{ "cells": [ { "cell_type": "markdown", "id": "cb0d308b-8f50-4ac9-a8a3-550d8a2e3feb", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ " \n", "\n", "# OpenFE Showcase: Relative Binding Free Energies in the TYK2 System" ] }, { "cell_type": "markdown", "id": "8eea1a56-3241-4ccf-9d3a-88de6bbf0e0e", "metadata": {}, "source": [ "# Intro\n", "\n", "Welcome to the Open Free Energy toolkit!\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", "\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", "\n", "* Atom mappers\n", "* Scorers (for atom mappings)\n", "* Ligand networks\n", "\n", "This showcase currently focuses on relative binding free energy (RBFE) calculations. However, OpenFE also provides protocols for running [absolute hydration free energy calculations](https://docs.openfree.energy/en/latest/tutorials/ahfe_tutorial.html) and [Molecular Dynamics (MD) simulations](https://docs.openfree.energy/en/latest/tutorials/md_tutorial.html). In the future, other methods will become available, such as absolute binding free energy calculations and RBFE calculations using a Separated Topologies approach.\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." ] }, { "cell_type": "markdown", "id": "28f40a29-4ddd-4f25-8c08-154bb3139bb3", "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" ] }, { "cell_type": "markdown", "id": "AUgaKFN7eCt5", "metadata": { "id": "AUgaKFN7eCt5", "nbsphinx": "hidden" }, "source": [ "# 0. Setup for Google Colab\n", "\n", "If you are running this example in Google Colab, run the following cells to setup the environment. If you are running this notebook locally, skip down to `1. Overview`" ] }, { "cell_type": "code", "execution_count": 1, "id": "-jX9udRGehD3", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-jX9udRGehD3", "nbsphinx": "hidden", "outputId": "78a0d40d-a6d2-4a93-a041-406a8cb551b9", "tags": [] }, "outputs": [], "source": [ "# NBVAL_SKIP\n", "# Only run this cell if on google colab\n", "import os\n", "if \"COLAB_RELEASE_TAG\" in os.environ:\n", " # fix for colab's torchvision causing issues\n", " !rm -r /usr/local/lib/python3.12/dist-packages/torchvision\n", " \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\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "dlCARZ2_fAI4", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dlCARZ2_fAI4", "nbsphinx": "hidden", "outputId": "d528f4dc-5105-4288-ea08-9b2e75d442ff", "tags": [] }, "outputs": [], "source": [ "# NBVAL_SKIP\n", "# Only run this cell if on google colab\n", "# Colab will say the runtime crashed, but this is expected with `install_from_url`, and you should continue running the next cell\n", "\n", "import os\n", "if \"COLAB_RELEASE_TAG\" in os.environ:\n", " import condacolab\n", " import locale\n", " locale.getpreferredencoding = lambda: \"UTF-8\"\n", " !mkdir inputs && cd inputs && openfe fetch rbfe-tutorial\n", " # quick fix for https://github.com/conda-incubator/condacolab/issues/75\n", " # if deprecation warnings persist, rerun this cell\n", " import warnings\n", " warnings.filterwarnings(action=\"ignore\", message=r\"datetime.datetime.utcnow\") \n", " for _ in range(3):\n", " # Sometimes we have to re-run the check\n", " try:\n", " condacolab.check()\n", " except:\n", " pass\n", " else:\n", " break" ] }, { "cell_type": "markdown", "id": "0eaea8f6", "metadata": { "id": "0eaea8f6" }, "source": [ "# 1. Overview\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", "\n", "For convenience, a prepared (capped and protonated) PDB structure of the\n", "TYK2 protein is provided under `inputs/tyk2_protein.pdb`. " ] }, { "cell_type": "markdown", "id": "31608e5a-cedf-4840-b809-a7df32b319ea", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "e9a7f137", "metadata": { "id": "e9a7f137" }, "source": [ "## 1.1. The dataset: Alchemical transformations of TYK2 ligands\n", "\n", "Here we explore how OpenFE can be used to build a network of alchemical transformations between the TYK2 ligands.