{'system': PosixPath('hybrid_system.xml.bz2'),\n",
- " 'positions': PosixPath('hybrid_positions.npy'),\n",
- " 'pdb_structure': PosixPath('hybrid_system.pdb'),\n",
- " 'selection_indices': array([ 0, 1, 2, ..., 37051, 37052, 37053], shape=(4755,)),\n",
- " 'hybrid_factory': ,\n",
- " 'hybrid_system': >,\n",
- " 'hybrid_positions': Quantity(value=array([[-1.5732 , -1.2864 , 0.422 ],\n",
- " [-1.5275 , -1.4196 , 0.3637 ],\n",
- " [-1.4652 , -1.4991 , 0.434 ],\n",
- " ...,\n",
- " [-0.79803, -1.16755, -2.20897],\n",
- " [-0.9577 , -1.23187, -2.25983],\n",
- " [-0.98902, -1.35295, -2.04978]], shape=(37065, 3)), unit=nanometer)}"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
}
],
"source": [
- "# complex dry-run\n",
- "complex_unit = list(complex_dag.protocol_units)[0]\n",
+ "legs = {\n",
+ " \"solvent\": {\"solvent\": solvent},\n",
+ " \"complex\": {\"protein\": protein, \"solvent\": solvent},\n",
+ "}\n",
+ "\n",
+ "transformations = []\n",
+ "for edge in mst_network.edges:\n",
+ " for leg_name, leg_components in legs.items():\n",
+ " state_a = openfe.ChemicalSystem({\"ligand\": edge.componentA, **leg_components})\n",
+ " state_b = openfe.ChemicalSystem({\"ligand\": edge.componentB, **leg_components})\n",
+ "\n",
+ " settings = RelativeHybridTopologyProtocol._adaptive_settings(\n",
+ " stateA=state_a,\n",
+ " stateB=state_b,\n",
+ " mapping=edge,\n",
+ " initial_settings=base_settings,\n",
+ " )\n",
"\n",
- "complex_unit.run(dry=True, verbose=True)"
+ " transformations.append(\n",
+ " openfe.Transformation(\n",
+ " stateA=state_a,\n",
+ " stateB=state_b,\n",
+ " mapping=edge,\n",
+ " protocol=RelativeHybridTopologyProtocol(settings=settings),\n",
+ " name=f\"rbfe_{edge.componentA.name}_{leg_name}_{edge.componentB.name}_{leg_name}\",\n",
+ " validate=True,\n",
+ " )\n",
+ " )\n",
+ "\n",
+ "print(f\"{len(transformations)} validated transformations\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc4a30d5-bdd1-4984-a4ee-d1b87171186a",
+ "metadata": {},
+ "source": [
+ "We can check what the adaptive settings did. The complex legs have the smaller padding, and none of the TYK2 edges change net charge, so no transformation needed the charge-change settings:"
]
},
{
"cell_type": "code",
- "execution_count": 27,
- "id": "77accb06",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "77accb06",
- "outputId": "e0b977c0-7e72-4a49-d3ea-eb0cdb85ed5d"
- },
+ "execution_count": 26,
+ "id": "0333885c-d858-4ee1-ac64-c443b58faa97",
+ "metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "INFO:\tStarting system setup unit\n",
- "INFO:\tParameterizing systems\n",
- "INFO:\tRequested to generate parameters for residue \n",
- "INFO:\tRequested to generate parameters for residue \n",
- "INFO:\tCreating hybrid system\n",
- "INFO:\tSetting force field terms\n",
- "INFO:\tAdding forces\n",
- "INFO:\tNo CMAPTorsionForce found. Skipping adding force.\n",
- "INFO:\tHybrid system created\n",
- "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/site-packages/mdtraj/core/topology.py:97: UserWarning: atom_indices are not monotonically increasing\n",
- " warnings.warn(\"atom_indices are not monotonically increasing\")\n"
+ "rbfe_lig_jmc_27_solvent_lig_jmc_28_solvent 1.5 nanometer\n",
+ "rbfe_lig_jmc_27_complex_lig_jmc_28_complex 1 nanometer\n",
+ "Charge-changing edges: 0\n"
]
- },
- {
- "data": {
- "text/plain": [
- "{'system': PosixPath('hybrid_system.xml.bz2'),\n",
- " 'positions': PosixPath('hybrid_positions.npy'),\n",
- " 'pdb_structure': PosixPath('hybrid_system.pdb'),\n",
- " 'selection_indices': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n",
- " 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
- " 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1755,\n",
- " 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1751,\n",
- " 1752, 1753, 1754]),\n",
- " 'hybrid_factory': ,\n",
- " 'hybrid_system': >,\n",
- " 'hybrid_positions': Quantity(value=array([[-0.4771 , -0.27958, -1.64978],\n",
- " [-0.53622, -0.36601, -1.62251],\n",
- " [-0.47773, -0.49364, -1.61997],\n",
- " ...,\n",
- " [-0.79803, -1.16755, -2.20897],\n",
- " [-0.9577 , -1.23187, -2.25983],\n",
- " [-0.98902, -1.35295, -2.04978]], shape=(1766, 3)), unit=nanometer)}"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
}
],
"source": [
- "# solvent dry-run\n",
- "solvent_unit = list(solvent_dag.protocol_units)[0]\n",
+ "for transformation in transformations[:2]:\n",
+ " print(transformation.name, transformation.protocol.settings.solvation_settings.solvent_padding)\n",
"\n",
- "solvent_unit.run(dry=True, verbose=True)"
+ "charge_changes = [edge for edge in mst_network.edges if edge.get_alchemical_charge_difference() != 0]\n",
+ "print(f\"Charge-changing edges: {len(charge_changes)}\")"
]
},
{
"cell_type": "markdown",
- "id": "ceba2266-8ad5-4205-96df-4b6403fc3b50",
+ "id": "fa1f99ad-166c-4eb8-b045-85db984f2d98",
"metadata": {},
"source": [
- "## 3.2. Using the CLI\n",
- "\n",
- "Even when using the Python API to set up the RBFE calculations, you can dump all `Transformation`s to a JSON file and run the calculations using the `openfe quickrun` command. Here, we will show you how to save the `Transformation`s to the JSON file."
+ "Together, the transformations form an `AlchemicalNetwork`, which describes the whole campaign:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "714c1b69-226c-41dc-b73e-50611e873c34",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "alchemical_network = openfe.AlchemicalNetwork(edges=transformations, name=\"tyk2_rbfe\")"
]
},
{
"cell_type": "markdown",
- "id": "043672e9-20e5-4b2c-8977-cca0e2ddf23e",
+ "id": "7f066f3c-c683-4f45-9adb-1d46c56b46c2",
"metadata": {},
"source": [
- "We’ll write out the transformation to disk, so that it can be run using the `openfe quickrun` command:"
+ "### Writing transformations to disk\n",
+ "\n",
+ "Each transformation is written to its own JSON file, which `openfe quickrun` takes as input:"
]
},
{
"cell_type": "code",
"execution_count": 28,
- "id": "12d35b4c-b737-4c3c-9fda-dc387aaa6915",
+ "id": "7ca4833a-6e2c-4cc5-aae2-63e41a8419c5",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['rbfe_lig_ejm_31_complex_lig_ejm_46_complex.json',\n",
+ " 'rbfe_lig_ejm_31_complex_lig_ejm_47_complex.json',\n",
+ " 'rbfe_lig_ejm_31_complex_lig_ejm_48_complex.json',\n",
+ " 'rbfe_lig_ejm_31_complex_lig_ejm_50_complex.json']"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import pathlib\n",
- "# first we create the directory\n",
+ "\n",
"transformation_dir = pathlib.Path(\"tyk2_json\")\n",
"transformation_dir.mkdir(exist_ok=True)\n",
"\n",
- "# then we write out the transformations\n",
- "transformation_complex.to_json(transformation_dir / f\"{transformation_complex.name}.json\")\n",
- "transformation_solvent.to_json(transformation_dir / f\"{transformation_solvent.name}.json\")"
+ "for transformation in transformations:\n",
+ " transformation.to_json(transformation_dir / f\"{transformation.name}.json\")\n",
+ "\n",
+ "sorted(path.name for path in transformation_dir.iterdir())[:4]"
]
},
{
"cell_type": "markdown",
- "id": "05d4c16c-1d16-4ef1-a1b3-2eec6275c778",
+ "id": "cc2d714e-3875-4f6f-871f-361d211e448a",
"metadata": {},
"source": [
- "You can run the RBFE simulations from the CLI by using the `openfe quickrun` command, as described in Section 5. below."
+ "### Additional validation checks\n",
+ "\n",
+ "By setting `validate=True` when creating each `Transformation`, we have already checked that the end states, mapping, and protocol settings are compatible.\n",
+ "\n",
+ "We can also call validation methods directly to check specific inputs. These methods raise an error if validation fails and return without an error otherwise.\n",
+ "\n",
+ "For example, `ProteinComponent.validate()` looks for missing residues or caps by flagging any peptide bond longer than 3 Å. The TYK2 protein passes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "cc4e6b7e-508f-4880-8f96-f91d0b913d50",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "protein.validate()"
]
},
{
"cell_type": "markdown",
- "id": "e07d1e29",
- "metadata": {
- "id": "e07d1e29"
- },
+ "id": "76c1f95a-e141-499b-a1e0-c20fa5a8be91",
+ "metadata": {},
"source": [
- "# 4. Analysis"
+ "`Protocol.validate()` can also be called directly to check whether the inputs are compatible with a given protocol and its settings.\n",
+ "\n",
+ "For example, let's increase the number of λ windows without increasing the number of replicas. This creates an inconsistent configuration, which validation catches:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "429e3928-b9f2-4a33-a771-4689fc4caab7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of replicas in ``simulation_settings``: 11 must equal the number of lambda windows in lambda_settings: 15.\n"
+ ]
+ }
+ ],
+ "source": [
+ "inconsistent_settings = RelativeHybridTopologyProtocol.default_settings()\n",
+ "inconsistent_settings.lambda_settings.lambda_windows = 15\n",
+ "\n",
+ "transformation = transformations[0]\n",
+ "try:\n",
+ " RelativeHybridTopologyProtocol(settings=inconsistent_settings).validate(\n",
+ " stateA=transformation.stateA,\n",
+ " stateB=transformation.stateB,\n",
+ " mapping=transformation.mapping,\n",
+ " )\n",
+ "except ValueError as error:\n",
+ " print(error)"
]
},
{
"cell_type": "markdown",
- "id": "2accbbd1",
- "metadata": {
- "id": "2accbbd1"
- },
+ "id": "21524545-1a2c-46f5-a783-682a57bb6bd8",
+ "metadata": {},
"source": [
- "Finally now that we've \"run\" our simulations, let's go ahead and gather the free energies for both phases. \n",
- "First we will take a look at the way to do this using our python api, then we will show how to use our `openfe gather` command on pre-computed results.\n",
+ "
\n",
+ "\n",
+ "**Learn more:**\n",
"\n",
- "## 4.1 Analysis - Python API\n",
+ "- **User guide:** [Alchemical networks: planning a simulation campaign](https://docs.openfree.energy/en/stable/guide/setup/alchemical_network_model.html) – how chemical systems, transformations and networks fit together.\n",
+ "- **User guide:** [Components, Chemical Systems and Thermodynamic cycles](https://docs.openfree.energy/en/stable/guide/setup/chemical_systems_and_thermodynamic_cycles.html) - which component types exist, and how chemical systems define the end states of each leg of a calculation.\n",
+ "- **How to:** [Dumping a Transformation to JSON](https://docs.openfree.energy/en/stable/cookbook/dumping_transformation.html) – writing transformations to files to run elsewhere, for example on an HPC cluster.\n",
"\n",
- "We can use the python API to gather the free energies for both phases by passing the results of executing the DAGs and calling the `gather()` methods of `RelativeLigandTransform`.\n",
- "This takes a **list** of completed DAG results, catering for when simulations have been extended."