\n", "\n", "First, we will use rdkit to visualize the TYK2 ligands." ] }, { "cell_type": "code", "execution_count": 3, "id": "711d73bc-519f-4b81-9daf-c7594c7bbf8f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "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)" ] }, { "cell_type": "markdown", "id": "0870f84e", "metadata": { "id": "0870f84e" }, "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", "\n", "| **Actions** | **Software** |\n", "|:------------------------------|:-----------------------------------------------------------|\n", "| Create OpenFE Molecules | OpenFE RDKit |\n", "| Create Network | OpenFE Lomap, 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 |" ] }, { "cell_type": "markdown", "id": "14ade1f0", "metadata": { "id": "14ade1f0" }, "source": [ "# 2. Setup" ] }, { "cell_type": "markdown", "id": "5962dec2-d9ef-475d-8086-ee15954e81f6", "metadata": {}, "source": [ "## 2.1. Loading Ligands and Defining Ligand Atom Mappings" ] }, { "cell_type": "markdown", "id": "f734578f", "metadata": { "id": "f734578f" }, "source": [ "### Creating OpenFE SmallMoleculeComponents\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", "\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." ] }, { "cell_type": "markdown", "id": "fbc94d04-5d68-4123-82b2-97a1b8872375", "metadata": {}, "source": [ "Load ligands using RDKit:" ] }, { "cell_type": "code", "execution_count": 4, "id": "4096ce97", "metadata": { "id": "4096ce97" }, "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]" ] }, { "cell_type": "markdown", "id": "044045af-020c-4926-b295-0c9db5bd6a0c", "metadata": {}, "source": [ "Load ligands using the OpenFF toolkit:" ] }, { "cell_type": "code", "execution_count": 5, "id": "b5e5b538-4847-42f5-b29e-6e1f25bbbeed", "metadata": {}, "outputs": [], "source": [ "from openff.toolkit import Molecule\n", "from openfe import SmallMoleculeComponent\n", "\n", "# Load ligands using OpenFF toolkit\n", "ligands_sdf = Molecule.from_file('inputs/tyk2_ligands.sdf')\n", "\n", "# Now pass these to form a list of Molecules\n", "ligand_mols = [SmallMoleculeComponent.from_openff(sdf) for sdf in ligands_sdf]" ] }, { "cell_type": "markdown", "id": "3a8d5433", "metadata": { "id": "3a8d5433" }, "source": [ "OpenFE `SmallMoleculeComponent`s have some useful built-in attributes and methods.\n", "\n", "For example, the molecule's name (as defined by the SDF file) can be accessed:" ] }, { "cell_type": "code", "execution_count": 6, "id": "faacbebb", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "faacbebb", "outputId": "bc0ad1d9-72ad-49d0-e322-61b2a87d5301" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name: lig_ejm_31\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": 7, "id": "29b7c68a", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "29b7c68a", "outputId": "39e7a77a-d2b8-49d2-8f70-fd8118845a99" }, "outputs": [ { "data": { "text/plain": [ "openff.toolkit.topology.molecule.Molecule" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(ligand_mols[0].to_openff())" ] }, { "cell_type": "markdown", "id": "9e72bee2-bcb3-466f-86e7-8b51c8932aa7", "metadata": {}, "source": [ "### Ligand Atom Mapping" ] }, { "cell_type": "markdown", "id": "3128085c-db8a-4a70-89ef-0fde956fdb40", "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. `LomapAtomMapper`\n", " * based on the maximum common substructure (MCS)\n", "2. `KartografAtomMapper`\n", " * based on the 3D geometries of the ligands\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." ] }, { "cell_type": "markdown", "id": "1eec9783-4620-43ff-8ef7-a647fd6f54ac", "metadata": {}, "source": [ "**1. `LomapAtomMapper`**" ] }, { "cell_type": "code", "execution_count": 8, "id": "01384f2a-99ce-400d-a988-82fd393c4ee5", "metadata": {}, "outputs": [], "source": [ "from openfe.setup import LomapAtomMapper\n", "mapper = LomapAtomMapper()\n", "lomap_mapping = next(mapper.suggest_mappings(ligand_mols[0], ligand_mols[4]))" ] }, { "cell_type": "markdown", "id": "140f4e46-a58b-425d-b9cf-ab62078fc050", "metadata": {}, "source": [ "We can also visualize the atom mappings by invoking the individual OpenFE `AtomMapping` objects directly.\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." ] }, { "cell_type": "code", "execution_count": 9, "id": "00fb84d6-9fa7-4114-96e5-c656ad6f3a3f", "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", "lomap_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": "