+ "
"
]
},
{
"cell_type": "markdown",
- "id": "f69eb5cb-88ce-4e11-b05e-5591a2ed66c2",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "fd1332db",
- "outputId": "5b7d8379-d10d-4bf7-eb32-527998843e91",
- "tags": [
- "nbval-skip"
- ]
- },
+ "id": "7b6f7dc9-e3fa-4a96-8e16-eb372f69fa31",
+ "metadata": {},
"source": [
- "For production use we recommend saving the transformations to disk and using `openfe quickrun` to run them in an HPC environment, but for completeness, below is a python snippet that will run the transformations we defined earlier and then analyze the results. \n",
+ "## 9. Running the calculations\n",
"\n",
- "``` python\n",
- "# Finally we can run the simulations\n",
- "complex_path = pathlib.Path('./complex')\n",
- "complex_path.mkdir()\n",
+ "Each transformation JSON is run with `openfe quickrun`:\n",
"\n",
- "# First the complex transformation\n",
- "complex_dag_results = execute_DAG(complex_dag, scratch_basedir=complex_path, shared_basedir=complex_path)\n",
+ "```bash\n",
+ "openfe quickrun tyk2_json/rbfe_lig_ejm_31_solvent_lig_ejm_47_solvent.json \\\n",
+ " -o results/lig_ejm_31_solvent_lig_ejm_47_solvent.json \\\n",
+ " -d results/lig_ejm_31_solvent_lig_ejm_47_solvent/\n",
+ "```\n",
"\n",
- "# Next the solvent state transformation\n",
- "solvent_path = pathlib.Path('./solvent')\n",
- "solvent_path.mkdir()\n",
+ "`-o` is the results JSON, holding the free energy estimate and uncertainty, and `-d` is the directory for the simulation files (trajectories, analysis plots and so on). By default each transformation runs three independent repeats, whose spread gives the uncertainty in the estimate.\n",
"\n",
- "solvent_dag_results = execute_DAG(solvent_dag, scratch_basedir=solvent_path, shared_basedir=solvent_path)\n",
+ "
\n",
"\n",
- "# Get the complex and solvent results\n",
- "complex_results = rbfe_protocol.gather([complex_dag_results])\n",
- "solvent_results = rbfe_protocol.gather([solvent_dag_results])\n",
+ "We don't run the simulations in this notebook: even one leg takes hours on a GPU. Instead, section 10 uses precomputed results. In a real campaign, you would typically submit each transformation as a separate job on an HPC cluster.\n",
"\n",
- "print(f\"Complex dG: {complex_results.get_estimate()}, err {complex_results.get_uncertainty()}\")\n",
- "print(f\"Solvent dG: {solvent_results.get_estimate()}, err {solvent_results.get_uncertainty()}\")\n",
- "```"
+ "
\n",
+ "\n",
+ "**Learn more:**\n",
+ "\n",
+ "- **Tutorial:** [RBFE with the CLI](https://docs.openfree.energy/en/stable/tutorials/rbfe_cli_tutorial.html) – the same campaign set up and run entirely from the command line.\n",
+ "- **User guide:** [Customising CLI planning with YAML settings](https://docs.openfree.energy/en/stable/guide/cli/cli_yaml.html) – changing the mapper, network and charge method used by the CLI.\n",
+ "- **User guide:** [Execution with quickrun](https://docs.openfree.energy/en/stable/guide/execution/quickrun_execution.html) – running transformation files, the output directory, resuming halted jobs and job submission scripts.\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e07d1e29",
+ "metadata": {
+ "id": "e07d1e29"
+ },
+ "source": [
+ "## 10. Results\n",
+ "\n",
+ "The following precomputed results come from a TYK2 campaign planned with the CLI defaults. Its network differs slightly from the MST generated in section 6, so not every ligand pair matches the transformations created above.\n",
"\n",
- "First we will download some TYK2 transformations we already ran. These results are from an entire TYK2 network and not a single edge. "
+ "We use these results to demonstrate how to gather and analyse the outputs of a completed campaign."