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\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Visualize the mapping in 3D\n", "lomap_mapping.view_3d(show_atomIDs=True)" ] }, { "cell_type": "markdown", "id": "f21bf9e5-6cb8-41c1-b0fd-e70ee2e71852", "metadata": {}, "source": [ "**2. `KartografAtomMapper`**\n", "\n", "We can also use the `KartografAtomMapper` which is based on the 3D geometries of the ligands." ] }, { "cell_type": "code", "execution_count": 11, "id": "c710a20e-2098-461f-8fa6-5e44b19ad6aa", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:\t#################################\n", "INFO:\tMap Heavy Atoms \n", "INFO:\t#################################\n", "INFO:\tMasking Atoms\n", "INFO:\tBuild Distance Matrix\n", "INFO:\tCalculate Mapping\n", "INFO:\tFind Maximal overlapping connected sets of mapped atoms\n", "INFO:\t#################################\n", "INFO:\tMap Hydrogen Atoms: \n", "INFO:\t#################################\n", "INFO:\tMasking Atoms\n", "INFO:\tBuild Distance Matrix\n", "INFO:\tCalculate Mapping\n", "INFO:\tFind Maximal overlapping connected sets of mapped atoms\n", "INFO:\tFiltering bond breaks\n" ] } ], "source": [ "from kartograf import KartografAtomMapper\n", "# Build Kartograf Atom Mapper\n", "mapper = KartografAtomMapper(atom_map_hydrogens=True)\n", "\n", "# Get Mapping\n", "kartograf_mapping = next(mapper.suggest_mappings(ligand_mols[0], ligand_mols[4]))" ] }, { "cell_type": "code", "execution_count": 12, "id": "56117a03-6503-4317-9364-27e343a4911e", "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": "de4592a7", "metadata": { "id": "de4592a7" }, "source": [ "## 2.2. Creating a ligand network\n", "\n", "A `LigandNetwork` is a set of `SmallMoleculeComponent`s that are connected by `AtomMapping`s of two small molecules. \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", "\n", "In this section, we will create and visualize the MST, LOMAP, and radial networks for the TYK2 dataset.\n", "\n", "Here, we will be using the `LomapAtomMapper` as the atom mapper for all networks." ] }, { "cell_type": "code", "execution_count": 13, "id": "5b89da93", "metadata": { "id": "5b89da93", "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:\tTrying to remove edge 0-2 with similarity 0.818731\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 0.860708\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 0-1 with similarity 0.904837\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 0-6 with similarity 0.904837\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 1-2 with similarity 0.904837\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 1-6 with similarity 0.951229\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 0.904837\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 0.904837\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 3-7 with similarity 0.904837\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 8-7 with similarity 0.951229\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 8-9 with similarity 0.951229\n", "INFO:\tRejecting edge deletion on cycle covering\n", "INFO:\tTrying to remove edge 9-7 with similarity 0.951229\n", "INFO:\tChecking edge deletion on distance-to-actives 0 vs 0\n", "INFO:\tRemoved edge 9-7\n" ] } ], "source": [ "# Create network from the two molecules\n", "import openfe\n", "from openfe.setup.ligand_network_planning import generate_radial_network\n", "from openfe.setup.ligand_network_planning import generate_minimal_spanning_network\n", "from openfe.setup.ligand_network_planning import generate_lomap_network\n", "from openfe.setup import LomapAtomMapper\n", "\n", "# Create an MST network\n", "mst_network = generate_minimal_spanning_network(\n", " ligands=ligand_mols,\n", " scorer=openfe.lomap_scorers.default_lomap_score,\n", " mappers=[LomapAtomMapper(),])\n", "\n", "# Create a LOMAP network\n", "lomap_network = generate_lomap_network(\n", " ligands=ligand_mols,\n", " scorer=openfe.lomap_scorers.default_lomap_score,\n", " mappers=[LomapAtomMapper(),])\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=[LomapAtomMapper(),])" ] }, { "cell_type": "markdown", "id": "41cd9718-0c2e-4b59-86ee-8dc429915de1", "metadata": {}, "source": [ "We can plot out the different networks to visualize their structure and to see how ligands are being tranformed." ] }, { "cell_type": "code", "execution_count": 14, "id": "f4e3ac10-236b-4ff4-a69e-600706288164", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize the MST network\n", "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n", "\n", "plot_atommapping_network(mst_network)" ] }, { "cell_type": "code", "execution_count": 15, "id": "3335c478-ce7c-41a4-b36f-2e4c79581050", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Visualize the LOMAP network\n", "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n", "\n", "plot_atommapping_network(lomap_network)" ] }, { "cell_type": "code", "execution_count": 16, "id": "e1c09036-efc7-4bc4-9851-ac16992bf041", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Visualize the radial network\n", "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n", "\n", "plot_atommapping_network(radial_network)" ] }, { "cell_type": "markdown", "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**" ] }, { "cell_type": "code", "execution_count": 17, "id": "ad80c243-82d7-4599-a4a6-5d651c662f24", "metadata": {}, "outputs": [ { "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)C2CCCC2)c1)c1c(Cl)cccc1Cl\n", "map between molecule A and B: {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 28: 38, 29: 39, 30: 40, 31: 41}\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": 18, "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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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can display the atom mapping of an edge by calling it\n", "edge" ] }, { "cell_type": "code", "execution_count": 19, "id": "66dd7d32", "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": 19, "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" }, "source": [ "### Storing the ligand network\n", "\n", "Created networks can easily be converted to (and also loaded from) a GraphML representation.\n", "\n", "This can allow users of OpenFE to store the network to disk for later use." ] }, { "cell_type": "code", "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())" ] }, { "cell_type": "markdown", "id": "feb4d54a-01a8-4bbe-ac13-1a15cdb79254", "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", "`ChemicalSystems` take in:\n", "\n", "1. A dictionary of the chemical components (e.g. `SmallMoleculeComponent`,\n", " `ProteinComponent`, `SolventComponent`) defining the system.\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": 21, "id": "0b6c9ec3", "metadata": { "id": "0b6c9ec3" }, "outputs": [], "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", "\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)" ] }, { "cell_type": "code", "execution_count": 22, "id": "8c6d6504", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 317 }, "id": "8c6d6504", "outputId": "47026705-938a-4e62-f1af-66e9c50334aa" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 23, "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", "\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`)." ] }, { "cell_type": "markdown", "id": "d7c8ab9e-0185-40d7-a441-7a4cd948dda9", "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.**" ] }, { "cell_type": "code", "execution_count": 24, "id": "309c059b-85c7-4911-a417-69889a474fe1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'early_termination_target_error': {'unit': 'kilocalorie_per_mole', 'val': 0.0},\n", " 'equilibration_length': {'unit': 'nanosecond', 'val': 0.009999999999999998},\n", " 'minimization_steps': 5000,\n", " 'n_replicas': 11,\n", " 'production_length': {'unit': 'nanosecond', 'val': 0.04999999999999999},\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", " 'sams_flatness_criteria': 'logZ-flatness',\n", " 'sams_gamma0': 1.0,\n", " 'time_per_iteration': {'unit': 'picosecond', 'val': 2.5}}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/gufe/settings/models.py:30: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.11/migration/\n", " pprint.pprint(self.dict())\n" ] } ], "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." ] }, { "cell_type": "code", "execution_count": 25, "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", "metadata": {}, "source": [ "# 3. Running a Relative Ligand Binding Free Energy Calculation" ] }, { "cell_type": "markdown", "id": "06d9d8a4-d570-42ea-9936-222b5e1728b2", "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", "\n", "As previously detailed, we create two sets of transformations for the complex and the solvent legs of the thermodynamic cycle." ] }, { "cell_type": "code", "execution_count": 26, "id": "853d6eef-e716-4284-b02f-8b06f2beb916", "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", " )" ] }, { "cell_type": "markdown", "id": "026ad9fc-635f-44d2-937c-bbc58edf64da", "metadata": {}, "source": [ "## 3.1. Using the Python API" ] }, { "cell_type": "markdown", "id": "7fd71a0e-7bf6-4529-961b-be8dfa7bbbce", "metadata": {}, "source": [ "### Creating the `ProtocolDAG`" ] }, { "cell_type": "markdown", "id": "0e2e7d25-5bb6-4344-963d-8cf5298982b4", "metadata": {}, "source": [ "With the `Transformation` defined, we can move onto creating the `ProtocolDAG`.