]
},
{
"cell_type": "code",
- "execution_count": 29,
- "id": "059068d3-d9e2-4923-80ca-c2989a559d1f",
+ "execution_count": 31,
+ "id": "70f53876-99c3-42b3-92d7-14526a167320",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=68413) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+ "/Users/hannahbaumann/.local/share/mamba/envs/openfe_dev/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=42011) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
" pid, fd = os.forkpty()\n"
]
},
@@ -3235,37 +6117,89 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Fetching /Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/site-packages/openfecli/tests/data/rbfe_results.tar.gz\n"
+ "Fetching /Users/hannahbaumann/.local/share/mamba/envs/openfe_dev/lib/python3.13/site-packages/openfecli/tests/data/rbfe_results.tar.gz\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=68413) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+ "/Users/hannahbaumann/.local/share/mamba/envs/openfe_dev/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=42011) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
" pid, fd = os.forkpty()\n"
]
}
],
"source": [
- "# Results from our cli tutorial\n",
- "locale.getpreferredencoding = lambda: \"UTF-8\" # hack for google colab, not needed for local execution\n",
+ "import locale\n",
+ "locale.getpreferredencoding = lambda: \"UTF-8\" # needed on Google Colab only\n",
+ "\n",
"!openfe fetch rbfe-tutorial-results\n",
- "# Extract results\n",
"!tar -xf rbfe_results.tar.gz"
]
},
{
"cell_type": "markdown",
- "id": "9fe99e84-1857-47a5-adea-bf0983e8a750",
+ "id": "5038cee6-99b1-4c97-8a97-a5597e5c279f",
"metadata": {},
"source": [
- "Now we can use the `openfe gather` command to look at the results (see section 5.3 for more details)"
+ "### Gathering free energies\n",
+ "\n",
+ "`openfe gather` combines the results JSON files into a table. The `--report` option selects what is reported:\n",
+ "\n",
+ "* `raw`: the free energy of every leg and repeat.\n",
+ "* `ddg`: the relative binding free energy ΔΔG for each edge, combining both legs and averaging over repeats. The uncertainty is the standard deviation between repeats.\n",
+ "* `dg`: a binding free energy ΔG per ligand, derived from all ΔΔG values with a maximum likelihood estimator (MLE). These values are relative to the mean of the series, which is set to 0 kcal/mol, so they must be shifted before comparing them with experiment."
]
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 32,
+ "id": "ce5cad65-100c-4203-9b0b-7ca7994545e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/hannahbaumann/.local/share/mamba/envs/openfe_dev/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=42011) 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": [
+ "\u001b[?25lLoading results: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54/54 files\u001b[?25h\n",
+ "writing ddg output to 'tyk2_ddg.tsv'\n",
+ "ligand_i\tligand_j\tDDG(i->j) (kcal/mol)\tuncertainty (kcal/mol)\n",
+ "lig_ejm_31\tlig_ejm_42\t0.8\t0.2\n",
+ "lig_ejm_31\tlig_ejm_46\t-0.89\t0.08\n",
+ "lig_ejm_31\tlig_ejm_47\t0.0\t0.2\n",
+ "lig_ejm_31\tlig_ejm_48\t0.6\t0.1\n",
+ "lig_ejm_31\tlig_ejm_50\t1.00\t0.05\n",
+ "lig_ejm_42\tlig_ejm_43\t1.4\t0.2\n",
+ "lig_ejm_46\tlig_jmc_23\t0.3\t0.1\n",
+ "lig_ejm_46\tlig_jmc_27\t-0.1\t0.1\n",
+ "lig_ejm_46\tlig_jmc_28\t-0.27\t0.07\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/hannahbaumann/.local/share/mamba/envs/openfe_dev/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=42011) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+ " pid, fd = os.forkpty()\n"
+ ]
+ }
+ ],
+ "source": [
+ "!openfe gather results/ --report ddg -o tyk2_ddg.tsv\n",
+ "!cat tyk2_ddg.tsv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
"id": "ab9f2cca-8458-4515-a64a-20370151e1a5",
"metadata": {},
"outputs": [
@@ -3273,7 +6207,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=68413) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+ "/Users/hannahbaumann/.local/share/mamba/envs/openfe_dev/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=42011) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
" pid, fd = os.forkpty()\n"
]
},
@@ -3281,8 +6215,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001B[?25lLoading results: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54/54 files\u001B[?25h\n",
- "writing dg output to 'final_results.tsv'\n",
+ "\u001b[?25lLoading results: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54/54 files\u001b[?25h\n",
+ "writing dg output to 'tyk2_dg.tsv'\n",
"ligand\tDG(MLE) (kcal/mol)\tuncertainty (kcal/mol)\n",
"lig_ejm_31\t-0.09\t0.06\n",
"lig_ejm_42\t0.7\t0.1\n",
@@ -3300,132 +6234,5453 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/Users/hannahbaumann/.local/share/mamba/envs/openfe/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=68413) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+ "/Users/hannahbaumann/.local/share/mamba/envs/openfe_dev/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=42011) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
" pid, fd = os.forkpty()\n"
]
}
],
"source": [
- "!openfe gather results/ --report dg -o final_results.tsv\n",
- "!cat final_results.tsv"
+ "!openfe gather results/ --report dg -o tyk2_dg.tsv\n",
+ "!cat tyk2_dg.tsv"
]
},
{
"cell_type": "markdown",
- "id": "524015c7-6c42-4b84-98b9-361c17945fbe",
+ "id": "89fdb7e1-dd2c-48e4-98d7-3b8eb550a090",
"metadata": {},
"source": [
- "# 5. Relative Free Energies with the OpenFE CLI"
+ "
\n",
+ "\n",
+ "**Learn more:**\n",
+ "\n",
+ "- **User guide:** [Analysis of the Relative Hybrid Topology Protocol](https://docs.openfree.energy/en/stable/guide/protocols/relativehybridtopology.html#analysis) – the diagnostic plots produced for each transformation.\n",
+ "- **Reference:** [openfe gather](https://docs.openfree.energy/en/stable/reference/cli/gather.html) – all options and report types.\n",
+ "- **User guide:** [Working with networks of results](https://docs.openfree.energy/en/stable/guide/results/working_with_networks.html) – turning relative results into absolute estimates with MLE.\n",
+ "\n",
+ "
"
]
},
{
"cell_type": "markdown",
- "id": "11074a41-2827-48c5-9493-e0e4e2d94838",
+ "id": "ef56a751-4181-4fbd-b300-5c102c8e028f",
"metadata": {},
"source": [
- "You can also do all the above using the OpenFE command line interface – with no Python at all!\n",
- "\n",
- "The entire process of running the campaign of simulations is split into 3 stages; each of which corresponds to a CLI command:\n",
- "\n",
- "1. Setting up the necessary files to describe each of the individual simulations to run.\n",
+ "### Checking simulation health\n",
"\n",
- "2. Running the simulations.\n",
+ "Before interpreting the free energy estimates, check the simulation\n",
+ "diagnostics for signs of insufficient sampling or structural instability.\n",
+ "The protocol generates plots for energetic overlap, replica mixing, convergence\n",
+ "and structural changes, including ligand RMSD and COM drift.\n",
"\n",
- "3. Gathering the results of separate simulations into a single table.\n",
- "\n",
- "For more details, please visit our tutorial: [Relative binding free energies with the OpenFE CLI](https://docs.openfree.energy/en/latest/tutorials/rbfe_cli_tutorial.html)."