\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": 27, "id": "77e72e9a-bac0-4059-b0bf-96c15c9b2696", "metadata": {}, "outputs": [], "source": [ "complex_dag = transformation_complex.create()\n", "\n", "solvent_dag = transformation_solvent.create()" ] }, { "cell_type": "markdown", "id": "051fb438-1ab0-4d3f-9615-4b2caff9a9b2", "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", "\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", "\n", "Individual Units can then be executed by calling the `.execute()` method.\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." ] }, { "cell_type": "code", "execution_count": 28, "id": "981cde0c", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "981cde0c", "outputId": "812389bc-3730-416b-8154-79e0e1fb4346" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:\tPreparing the hybrid topology simulation\n", "INFO:\tParameterizing molecules\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:\tHybrid system created\n", "WARNING:\tWarning: The openmmtools.multistate API is experimental and may change in future releases\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_rfe/equil_rfe_methods.py:1018: DeprecationWarning: `in1d` is deprecated. Use `np.isin` instead.\n", " bfactors[np.in1d(selection_indices, list(hybrid_factory._atom_classes['unique_old_atoms']))] = 0.25 # lig A\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_rfe/equil_rfe_methods.py:1019: DeprecationWarning: `in1d` is deprecated. Use `np.isin` instead.\n", " bfactors[np.in1d(selection_indices, list(hybrid_factory._atom_classes['core_atoms']))] = 0.50 # core\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_rfe/equil_rfe_methods.py:1020: DeprecationWarning: `in1d` is deprecated. Use `np.isin` instead.\n", " bfactors[np.in1d(selection_indices, list(hybrid_factory._atom_classes['unique_new_atoms']))] = 0.75 # lig B\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/mdtraj/core/topology.py:97: UserWarning: atom_indices are not monotonically increasing\n", " warnings.warn(\"atom_indices are not monotonically increasing\")\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_utils/omm_compute.py:76: UserWarning: Non-CUDA platform selected: CPU, this may significantly impact simulation performance\n", " warnings.warn(wmsg)\n", "WARNING:\tNon-CUDA platform selected: CPU, this may significantly impact simulation performance\n", "INFO:\tCreating and setting up the sampler\n", "WARNING:\tWarning: The openmmtools.multistate API is experimental and may change in future releases\n", "WARNING:\tAn online_analysis_interval that is not a multiple of the checkpoint_interval can lead to redundant information in the real time yaml file after recovering from checkpoints.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Please cite the following:\n", "\n", " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", " \n" ] }, { "data": { "text/plain": [ "{'debug': {'sampler': }}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# complex dry-run\n", "complex_unit = list(complex_dag.protocol_units)[0]\n", "\n", "complex_unit.run(dry=True, verbose=True)" ] }, { "cell_type": "code", "execution_count": 29, "id": "77accb06", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "77accb06", "outputId": "e0b977c0-7e72-4a49-d3ea-eb0cdb85ed5d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:\tPreparing the hybrid topology simulation\n", "INFO:\tParameterizing molecules\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:\tHybrid system created\n", "WARNING:\tWarning: The openmmtools.multistate API is experimental and may change in future releases\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_rfe/equil_rfe_methods.py:1018: DeprecationWarning: `in1d` is deprecated. Use `np.isin` instead.\n", " bfactors[np.in1d(selection_indices, list(hybrid_factory._atom_classes['unique_old_atoms']))] = 0.25 # lig A\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_rfe/equil_rfe_methods.py:1019: DeprecationWarning: `in1d` is deprecated. Use `np.isin` instead.\n", " bfactors[np.in1d(selection_indices, list(hybrid_factory._atom_classes['core_atoms']))] = 0.50 # core\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_rfe/equil_rfe_methods.py:1020: DeprecationWarning: `in1d` is deprecated. Use `np.isin` instead.\n", " bfactors[np.in1d(selection_indices, list(hybrid_factory._atom_classes['unique_new_atoms']))] = 0.75 # lig B\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/mdtraj/core/topology.py:97: UserWarning: atom_indices are not monotonically increasing\n", " warnings.warn(\"atom_indices are not monotonically increasing\")\n", "/Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfe/protocols/openmm_utils/omm_compute.py:76: UserWarning: Non-CUDA platform selected: CPU, this may significantly impact simulation performance\n", " warnings.warn(wmsg)\n", "WARNING:\tNon-CUDA platform selected: CPU, this may significantly impact simulation performance\n", "INFO:\tCreating and setting up the sampler\n", "WARNING:\tWarning: The openmmtools.multistate API is experimental and may change in future releases\n", "WARNING:\tAn online_analysis_interval that is not a multiple of the checkpoint_interval can lead to redundant information in the real time yaml file after recovering from checkpoints.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Please cite the following:\n", "\n", " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", " \n" ] }, { "data": { "text/plain": [ "{'debug': {'sampler': }}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# solvent dry-run\n", "solvent_unit = list(solvent_dag.protocol_units)[0]\n", "\n", "solvent_unit.run(dry=True, verbose=True)" ] }, { "cell_type": "markdown", "id": "ceba2266-8ad5-4205-96df-4b6403fc3b50", "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." ] }, { "cell_type": "markdown", "id": "043672e9-20e5-4b2c-8977-cca0e2ddf23e", "metadata": {}, "source": [ "We’ll write out the transformation to disk, so that it can be run using the `openfe quickrun` command:" ] }, { "cell_type": "code", "execution_count": 30, "id": "12d35b4c-b737-4c3c-9fda-dc387aaa6915", "metadata": {}, "outputs": [], "source": [ "import pathlib\n", "# first we create the directory\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\")" ] }, { "cell_type": "markdown", "id": "05d4c16c-1d16-4ef1-a1b3-2eec6275c778", "metadata": {}, "source": [ "You can run the RBFE simulations from the CLI by using the `openfe quickrun` command, as described in Section 5. below." ] }, { "cell_type": "markdown", "id": "e07d1e29", "metadata": { "id": "e07d1e29" }, "source": [ "# 4. Analysis" ] }, { "cell_type": "markdown", "id": "2accbbd1", "metadata": { "id": "2accbbd1" }, "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", "## 4.1 Analysis - Python API\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" ] }, "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", "\n", "``` python\n", "# Finally we can run the simulations\n", "complex_path = pathlib.Path('./complex')\n", "complex_path.mkdir()\n", "\n", "# First the complex transformation\n", "complex_dag_results = execute_DAG(complex_dag, scratch_basedir=complex_path, shared_basedir=complex_path)\n", "\n", "# Next the solvent state transformation\n", "solvent_path = pathlib.Path('./solvent')\n", "solvent_path.mkdir()\n", "\n", "solvent_dag_results = execute_DAG(solvent_dag, scratch_basedir=solvent_path, shared_basedir=solvent_path)\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", "\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", "```" ] }, { "cell_type": "markdown", "id": "d53f72e1-ebec-4cad-8054-1746e1e79074", "metadata": {}, "source": [ "## 4.2 Analysis - `openfe gather`\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. " ] }, { "cell_type": "code", "execution_count": 31, "id": "059068d3-d9e2-4923-80ca-c2989a559d1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fetching /Users/atravitz/micromamba/envs/openfe-conda/lib/python3.11/site-packages/openfecli/tests/data/rbfe_results.tar.gz\n" ] } ], "source": [ "# Results from our cli tutorial\n", "locale.getpreferredencoding = lambda: \"UTF-8\" # hack for google colab, not needed for local execution\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", "metadata": {}, "source": [ "Now we can use the `openfe gather` command to look at the results (see section 5.3 for more details)" ] }, { "cell_type": "code", "execution_count": 32, "id": "ab9f2cca-8458-4515-a64a-20370151e1a5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "writing