+ "See the [Assessing simulation health](https://docs.openfree.energy/en/stable/guide/results/simulation_health.html)\n",
+ "user guide for interpreting these diagnostics."
]
},
{
"cell_type": "markdown",
- "id": "68cc5f26-71e2-4135-b72b-6c5a4a1d4072",
+ "id": "524015c7-6c42-4b84-98b9-361c17945fbe",
"metadata": {},
"source": [
- "## 5.1. Setup\n",
- "\n",
- "The setup, as described above, can also be carried out using the CLI command `openfe plan-rbfe-network`.\n",
+ "## 11. Analysing results with cinnabar\n",
"\n",
- "`openfe plan-rbfe-network -M inputs/tyk2_ligands.sdf -p inputs/tyk2_protein.pdb -o tyk2_json/`\n",
+ "[cinnabar](https://cinnabar.openfree.energy/en/latest/) analyses networks of free energy results. It is what `openfe gather --report dg` uses for the MLE, and it also provides comparisons with experiment and plotting.\n",
"\n",
- "This command plans a relative binding free energy network and saves it as JSON files for the `openfe quickrun` command.\n",
+ "### Loading the results\n",
"\n",
- "By default, this tool makes the following choices:\n",
- "\n",
- "* Atom mappings performed by Kartograf\n",
- "* Minimal spanning network as the network planner, with LOMAP default score as the weight function\n",
- "* Water as solvent, with NaCl counter ions at 0.15 M concentration.\n",
- "* Protocol is the OpenMM-based relative hybrid topology protocol, with default settings.\n",
- "\n",
- "These choices can be customized by creating a settings yaml file, which is\n",
- "passed in via the ``-s settings.yaml`` option. For more details, please visit our user guide section about [Customising CLI planning with yaml settings](https://docs.openfree.energy/en/latest/guide/cli/cli_yaml.html)"
+ "We need the calculated ΔΔG values and, to compare with experiment, the experimental ΔG values. The experimental TYK2 data comes from the [openfe-benchmarks](https://github.com/OpenFreeEnergy/openfe-benchmarks) repository:"
]
},
{
- "cell_type": "markdown",
- "id": "26fef157-6989-406d-b719-0ed597ef820e",
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "04907653-a4ad-4598-b7ee-02e52a9a0868",
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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1.00
\n",
+ "
0.05
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ligand_i ligand_j DDG(i->j) (kcal/mol) uncertainty (kcal/mol)\n",
+ "0 lig_ejm_31 lig_ejm_42 0.80 0.20\n",
+ "1 lig_ejm_31 lig_ejm_46 -0.89 0.08\n",
+ "2 lig_ejm_31 lig_ejm_47 0.00 0.20\n",
+ "3 lig_ejm_31 lig_ejm_48 0.60 0.10\n",
+ "4 lig_ejm_31 lig_ejm_50 1.00 0.05"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "## 5.2. Execution\n",
- "\n",
- "You can run each leg individually by using the `openfe quickrun` command. It takes a transformation JSON as input, and the flags `-o` to give the final output JSON file and `-d` for the directory where simulation results should be stored. For example,\n",
- "\n",
- "`openfe quickrun tyk2_json/lig_ejm_31_lig_ejm_47_complex.json -o results_complex.json -d working-directory`\n",
- "\n",
- "`openfe quickrun tyk2_json/lig_ejm_31_lig_ejm_47_solvent.json -o results_solvent.json -d working-directory`\n",
- "\n",
- "## 5.3. Analysis\n",
+ "import json\n",
+ "import urllib.request\n",
"\n",
- "To gather the \n",
- " estimates into a single file, use the `openfe gather` command from within the working directory used above:\n",
+ "import pandas as pd\n",
"\n",
- "`openfe gather results/ --report dg -o final_results.tsv`\n",
+ "calculated = pd.read_csv(\"tyk2_ddg.tsv\", sep=\"\\t\")\n",
"\n",
- "This will write out a tab-separated table of results, where the results reported are controlled by the `--report` option:\n",
- "\n",
- "* `dg` (default) reports the ligand and the results are the maximum likelihood estimate of its absolute free, and the associated uncertainty from DDG replica averages and standard deviations.\n",
+ "experimental_url = (\n",
+ " \"https://raw.githubusercontent.com/OpenFreeEnergy/openfe-benchmarks/main/\"\n",
+ " \"openfe_benchmarks/data/benchmark_systems/jacs_set/tyk2/experimental_binding_data.json\"\n",
+ ")\n",
+ "with urllib.request.urlopen(experimental_url) as response:\n",
+ " experimental = json.load(response)\n",
"\n",
- "* `ddg` reports pairs of ligand_i and ligand_j, the calculated relative free energy DDG(i->j) = DG(j) - DG(i) and its uncertainty.\n",
+ "calculated.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7477fd2f-fc45-438a-9456-243b5e595d69",
+ "metadata": {},
+ "source": [
+ "Both are added to a cinnabar `FEMap`, which holds the network of calculated and experimental values. The experimental data names ligands without the `lig_` prefix, so we add it back to match our ligand names:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "e6a2b311-9e49-443e-9006-d9c59e9bb6cd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from cinnabar import FEMap, plotting\n",
+ "\n",
+ "fe_map = FEMap()\n",
+ "\n",
+ "for _, row in calculated.iterrows():\n",
+ " fe_map.add_relative_calculation(\n",
+ " labelA=row[\"ligand_i\"],\n",
+ " labelB=row[\"ligand_j\"],\n",
+ " value=row[\"DDG(i->j) (kcal/mol)\"] * unit.kilocalories_per_mole,\n",
+ " uncertainty=row[\"uncertainty (kcal/mol)\"] * unit.kilocalories_per_mole,\n",
+ " source=\"openfe\",\n",
+ " )\n",
+ "\n",
+ "for ligand in ligands:\n",
+ " fe_map.add_experimental_measurement(\n",
+ " label=ligand.name,\n",
+ " value=experimental[ligand.name.removeprefix(\"lig_\")][\"dg\"][\"magnitude\"] * unit.kilocalories_per_mole,\n",
+ " uncertainty=0.0 * unit.kilocalories_per_mole,\n",
+ " source=\"experimental\",\n",
+ " )\n",
+ "\n",
+ "fe_map.draw_graph()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "acbe80a2-f330-4464-a31f-aaac7f51a7c6",
+ "metadata": {},
+ "source": [
+ "### Relative free energies\n",
"\n",
- "* `raw` reports the raw results, giving the leg (vacuum, solvent, or complex), ligand_i, ligand_j, the raw DG(i->j) associated with it."
+ "First we compare each calculated ΔΔG with the experimental ΔΔG for the same edge. Points on the diagonal indicate perfect agreement, and the shaded bands mark ±0.5 and ±1 kcal/mol."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "8af7ef81-5548-4408-ae44-d1f5503f3133",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plotting.plot_DDGs(fe_map, source=\"openfe\", figsize=5, title=\"TYK2 relative binding free energies\")"
]
},
{
"cell_type": "markdown",
- "id": "c3a26c6b-c958-4607-a191-f84c626d2db1",
+ "id": "7d3bd710-957f-48aa-bf5e-14262399eaf8",
"metadata": {},
"source": [
- "# 6. Useful References for Getting Started"
+ "### Binding free energies per ligand\n",
+ "\n",
+ "To rank compounds, we combine the ΔΔG values into a ΔG per ligand with the MLE. Because MLE values are centred on 0, we shift them by the mean experimental ΔG to place both on the same scale:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "89ae8f5c-37c8-41a2-ba7b-d93c5d509dfa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fe_map.generate_absolute_values()\n",
+ "\n",
+ "absolute = fe_map.get_absolute_dataframe()\n",
+ "experimental_mean = absolute[absolute[\"source\"] == \"experimental\"][\"DG (kcal/mol)\"].mean()\n",
+ "\n",
+ "plotting.plot_DGs(fe_map, source=\"MLE\", figsize=5, shift=experimental_mean, title=\"TYK2 binding free energies\")"
]
},
{
"cell_type": "markdown",
- "id": "2dc92d40-5a28-404f-bbff-5081cec5f995",
+ "id": "4fed0727-56db-4e80-977e-22aa367c5ee0",
"metadata": {},
"source": [
- "In our [documentation](https://docs.openfree.energy/en/latest/index.html), \n",
- "we provide tutorials for every protocol to walk you through setup, execution and analysis step by step.\n",
+ "
\n",
"\n",
- "* [RBFE CLI tutorial](https://docs.openfree.energy/en/latest/tutorials/rbfe_cli_tutorial.html)\n",
- "* [RBFE Python tutorial](https://docs.openfree.energy/en/latest/tutorials/rbfe_python_tutorial.html)\n",
- "* [AHFE tutorial](https://docs.openfree.energy/en/latest/tutorials/ahfe_tutorial.html)\n",
- "* [MD tutorial](https://docs.openfree.energy/en/latest/tutorials/md_tutorial.html)\n",
- "* [ABFE tutorial](https://docs.openfree.energy/en/latest/tutorials/abfe_tutorial.html)\n",
- "* [RBFE using a Separated Topologies (SepTop) approach tutorial](https://docs.openfree.energy/en/latest/tutorials/septop_tutorial.html)\n",
+ "**Cycle closure.** In a network with cycles, such as the LOMAP network from section 6, the ΔΔG values around each closed cycle should sum to zero, which gives a useful consistency check on the results. The minimal spanning tree used here has no cycles.\n",
"\n",
- "In addition to the tutorials, you can find [cookbooks](https://docs.openfree.energy/en/latest/cookbook/index.html), written as How-to guides on how to utilize different components of the toolkit, as well as a [User Guide](https://docs.openfree.energy/en/latest/guide/index.html) that goes into the underlying concepts of the OpenFE toolkit.\n",
+ "**Learn more:**\n",
"\n",
- "For details about the toolkit's core methods and classes, please visit our [API Reference](https://docs.openfree.energy/en/latest/reference/index.html) or our [Github page](https://github.com/OpenFreeEnergy/openfe).\n",
+ "- **cinnabar docs:** [cinnabar documentation](https://cinnabar.openfree.energy/en/latest/) – the full API, including statistics and plotting.\n",
+ "- **Tutorial:** [Plotting results with cinnabar](https://docs.openfree.energy/en/stable/tutorials/plotting_with_cinnabar.html) – comparing calculated and experimental values.\n",
"\n",
- "To learn more about the project, our team and how you can get involved, please visit our [Homepage](https://openfree.energy/) or get in touch at OpenFreeEnergy@omsf.io."
+ "
"
]
},
{
+ "cell_type": "markdown",
+ "id": "2ada4410-a596-4fe1-ae5c-44b658ca8279",
"metadata": {},
- "cell_type": "code",
- "outputs": [],
- "execution_count": null,
- "source": "",
- "id": "eb2308d6adfdda77"
+ "source": [
+ "## 12. Next steps\n",
+ "\n",
+ "In this showcase, we planned a TYK2 RBFE campaign with the Python API: assigning partial charges, mapping and scoring ligand pairs, building a ligand network, configuring the protocol and writing out the transformations. We then gathered precomputed results and compared them with experiment using cinnabar.\n",
+ "\n",
+ "### Beyond this showcase\n",
+ "\n",
+ "There are additional steps you may encounter when running and analysing your own RBFE campaigns that we haven't covered here:\n",
+ "\n",
+ "- **Analysing simulation trajectories:** [openfe-analysis](https://openfe-analysis.openfree.energy/en/latest/) allows you to load simulation trajectories into MDAnalysis for further inspection and analysis.\n",
+ "- **Troubleshooting failed simulations:** The [Troubleshooting guide](https://docs.openfree.energy/en/stable/guide/troubleshooting.html) covers common errors, including issues with box size, force field parameter assignment and NaN errors.\n",
+ "\n",
+ "### Further reading\n",
+ "\n",
+ "To learn more about OpenFE and explore other workflows:\n",
+ "\n",
+ "- **Step-by-step tutorials:** [RBFE with the CLI](https://docs.openfree.energy/en/stable/tutorials/rbfe_cli_tutorial.html), [RBFE with the Python API](https://docs.openfree.energy/en/stable/tutorials/rbfe_python_tutorial.html), and tutorials for [other protocols](https://docs.openfree.energy/en/stable/tutorials/index.html).\n",
+ "- **Choosing a protocol and checking what it supports:** [Protocols](https://docs.openfree.energy/en/stable/guide/protocols/index.html).\n",
+ "- **How-to guides** for individual tasks: the [How-to guides](https://docs.openfree.energy/en/stable/cookbook/index.html).\n",
+ "\n",
+ "To learn more about the project, visit [openfree.energy](https://openfree.energy/), or ask questions on the [OpenFE discussions board](https://github.com/orgs/OpenFreeEnergy/discussions)."
+ ]
}
],
"metadata": {
@@ -3449,7 +11704,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.13.11"
+ "version": "3.13.14"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
![](\"OFE-color-horizontal.png\")
\n",
+ "![](\"images/OFE-color-horizontal.png\")
\n",
"\n",
"# OpenFE Showcase: Relative Binding Free Energies in the TYK2 System"
]
},
{
"cell_type": "markdown",
- "id": "8eea1a56-3241-4ccf-9d3a-88de6bbf0e0e",
+ "id": "f347f95a-ce31-4d2f-958e-0a6e33374dea",
"metadata": {},
"source": [
- "# Intro\n",
- "\n",
- "Welcome to the Open Free Energy toolkit!\n",
+ "## Introduction\n",
"\n",
- "The OpenFE toolkit provides open-source frameworks for calculating alchemical free energies. This notebook showcases the methods that are available in OpenFE and their usage.\n",
+ "Welcome to the Open Free Energy toolkit! **openfe** is an open-source package for planning, running and analyzing alchemical free energy calculations.\n",
"\n",
- "Throughout this showcase, we will introduce different interchangeable components that users can choose from during the setup of free energy calculations. OpenFE allows you to mix and match different components, such as:\n",
+ "In this showcase we calculate relative binding free energies (RBFEs) for a congeneric series of ligands binding to tyrosine kinase 2 (TYK2), using the Relative Hybrid Topology Protocol. Along the way, we introduce the main building blocks of **openfe** and the choices you can make at each step, such as:\n",
"\n",
"* Atom mappers\n",
"* Scorers (for atom mappings)\n",
- "* Ligand networks\n",
- "\n",
- "This showcase currently focuses on relative binding free energy (RBFE) calculations using a hybrid topology approach. However, OpenFE provides a wide range of protocols for:\n",
- "- [Absolute binding free energy calculations](https://docs.openfree.energy/en/latest/tutorials/abfe_tutorial.html)\n",
- "- [Absolute hydration free energy calculations](https://docs.openfree.energy/en/latest/tutorials/ahfe_tutorial.html)\n",
- "- [Molecular Dynamics (MD) simulations](https://docs.openfree.energy/en/latest/tutorials/md_tutorial.html)\n",
- "- [Relative binding free energy calculations using a Separated Topologies (SepTop) approach](https://docs.openfree.energy/en/latest/tutorials/septop_tutorial.html)\n",
- "\n",
- "If you are planning your own calculations, please also check out our [tutorials](https://docs.openfree.energy/en/stable/tutorials/index.html) which will walk you step-by-step through setup, execution and analysis of different protocols."
+ "* Ligand networks"
]
},
{
"cell_type": "markdown",
- "id": "28f40a29-4ddd-4f25-8c08-154bb3139bb3",
+ "id": "b74dcaf2-5fae-4bff-bd76-e7aa7c2be213",
"metadata": {},
"source": [
- "# Outline\n",
- "\n",
- "0. Setup for Google Colab \n",
- "1. Overview \n",
- "2. Setup \n",
- " 2.1. Loading Ligands and Defining Ligand Atom Mappings \n",
- " 2.2. Creating a ligand network \n",
- " 2.3. Defining ChemicalSystems \n",
- " 2.4. Defining the RBFE simulation settings and protocol \n",
- "3. Running a Relative Ligand Binding Free Energy Calculation \n",
- " 3.1. Using the Python API \n",
- " 3.2. Using the CLI \n",
- "4. Analysis\n",
- "5. Relative Free Energies with the OpenFE CLI\n",
- "6. Useful References for Getting Started"
+ "## Outline\n",
+ "\n",
+ "0. Setup for Google Colab\n",
+ "1. Overview\n",
+ "2. Background: hybrid topology RBFEs\n",
+ "3. The TYK2 inputs\n",
+ "4. Assigning partial charges\n",
+ "5. Atom mappings and scoring\n",
+ "6. Ligand networks\n",
+ "7. Protocol settings\n",
+ "8. Creating transformations\n",
+ "9. Running the calculations\n",
+ "10. Results\n",
+ "11. Analysing results with cinnabar\n",
+ "12. Next steps"
]
},
{
@@ -97,12 +85,12 @@
" \n",
" !pip install -q condacolab\n",
" import condacolab\n",
- " condacolab.install_from_url(\"https://github.com/OpenFreeEnergy/openfe/releases/download/v1.7.0/OpenFEforge-1.7.0-Linux-x86_64.sh\")"
+ " condacolab.install_from_url(\"https://github.com/OpenFreeEnergy/openfe/releases/download/v1.13.0/OpenFEforge-1.13.0-Linux-x86_64.sh\")"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "dlCARZ2_fAI4",
"metadata": {
"colab": {
@@ -141,51 +129,151 @@
},
{
"cell_type": "markdown",
- "id": "0eaea8f6",
- "metadata": {
- "id": "0eaea8f6"
- },
+ "id": "8421015e-4d1e-4206-9d79-db9204e083bd",
+ "metadata": {},
+ "source": [
+ "## 1. Overview\n",
+ "\n",
+ "An **openfe** calculation has three stages:\n",
+ "\n",
+ "1. **Setup**: load the protein and ligands, decide which ligand pairs to compare, and define how each transformation is simulated.\n",
+ "2. **Run**: execute the simulations, usually on GPUs.\n",
+ "3. **Gather**: combine the simulation outputs into free energy estimates and analyze them.\n",
+ "\n",
+ "Each stage can be carried out with the Python API or with the command line interface (CLI). This notebook uses the Python API for planning, and the CLI for running and gathering, which is how most production campaigns are run.\n",
+ "\n",
+ "![](\"images/openfe_workflow.svg\")
\n",
+ "\n",
+ "![](\"images/rbfe_thermocycle.png\")
\n",
"\n",
- "In this example we show how to set up a network of transformations using\n",
- "the OpenFE toolkit for small chemical modifications of ligands binding to tyrosine kinase 2 (TYK2).\n",
+ "The horizontal legs (binding) are very hard to converge, so instead we simulate the vertical legs, alchemically transforming ligand A into ligand B both in solvent and in the protein-bound complex:\n",
"\n",
- "For convenience, a prepared (capped and protonated) PDB structure of the\n",
- "TYK2 protein is provided under `inputs/tyk2_protein.pdb`. "
+ "$$\\Delta\\Delta G_{\\mathrm{bind}}(A \\rightarrow B) = \\Delta G_{\\mathrm{complex}}(A \\rightarrow B) - \\Delta G_{\\mathrm{solvent}}(A \\rightarrow B)$$\n",
+ "\n",
+ "Every ligand pair therefore needs **two** simulations: a solvent leg and a complex leg.\n",
+ "\n",
+ "**Sampling along the alchemical path**\n",
+ "\n",
+ "The transformation from A to B is controlled by a coupling parameter, λ, which interpolates the interactions of the atoms that differ between the two ligands. The system is simulated at a series of discrete λ values (λ windows), chosen so that neighbouring windows overlap, and the free energy is estimated from all windows with the multistate Bennett acceptance ratio (MBAR).\n",
+ "\n",
+ "![](\"images/lambda_scaling.png\")
\n",
+ "\n",
+ "*Default λ schedule for the Relative Hybrid Topology protocol. Markers indicate the 11 λ windows.*\n",
+ "\n",
+ "For atoms mapped between the two ligands (the common core), nonbonded parameters are interpolated between their values in states A and B. Atoms unique to either ligand instead have their interactions progressively turned off or on, as illustrated above.\n",
+ "\n",
+ "To improve sampling, **openfe** uses Hamiltonian replica exchange (HREX), where swaps between neighbouring windows are attempted at regular intervals. This lets configurations move along the alchemical path and escape local minima.\n",
+ "\n",
+ "![](\"images/hrex.jpg\")
\n",
+ "\n",
+ "*HREX sampling across λ windows, reproduced from Mey et al., [Best Practices for Alchemical Free Energy Calculations](https://pmc.ncbi.nlm.nih.gov/articles/PMC8388617/).*\n",
+ "\n",
+ "**The hybrid topology approach**\n",
+ "\n",
+ "In a hybrid topology, both ligands are represented by a single combined molecule. Atoms shared by the two ligands (the core) are interpolated between their parameters in A and B, while atoms unique to either ligand are switched on or off as dummy atoms. Because only the differing atoms are perturbed, these transformations are usually cheaper to converge than perturbing whole molecules.\n",
+ "\n",
+ "![](\"images/hybrid_topology.png\")
\n",
+ "\n",
+ "*Hybrid topology scheme, reproduced from the [alchemistry.org wiki](https://www.alchemistry.org/wiki/Constructing_a_Pathway_of_Intermediate_States).*\n",
+ "\n",
+ "![](\"tyk2.png\")
"
+ "![](\"images/tyk2.png\")
\n",
+ "\n",
+ "