dg output to 'final_results.tsv'\n", "ligand\tDG(MLE) (kcal/mol)\tuncertainty (kcal/mol)\n", "lig_ejm_31\t-0.09\t0.05\n", "lig_ejm_42\t0.7\t0.1\n", "lig_ejm_46\t-0.98\t0.05\n", "lig_ejm_47\t-0.1\t0.1\n", "lig_ejm_48\t0.53\t0.09\n", "lig_ejm_50\t0.91\t0.06\n", "lig_ejm_43\t2.0\t0.2\n", "lig_jmc_23\t-0.68\t0.09\n", "lig_jmc_27\t-1.1\t0.1\n", "lig_jmc_28\t-1.25\t0.08\n" ] } ], "source": [ "!openfe gather results/ --report dg -o final_results.tsv\n", "!cat final_results.tsv" ] }, { "cell_type": "markdown", "id": "524015c7-6c42-4b84-98b9-361c17945fbe", "metadata": {}, "source": [ "# 5. Relative Free Energies with the OpenFE CLI" ] }, { "cell_type": "markdown", "id": "11074a41-2827-48c5-9493-e0e4e2d94838", "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", "\n", "2. Running the simulations.\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)." ] }, { "cell_type": "markdown", "id": "68cc5f26-71e2-4135-b72b-6c5a4a1d4072", "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", "\n", "`openfe plan-rbfe-network -M inputs/tyk2_ligands.sdf -p inputs/tyk2_protein.pdb -o tyk2_json/`\n", "\n", "This command plans a relative binding free energy network and saves it as JSON files for the `openfe quickrun` command.\n", "\n", "By default, this tool makes the following choices:\n", "\n", "* Atom mappings performed by LOMAP, with settings `max3d=1.0` and\n", " `element_change=False`\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)" ] }, { "cell_type": "markdown", "id": "26fef157-6989-406d-b719-0ed597ef820e", "metadata": {}, "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", "\n", "To gather the \n", " estimates into a single file, use the `openfe gather` command from within the working directory used above:\n", "\n", "`openfe gather results/ --report dg -o final_results.tsv`\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", "\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", "\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." ] }, { "cell_type": "markdown", "id": "c3a26c6b-c958-4607-a191-f84c626d2db1", "metadata": {}, "source": [ "# 6. Useful References for Getting Started" ] }, { "cell_type": "markdown", "id": "2dc92d40-5a28-404f-bbff-5081cec5f995", "metadata": {}, "source": [ "In our [documentation](https://docs.openfree.energy/en/latest/index.html), \n", "we provide tutorials for ever protocol to walk you through setup, execution and analysis step by step.\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", "\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", "\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", "\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": "code", "execution_count": null, "id": "6383b847-4270-43bb-9e17-4fdc0a1ecb5a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "44015ed54a6f47d7947fb5d27c8e4ed2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "73e8c79ac5724c09b52eb8ecb4f5cfd0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", "state": { "description_width": "" } }, "7b8c1a6c173d4413952e292cfb7a69a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_e1a9200af97841a994ac223eccaa993c", "style": "IPY_MODEL_a4b7a5020375491781da4b01f05d5edb", "value": " 45/45 [00:00<00:00, 126.47it/s]" } }, "9f9eaaac3a334054aa8d457e992a64c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_44015ed54a6f47d7947fb5d27c8e4ed2", "max": 45, "style": "IPY_MODEL_73e8c79ac5724c09b52eb8ecb4f5cfd0", "value": 45 } }, "a4b7a5020375491781da4b01f05d5edb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "a886854e697644ebb35ccc0dfa894323": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_b58368f0e2174d859b32afc556150734", "style": "IPY_MODEL_c6fc3bd7fa0a42abbac6f39112a8d246", "value": "Mapping: 100%" } }, "b473b59f6e8b4d01873b886d46cd83f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_a886854e697644ebb35ccc0dfa894323", "IPY_MODEL_9f9eaaac3a334054aa8d457e992a64c2", "IPY_MODEL_7b8c1a6c173d4413952e292cfb7a69a5" ], "layout": "IPY_MODEL_c6dba2d9b7184e2785d2aebc8dc00909" } }, "b58368f0e2174d859b32afc556150734": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c6dba2d9b7184e2785d2aebc8dc00909": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c6fc3bd7fa0a42abbac6f39112a8d246": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "e1a9200af97841a994ac223eccaa993c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }