From 307715a9387c9d74983bf890e441288bb1f564d9 Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Thu, 30 Apr 2026 11:05:53 -0700
Subject: [PATCH 01/15] add openfold output cell

---
 cookbook/openfold3_to_openfe.ipynb | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index e048e05..b1f1177 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -218,6 +218,24 @@
     "`run_openfold predict --query_json=queries.json --runner_yaml output_settings.yml`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9c1ab9ee-71f5-4587-bea1-35097621c039",
+   "metadata": {},
+   "source": [
+    "### Evaluating OpenFold output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "32cd3cbd-9d5f-4214-90da-a84ed645d35b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# confidence scores for each ligand are at assets/of3_tyk2_output/{lig_name}/*/*confidences*.json"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b4adde29-282a-45f6-96a6-c8de0700a266",

From a968c9fe893fffee95009c006e4ebd92926069ce Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Thu, 30 Apr 2026 14:21:39 -0700
Subject: [PATCH 02/15] reorganizing sections

---
 cookbook/openfold3_to_openfe.ipynb | 644 +++++++++++++----------------
 1 file changed, 298 insertions(+), 346 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index b1f1177..815584e 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -35,48 +35,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e89d5ef-e362-4335-9cbb-9752341b29f9",
+   "id": "2fea29c3",
    "metadata": {},
    "source": [
-    "## Overview\n",
-    "\n",
-    "In this example notebook we demonstrate how to predict and run simulations for different ligands bound to tyrosine kinase 2 (TYK2). The dataset comes from the [Wang et al. JACS 2015 dataset](https://pubs.acs.org/doi/10.1021/ja512751q).\n",
-    "\n",
-    "<center><img src=\"assets/tyk2.png\" width=\"400\"></center>\n",
-    "\n",
-    "\n",
-    "**The tutorial is broken into the following steps:**\n",
-    "\n",
-    "1. Running cofolding with OpenFold3 on a set of TYK2 ligands\n",
-    "\n",
-    "2. Processing the OpenFold3 output for use with OpenFE\n",
-    "\n",
-    "    a) Choosing a reference protein structure\n",
-    "\n",
-    "    b) Aligning & extracting the necessary ligands & protein structures.\n",
-    "\n",
-    "    c) Fixing & protonating the reference protein structure.\n",
-    "\n",
-    "\n",
-    "3. Loading structures into OpenFE Components\n",
-    "\n",
-    "4. Creating a free energy transformation with 3 protocol options\n",
-    "\n",
-    "    a) Absolute Binding Free Energy Transformation\n",
-    "\n",
-    "    b) SepTop Relative Binding Free Energy Transformation\n",
-    "\n",
-    "    c) Hybrid Topology Relative Binding Free Energy Transformation"
+    "## 1. Run cofolding with OpenFold3 on a set of TYK2 ligands"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2fea29c3",
+   "id": "ef3bdb1f-504a-4311-8e0a-c28265b006cb",
    "metadata": {},
    "source": [
-    "## 1. Run cofolding with OpenFold3 on a set of TYK2 ligands\n",
-    "\n",
-    "### Preparing the Input JSON for OpenFold\n",
+    "### Preparing the input JSON for OpenFold\n",
     "\n",
     "First we need to create an input JSON file for OpenFold. This step requires assembling two key pieces of information:\n",
     "\n",
@@ -84,6 +54,7 @@
     "- Protein sequence (extracted from the PDB file)\n",
     "\n",
     "In this workflow, we begin with an SDF file containing the ligands.\n",
+    "\n",
     "We extract the SMILES representation for each ligand and store them in a dictionary alongside their ligand identifiers."
    ]
   },
@@ -96,12 +67,14 @@
    },
    "outputs": [],
    "source": [
-    "ligands_file = \"assets/tyk2_ligands.sdf\"\n",
     "from rdkit.Chem.Descriptors3D import Asphericity\n",
     "from rdkit import Chem\n",
     "\n",
+    "ligands_file = \"assets/tyk2_ligands.sdf\"\n",
+    "\n",
     "ligands_dict = {}\n",
     "sppl = Chem.SDMolSupplier(ligands_file, removeHs=True)\n",
+    "\n",
     "for mol in sppl:\n",
     "    smi = Chem.MolToSmiles(mol)\n",
     "    name = mol.GetProp(\"_Name\")\n",
@@ -143,7 +116,7 @@
    "id": "80da7967-8046-44e4-aef7-c81ac6712f98",
    "metadata": {},
    "source": [
-    "We then create the JSON file that will be the OpenFold input."
+    "Next we create the JSON file that will be the OpenFold input."
    ]
   },
   {
@@ -199,23 +172,29 @@
    "source": [
     "### Running OpenFold\n",
     "\n",
-    "#### Defining the output settings\n",
+    "#### Defining the OpenFold settings\n",
     "\n",
-    "By default OpenFold3 outputs `.mmcif` files, but OpenFE requires `.pdb` files. We can ask OpenFold to generate this instead by defining it in the output settings.\n",
+    "we define the following settings in a `settings.yaml` file to be passed to OpenFold:\n",
     "\n",
-    "To do this, create an `output_settings.yml` file to specify .pdb as the output file format:\n",
+    "```json\n",
+    "model_update:\n",
+    "  presets:\n",
+    "      - predict\n",
+    "      - low_mem\n",
+    "  custom:\n",
+    "      settings:\n",
+    "          memory:\n",
+    "               eval:\n",
+    "                   use_cueq_triangle_kernels: true\n",
+    "                   use_deepspeed_evo_attention: false\n",
     "\n",
-    "```\n",
-    "output_writer_settings:\n",
-    "  # change output format to pdb (default: mmcif):\n",
-    "  structure_format: pdb\n",
-    "```\n",
     "\n",
+    "```\n",
     "#### Running OpenFold\n",
     "\n",
     "With all these inputs, you can now run OpenFold:\n",
     "\n",
-    "`run_openfold predict --query_json=queries.json --runner_yaml output_settings.yml`"
+    "`run_openfold predict --query_json=queries.json --runner_yaml settings.yaml`"
    ]
   },
   {
@@ -223,361 +202,196 @@
    "id": "9c1ab9ee-71f5-4587-bea1-35097621c039",
    "metadata": {},
    "source": [
-    "### Evaluating OpenFold output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "32cd3cbd-9d5f-4214-90da-a84ed645d35b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# confidence scores for each ligand are at assets/of3_tyk2_output/{lig_name}/*/*confidences*.json"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4adde29-282a-45f6-96a6-c8de0700a266",
-   "metadata": {},
-   "source": [
-    "## 2. Processing the OpenFold3 output for use with OpenFE\n",
+    "#### Evaluating OpenFold output\n",
+    "\n",
+    "confidence scores for each ligand are at ``assets/of3_tyk2_output/{lig_name}/seed_42/*confidences.json``\n",
     "\n",
     "<div class=\"alert alert-block alert-warning\"> \n",
     "⚠️ <b>Important Note:</b>\n",
-    "    Here we use the first model for each complex predicted by OpenFold3. In practice you would filter through all predicted models, possibly using pose busters (github.com/maabuu/posebusters) and some other scoring metrics to identify the most plausible inputs.\n",
+    "    Here we use the first model for each complex predicted by OpenFold3. In practice you would filter through all predicted models using confidence scores, and possibly using pose busters (github.com/maabuu/posebusters).\n",
     "</div>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eaf920be-9e16-402b-9fc5-8e1d4f3712cf",
+   "id": "721074db-8266-4c3e-b592-64220e0cfd8f",
    "metadata": {},
    "source": [
-    "### Preparing & extracting the structures\n",
-    "\n",
-    "Now that we have a set of predicted structures, we must extract the relevant information from the PDB files.\n",
-    "This means:\n",
-    "\n",
-    "a) Choosing a reference protein structure which will be used for all binding affinity predictions\n",
-    "\n",
-    "  - This is necessary for RBFE simulations, where a single structure must be kept constant throughout the transformations. Note that for ABFE simulations, this step can be avoided.\n",
-    "  - For the sake of this demonstration, we will be choosing the predicted structure that is bound to the ligand occupying the largest volume. The idea here being that hopefully the binding site of that structure can accomodate all the other ligands. We note that in practice this is not always true.\n",
-    "\n",
-    "b) Aligning the structures to the reference & extracting the necessary structures\n",
-    "\n",
-    "  - This is so that we can extract the ligands and use them with the chosen structure.\n",
-    "\n",
-    "c) Fixing & protonating the reference protein structure.\n",
-    "\n",
-    "\n",
-    "#### a) Choosing a reference protein structure\n",
-    "\n",
-    "As mentioned above, we will select a reference protein structure for all simulations going forward. To do this, we naively select the structure bound to a ligand with the largest volume.\n",
-    "\n",
-    "Here we load in all the structures into MDAnalysis, extract the ligands into RDKit molecules using their initial SMILES definitions as bond/formal charge templates, and then get the ligand with the largest volume using the `Asphericity` 3D descriptor."
+    "## 2. Running OpenFE Protocols with OpenFold structures"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
+   "cell_type": "markdown",
+   "id": "249ec515-cfad-4f49-8dba-ee2438b1adf4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ligand from lig_ejm_50_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_23_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_43_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_42_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_31_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_47_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_46_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_48_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_28_seed_42_sample_1_model.pdb\n",
-      "\n",
-      "Largest ligand is lig_jmc_27 with volume 0.5209626577896912\n"
-     ]
-    }
-   ],
    "source": [
-    "import pathlib\n",
-    "import MDAnalysis as mda\n",
-    "from MDAnalysis.analysis import align\n",
-    "from rdkit import Chem, rdBase\n",
-    "from rdkit.Chem import AllChem\n",
-    "from rdkit.Chem.Descriptors3D import Asphericity\n",
-    "import warnings\n",
+    "We will demonstrate how to use OpenFold generated structures with three different OpenFE protocols:\n",
     "\n",
-    "# don't show warnings that are expected and not an issue\n",
-    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
-    "\n",
-    "# gather all the complexes and align to a single structure\n",
-    "# which has the largest ligand by volume \n",
-    "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
-    "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
-    "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
-    "temp_dir.mkdir(exist_ok=True,parents=True)\n",
-    "# load all the output pdbs and calculate ligand volumes\n",
-    "ligand_volumes = {}\n",
+    "a) Absolute Binding Free Energy\n",
     "\n",
-    "for output_file in of_results_root.glob(\"*/*/*.pdb\"):\n",
-    "    ligand_name = output_file.name.split('_seed')[0]\n",
-    "    print(f\"Extracting ligand from {output_file.name}\")\n",
-    "    with warnings.catch_warnings():   \n",
-    "        warnings.simplefilter(\"ignore\")\n",
-    "        u = mda.Universe(str(output_file))\n",
-    "    ligand = u.select_atoms(\"resname LIG\")\n",
-    "    # write the ligand to a temporary file\n",
-    "    ligand.write(str(temp_dir / f\"{ligand_name}.pdb\"))\n",
-    "    # now load back with rdkit\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(str(temp_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
-    "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
-    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
-    "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
-    "    with rdBase.BlockLogs():  \n",
-    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
-    "    # calculate the volume\n",
-    "    vsa = Asphericity(rdkit_mol)\n",
-    "    ligand_volumes[ligand_name] = vsa\n",
+    "b) Separated Topologies\n",
     "\n",
-    "# find the ligand with the largest volume\n",
-    "largest_ligand = max(ligand_volumes, key=ligand_volumes.get)\n",
-    "print(f\"\\nLargest ligand is {largest_ligand} with volume {ligand_volumes[largest_ligand]}\")"
+    "c) Hybrid Topology\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6056e755-0da0-4712-b4ae-7a721c94851d",
+   "id": "48d2bae8-79d2-41d9-a3eb-61d6c2c25924",
    "metadata": {},
    "source": [
-    "#### b) Aligning & extracting the necessary structures\n",
-    "\n",
-    "Next we align all the models back to the reference structure. We then extract all the ligands and the protein atoms from the reference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aligning lig_ejm_50_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_jmc_23_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_ejm_43_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_ejm_42_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_ejm_31_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_ejm_47_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_jmc_27_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_ejm_46_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_ejm_48_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_jmc_28_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n"
-     ]
-    }
-   ],
-   "source": [
-    "reference_pdb = pathlib.Path(of_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")\n",
-    "other_pdbs = of_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
-    "output_dir = pathlib.Path(\"openfold_to_openfe/p_aligned/\")\n",
-    "output_dir.mkdir(exist_ok=True)\n",
-    "\n",
-    "ref = mda.Universe(reference_pdb)\n",
-    "for pdb in other_pdbs:\n",
-    "    print(f\"Aligning {pdb.name} to {reference_pdb.name}\")\n",
-    "    ligand = pdb.name.split('_seed')[0]\n",
-    "    mob = mda.Universe(pdb)\n",
+    "### Creating OpenFE components from the inputs\n",
     "\n",
-    "    # Align mobile → reference using protein backbone\n",
-    "    ref_prot = ref.select_atoms(\"protein and backbone\")\n",
-    "    mob_prot = mob.select_atoms(\"protein and backbone\")\n",
-    "    \n",
-    "    # Perform alignment in-place\n",
-    "    align.alignto(mob_prot, ref_prot)\n",
-    "    aligned_ligand = mob.select_atoms(\"resname LIG\")\n",
-    "    aligned_ligand.write(f'{output_dir}/{ligand}.pdb')\n",
-    "    # load back into rdkit to add hydrogens\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand}.pdb', removeHs=True)\n",
-    "    # use the template to assign bond orders\n",
-    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand])\n",
-    "    with rdBase.BlockLogs():  \n",
-    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
-    "    # Add hydrogens\n",
-    "    rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
-    "    # Set the molecule name\n",
-    "    rdkit_mol.SetProp('_Name', f\"{ligand}\")\n",
-    "    # write to sdf\n",
-    "    Chem.MolToMolFile(rdkit_mol, output_dir / f\"{ligand}_prepped.sdf\")\n",
+    "Now we can create OpenFE components from the files we prepared above.\n",
+    "#### Loading ligands and assigning partial charges\n",
     "\n",
-    "    # For the reference pdb, save the protein alone\n",
-    "    if pdb.name.split('_seed')[0] == largest_ligand:\n",
-    "        full_protein = mob.select_atoms(\"protein\")\n",
-    "        full_protein.write(f'{output_dir}/protein.pdb')"
+    "First we load the ligands and assign partial charges to them using OpenFF's NAGL model."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fe3ca792-4615-4be3-8a41-feb0819e968d",
+   "id": "6419b4ec-6513-47ca-bb66-0eb17a7224c3",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-warning\"> \n",
-    "⚠️ <b>Important Note:</b>\n",
-    "    <br><br>\n",
-    "    Here simply we assign ligand protonation states to those we defined at the start of this exercise. Additionally, below we will assign protein protonation states using PDBFixer at pH 7 without accounting for the ligand.<br><br>\n",
-    "    In practice more care would be taken when protonating the complexes, using more complex tools such as Maestro or OpenEye's Spruce. For example, ligand protonation states may change depending on the chemical environment they are bound to.\n",
-    "</div>"
+    "### 2a) Absolute Binding Free Energy (ABFE) Transformation\n",
+    "\n",
+    "The ABFE protocol requires the least amount of post-processing of the OpenFold outputs.\n",
+    "\n",
+    "For the sake of simplicity, we will only create a Transformation for `lig_jmc_27`, but this process is generalizable to the rest of the ligands in the series.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "10b42b56-2f47-4215-8397-fcf7473c4860",
+   "id": "cdeee4e3-ce8c-4151-9ef7-5a6017ac3dcb",
    "metadata": {},
    "source": [
-    "#### c) Fixing & protonating the reference protein structure\n",
-    "\n",
-    "The PDB contains the heavy atoms of the protein but is missing hydrogens and caps. Here we call PDBFixer to add hydrogens at pH 7 and add protein caps."
+    "#### Loading and assigning partial charges to the ligand(s)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
+   "id": "09725e7d-b959-4221-b027-1488b6fea22a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# now run the reference protein through pdbfixer\n",
-    "! pdbfixer openfold_to_openfe/p_aligned/protein.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/p_aligned/protein_fixed.pdb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "89e52270-514c-4b21-8179-07c7319ff468",
-   "metadata": {},
-   "source": [
-    "## 3. Creating OpenFE components from the inputs\n",
-    "\n",
-    "Now we can create OpenFE components from the files we prepared above.\n",
-    "\n",
-    "### Loading ligands and assigning partial charges\n",
-    "\n",
-    "First we load the ligands and assign them partial charges using OpenFF's NAGL model."
+    "import openfe\n",
+    "supp = Chem.SDMolSupplier(\"assets/tyk2_ligands.sdf\", removeHs=False)\n",
+    "all_ligands = [openfe.SmallMoleculeComponent.from_rdkit(mol) for mol in supp]\n",
+    "ligand = [lig for lig in all_ligands if lig.name == 'lig_jmc_27'][0]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "fc97de03",
+   "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.85it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.57it/s]\n"
      ]
     }
    ],
    "source": [
-    "import glob\n",
-    "import openfe\n",
     "from openfe.protocols.openmm_utils.omm_settings import OpenFFPartialChargeSettings\n",
     "from openfe.protocols.openmm_utils.charge_generation import bulk_assign_partial_charges\n",
     "\n",
-    "# Load all the ligands\n",
-    "ligand_sdfs = glob.glob('openfold_to_openfe/p_aligned/*_prepped.sdf')\n",
-    "ligands = []\n",
-    "for l in ligand_sdfs:\n",
-    "    ligand = openfe.SmallMoleculeComponent.from_sdf_file(l)\n",
-    "    ligands.append(ligand)\n",
-    "\n",
     "# Generate partial charges\n",
     "charge_settings = OpenFFPartialChargeSettings(partial_charge_method=\"nagl\")\n",
     "\n",
     "charged_ligands = bulk_assign_partial_charges(\n",
-    "    molecules=ligands,\n",
+    "    molecules=[ligand],\n",
     "    overwrite=False,\n",
     "    method=charge_settings.partial_charge_method,\n",
     "    toolkit_backend=charge_settings.off_toolkit_backend,\n",
     "    generate_n_conformers=1,\n",
     "    nagl_model=None,\n",
-    "    processors=1\n",
+    "    processors=1,\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0d1e9f9d-ebe0-4fd5-be6f-fe03f7db75ee",
+   "id": "a84b9dfa-e92a-43fa-baae-a725778122cb",
    "metadata": {},
    "source": [
-    "Then we can load the reference protein to a ProteinComponent."
+    "#### Fixing & protonating the protein structure\n",
+    "\n",
+    "The PDB contains the heavy atoms of the protein but is missing hydrogens and caps. Here we call PDBFixer to add hydrogens at pH 7 and add protein caps."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
+   "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/site-packages/gufe/components/explicitmoleculecomponent.py:59: UserWarning: Molecule (name='tyk2') doesn't have any hydrogen atoms present. If this is unexpected, consider loading the molecule with `removeHs=False`\n",
-      "  warnings.warn(\n"
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=37394) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "  pid, fd = os.forkpty()\n"
      ]
     }
    ],
    "source": [
-    "protein = openfe.ProteinComponent.from_pdb_file(\"openfold_to_openfe/p_aligned/protein.pdb\", name=\"tyk2\")"
+    "# now run the reference protein through pdbfixer\n",
+    "! pdbfixer assets/of3_tyk2_output/lig_jmc_27/seed_42/lig_jmc_27_seed_42_sample_1_model.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7936a780-99a5-4bcb-addf-64646b9f3a4d",
+   "id": "8c55d160-558f-426f-8a8c-9ae46d191cff",
    "metadata": {},
    "source": [
-    "Finally, let's also define a SolventComponent which we will use to define our Transformations."
+    "Then we can load the reference protein to a ProteinComponent."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
-   "metadata": {},
-   "outputs": [],
+   "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ProteinComponent(name=tyk2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "solvent = openfe.SolventComponent()"
+    "protein = openfe.ProteinComponent.from_pdb_file(\"openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb\", name=\"tyk2\")\n",
+    "protein"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8a83aff9-a356-4a3f-aeed-cc3a0e989cf5",
+   "id": "4d11803b-6d4b-4507-8c98-6ff40f75680e",
    "metadata": {},
    "source": [
-    "## 4. Creating a Binding Free Energy Transformation\n",
-    "\n",
-    "### 4a) Absolute Binding Free Energy\n",
-    "Using these inputs, we can create ABFE Transformations. For the sake of simplicity, we will only create a Transformation for `lig_jmc_27`, but this process is generalizable to the rest of the ligands in the series.\n",
-    "\n",
-    "First we create the `ChemicalSystems` defining the end states of the simulation:"
+    "We must also define a SolventComponent which we will use to define our Transformations."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "c405e50c-1df3-4bef-8278-393550e1fc6a",
+   "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "SmallMoleculeComponent(name=lig_jmc_27)"
+       "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
      "execution_count": 10,
@@ -586,9 +400,24 @@
     }
    ],
    "source": [
-    "# choose the first ligand\n",
-    "ligand = [l for l in charged_ligands if l.name == 'lig_jmc_27'][0]\n",
-    "ligand"
+    "solvent = openfe.SolventComponent()\n",
+    "solvent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1ef09d2-ec8b-4577-8510-c67cb89fc5b5",
+   "metadata": {},
+   "source": [
+    "#### Creating the ``Transformation``"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "695c61c4-3fa6-4e1a-939f-5349e6829627",
+   "metadata": {},
+   "source": [
+    "Create the `ChemicalSystems` defining the end states of the simulation:"
    ]
   },
   {
@@ -598,15 +427,13 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(ChemicalSystem(name=state_A, components={'ligand': SmallMoleculeComponent(name=lig_jmc_27), 'protein': ProteinComponent(name=tyk2), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n",
-       " ChemicalSystem(name=state_B, components={'protein': ProteinComponent(name=tyk2), 'solvent': SolventComponent(name=O, Na+, Cl-)}))"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChemicalSystem(name=complex, components={'ligand': SmallMoleculeComponent(name=lig_jmc_27), 'protein': ProteinComponent(name=tyk2), 'solvent': SolventComponent(name=O, Na+, Cl-)})\n",
+      "\n",
+      "ChemicalSystem(name=solvent, components={'protein': ProteinComponent(name=tyk2), 'solvent': SolventComponent(name=O, Na+, Cl-)})\n"
+     ]
     }
    ],
    "source": [
@@ -617,20 +444,22 @@
     "    'ligand': ligand,\n",
     "    'protein': protein,\n",
     "    'solvent': solvent,\n",
-    "}, name=\"state_A\")\n",
+    "}, name=\"complex\")\n",
     "\n",
     "# state B is the system without the ligand\n",
     "stateB = openfe.ChemicalSystem({\n",
     "    'protein': protein,\n",
     "    'solvent': solvent,\n",
-    "}, name=\"state_B\")\n",
+    "}, name=\"solvent\")\n",
     "\n",
-    "stateA, stateB"
+    "print(stateA)\n",
+    "print(\"\")\n",
+    "print(stateB)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b576d9b0-b81e-4dc1-8bf0-26fc1585b757",
+   "id": "e32fb4ef-dbf5-4a9a-90c4-b64d9e5af6ce",
    "metadata": {},
    "source": [
     "Next we create the Protocol. We use the default settings, except to help us execute in parallel we set the number of repeats to 1."
@@ -652,7 +481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a643c9b-5510-4ae4-9b10-0af779821e66",
+   "id": "6bf8af4c-9894-4af3-84f8-2ed3830120ad",
    "metadata": {},
    "source": [
     "Finally we create the `Transformation` object."
@@ -676,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f89a9800-9d25-470c-845f-87479095806e",
+   "id": "34ec9c8d-cb7f-4cad-9794-b73629a11246",
    "metadata": {},
    "source": [
     "This transformation can now be run using `openfe quickrun`."
@@ -684,17 +513,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9e4f4a1-ed28-4d33-b73b-865dc5023b93",
+   "id": "cc023ddb-d32a-4f78-bea1-a4f9efddf902",
+   "metadata": {},
+   "source": [
+    "### 2b) Separated Topologies RBFE Transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a90d2205-1c68-4349-95b1-e20f607d4a86",
    "metadata": {},
    "source": [
-    "### 4b) Separated Topologies Relative Binding Free Energy Transformation\n",
-    "\n",
     "We can instead build a SepTop RBFE Transformation. Here we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {},
    "outputs": [],
@@ -705,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {},
    "outputs": [],
@@ -729,7 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebde65ab-8a9c-4426-a0c4-91aefd6dea68",
+   "id": "54484f2f-347b-498e-b5e2-60e8c8db446a",
    "metadata": {},
    "source": [
     "Next we create the Protocol. We use the default settings, except to help us execute in parallel we set the number of repeats to 1."
@@ -737,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {},
    "outputs": [],
@@ -751,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {},
    "outputs": [],
@@ -767,7 +602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d59f5d1b-f078-49c2-9965-9cb8abc64a71",
+   "id": "db4344c5-c300-4b6e-aa0a-a7cae9c6380f",
    "metadata": {},
    "source": [
     "This transformation can now be run using `openfe quickrun`."
@@ -775,17 +610,166 @@
   },
   {
    "cell_type": "markdown",
-   "id": "316727a4-aa8b-43dd-916d-b73244ee829a",
+   "id": "6e16e781-c6dc-4a62-85a7-0bc59678e916",
    "metadata": {},
    "source": [
-    "### 4c) Creating a Hybrid Topology Relative Binding Free Energy Transformation\n",
+    "### 2c) Creating a Hybrid Topology Relative Binding Free Energy Transformation\n",
     "\n",
     "Finally we create a Hybrid Topology RBFE Transformation. Again, as per the SepTop example above, we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "eaf920be-9e16-402b-9fc5-8e1d4f3712cf",
+   "metadata": {},
+   "source": [
+    "### Preparing & extracting the structures\n",
+    "\n",
+    "Now that we have a set of predicted structures, we must extract the relevant information from the PDB files.\n",
+    "This means:\n",
+    "\n",
+    "a) Choosing a reference protein structure which will be used for all binding affinity predictions\n",
+    "\n",
+    "  - This is necessary for RBFE simulations, where a single structure must be kept constant throughout the transformations. Note that for ABFE simulations, this step can be avoided.\n",
+    "  - For the sake of this demonstration, we will be choosing the predicted structure that is bound to the ligand occupying the largest volume. The idea here being that hopefully the binding site of that structure can accomodate all the other ligands. We note that in practice this is not always true.\n",
+    "\n",
+    "b) Aligning the structures to the reference & extracting the necessary structures\n",
+    "\n",
+    "  - This is so that we can extract the ligands and use them with the chosen structure.\n",
+    "\n",
+    "c) Fixing & protonating the reference protein structure.\n",
+    "\n",
+    "\n",
+    "#### a) Choosing a reference protein structure\n",
+    "\n",
+    "As mentioned above, we will select a reference protein structure for all simulations going forward. To do this, we naively select the structure bound to a ligand with the largest volume.\n",
+    "\n",
+    "Here we load in all the structures into MDAnalysis, extract the ligands into RDKit molecules using their initial SMILES definitions as bond/formal charge templates, and then get the ligand with the largest volume using the `Asphericity` 3D descriptor."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import MDAnalysis as mda\n",
+    "from MDAnalysis.analysis import align\n",
+    "from rdkit import Chem, rdBase\n",
+    "from rdkit.Chem import AllChem\n",
+    "from rdkit.Chem.Descriptors3D import Asphericity\n",
+    "import warnings\n",
+    "\n",
+    "# don't show warnings that are expected and not an issue\n",
+    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
+    "\n",
+    "# gather all the complexes and align to a single structure\n",
+    "# which has the largest ligand by volume \n",
+    "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
+    "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
+    "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
+    "temp_dir.mkdir(exist_ok=True,parents=True)\n",
+    "# load all the output pdbs and calculate ligand volumes\n",
+    "ligand_volumes = {}\n",
+    "\n",
+    "for output_file in of_results_root.glob(\"*/*/*.pdb\"):\n",
+    "    ligand_name = output_file.name.split('_seed')[0]\n",
+    "    print(f\"Extracting ligand from {output_file.name}\")\n",
+    "    with warnings.catch_warnings():   \n",
+    "        warnings.simplefilter(\"ignore\")\n",
+    "        u = mda.Universe(str(output_file))\n",
+    "    ligand = u.select_atoms(\"resname LIG\")\n",
+    "    # write the ligand to a temporary file\n",
+    "    ligand.write(str(temp_dir / f\"{ligand_name}.pdb\"))\n",
+    "    # now load back with rdkit\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(str(temp_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
+    "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
+    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
+    "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
+    "    with rdBase.BlockLogs():  \n",
+    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "    # calculate the volume\n",
+    "    vsa = Asphericity(rdkit_mol)\n",
+    "    ligand_volumes[ligand_name] = vsa\n",
+    "\n",
+    "# find the ligand with the largest volume\n",
+    "largest_ligand = max(ligand_volumes, key=ligand_volumes.get)\n",
+    "print(f\"\\nLargest ligand is {largest_ligand} with volume {ligand_volumes[largest_ligand]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6056e755-0da0-4712-b4ae-7a721c94851d",
+   "metadata": {},
+   "source": [
+    "#### b) Aligning & extracting the necessary structures\n",
+    "\n",
+    "Next we align all the models back to the reference structure. We then extract all the ligands and the protein atoms from the reference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_pdb = pathlib.Path(of_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")\n",
+    "other_pdbs = of_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
+    "output_dir = pathlib.Path(\"openfold_to_openfe/p_aligned/\")\n",
+    "output_dir.mkdir(exist_ok=True)\n",
+    "\n",
+    "ref = mda.Universe(reference_pdb)\n",
+    "for pdb in other_pdbs:\n",
+    "    print(f\"Aligning {pdb.name} to {reference_pdb.name}\")\n",
+    "    ligand = pdb.name.split('_seed')[0]\n",
+    "    mob = mda.Universe(pdb)\n",
+    "\n",
+    "    # Align mobile → reference using protein backbone\n",
+    "    ref_prot = ref.select_atoms(\"protein and backbone\")\n",
+    "    mob_prot = mob.select_atoms(\"protein and backbone\")\n",
+    "    \n",
+    "    # Perform alignment in-place\n",
+    "    align.alignto(mob_prot, ref_prot)\n",
+    "    aligned_ligand = mob.select_atoms(\"resname LIG\")\n",
+    "    aligned_ligand.write(f'{output_dir}/{ligand}.pdb')\n",
+    "    # load back into rdkit to add hydrogens\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand}.pdb', removeHs=True)\n",
+    "    # use the template to assign bond orders\n",
+    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand])\n",
+    "    with rdBase.BlockLogs():  \n",
+    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "    # Add hydrogens\n",
+    "    rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
+    "    # Set the molecule name\n",
+    "    rdkit_mol.SetProp('_Name', f\"{ligand}\")\n",
+    "    # write to sdf\n",
+    "    Chem.MolToMolFile(rdkit_mol, output_dir / f\"{ligand}_prepped.sdf\")\n",
+    "\n",
+    "    # For the reference pdb, save the protein alone\n",
+    "    if pdb.name.split('_seed')[0] == largest_ligand:\n",
+    "        full_protein = mob.select_atoms(\"protein\")\n",
+    "        full_protein.write(f'{output_dir}/protein.pdb')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe3ca792-4615-4be3-8a41-feb0819e968d",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\"> \n",
+    "⚠️ <b>Important Note:</b>\n",
+    "    <br><br>\n",
+    "    Here simply we assign ligand protonation states to those we defined at the start of this exercise. Additionally, below we will assign protein protonation states using PDBFixer at pH 7 without accounting for the ligand.<br><br>\n",
+    "    In practice more care would be taken when protonating the complexes, using more complex tools such as Maestro or OpenEye's Spruce. For example, ligand protonation states may change depending on the chemical environment they are bound to.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {},
    "outputs": [],
@@ -815,7 +799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {},
    "outputs": [],
@@ -837,42 +821,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:kartograf.atom_mapper:#################################\n",
-      "INFO:kartograf.atom_mapper:Map Heavy Atoms \n",
-      "INFO:kartograf.atom_mapper:#################################\n",
-      "INFO:kartograf.atom_mapper:Masking Atoms\n",
-      "INFO:kartograf.atom_mapper:Build Distance Matrix\n",
-      "INFO:kartograf.atom_mapper:Calculate Mapping\n",
-      "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n",
-      "INFO:kartograf.atom_mapper:#################################\n",
-      "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n",
-      "INFO:kartograf.atom_mapper:#################################\n",
-      "INFO:kartograf.atom_mapper:Masking Atoms\n",
-      "INFO:kartograf.atom_mapper:Build Distance Matrix\n",
-      "INFO:kartograf.atom_mapper:Calculate Mapping\n",
-      "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n",
-      "INFO:kartograf.atom_mapper:Filtering bond breaks\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from kartograf import KartografAtomMapper\n",
     "\n",
@@ -891,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {},
    "outputs": [],
@@ -915,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {},
    "outputs": [],
@@ -945,7 +897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {},
    "outputs": [],
@@ -975,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {},
    "outputs": [],

From 96b8b76e28691729a92761caddbb20ef679d5585 Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Thu, 30 Apr 2026 14:42:34 -0700
Subject: [PATCH 03/15] adjusting ordering logic

---
 cookbook/openfold3_to_openfe.ipynb | 511 +++++++++++++++++++++--------
 1 file changed, 368 insertions(+), 143 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 815584e..999264d 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -226,25 +226,7 @@
    "metadata": {},
    "source": [
     "We will demonstrate how to use OpenFold generated structures with three different OpenFE protocols:\n",
-    "\n",
-    "a) Absolute Binding Free Energy\n",
-    "\n",
-    "b) Separated Topologies\n",
-    "\n",
-    "c) Hybrid Topology\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48d2bae8-79d2-41d9-a3eb-61d6c2c25924",
-   "metadata": {},
-   "source": [
-    "### Creating OpenFE components from the inputs\n",
-    "\n",
-    "Now we can create OpenFE components from the files we prepared above.\n",
-    "#### Loading ligands and assigning partial charges\n",
-    "\n",
-    "First we load the ligands and assign partial charges to them using OpenFF's NAGL model."
+    "Absolute Binding Free Energy (ABFE), Separated Topologies RBFE, and Hybrid Topology RBFE.\n"
    ]
   },
   {
@@ -252,7 +234,7 @@
    "id": "6419b4ec-6513-47ca-bb66-0eb17a7224c3",
    "metadata": {},
    "source": [
-    "### 2a) Absolute Binding Free Energy (ABFE) Transformation\n",
+    "### Creating an Absolute Binding Free Energy (ABFE) Transformation\n",
     "\n",
     "The ABFE protocol requires the least amount of post-processing of the OpenFold outputs.\n",
     "\n",
@@ -269,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "09725e7d-b959-4221-b027-1488b6fea22a",
    "metadata": {},
    "outputs": [],
@@ -282,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {},
    "outputs": [
@@ -290,7 +272,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.57it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.68it/s]\n"
      ]
     }
    ],
@@ -324,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
    "metadata": {},
    "outputs": [
@@ -332,7 +314,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=37394) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=39178) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -352,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
    "metadata": {
     "scrolled": true
@@ -364,7 +346,7 @@
        "ProteinComponent(name=tyk2)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -384,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {},
    "outputs": [
@@ -394,7 +376,7 @@
        "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,7 +391,7 @@
    "id": "e1ef09d2-ec8b-4577-8510-c67cb89fc5b5",
    "metadata": {},
    "source": [
-    "#### Creating the ``Transformation``"
+    "#### Creating the ABFE ``Transformation``"
    ]
   },
   {
@@ -422,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
    "metadata": {},
    "outputs": [
@@ -467,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
    "metadata": {},
    "outputs": [],
@@ -489,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
    "metadata": {},
    "outputs": [],
@@ -511,12 +493,287 @@
     "This transformation can now be run using `openfe quickrun`."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "df8cb66d-c201-4a22-b87a-a1e5501d4be4",
+   "metadata": {},
+   "source": [
+    "### Preparing OpenFold outputs for Relative Binding Free Energies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dd96548-7190-4851-b1ed-fb2d8c1bd87b",
+   "metadata": {},
+   "source": [
+    "RBFE transformations require additional preparation, including:\n",
+    "\n",
+    "a) Choosing a reference protein structure which will be used for all binding affinity predictions\n",
+    "\n",
+    "  - This is necessary for RBFE simulations, where a single structure must be kept constant throughout the transformations. Note that for ABFE simulations, this step can be avoided.\n",
+    "  - For the sake of this demonstration, we will be choosing the predicted structure that is bound to the ligand occupying the largest volume. The idea here being that hopefully the binding site of that structure can accomodate all the other ligands. We note that in practice this is not always true.\n",
+    "\n",
+    "b) Aligning the structures to the reference & extracting the necessary structures\n",
+    "\n",
+    "  - This is so that we can extract the ligands and use them with the chosen structure.\n",
+    "\n",
+    "c) Fixing & protonating the reference protein structure, as we did above for the ABFE protein structure.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02a82dbd-5a01-43c5-af33-654b706a5721",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### a) Choosing a reference protein structure\n",
+    "\n",
+    "As mentioned above, we will select a reference protein structure for all simulations going forward. To do this, we naively select the structure bound to a ligand with the largest volume.\n",
+    "\n",
+    "Here we load in all the structures into MDAnalysis, extract the ligands into RDKit molecules using their initial SMILES definitions as bond/formal charge templates, and then get the ligand with the largest volume using the `Asphericity` 3D descriptor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ligand from lig_ejm_50_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_jmc_23_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_43_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_42_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_31_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_47_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_46_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_48_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_jmc_28_seed_42_sample_1_model.pdb\n",
+      "\n",
+      "Largest ligand is lig_jmc_27 with volume 0.5209626577896912\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pathlib\n",
+    "import MDAnalysis as mda\n",
+    "from MDAnalysis.analysis import align\n",
+    "from rdkit import Chem, rdBase\n",
+    "from rdkit.Chem import AllChem\n",
+    "from rdkit.Chem.Descriptors3D import Asphericity\n",
+    "import warnings\n",
+    "\n",
+    "# don't show warnings that are expected and not an issue\n",
+    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
+    "\n",
+    "# gather all the complexes and align to a single structure\n",
+    "# which has the largest ligand by volume \n",
+    "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
+    "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
+    "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
+    "temp_dir.mkdir(exist_ok=True,parents=True)\n",
+    "# load all the output pdbs and calculate ligand volumes\n",
+    "ligand_volumes = {}\n",
+    "\n",
+    "for output_file in of_results_root.glob(\"*/*/*.pdb\"):\n",
+    "    ligand_name = output_file.name.split('_seed')[0]\n",
+    "    print(f\"Extracting ligand from {output_file.name}\")\n",
+    "    with warnings.catch_warnings():   \n",
+    "        warnings.simplefilter(\"ignore\")\n",
+    "        u = mda.Universe(str(output_file))\n",
+    "    ligand = u.select_atoms(\"resname LIG\")\n",
+    "    # write the ligand to a temporary file\n",
+    "    ligand.write(str(temp_dir / f\"{ligand_name}.pdb\"))\n",
+    "    # now load back with rdkit\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(str(temp_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
+    "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
+    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
+    "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
+    "    with rdBase.BlockLogs():  \n",
+    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "    # calculate the volume\n",
+    "    vsa = Asphericity(rdkit_mol)\n",
+    "    ligand_volumes[ligand_name] = vsa\n",
+    "\n",
+    "# find the ligand with the largest volume\n",
+    "largest_ligand = max(ligand_volumes, key=ligand_volumes.get)\n",
+    "print(f\"\\nLargest ligand is {largest_ligand} with volume {ligand_volumes[largest_ligand]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "193dc76a-6e43-4e25-8b9d-5c0e4a79c3e9",
+   "metadata": {},
+   "source": [
+    "#### b) Aligning & extracting the necessary structures\n",
+    "\n",
+    "Next we align all the models back to the reference structure. We then extract all the ligands and the protein atoms from the reference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aligning lig_ejm_50_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_jmc_23_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_ejm_43_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_ejm_42_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_ejm_31_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_ejm_47_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_jmc_27_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_ejm_46_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_ejm_48_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Aligning lig_jmc_28_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n"
+     ]
+    }
+   ],
+   "source": [
+    "reference_pdb = pathlib.Path(of_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")\n",
+    "other_pdbs = of_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
+    "output_dir = pathlib.Path(\"openfold_to_openfe/p_aligned/\")\n",
+    "output_dir.mkdir(exist_ok=True)\n",
+    "\n",
+    "ref = mda.Universe(reference_pdb)\n",
+    "for pdb in other_pdbs:\n",
+    "    print(f\"Aligning {pdb.name} to {reference_pdb.name}\")\n",
+    "    ligand = pdb.name.split('_seed')[0]\n",
+    "    mob = mda.Universe(pdb)\n",
+    "\n",
+    "    # Align mobile → reference using protein backbone\n",
+    "    ref_prot = ref.select_atoms(\"protein and backbone\")\n",
+    "    mob_prot = mob.select_atoms(\"protein and backbone\")\n",
+    "    \n",
+    "    # Perform alignment in-place\n",
+    "    align.alignto(mob_prot, ref_prot)\n",
+    "    aligned_ligand = mob.select_atoms(\"resname LIG\")\n",
+    "    aligned_ligand.write(f'{output_dir}/{ligand}.pdb')\n",
+    "    # load back into rdkit to add hydrogens\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand}.pdb', removeHs=True)\n",
+    "    # use the template to assign bond orders\n",
+    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand])\n",
+    "    with rdBase.BlockLogs():  \n",
+    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "    # Add hydrogens\n",
+    "    rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
+    "    # Set the molecule name\n",
+    "    rdkit_mol.SetProp('_Name', f\"{ligand}\")\n",
+    "    # write to sdf\n",
+    "    Chem.MolToMolFile(rdkit_mol, output_dir / f\"{ligand}_prepped.sdf\")\n",
+    "\n",
+    "    # For the reference pdb, save the protein alone\n",
+    "    if pdb.name.split('_seed')[0] == largest_ligand:\n",
+    "        full_protein = mob.select_atoms(\"protein\")\n",
+    "        full_protein.write(f'{output_dir}/protein.pdb')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5b5141e-efe0-4bea-89e9-4896c00f4311",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\"> \n",
+    "⚠️ <b>Important Note:</b>\n",
+    "    <br><br>\n",
+    "    Here simply we assign ligand protonation states to those we defined at the start of this exercise. Additionally, below we will assign protein protonation states using PDBFixer at pH 7 without accounting for the ligand.<br><br>\n",
+    "    In practice more care would be taken when protonating the complexes, using more complex tools such as Maestro or OpenEye's Spruce. For example, ligand protonation states may change depending on the chemical environment they are bound to.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf56d93-222a-4651-b8cf-f451f0360878",
+   "metadata": {},
+   "source": [
+    "#### c) Fixing & protonating the reference protein structure\n",
+    "\n",
+    "The PDB contains the heavy atoms of the protein but is missing hydrogens and caps. Here we call PDBFixer to add hydrogens at pH 7 and add protein caps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=39178) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "  pid, fd = os.forkpty()\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now run the reference protein through pdbfixer\n",
+    "! pdbfixer openfold_to_openfe/p_aligned/protein.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/p_aligned/protein_fixed.pdb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1153dbd0-998a-4f3f-8e47-a950c806d1dd",
+   "metadata": {},
+   "source": [
+    "#### d) Loading and assigning partial charges to the aligned ligands"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "992d53f3-d086-4283-9d6a-055be01588ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.86it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import glob\n",
+    "import openfe\n",
+    "from openfe.protocols.openmm_utils.omm_settings import OpenFFPartialChargeSettings\n",
+    "from openfe.protocols.openmm_utils.charge_generation import bulk_assign_partial_charges\n",
+    "\n",
+    "# Load all the ligands\n",
+    "ligand_sdfs = glob.glob('openfold_to_openfe/p_aligned/*_prepped.sdf')\n",
+    "ligands = []\n",
+    "for l in ligand_sdfs:\n",
+    "    ligand = openfe.SmallMoleculeComponent.from_sdf_file(l)\n",
+    "    ligands.append(ligand)\n",
+    "\n",
+    "# Generate partial charges\n",
+    "charge_settings = OpenFFPartialChargeSettings(partial_charge_method=\"nagl\")\n",
+    "\n",
+    "charged_ligands = bulk_assign_partial_charges(\n",
+    "    molecules=ligands,\n",
+    "    overwrite=False,\n",
+    "    method=charge_settings.partial_charge_method,\n",
+    "    toolkit_backend=charge_settings.off_toolkit_backend,\n",
+    "    generate_n_conformers=1,\n",
+    "    nagl_model=None,\n",
+    "    processors=1\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cc023ddb-d32a-4f78-bea1-a4f9efddf902",
    "metadata": {},
    "source": [
-    "### 2b) Separated Topologies RBFE Transformation"
+    "### Creating a Separated Topologies RBFE Transformation"
    ]
   },
   {
@@ -524,12 +781,12 @@
    "id": "a90d2205-1c68-4349-95b1-e20f607d4a86",
    "metadata": {},
    "source": [
-    "We can instead build a SepTop RBFE Transformation. Here we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
+    "We can now SepTop RBFE Transformation. Here we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {},
    "outputs": [],
@@ -540,7 +797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {},
    "outputs": [],
@@ -572,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {},
    "outputs": [],
@@ -586,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {},
    "outputs": [],
@@ -613,7 +870,7 @@
    "id": "6e16e781-c6dc-4a62-85a7-0bc59678e916",
    "metadata": {},
    "source": [
-    "### 2c) Creating a Hybrid Topology Relative Binding Free Energy Transformation\n",
+    "### Creating a Hybrid Topology Relative Binding Free Energy Transformation\n",
     "\n",
     "Finally we create a Hybrid Topology RBFE Transformation. Again, as per the SepTop example above, we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
    ]
@@ -622,37 +879,33 @@
    "cell_type": "markdown",
    "id": "eaf920be-9e16-402b-9fc5-8e1d4f3712cf",
    "metadata": {},
-   "source": [
-    "### Preparing & extracting the structures\n",
-    "\n",
-    "Now that we have a set of predicted structures, we must extract the relevant information from the PDB files.\n",
-    "This means:\n",
-    "\n",
-    "a) Choosing a reference protein structure which will be used for all binding affinity predictions\n",
-    "\n",
-    "  - This is necessary for RBFE simulations, where a single structure must be kept constant throughout the transformations. Note that for ABFE simulations, this step can be avoided.\n",
-    "  - For the sake of this demonstration, we will be choosing the predicted structure that is bound to the ligand occupying the largest volume. The idea here being that hopefully the binding site of that structure can accomodate all the other ligands. We note that in practice this is not always true.\n",
-    "\n",
-    "b) Aligning the structures to the reference & extracting the necessary structures\n",
-    "\n",
-    "  - This is so that we can extract the ligands and use them with the chosen structure.\n",
-    "\n",
-    "c) Fixing & protonating the reference protein structure.\n",
-    "\n",
-    "\n",
-    "#### a) Choosing a reference protein structure\n",
-    "\n",
-    "As mentioned above, we will select a reference protein structure for all simulations going forward. To do this, we naively select the structure bound to a ligand with the largest volume.\n",
-    "\n",
-    "Here we load in all the structures into MDAnalysis, extract the ligands into RDKit molecules using their initial SMILES definitions as bond/formal charge templates, and then get the ligand with the largest volume using the `Asphericity` 3D descriptor."
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ligand from lig_ejm_50_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_jmc_23_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_43_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_42_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_31_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_47_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_46_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_ejm_48_seed_42_sample_1_model.pdb\n",
+      "Extracting ligand from lig_jmc_28_seed_42_sample_1_model.pdb\n",
+      "\n",
+      "Largest ligand is lig_jmc_27 with volume 0.5209626577896912\n"
+     ]
+    }
+   ],
    "source": [
     "import pathlib\n",
     "import MDAnalysis as mda\n",
@@ -699,77 +952,9 @@
     "print(f\"\\nLargest ligand is {largest_ligand} with volume {ligand_volumes[largest_ligand]}\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6056e755-0da0-4712-b4ae-7a721c94851d",
-   "metadata": {},
-   "source": [
-    "#### b) Aligning & extracting the necessary structures\n",
-    "\n",
-    "Next we align all the models back to the reference structure. We then extract all the ligands and the protein atoms from the reference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reference_pdb = pathlib.Path(of_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")\n",
-    "other_pdbs = of_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
-    "output_dir = pathlib.Path(\"openfold_to_openfe/p_aligned/\")\n",
-    "output_dir.mkdir(exist_ok=True)\n",
-    "\n",
-    "ref = mda.Universe(reference_pdb)\n",
-    "for pdb in other_pdbs:\n",
-    "    print(f\"Aligning {pdb.name} to {reference_pdb.name}\")\n",
-    "    ligand = pdb.name.split('_seed')[0]\n",
-    "    mob = mda.Universe(pdb)\n",
-    "\n",
-    "    # Align mobile → reference using protein backbone\n",
-    "    ref_prot = ref.select_atoms(\"protein and backbone\")\n",
-    "    mob_prot = mob.select_atoms(\"protein and backbone\")\n",
-    "    \n",
-    "    # Perform alignment in-place\n",
-    "    align.alignto(mob_prot, ref_prot)\n",
-    "    aligned_ligand = mob.select_atoms(\"resname LIG\")\n",
-    "    aligned_ligand.write(f'{output_dir}/{ligand}.pdb')\n",
-    "    # load back into rdkit to add hydrogens\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand}.pdb', removeHs=True)\n",
-    "    # use the template to assign bond orders\n",
-    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand])\n",
-    "    with rdBase.BlockLogs():  \n",
-    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
-    "    # Add hydrogens\n",
-    "    rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
-    "    # Set the molecule name\n",
-    "    rdkit_mol.SetProp('_Name', f\"{ligand}\")\n",
-    "    # write to sdf\n",
-    "    Chem.MolToMolFile(rdkit_mol, output_dir / f\"{ligand}_prepped.sdf\")\n",
-    "\n",
-    "    # For the reference pdb, save the protein alone\n",
-    "    if pdb.name.split('_seed')[0] == largest_ligand:\n",
-    "        full_protein = mob.select_atoms(\"protein\")\n",
-    "        full_protein.write(f'{output_dir}/protein.pdb')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fe3ca792-4615-4be3-8a41-feb0819e968d",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\"> \n",
-    "⚠️ <b>Important Note:</b>\n",
-    "    <br><br>\n",
-    "    Here simply we assign ligand protonation states to those we defined at the start of this exercise. Additionally, below we will assign protein protonation states using PDBFixer at pH 7 without accounting for the ligand.<br><br>\n",
-    "    In practice more care would be taken when protonating the complexes, using more complex tools such as Maestro or OpenEye's Spruce. For example, ligand protonation states may change depending on the chemical environment they are bound to.\n",
-    "</div>"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {},
    "outputs": [],
@@ -799,7 +984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {},
    "outputs": [],
@@ -821,10 +1006,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:kartograf.atom_mapper:#################################\n",
+      "INFO:kartograf.atom_mapper:Map Heavy Atoms \n",
+      "INFO:kartograf.atom_mapper:#################################\n",
+      "INFO:kartograf.atom_mapper:Masking Atoms\n",
+      "INFO:kartograf.atom_mapper:Build Distance Matrix\n",
+      "INFO:kartograf.atom_mapper:Calculate Mapping\n",
+      "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n",
+      "INFO:kartograf.atom_mapper:#################################\n",
+      "INFO:kartograf.atom_mapper:Map Hydrogen Atoms: \n",
+      "INFO:kartograf.atom_mapper:#################################\n",
+      "INFO:kartograf.atom_mapper:Masking Atoms\n",
+      "INFO:kartograf.atom_mapper:Build Distance Matrix\n",
+      "INFO:kartograf.atom_mapper:Calculate Mapping\n",
+      "INFO:kartograf.atom_mapper:Find Maximal overlapping connected sets of mapped atoms\n",
+      "INFO:kartograf.atom_mapper:Filtering bond breaks\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from kartograf import KartografAtomMapper\n",
     "\n",
@@ -843,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {},
    "outputs": [],
@@ -867,7 +1084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {},
    "outputs": [],
@@ -897,7 +1114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {},
    "outputs": [],
@@ -927,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {},
    "outputs": [],
@@ -962,11 +1179,19 @@
    "id": "6cae5156-f80f-4520-baee-4199774d441a",
    "metadata": {},
    "source": [
-    "## 5. Creating networks and running the simulations\n",
+    "## 5. Running the ``Transformation`` simulations\n",
     "\n",
     "In practice, one would create entire network of Transformations, and then run all the simulations.\n",
     "This notebook will not cover these aspects. To find out more about this, please see the [OpenFE Tutorials](https://docs.openfree.energy/en/latest/tutorials/index.html)."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f09a0d21-c0d3-4c39-9c9d-b0b1d61be2ae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 9c1bd177a2c983791172fa9c7d1bb54f24135bce Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Fri, 1 May 2026 18:26:42 -0700
Subject: [PATCH 04/15] add example portal image

---
 .../tyk2_lig_ejm_31_openfold_portal.png       | Bin 0 -> 187526 bytes
 cookbook/openfold3_to_openfe.ipynb            |  56 ++++++++++++++++--
 2 files changed, 51 insertions(+), 5 deletions(-)
 create mode 100644 cookbook/assets/tyk2_lig_ejm_31_openfold_portal.png

diff --git a/cookbook/assets/tyk2_lig_ejm_31_openfold_portal.png b/cookbook/assets/tyk2_lig_ejm_31_openfold_portal.png
new file mode 100644
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diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 999264d..932bfbe 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "This notebook offers a brief preview of some of the steps you may need to go through to calculate binding affinities using [OpenFE](https://docs.openfree.energy/en/latest/) with [OpenFold3](https://openfold-3.readthedocs.io/en/latest/) generated structures.\n",
+    "Here we show a brief preview of some of the steps you may need to go through to calculate binding affinities using [OpenFE](https://docs.openfree.energy/en/latest/) with [OpenFold3](https://openfold-3.readthedocs.io/en/latest/) generated structures.\n",
     "\n",
     "Notably, we will demonstrate how to generate a set of predictions, clean them up for use in molecular dynamics simulations, and then apply them to both relative binding free energy (RBFE) and absolute binding free energy (ABFE) methods."
    ]
@@ -33,6 +33,42 @@
     "</div>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f0d1c067-d461-4a28-b154-396f1517161b",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this example notebook we demonstrate how to predict and run simulations for different ligands bound to tyrosine kinase 2 (TYK2). The dataset comes from the [Wang et al. JACS 2015 dataset](https://pubs.acs.org/doi/10.1021/ja512751q).\n",
+    "\n",
+    "<center><img src=\"assets/tyk2.png\" width=\"200\"></center>\n",
+    "\n",
+    "\n",
+    "**The tutorial is broken into the following steps:**\n",
+    "\n",
+    "1. Running cofolding with OpenFold3 on a set of TYK2 ligands\n",
+    "\n",
+    "2. Processing the OpenFold3 output for use with OpenFE\n",
+    "\n",
+    "    a) Choosing a reference protein structure\n",
+    "\n",
+    "    b) Aligning & extracting the necessary ligands & protein structures.\n",
+    "\n",
+    "    c) Fixing & protonating the reference protein structure.\n",
+    "\n",
+    "\n",
+    "3. Loading structures into OpenFE Components\n",
+    "\n",
+    "4. Creating a free energy transformation with 3 protocol options\n",
+    "\n",
+    "    a) Absolute Binding Free Energy Transformation\n",
+    "\n",
+    "    b) SepTop Relative Binding Free Energy Transformation\n",
+    "\n",
+    "    c) Hybrid Topology Relative Binding Free Energy Transformation"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "2fea29c3",
@@ -206,6 +242,16 @@
     "\n",
     "confidence scores for each ligand are at ``assets/of3_tyk2_output/{lig_name}/seed_42/*confidences.json``\n",
     "\n",
+    "If you are using the OpenFold portal, you can view the confidence scores interactively:\n",
+    "\n",
+    "<center><img src=\"assets/tyk2_lig_ejm_31_openfold_portal.png\" width=\"800\"></center>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9481baf0-9f31-41f2-acf2-ec9b62552345",
+   "metadata": {},
+   "source": [
     "<div class=\"alert alert-block alert-warning\"> \n",
     "⚠️ <b>Important Note:</b>\n",
     "    Here we use the first model for each complex predicted by OpenFold3. In practice you would filter through all predicted models using confidence scores, and possibly using pose busters (github.com/maabuu/posebusters).\n",
@@ -272,7 +318,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.68it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.49it/s]\n"
      ]
     }
    ],
@@ -314,7 +360,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=39178) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=74212) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -709,7 +755,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=39178) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=74212) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -737,7 +783,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.86it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.83it/s]\n"
      ]
     }
    ],

From 23acdb67d5380963abb2a9f93b2dd51b99a92954 Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Mon, 4 May 2026 09:32:40 +0200
Subject: [PATCH 05/15] add images

---
 cookbook/assets/abfe-cycle.png     |   1 +
 cookbook/assets/rbfe-cycle.png     | Bin 0 -> 134741 bytes
 cookbook/assets/septop_cycle.png   |   1 +
 cookbook/openfold3_to_openfe.ipynb | 160 +++++++++++++++++------------
 4 files changed, 97 insertions(+), 65 deletions(-)
 create mode 120000 cookbook/assets/abfe-cycle.png
 create mode 100644 cookbook/assets/rbfe-cycle.png
 create mode 120000 cookbook/assets/septop_cycle.png

diff --git a/cookbook/assets/abfe-cycle.png b/cookbook/assets/abfe-cycle.png
new file mode 120000
index 0000000..c23d1d8
--- /dev/null
+++ b/cookbook/assets/abfe-cycle.png
@@ -0,0 +1 @@
+../../abfe_tutorial/abfe-cycle.png
\ No newline at end of file
diff --git a/cookbook/assets/rbfe-cycle.png b/cookbook/assets/rbfe-cycle.png
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diff --git a/cookbook/assets/septop_cycle.png b/cookbook/assets/septop_cycle.png
new file mode 120000
index 0000000..49442dd
--- /dev/null
+++ b/cookbook/assets/septop_cycle.png
@@ -0,0 +1 @@
+../../openmm_septop/septop_cycle.png
\ No newline at end of file
diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 932bfbe..eeee630 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -42,31 +42,7 @@
     "\n",
     "In this example notebook we demonstrate how to predict and run simulations for different ligands bound to tyrosine kinase 2 (TYK2). The dataset comes from the [Wang et al. JACS 2015 dataset](https://pubs.acs.org/doi/10.1021/ja512751q).\n",
     "\n",
-    "<center><img src=\"assets/tyk2.png\" width=\"200\"></center>\n",
-    "\n",
-    "\n",
-    "**The tutorial is broken into the following steps:**\n",
-    "\n",
-    "1. Running cofolding with OpenFold3 on a set of TYK2 ligands\n",
-    "\n",
-    "2. Processing the OpenFold3 output for use with OpenFE\n",
-    "\n",
-    "    a) Choosing a reference protein structure\n",
-    "\n",
-    "    b) Aligning & extracting the necessary ligands & protein structures.\n",
-    "\n",
-    "    c) Fixing & protonating the reference protein structure.\n",
-    "\n",
-    "\n",
-    "3. Loading structures into OpenFE Components\n",
-    "\n",
-    "4. Creating a free energy transformation with 3 protocol options\n",
-    "\n",
-    "    a) Absolute Binding Free Energy Transformation\n",
-    "\n",
-    "    b) SepTop Relative Binding Free Energy Transformation\n",
-    "\n",
-    "    c) Hybrid Topology Relative Binding Free Energy Transformation"
+    "<center><img src=\"assets/tyk2.png\" width=\"200\"></center>\n"
    ]
   },
   {
@@ -242,7 +218,7 @@
     "\n",
     "confidence scores for each ligand are at ``assets/of3_tyk2_output/{lig_name}/seed_42/*confidences.json``\n",
     "\n",
-    "If you are using the OpenFold portal, you can view the confidence scores interactively:\n",
+    "If you are using the OpenFold portal, you can view the confidence scores interactively, where darker regions have higher confidence scores:\n",
     "\n",
     "<center><img src=\"assets/tyk2_lig_ejm_31_openfold_portal.png\" width=\"800\"></center>\n"
    ]
@@ -280,8 +256,22 @@
    "id": "6419b4ec-6513-47ca-bb66-0eb17a7224c3",
    "metadata": {},
    "source": [
-    "### Creating an Absolute Binding Free Energy (ABFE) Transformation\n",
-    "\n",
+    "### Creating an Absolute Binding Free Energy (ABFE) Transformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd51d74c-5cdf-445a-8a89-7ff365bc1e1c",
+   "metadata": {},
+   "source": [
+    "<center><img src=\"assets/abfe-cycle.png\" width=\"400\"></center>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d34aab3e-8275-46ab-abc6-2a588224d889",
+   "metadata": {},
+   "source": [
     "The ABFE protocol requires the least amount of post-processing of the OpenFold outputs.\n",
     "\n",
     "For the sake of simplicity, we will only create a Transformation for `lig_jmc_27`, but this process is generalizable to the rest of the ligands in the series.\n"
@@ -292,7 +282,9 @@
    "id": "cdeee4e3-ce8c-4151-9ef7-5a6017ac3dcb",
    "metadata": {},
    "source": [
-    "#### Loading and assigning partial charges to the ligand(s)"
+    "#### Loading and assigning partial charges to the ligand(s)\n",
+    "\n",
+    "Load the ligands as openfe `SmallMoleculeComponent`s so that they can be understood by the OpenFE ecosystem. "
    ]
   },
   {
@@ -303,14 +295,40 @@
    "outputs": [],
    "source": [
     "import openfe\n",
+    "\n",
     "supp = Chem.SDMolSupplier(\"assets/tyk2_ligands.sdf\", removeHs=False)\n",
-    "all_ligands = [openfe.SmallMoleculeComponent.from_rdkit(mol) for mol in supp]\n",
-    "ligand = [lig for lig in all_ligands if lig.name == 'lig_jmc_27'][0]"
+    "all_ligands = [openfe.SmallMoleculeComponent.from_rdkit(mol) for mol in supp]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "257a7370-9c10-4cef-96f2-d4ae411c6222",
+   "metadata": {},
+   "source": [
+    "Choose one ligand for demonstration"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "b6f6fe49-08c2-462d-87ed-55f468dca669",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ligand = [lig for lig in all_ligands if lig.name == 'lig_jmc_27'][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0cd4185-3491-4bcb-9b4d-d160d84ad40e",
+   "metadata": {},
+   "source": [
+    "Assign partial charges to the ligands:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {},
    "outputs": [
@@ -318,7 +336,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.49it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.52it/s]\n"
      ]
     }
    ],
@@ -352,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
    "metadata": {},
    "outputs": [
@@ -360,7 +378,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=74212) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=5309) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -380,11 +398,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -392,7 +408,7 @@
        "ProteinComponent(name=tyk2)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {},
    "outputs": [
@@ -422,7 +438,7 @@
        "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -450,7 +466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
    "metadata": {},
    "outputs": [
@@ -495,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
    "metadata": {},
    "outputs": [],
@@ -517,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
    "metadata": {},
    "outputs": [],
@@ -581,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
    "metadata": {},
    "outputs": [
@@ -662,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
    "metadata": {},
    "outputs": [
@@ -747,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
    "metadata": {},
    "outputs": [
@@ -755,7 +771,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=74212) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=5309) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -775,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
    "metadata": {},
    "outputs": [
@@ -783,7 +799,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.83it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.88it/s]\n"
      ]
     }
    ],
@@ -822,6 +838,14 @@
     "### Creating a Separated Topologies RBFE Transformation"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "97f84495-88c8-49fe-987f-25ca25cdf210",
+   "metadata": {},
+   "source": [
+    "<center><img src=\"assets/septop_cycle.png\" width=\"400\"></center>\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a90d2205-1c68-4349-95b1-e20f607d4a86",
@@ -832,7 +856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {},
    "outputs": [],
@@ -843,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {},
    "outputs": [],
@@ -875,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {},
    "outputs": [],
@@ -889,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {},
    "outputs": [],
@@ -918,18 +942,24 @@
    "source": [
     "### Creating a Hybrid Topology Relative Binding Free Energy Transformation\n",
     "\n",
-    "Finally we create a Hybrid Topology RBFE Transformation. Again, as per the SepTop example above, we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
+    "<center><img src=\"assets/rbfe-cycle.png\" width=\"400\"></center>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eaf920be-9e16-402b-9fc5-8e1d4f3712cf",
+   "id": "884c5615-b2d4-4dde-b949-7760733ec10a",
    "metadata": {},
-   "source": []
+   "source": [
+    "Finally we create a Hybrid Topology RBFE Transformation.\n",
+    "\n",
+    "In addition to aligning the protein structures, \n",
+    "\n",
+    "Again, as per the SepTop example above, we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {},
    "outputs": [
@@ -1000,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {},
    "outputs": [],
@@ -1030,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {},
    "outputs": [],
@@ -1052,7 +1082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {},
    "outputs": [
@@ -1106,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {},
    "outputs": [],
@@ -1130,7 +1160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {},
    "outputs": [],
@@ -1160,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {},
    "outputs": [],
@@ -1190,7 +1220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {},
    "outputs": [],

From f6367c088c980d91c00025567673cb2068e7bea0 Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Mon, 4 May 2026 10:00:02 +0200
Subject: [PATCH 06/15] add slide metadata

---
 cookbook/openfold3_to_openfe.ipynb | 631 +++++++++++++++++++++++++----
 1 file changed, 552 insertions(+), 79 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index eeee630..67ec59e 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -3,7 +3,13 @@
   {
    "cell_type": "markdown",
    "id": "35354229",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "<img src=\"assets/OFE-color-horizontal.png\" width=\"300\"> &nbsp;&nbsp;&nbsp;&nbsp;  <img src=\"assets/openfold.png\" width=\"300\"> \n",
     "\n",
@@ -13,7 +19,13 @@
   {
    "cell_type": "markdown",
    "id": "0d905dc8-e11b-4088-82f5-2c2cefef3973",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "## Introduction\n",
     "\n",
@@ -25,7 +37,13 @@
   {
    "cell_type": "markdown",
    "id": "ada5c811-8603-4473-8040-7fd370b4a1cf",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "<div class=\"alert alert-block alert-danger\"> \n",
     "⚠️ <b>Important Note:</b>\n",
@@ -36,7 +54,13 @@
   {
    "cell_type": "markdown",
    "id": "f0d1c067-d461-4a28-b154-396f1517161b",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "## Overview\n",
     "\n",
@@ -48,7 +72,13 @@
   {
    "cell_type": "markdown",
    "id": "2fea29c3",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "## 1. Run cofolding with OpenFold3 on a set of TYK2 ligands"
    ]
@@ -56,15 +86,33 @@
   {
    "cell_type": "markdown",
    "id": "ef3bdb1f-504a-4311-8e0a-c28265b006cb",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "### Preparing the input JSON for OpenFold\n",
     "\n",
     "First we need to create an input JSON file for OpenFold. This step requires assembling two key pieces of information:\n",
     "\n",
     "- Ligand SMILES strings\n",
-    "- Protein sequence (extracted from the PDB file)\n",
-    "\n",
+    "- Protein sequence (extracted from the PDB file)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cfa0d3a-9eb5-4ddc-854f-b278469ae2e1",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
     "In this workflow, we begin with an SDF file containing the ligands.\n",
     "\n",
     "We extract the SMILES representation for each ligand and store them in a dictionary alongside their ligand identifiers."
@@ -75,7 +123,12 @@
    "execution_count": 1,
    "id": "08962d91-a334-4c89-adf1-19be6f2bdb16",
    "metadata": {
-    "scrolled": true
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -97,7 +150,13 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "1c55bfeb-c1bc-4af8-98da-43ff8c1de141",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -126,7 +185,13 @@
   {
    "cell_type": "markdown",
    "id": "80da7967-8046-44e4-aef7-c81ac6712f98",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "Next we create the JSON file that will be the OpenFold input."
    ]
@@ -134,7 +199,13 @@
   {
    "cell_type": "markdown",
    "id": "b3f0be0c-144c-4e96-9310-a8778111291c",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "<div class=\"alert alert-block alert-info\"> \n",
     "  <b>Note:</b> In this example, we did not use the full FASTA sequence from UNIPROT for TYK2, but rather the sequence of the crystallographic structure.\n",
@@ -145,7 +216,13 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "25a8ba0c-311c-476e-9913-9b39611c708d",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -180,7 +257,13 @@
   {
    "cell_type": "markdown",
    "id": "a2546f36-2ca2-4793-ad4b-19b6e76574ae",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "### Running OpenFold\n",
     "\n",
@@ -212,12 +295,30 @@
   {
    "cell_type": "markdown",
    "id": "9c1ab9ee-71f5-4587-bea1-35097621c039",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "#### Evaluating OpenFold output\n",
     "\n",
-    "confidence scores for each ligand are at ``assets/of3_tyk2_output/{lig_name}/seed_42/*confidences.json``\n",
-    "\n",
+    "confidence scores for each ligand are at ``assets/of3_tyk2_output/{lig_name}/seed_42/*confidences.json``\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73368db9-350f-4851-922a-01469736dcc7",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "source": [
     "If you are using the OpenFold portal, you can view the confidence scores interactively, where darker regions have higher confidence scores:\n",
     "\n",
     "<center><img src=\"assets/tyk2_lig_ejm_31_openfold_portal.png\" width=\"800\"></center>\n"
@@ -226,7 +327,13 @@
   {
    "cell_type": "markdown",
    "id": "9481baf0-9f31-41f2-acf2-ec9b62552345",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "<div class=\"alert alert-block alert-warning\"> \n",
     "⚠️ <b>Important Note:</b>\n",
@@ -237,7 +344,13 @@
   {
    "cell_type": "markdown",
    "id": "721074db-8266-4c3e-b592-64220e0cfd8f",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "## 2. Running OpenFE Protocols with OpenFold structures"
    ]
@@ -245,7 +358,13 @@
   {
    "cell_type": "markdown",
    "id": "249ec515-cfad-4f49-8dba-ee2438b1adf4",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "We will demonstrate how to use OpenFold generated structures with three different OpenFE protocols:\n",
     "Absolute Binding Free Energy (ABFE), Separated Topologies RBFE, and Hybrid Topology RBFE.\n"
@@ -254,7 +373,13 @@
   {
    "cell_type": "markdown",
    "id": "6419b4ec-6513-47ca-bb66-0eb17a7224c3",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "### Creating an Absolute Binding Free Energy (ABFE) Transformation"
    ]
@@ -262,7 +387,13 @@
   {
    "cell_type": "markdown",
    "id": "fd51d74c-5cdf-445a-8a89-7ff365bc1e1c",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "<center><img src=\"assets/abfe-cycle.png\" width=\"400\"></center>\n"
    ]
@@ -270,7 +401,13 @@
   {
    "cell_type": "markdown",
    "id": "d34aab3e-8275-46ab-abc6-2a588224d889",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "The ABFE protocol requires the least amount of post-processing of the OpenFold outputs.\n",
     "\n",
@@ -280,7 +417,13 @@
   {
    "cell_type": "markdown",
    "id": "cdeee4e3-ce8c-4151-9ef7-5a6017ac3dcb",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "#### Loading and assigning partial charges to the ligand(s)\n",
     "\n",
@@ -291,7 +434,13 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "09725e7d-b959-4221-b027-1488b6fea22a",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import openfe\n",
@@ -321,7 +470,13 @@
   {
    "cell_type": "markdown",
    "id": "f0cd4185-3491-4bcb-9b4d-d160d84ad40e",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "Assign partial charges to the ligands:"
    ]
@@ -330,7 +485,13 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -361,7 +522,12 @@
   {
    "cell_type": "markdown",
    "id": "a84b9dfa-e92a-43fa-baae-a725778122cb",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "#### Fixing & protonating the protein structure\n",
     "\n",
@@ -372,7 +538,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -391,7 +564,13 @@
   {
    "cell_type": "markdown",
    "id": "8c55d160-558f-426f-8a8c-9ae46d191cff",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "Then we can load the reference protein to a ProteinComponent."
    ]
@@ -400,7 +579,13 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -421,7 +606,13 @@
   {
    "cell_type": "markdown",
    "id": "4d11803b-6d4b-4507-8c98-6ff40f75680e",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "We must also define a SolventComponent which we will use to define our Transformations."
    ]
@@ -430,7 +621,13 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -451,7 +648,13 @@
   {
    "cell_type": "markdown",
    "id": "e1ef09d2-ec8b-4577-8510-c67cb89fc5b5",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "#### Creating the ABFE ``Transformation``"
    ]
@@ -459,7 +662,13 @@
   {
    "cell_type": "markdown",
    "id": "695c61c4-3fa6-4e1a-939f-5349e6829627",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "Create the `ChemicalSystems` defining the end states of the simulation:"
    ]
@@ -468,7 +677,13 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -504,7 +719,13 @@
   {
    "cell_type": "markdown",
    "id": "e32fb4ef-dbf5-4a9a-90c4-b64d9e5af6ce",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "Next we create the Protocol. We use the default settings, except to help us execute in parallel we set the number of repeats to 1."
    ]
@@ -513,7 +734,13 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from openfe.protocols.openmm_afe import AbsoluteBindingProtocol\n",
@@ -526,7 +753,13 @@
   {
    "cell_type": "markdown",
    "id": "6bf8af4c-9894-4af3-84f8-2ed3830120ad",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "Finally we create the `Transformation` object."
    ]
@@ -535,7 +768,13 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "abfe_transformation = openfe.Transformation(\n",
@@ -550,7 +789,13 @@
   {
    "cell_type": "markdown",
    "id": "34ec9c8d-cb7f-4cad-9794-b73629a11246",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "This transformation can now be run using `openfe quickrun`."
    ]
@@ -558,7 +803,13 @@
   {
    "cell_type": "markdown",
    "id": "df8cb66d-c201-4a22-b87a-a1e5501d4be4",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "### Preparing OpenFold outputs for Relative Binding Free Energies"
    ]
@@ -566,7 +817,13 @@
   {
    "cell_type": "markdown",
    "id": "9dd96548-7190-4851-b1ed-fb2d8c1bd87b",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "RBFE transformations require additional preparation, including:\n",
     "\n",
@@ -585,7 +842,13 @@
   {
    "cell_type": "markdown",
    "id": "02a82dbd-5a01-43c5-af33-654b706a5721",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "\n",
     "#### a) Choosing a reference protein structure\n",
@@ -599,7 +862,13 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -669,7 +938,13 @@
   {
    "cell_type": "markdown",
    "id": "193dc76a-6e43-4e25-8b9d-5c0e4a79c3e9",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "#### b) Aligning & extracting the necessary structures\n",
     "\n",
@@ -680,7 +955,13 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -741,7 +1022,13 @@
   {
    "cell_type": "markdown",
    "id": "e5b5141e-efe0-4bea-89e9-4896c00f4311",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "<div class=\"alert alert-block alert-warning\"> \n",
     "⚠️ <b>Important Note:</b>\n",
@@ -754,7 +1041,13 @@
   {
    "cell_type": "markdown",
    "id": "6bf56d93-222a-4651-b8cf-f451f0360878",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "#### c) Fixing & protonating the reference protein structure\n",
     "\n",
@@ -765,7 +1058,13 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -784,7 +1083,13 @@
   {
    "cell_type": "markdown",
    "id": "1153dbd0-998a-4f3f-8e47-a950c806d1dd",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "#### d) Loading and assigning partial charges to the aligned ligands"
    ]
@@ -793,7 +1098,13 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -833,7 +1144,13 @@
   {
    "cell_type": "markdown",
    "id": "cc023ddb-d32a-4f78-bea1-a4f9efddf902",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "### Creating a Separated Topologies RBFE Transformation"
    ]
@@ -841,7 +1158,13 @@
   {
    "cell_type": "markdown",
    "id": "97f84495-88c8-49fe-987f-25ca25cdf210",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "<center><img src=\"assets/septop_cycle.png\" width=\"400\"></center>\n"
    ]
@@ -849,7 +1172,13 @@
   {
    "cell_type": "markdown",
    "id": "a90d2205-1c68-4349-95b1-e20f607d4a86",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "We can now SepTop RBFE Transformation. Here we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
    ]
@@ -858,7 +1187,13 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ligandA = [l for l in charged_ligands if l.name == 'lig_jmc_27'][0]\n",
@@ -869,7 +1204,13 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Create the two end states\n",
@@ -892,7 +1233,13 @@
   {
    "cell_type": "markdown",
    "id": "54484f2f-347b-498e-b5e2-60e8c8db446a",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "Next we create the Protocol. We use the default settings, except to help us execute in parallel we set the number of repeats to 1."
    ]
@@ -901,7 +1248,13 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from openfe.protocols.openmm_septop import SepTopProtocol\n",
@@ -915,7 +1268,13 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "septop_transformation = openfe.Transformation(\n",
@@ -930,7 +1289,13 @@
   {
    "cell_type": "markdown",
    "id": "db4344c5-c300-4b6e-aa0a-a7cae9c6380f",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "This transformation can now be run using `openfe quickrun`."
    ]
@@ -938,7 +1303,13 @@
   {
    "cell_type": "markdown",
    "id": "6e16e781-c6dc-4a62-85a7-0bc59678e916",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "### Creating a Hybrid Topology Relative Binding Free Energy Transformation\n",
     "\n",
@@ -948,7 +1319,13 @@
   {
    "cell_type": "markdown",
    "id": "884c5615-b2d4-4dde-b949-7760733ec10a",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "Finally we create a Hybrid Topology RBFE Transformation.\n",
     "\n",
@@ -961,7 +1338,13 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1032,7 +1415,13 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ligandA = [l for l in charged_ligands if l.name == 'lig_jmc_27'][0]\n",
@@ -1042,7 +1431,13 @@
   {
    "cell_type": "markdown",
    "id": "dfbf86dd-1162-4cf5-998a-2b2c01e6080c",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "Because the ligands are not guaranteed to be aligned, care must be taken to make sure that there is some core overlap between the ligands. Here we naively use the atom aligner in Kartograf to re-align the ligands."
    ]
@@ -1050,7 +1445,13 @@
   {
    "cell_type": "markdown",
    "id": "eb09cf7b-e9e9-4f98-b9a5-7f6cfc9c146b",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "<div class=\"alert alert-block alert-warning\"> \n",
     "⚠️ <b>Important Note:</b>\n",
@@ -1062,7 +1463,13 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from kartograf.atom_aligner import align_mol_shape\n",
@@ -1075,7 +1482,13 @@
   {
    "cell_type": "markdown",
    "id": "d8617ab9-77c3-4103-a9fe-233262f19b74",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "With these aligned ligands, we can get a mapping between the two."
    ]
@@ -1084,7 +1497,13 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -1129,7 +1548,13 @@
   {
    "cell_type": "markdown",
    "id": "aeb15ac9-9999-4bb9-b5e3-bda5dda01dc9",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "Next we define the end state `ChemicalSystem`s"
    ]
@@ -1138,7 +1563,13 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Complex transformation ChemicalSystems\n",
@@ -1162,7 +1593,13 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Solvent transformation ChemicalSystems\n",
@@ -1183,7 +1620,13 @@
   {
    "cell_type": "markdown",
    "id": "fe4f8dd1-ffdc-4167-97ca-a24b86002613",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "Then we create the Protocols for both the solvent and complex phases."
    ]
@@ -1192,7 +1635,13 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n",
@@ -1213,7 +1662,13 @@
   {
    "cell_type": "markdown",
    "id": "9030db1c-35fe-4213-930c-b967832d9c7d",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "Finally we create the Transformations"
    ]
@@ -1222,7 +1677,13 @@
    "cell_type": "code",
    "execution_count": 28,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "transformation_complex = openfe.Transformation(\n",
@@ -1245,7 +1706,13 @@
   {
    "cell_type": "markdown",
    "id": "f08aaff4-97d0-4f53-bb32-1c7e247432aa",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "This transformation can now be run using `openfe quickrun`."
    ]
@@ -1253,7 +1720,13 @@
   {
    "cell_type": "markdown",
    "id": "6cae5156-f80f-4520-baee-4199774d441a",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
    "source": [
     "## 5. Running the ``Transformation`` simulations\n",
     "\n",

From 64c3074eddc0bf5127bda32a735dbd4e2529544e Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Tue, 5 May 2026 17:27:10 +0200
Subject: [PATCH 07/15] updating some language

---
 cookbook/openfold3_to_openfe.ipynb | 63 +++++++++++++++++-------------
 1 file changed, 35 insertions(+), 28 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 67ec59e..5a0391c 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -27,11 +27,14 @@
     "tags": []
    },
    "source": [
-    "## Introduction\n",
-    "\n",
-    "Here we show a brief preview of some of the steps you may need to go through to calculate binding affinities using [OpenFE](https://docs.openfree.energy/en/latest/) with [OpenFold3](https://openfold-3.readthedocs.io/en/latest/) generated structures.\n",
+    "## Overview\n",
     "\n",
-    "Notably, we will demonstrate how to generate a set of predictions, clean them up for use in molecular dynamics simulations, and then apply them to both relative binding free energy (RBFE) and absolute binding free energy (ABFE) methods."
+    "1. Predict structures using OpenFold3\n",
+    "2. Clean and process the predicted structures for use in the following OpenFE protocols:\n",
+    "    - Absolute Binding Free Energy Protocol (ABFE)\n",
+    "    - Relative Binding Free Energy Protocols (RBFEs)\n",
+    "        - Separated Topology\n",
+    "        - Hybrid Topology\n"
    ]
   },
   {
@@ -47,13 +50,13 @@
    "source": [
     "<div class=\"alert alert-block alert-danger\"> \n",
     "⚠️ <b>Important Note:</b>\n",
-    "    Please note that this notebook does not offer an exhaustive look into everything you may need to do when parsing OpenFold3 inputs into OpenFE. Where possible, we offer insights into some of the other hurdles you may face, but you are likely to encounter more in production settings.\n",
+    "    We will not cover everything needed to use OpenFold3 inputs in OpenFE! We will offer insights into some common considerations, but expect more in production settings.\n",
     "</div>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f0d1c067-d461-4a28-b154-396f1517161b",
+   "id": "2fea29c3",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -62,25 +65,23 @@
     "tags": []
    },
    "source": [
-    "## Overview\n",
-    "\n",
-    "In this example notebook we demonstrate how to predict and run simulations for different ligands bound to tyrosine kinase 2 (TYK2). The dataset comes from the [Wang et al. JACS 2015 dataset](https://pubs.acs.org/doi/10.1021/ja512751q).\n",
-    "\n",
-    "<center><img src=\"assets/tyk2.png\" width=\"200\"></center>\n"
+    "## 1. Run cofolding with OpenFold3 on a set of TYK2 ligands"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2fea29c3",
+   "id": "6c14eb39-036b-44d3-93e3-b29a635a7e2b",
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     },
     "tags": []
    },
    "source": [
-    "## 1. Run cofolding with OpenFold3 on a set of TYK2 ligands"
+    "We will show how to predict and set up simulations for different ligands bound to tyrosine kinase 2 (TYK2).The dataset comes from the [Wang et al. JACS 2015 dataset](https://pubs.acs.org/doi/10.1021/ja512751q).\n",
+    "\n",
+    "<center><img src=\"assets/tyk2.png\" width=\"200\"></center>\n"
    ]
   },
   {
@@ -89,14 +90,14 @@
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     },
     "tags": []
    },
    "source": [
     "### Preparing the input JSON for OpenFold\n",
     "\n",
-    "First we need to create an input JSON file for OpenFold. This step requires assembling two key pieces of information:\n",
+    "This step requires assembling two key pieces of information:\n",
     "\n",
     "- Ligand SMILES strings\n",
     "- Protein sequence (extracted from the PDB file)\n"
@@ -108,14 +109,14 @@
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     },
     "tags": []
    },
    "source": [
-    "In this workflow, we begin with an SDF file containing the ligands.\n",
+    "The SDF file `tyk2_ligands.sdf` contains all ligands for this exercise.\n",
     "\n",
-    "We extract the SMILES representation for each ligand and store them in a dictionary alongside their ligand identifiers."
+    "For each ligand, extract SMILES representation store in a dictionary alongside its ligand identifier."
    ]
   },
   {
@@ -139,7 +140,6 @@
     "\n",
     "ligands_dict = {}\n",
     "sppl = Chem.SDMolSupplier(ligands_file, removeHs=True)\n",
-    "\n",
     "for mol in sppl:\n",
     "    smi = Chem.MolToSmiles(mol)\n",
     "    name = mol.GetProp(\"_Name\")\n",
@@ -265,11 +265,11 @@
     "tags": []
    },
    "source": [
-    "### Running OpenFold\n",
+    "### Generating Structures with OpenFold\n",
     "\n",
     "#### Defining the OpenFold settings\n",
     "\n",
-    "we define the following settings in a `settings.yaml` file to be passed to OpenFold:\n",
+    "We define the following settings in a `settings.yaml` file to be passed to OpenFold:\n",
     "\n",
     "```json\n",
     "model_update:\n",
@@ -284,10 +284,17 @@
     "                   use_deepspeed_evo_attention: false\n",
     "\n",
     "\n",
-    "```\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1557d01d-6a84-4531-83b8-b879a50a0ac6",
+   "metadata": {},
+   "source": [
     "#### Running OpenFold\n",
     "\n",
-    "With all these inputs, you can now run OpenFold:\n",
+    "With all these inputs, you can now run **OpenFold3**:\n",
     "\n",
     "`run_openfold predict --query_json=queries.json --runner_yaml settings.yaml`"
    ]
@@ -497,7 +504,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.52it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.65it/s]\n"
      ]
     }
    ],
@@ -551,7 +558,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=5309) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=64970) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -1070,7 +1077,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=5309) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=64970) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -1110,7 +1117,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.88it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.85it/s]\n"
      ]
     }
    ],

From c90d481705f0a1d38c50f4a3031ef4e86b8ff08b Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 09:48:00 +0200
Subject: [PATCH 08/15] update ligand prep for abfe

---
 cookbook/openfold3_to_openfe.ipynb | 165 ++++++++++++++++++-----------
 1 file changed, 104 insertions(+), 61 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 5a0391c..176f591 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -434,44 +434,100 @@
    "source": [
     "#### Loading and assigning partial charges to the ligand(s)\n",
     "\n",
-    "Load the ligands as openfe `SmallMoleculeComponent`s so that they can be understood by the OpenFE ecosystem. "
+    "Load the ligands as openfe `SmallMoleculeComponent`s so that they can be understood by the OpenFE ecosystem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ec171f2-b162-4801-b3d2-204768770e55",
+   "metadata": {},
+   "source": [
+    "Extract the ligand cofolded with the protein from the output .pdb:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "09725e7d-b959-4221-b027-1488b6fea22a",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": "fragment"
-    },
-    "tags": []
-   },
+   "id": "8e22e331-c533-4557-b1fb-ef570c0c054c",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import openfe\n",
+    "import pathlib\n",
+    "import warnings\n",
+    "import MDAnalysis as mda\n",
+    "from rdkit import Chem, rdBase\n",
+    "from rdkit.Chem import AllChem\n",
+    "\n",
+    "# don't show warnings that are expected and not an issue\n",
+    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
+    "\n",
+    "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
+    "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
+    "temp_dir.mkdir(exist_ok=True,parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6a6a999f-f961-4909-add8-e8c3bc804c99",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting protein and ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n"
+     ]
+    }
+   ],
+   "source": [
+    "ligand_name = 'lig_jmc_27'\n",
+    "pdb_path = pathlib.Path(f\"assets/of3_tyk2_output/{ligand_name}/seed_42/{ligand_name}_seed_42_sample_1_model.pdb\")\n",
+    "output_dir = pathlib.Path(\"openfold_to_openfe/\")\n",
+    "output_dir.mkdir(exist_ok=True)\n",
     "\n",
-    "supp = Chem.SDMolSupplier(\"assets/tyk2_ligands.sdf\", removeHs=False)\n",
-    "all_ligands = [openfe.SmallMoleculeComponent.from_rdkit(mol) for mol in supp]"
+    "pdb_universe = mda.Universe(pdb_path)\n",
+    "\n",
+    "print(f\"Extracting protein and ligand from {pdb_path.name}\")\n",
+    "aligned_ligand = pdb_universe.select_atoms(\"resname LIG\")\n",
+    "aligned_ligand.write(f'{output_dir}/{ligand_name}.pdb')\n",
+    "\n",
+    "# load back into rdkit to add hydrogens\n",
+    "rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand_name}.pdb', removeHs=True)\n",
+    "# use the template to assign bond orders\n",
+    "template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
+    "with rdBase.BlockLogs():  \n",
+    "    rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "# Add hydrogens\n",
+    "rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
+    "# Set the molecule name\n",
+    "rdkit_mol.SetProp('_Name', f\"{ligand_name}\")\n",
+    "# write to sdf\n",
+    "ligand_prepped_path = output_dir / f\"{ligand_name}_prepped.sdf\"\n",
+    "Chem.MolToMolFile(rdkit_mol, ligand_prepped_path)\n",
+    "\n",
+    "# save the protein alone\n",
+    "full_protein = pdb_universe.select_atoms(\"protein\")\n",
+    "full_protein.write(f'{output_dir}/protein.pdb')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "257a7370-9c10-4cef-96f2-d4ae411c6222",
+   "id": "b1b544a9-9b92-43f3-8b88-7af9205621b5",
    "metadata": {},
    "source": [
-    "Choose one ligand for demonstration"
+    "Load the ligand as an openfe ``SmallMoleculeComponent``"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "b6f6fe49-08c2-462d-87ed-55f468dca669",
+   "execution_count": 6,
+   "id": "c4dca30b-bafb-4d03-80c4-83684655ddcf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ligand = [lig for lig in all_ligands if lig.name == 'lig_jmc_27'][0]"
+    "import openfe\n",
+    "ligand = openfe.SmallMoleculeComponent.from_sdf_file(ligand_prepped_path)"
    ]
   },
   {
@@ -490,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {
     "editable": true,
@@ -504,7 +560,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.65it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.86it/s]\n"
      ]
     }
    ],
@@ -521,7 +577,7 @@
     "    method=charge_settings.partial_charge_method,\n",
     "    toolkit_backend=charge_settings.off_toolkit_backend,\n",
     "    generate_n_conformers=1,\n",
-    "    nagl_model=None,\n",
+    "    nagl_model=\"openff-gnn-am1bcc-1.0.0.pt\",\n",
     "    processors=1,\n",
     ")"
    ]
@@ -543,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
    "metadata": {
     "editable": true,
@@ -558,7 +614,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=64970) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=92981) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -584,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
    "metadata": {
     "editable": true,
@@ -600,7 +656,7 @@
        "ProteinComponent(name=tyk2)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {
     "editable": true,
@@ -642,7 +698,7 @@
        "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -682,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
    "metadata": {
     "editable": true,
@@ -739,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
    "metadata": {
     "editable": true,
@@ -773,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
    "metadata": {
     "editable": true,
@@ -857,7 +913,6 @@
     "tags": []
    },
    "source": [
-    "\n",
     "#### a) Choosing a reference protein structure\n",
     "\n",
     "As mentioned above, we will select a reference protein structure for all simulations going forward. To do this, we naively select the structure bound to a ligand with the largest volume.\n",
@@ -867,7 +922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
    "metadata": {
     "editable": true,
@@ -900,7 +955,6 @@
     "import pathlib\n",
     "import MDAnalysis as mda\n",
     "from MDAnalysis.analysis import align\n",
-    "from rdkit import Chem, rdBase\n",
     "from rdkit.Chem import AllChem\n",
     "from rdkit.Chem.Descriptors3D import Asphericity\n",
     "import warnings\n",
@@ -911,9 +965,7 @@
     "# gather all the complexes and align to a single structure\n",
     "# which has the largest ligand by volume \n",
     "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
-    "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
-    "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
-    "temp_dir.mkdir(exist_ok=True,parents=True)\n",
+    "\n",
     "# load all the output pdbs and calculate ligand volumes\n",
     "ligand_volumes = {}\n",
     "\n",
@@ -960,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
    "metadata": {
     "editable": true,
@@ -1063,7 +1115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
    "metadata": {
     "editable": true,
@@ -1077,7 +1129,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=64970) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=92981) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -1103,7 +1155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
    "metadata": {
     "editable": true,
@@ -1117,7 +1169,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.85it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.83it/s]\n"
      ]
     }
    ],
@@ -1192,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {
     "editable": true,
@@ -1209,7 +1261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {
     "editable": true,
@@ -1253,7 +1305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {
     "editable": true,
@@ -1273,7 +1325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {
     "editable": true,
@@ -1343,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {
     "editable": true,
@@ -1373,21 +1425,12 @@
     }
    ],
    "source": [
-    "import pathlib\n",
-    "import MDAnalysis as mda\n",
     "from MDAnalysis.analysis import align\n",
-    "from rdkit import Chem, rdBase\n",
-    "from rdkit.Chem import AllChem\n",
     "from rdkit.Chem.Descriptors3D import Asphericity\n",
-    "import warnings\n",
-    "\n",
-    "# don't show warnings that are expected and not an issue\n",
-    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
     "\n",
     "# gather all the complexes and align to a single structure\n",
     "# which has the largest ligand by volume \n",
     "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
-    "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
     "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
     "temp_dir.mkdir(exist_ok=True,parents=True)\n",
     "# load all the output pdbs and calculate ligand volumes\n",
@@ -1420,7 +1463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {
     "editable": true,
@@ -1468,7 +1511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {
     "editable": true,
@@ -1502,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {
     "editable": true,
@@ -1568,7 +1611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {
     "editable": true,
@@ -1598,7 +1641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {
     "editable": true,
@@ -1640,7 +1683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {
     "editable": true,
@@ -1682,7 +1725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {
     "editable": true,

From 8ac0ae9795853933a6b69b4bf8c93305ca57c02f Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 10:31:49 +0200
Subject: [PATCH 09/15] update env

---
 cookbook/openfold3_to_openfe.ipynb | 174 +++++++++++++++--------------
 environment.yaml                   |   2 +-
 2 files changed, 93 insertions(+), 83 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 176f591..0b4a93a 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -462,8 +462,12 @@
     "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
     "\n",
     "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
-    "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
-    "temp_dir.mkdir(exist_ok=True,parents=True)"
+    "\n",
+    "output_dir = pathlib.Path(output_root, \"abfe_prep/\")\n",
+    "output_dir.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "tmp_ligand_dir = pathlib.Path(output_root, \"temp_ligands\")\n",
+    "tmp_ligand_dir.mkdir(exist_ok=True)\n"
    ]
   },
   {
@@ -483,8 +487,6 @@
    "source": [
     "ligand_name = 'lig_jmc_27'\n",
     "pdb_path = pathlib.Path(f\"assets/of3_tyk2_output/{ligand_name}/seed_42/{ligand_name}_seed_42_sample_1_model.pdb\")\n",
-    "output_dir = pathlib.Path(\"openfold_to_openfe/\")\n",
-    "output_dir.mkdir(exist_ok=True)\n",
     "\n",
     "pdb_universe = mda.Universe(pdb_path)\n",
     "\n",
@@ -494,6 +496,7 @@
     "\n",
     "# load back into rdkit to add hydrogens\n",
     "rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand_name}.pdb', removeHs=True)\n",
+    "\n",
     "# use the template to assign bond orders\n",
     "template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
     "with rdBase.BlockLogs():  \n",
@@ -513,41 +516,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1b544a9-9b92-43f3-8b88-7af9205621b5",
-   "metadata": {},
-   "source": [
-    "Load the ligand as an openfe ``SmallMoleculeComponent``"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c4dca30b-bafb-4d03-80c4-83684655ddcf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openfe\n",
-    "ligand = openfe.SmallMoleculeComponent.from_sdf_file(ligand_prepped_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0cd4185-3491-4bcb-9b4d-d160d84ad40e",
+   "id": "30816423-6c25-47bb-a90c-4870e85567c1",
    "metadata": {
-    "editable": true,
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     },
     "tags": []
    },
    "source": [
-    "Assign partial charges to the ligands:"
+    "#### Fixing & protonating the protein structure\n",
+    "\n",
+    "The PDB contains the heavy atoms of the protein but is missing hydrogens and caps. Here we call PDBFixer to add hydrogens at pH 7 and add protein caps."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
+   "execution_count": 8,
+   "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -560,50 +545,44 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.86it/s]\n"
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=97949) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "  pid, fd = os.forkpty()\n"
      ]
     }
    ],
    "source": [
-    "from openfe.protocols.openmm_utils.omm_settings import OpenFFPartialChargeSettings\n",
-    "from openfe.protocols.openmm_utils.charge_generation import bulk_assign_partial_charges\n",
-    "\n",
-    "# Generate partial charges\n",
-    "charge_settings = OpenFFPartialChargeSettings(partial_charge_method=\"nagl\")\n",
-    "\n",
-    "charged_ligands = bulk_assign_partial_charges(\n",
-    "    molecules=[ligand],\n",
-    "    overwrite=False,\n",
-    "    method=charge_settings.partial_charge_method,\n",
-    "    toolkit_backend=charge_settings.off_toolkit_backend,\n",
-    "    generate_n_conformers=1,\n",
-    "    nagl_model=\"openff-gnn-am1bcc-1.0.0.pt\",\n",
-    "    processors=1,\n",
-    ")"
+    "# now run the reference protein through pdbfixer\n",
+    "! pdbfixer assets/of3_tyk2_output/lig_jmc_27/seed_42/lig_jmc_27_seed_42_sample_1_model.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93d67ad4-6c06-4555-8760-06294e45f05f",
+   "metadata": {},
+   "source": [
+    "#### Loading the OpenFE Components"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a84b9dfa-e92a-43fa-baae-a725778122cb",
+   "id": "eaa991f1-3ecf-4f15-b637-4538a0078b7e",
    "metadata": {
+    "editable": true,
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     },
     "tags": []
    },
    "source": [
-    "#### Fixing & protonating the protein structure\n",
-    "\n",
-    "The PDB contains the heavy atoms of the protein but is missing hydrogens and caps. Here we call PDBFixer to add hydrogens at pH 7 and add protein caps."
+    "Then we can load the reference protein to a ProteinComponent."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
+   "execution_count": 9,
+   "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
    "metadata": {
     "editable": true,
-    "scrolled": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -611,22 +590,43 @@
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=92981) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
-      "  pid, fd = os.forkpty()\n"
-     ]
+     "data": {
+      "text/plain": [
+       "ProteinComponent(name=tyk2)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# now run the reference protein through pdbfixer\n",
-    "! pdbfixer assets/of3_tyk2_output/lig_jmc_27/seed_42/lig_jmc_27_seed_42_sample_1_model.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb"
+    "protein = openfe.ProteinComponent.from_pdb_file(\"openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb\", name=\"tyk2\")\n",
+    "protein"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1b544a9-9b92-43f3-8b88-7af9205621b5",
+   "metadata": {},
+   "source": [
+    "Load the ligand as an openfe ``SmallMoleculeComponent``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c4dca30b-bafb-4d03-80c4-83684655ddcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openfe\n",
+    "ligand = openfe.SmallMoleculeComponent.from_sdf_file(ligand_prepped_path)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8c55d160-558f-426f-8a8c-9ae46d191cff",
+   "id": "f0cd4185-3491-4bcb-9b4d-d160d84ad40e",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -635,13 +635,13 @@
     "tags": []
    },
    "source": [
-    "Then we can load the reference protein to a ProteinComponent."
+    "Assign partial charges to the ligands:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
+   "execution_count": 7,
+   "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -651,19 +651,29 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "ProteinComponent(name=tyk2)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.85it/s]\n"
+     ]
     }
    ],
    "source": [
-    "protein = openfe.ProteinComponent.from_pdb_file(\"openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb\", name=\"tyk2\")\n",
-    "protein"
+    "from openfe.protocols.openmm_utils.omm_settings import OpenFFPartialChargeSettings\n",
+    "from openfe.protocols.openmm_utils.charge_generation import bulk_assign_partial_charges\n",
+    "\n",
+    "# Generate partial charges\n",
+    "charge_settings = OpenFFPartialChargeSettings(partial_charge_method=\"nagl\")\n",
+    "\n",
+    "charged_ligands = bulk_assign_partial_charges(\n",
+    "    molecules=[ligand],\n",
+    "    overwrite=False,\n",
+    "    method=charge_settings.partial_charge_method,\n",
+    "    toolkit_backend=charge_settings.off_toolkit_backend,\n",
+    "    generate_n_conformers=1,\n",
+    "    nagl_model=\"openff-gnn-am1bcc-1.0.0.pt\",\n",
+    "    processors=1,\n",
+    ")"
    ]
   },
   {
@@ -977,9 +987,9 @@
     "        u = mda.Universe(str(output_file))\n",
     "    ligand = u.select_atoms(\"resname LIG\")\n",
     "    # write the ligand to a temporary file\n",
-    "    ligand.write(str(temp_dir / f\"{ligand_name}.pdb\"))\n",
+    "    ligand.write(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"))\n",
     "    # now load back with rdkit\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(str(temp_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
     "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
     "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
     "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
@@ -1129,7 +1139,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=92981) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=97949) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -1431,8 +1441,8 @@
     "# gather all the complexes and align to a single structure\n",
     "# which has the largest ligand by volume \n",
     "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
-    "temp_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
-    "temp_dir.mkdir(exist_ok=True,parents=True)\n",
+    "tmp_ligand_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
+    "tmp_ligand_dir.mkdir(exist_ok=True,parents=True)\n",
     "# load all the output pdbs and calculate ligand volumes\n",
     "ligand_volumes = {}\n",
     "\n",
@@ -1444,9 +1454,9 @@
     "        u = mda.Universe(str(output_file))\n",
     "    ligand = u.select_atoms(\"resname LIG\")\n",
     "    # write the ligand to a temporary file\n",
-    "    ligand.write(str(temp_dir / f\"{ligand_name}.pdb\"))\n",
+    "    ligand.write(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"))\n",
     "    # now load back with rdkit\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(str(temp_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
     "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
     "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
     "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
diff --git a/environment.yaml b/environment.yaml
index 6b1d00b..1bdcad7 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -14,5 +14,5 @@ dependencies:
   - pytest-cov
   - pytest-xdist
   - python
-  - openfe==1.11.0
+  - openfe==1.11.1
   - openff-interchange-base != 0.5.1  # https://github.com/openforcefield/openff-interchange/issues/1450

From a73f83322c8ab8c841abc06f2b596c65b86033cc Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 10:32:07 +0200
Subject: [PATCH 10/15] update slide formatting

---
 cookbook/openfold3_to_openfe.ipynb | 178 ++++++++++++++++++-----------
 1 file changed, 113 insertions(+), 65 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 0b4a93a..0ac1180 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -290,7 +290,13 @@
   {
    "cell_type": "markdown",
    "id": "1557d01d-6a84-4531-83b8-b879a50a0ac6",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "#### Running OpenFold\n",
     "\n",
@@ -437,19 +443,17 @@
     "Load the ligands as openfe `SmallMoleculeComponent`s so that they can be understood by the OpenFE ecosystem."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "5ec171f2-b162-4801-b3d2-204768770e55",
-   "metadata": {},
-   "source": [
-    "Extract the ligand cofolded with the protein from the output .pdb:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "8e22e331-c533-4557-b1fb-ef570c0c054c",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import pathlib\n",
@@ -470,11 +474,31 @@
     "tmp_ligand_dir.mkdir(exist_ok=True)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5ec171f2-b162-4801-b3d2-204768770e55",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Extract the ligand cofolded with the protein from the output .pdb:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "6a6a999f-f961-4909-add8-e8c3bc804c99",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -531,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
    "metadata": {
     "editable": true,
@@ -540,16 +564,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=97949) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
-      "  pid, fd = os.forkpty()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# now run the reference protein through pdbfixer\n",
     "! pdbfixer assets/of3_tyk2_output/lig_jmc_27/seed_42/lig_jmc_27_seed_42_sample_1_model.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb"
@@ -574,13 +589,28 @@
     "tags": []
    },
    "source": [
-    "Then we can load the reference protein to a ProteinComponent."
+    "Now we can define the protein, ligand, and solvent in OpenFE."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
+   "id": "97da58c4-b4da-478a-918a-a487e5c7317b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import openfe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca1b4341-f636-45d8-9b20-ed313cc95213",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -588,6 +618,21 @@
     },
     "tags": []
    },
+   "source": [
+    "Load the protein as an OpenFE ``ProteinComponent``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -595,7 +640,7 @@
        "ProteinComponent(name=tyk2)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -608,19 +653,30 @@
   {
    "cell_type": "markdown",
    "id": "b1b544a9-9b92-43f3-8b88-7af9205621b5",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
    "source": [
     "Load the ligand as an openfe ``SmallMoleculeComponent``"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "c4dca30b-bafb-4d03-80c4-83684655ddcf",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import openfe\n",
     "ligand = openfe.SmallMoleculeComponent.from_sdf_file(ligand_prepped_path)"
    ]
   },
@@ -640,12 +696,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": ""
     },
     "tags": []
    },
@@ -654,7 +710,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.85it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.65it/s]\n"
      ]
     }
    ],
@@ -692,12 +748,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": ""
     },
     "tags": []
    },
@@ -708,7 +764,7 @@
        "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
    "metadata": {
     "editable": true,
@@ -805,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
    "metadata": {
     "editable": true,
@@ -839,7 +895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
    "metadata": {
     "editable": true,
@@ -932,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
    "metadata": {
     "editable": true,
@@ -1022,7 +1078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
    "metadata": {
     "editable": true,
@@ -1125,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
    "metadata": {
     "editable": true,
@@ -1139,7 +1195,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.12/pty.py:95: DeprecationWarning: This process (pid=97949) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=1244) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -1165,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
    "metadata": {
     "editable": true,
@@ -1179,7 +1235,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.83it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.71it/s]\n"
      ]
     }
    ],
@@ -1254,7 +1310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {
     "editable": true,
@@ -1271,7 +1327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {
     "editable": true,
@@ -1315,7 +1371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {
     "editable": true,
@@ -1335,7 +1391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {
     "editable": true,
@@ -1405,7 +1461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {
     "editable": true,
@@ -1473,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {
     "editable": true,
@@ -1521,7 +1577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {
     "editable": true,
@@ -1555,7 +1611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {
     "editable": true,
@@ -1621,7 +1677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {
     "editable": true,
@@ -1651,7 +1707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {
     "editable": true,
@@ -1693,7 +1749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {
     "editable": true,
@@ -1735,7 +1791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {
     "editable": true,
@@ -1793,14 +1849,6 @@
     "In practice, one would create entire network of Transformations, and then run all the simulations.\n",
     "This notebook will not cover these aspects. To find out more about this, please see the [OpenFE Tutorials](https://docs.openfree.energy/en/latest/tutorials/index.html)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f09a0d21-c0d3-4c39-9c9d-b0b1d61be2ae",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1819,7 +1867,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3.13.13"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

From 0b3a7749db7fa030438f53324c5286d5c11f5b0f Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 10:35:07 +0200
Subject: [PATCH 11/15] update warning box

---
 cookbook/openfold3_to_openfe.ipynb | 115 ++++++++++++++++-------------
 1 file changed, 65 insertions(+), 50 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 0ac1180..b35864f 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -542,6 +542,7 @@
    "cell_type": "markdown",
    "id": "30816423-6c25-47bb-a90c-4870e85567c1",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -570,10 +571,35 @@
     "! pdbfixer assets/of3_tyk2_output/lig_jmc_27/seed_42/lig_jmc_27_seed_42_sample_1_model.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/lig_jmc_27_seed_42_sample_1_model_fixed.pdb"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3072cbd5-e273-4d44-aff4-e3c94bc53476",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "source": [
+    "<div class=\"alert alert-block alert-warning\"> \n",
+    "⚠️ <b>Note:</b>\n",
+    "    In practice more care would be taken when protonating the complexes, using more complex tools such as Maestro or OpenEye's Spruce.\n",
+    "    \n",
+    "For example, ligand protonation states may change depending on the chemical environment they are bound to.\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "93d67ad4-6c06-4555-8760-06294e45f05f",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "#### Loading the OpenFE Components"
    ]
@@ -594,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "97da58c4-b4da-478a-918a-a487e5c7317b",
    "metadata": {
     "editable": true,
@@ -624,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
    "metadata": {
     "editable": true,
@@ -640,7 +666,7 @@
        "ProteinComponent(name=tyk2)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -666,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "c4dca30b-bafb-4d03-80c4-83684655ddcf",
    "metadata": {
     "editable": true,
@@ -696,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {
     "editable": true,
@@ -710,7 +736,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.65it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.72it/s]\n"
      ]
     }
    ],
@@ -748,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {
     "editable": true,
@@ -764,7 +790,7 @@
        "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -804,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
    "metadata": {
     "editable": true,
@@ -861,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
    "metadata": {
     "editable": true,
@@ -870,7 +896,15 @@
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ERROR! Session/line number was not unique in database. History logging moved to new session 13\n"
+     ]
+    }
+   ],
    "source": [
     "from openfe.protocols.openmm_afe import AbsoluteBindingProtocol\n",
     "\n",
@@ -895,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
    "metadata": {
     "editable": true,
@@ -988,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
    "metadata": {
     "editable": true,
@@ -1078,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
    "metadata": {
     "editable": true,
@@ -1144,25 +1178,6 @@
     "        full_protein.write(f'{output_dir}/protein.pdb')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e5b5141e-efe0-4bea-89e9-4896c00f4311",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": "fragment"
-    },
-    "tags": []
-   },
-   "source": [
-    "<div class=\"alert alert-block alert-warning\"> \n",
-    "⚠️ <b>Important Note:</b>\n",
-    "    <br><br>\n",
-    "    Here simply we assign ligand protonation states to those we defined at the start of this exercise. Additionally, below we will assign protein protonation states using PDBFixer at pH 7 without accounting for the ligand.<br><br>\n",
-    "    In practice more care would be taken when protonating the complexes, using more complex tools such as Maestro or OpenEye's Spruce. For example, ligand protonation states may change depending on the chemical environment they are bound to.\n",
-    "</div>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "6bf56d93-222a-4651-b8cf-f451f0360878",
@@ -1181,7 +1196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
    "metadata": {
     "editable": true,
@@ -1195,7 +1210,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=1244) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
+      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=5400) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
       "  pid, fd = os.forkpty()\n"
      ]
     }
@@ -1221,7 +1236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
    "metadata": {
     "editable": true,
@@ -1235,7 +1250,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.71it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.78it/s]\n"
      ]
     }
    ],
@@ -1310,7 +1325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {
     "editable": true,
@@ -1327,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {
     "editable": true,
@@ -1371,7 +1386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {
     "editable": true,
@@ -1391,7 +1406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {
     "editable": true,
@@ -1461,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {
     "editable": true,
@@ -1529,7 +1544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {
     "editable": true,
@@ -1577,7 +1592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {
     "editable": true,
@@ -1611,7 +1626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {
     "editable": true,
@@ -1677,7 +1692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {
     "editable": true,
@@ -1707,7 +1722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {
     "editable": true,
@@ -1749,7 +1764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {
     "editable": true,
@@ -1791,7 +1806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {
     "editable": true,

From baf9da56de10769c9fb923f0f840bda1602c5f6f Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 12:20:36 +0200
Subject: [PATCH 12/15] factor out some functions

---
 cookbook/openfold3_to_openfe.ipynb | 294 ++++++++++++-----------------
 1 file changed, 116 insertions(+), 178 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index b35864f..5cc1f51 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "id": "35354229",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -20,7 +19,6 @@
    "cell_type": "markdown",
    "id": "0d905dc8-e11b-4088-82f5-2c2cefef3973",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -41,7 +39,6 @@
    "cell_type": "markdown",
    "id": "ada5c811-8603-4473-8040-7fd370b4a1cf",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -58,7 +55,6 @@
    "cell_type": "markdown",
    "id": "2fea29c3",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -72,7 +68,6 @@
    "cell_type": "markdown",
    "id": "6c14eb39-036b-44d3-93e3-b29a635a7e2b",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -88,7 +83,6 @@
    "cell_type": "markdown",
    "id": "ef3bdb1f-504a-4311-8e0a-c28265b006cb",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -107,7 +101,6 @@
    "cell_type": "markdown",
    "id": "2cfa0d3a-9eb5-4ddc-854f-b278469ae2e1",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -122,9 +115,25 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "65af114c-b151-4bcd-b1b5-448abbe43fc5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "# don't show warnings that are expected and not an issue\n",
+    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "08962d91-a334-4c89-adf1-19be6f2bdb16",
    "metadata": {
-    "editable": true,
     "scrolled": true,
     "slideshow": {
      "slide_type": "fragment"
@@ -138,20 +147,19 @@
     "\n",
     "ligands_file = \"assets/tyk2_ligands.sdf\"\n",
     "\n",
-    "ligands_dict = {}\n",
+    "ligand_smiles = {}\n",
     "sppl = Chem.SDMolSupplier(ligands_file, removeHs=True)\n",
     "for mol in sppl:\n",
     "    smi = Chem.MolToSmiles(mol)\n",
     "    name = mol.GetProp(\"_Name\")\n",
-    "    ligands_dict[name] = smi"
+    "    ligand_smiles[name] = smi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "1c55bfeb-c1bc-4af8-98da-43ff8c1de141",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -173,20 +181,19 @@
        " 'lig_jmc_28': 'C[C@@H]1C[C@@H]1C(=O)Nc1cc(NC(=O)c2c(Cl)cccc2Cl)ccn1'}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ligands_dict"
+    "ligand_smiles"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "80da7967-8046-44e4-aef7-c81ac6712f98",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -200,7 +207,6 @@
    "cell_type": "markdown",
    "id": "b3f0be0c-144c-4e96-9310-a8778111291c",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -214,10 +220,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "25a8ba0c-311c-476e-9913-9b39611c708d",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -237,7 +242,7 @@
     "# Build the queries dictionary\n",
     "queries = {}\n",
     "\n",
-    "for name, smiles in ligands_dict.items():\n",
+    "for name, smiles in ligand_smiles.items():\n",
     "    queries[name] = {\n",
     "        \"chains\": [\n",
     "            protein_info,\n",
@@ -258,7 +263,6 @@
    "cell_type": "markdown",
    "id": "a2546f36-2ca2-4793-ad4b-19b6e76574ae",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -271,7 +275,7 @@
     "\n",
     "We define the following settings in a `settings.yaml` file to be passed to OpenFold:\n",
     "\n",
-    "```json\n",
+    "```\n",
     "model_update:\n",
     "  presets:\n",
     "      - predict\n",
@@ -291,7 +295,6 @@
    "cell_type": "markdown",
    "id": "1557d01d-6a84-4531-83b8-b879a50a0ac6",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -309,7 +312,6 @@
    "cell_type": "markdown",
    "id": "9c1ab9ee-71f5-4587-bea1-35097621c039",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -325,7 +327,6 @@
    "cell_type": "markdown",
    "id": "73368db9-350f-4851-922a-01469736dcc7",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -341,7 +342,6 @@
    "cell_type": "markdown",
    "id": "9481baf0-9f31-41f2-acf2-ec9b62552345",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -358,7 +358,6 @@
    "cell_type": "markdown",
    "id": "721074db-8266-4c3e-b592-64220e0cfd8f",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -372,7 +371,6 @@
    "cell_type": "markdown",
    "id": "249ec515-cfad-4f49-8dba-ee2438b1adf4",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -387,7 +385,6 @@
    "cell_type": "markdown",
    "id": "6419b4ec-6513-47ca-bb66-0eb17a7224c3",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -401,7 +398,6 @@
    "cell_type": "markdown",
    "id": "fd51d74c-5cdf-445a-8a89-7ff365bc1e1c",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -415,7 +411,6 @@
    "cell_type": "markdown",
    "id": "d34aab3e-8275-46ab-abc6-2a588224d889",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -431,7 +426,6 @@
    "cell_type": "markdown",
    "id": "cdeee4e3-ce8c-4151-9ef7-5a6017ac3dcb",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -445,10 +439,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "8e22e331-c533-4557-b1fb-ef570c0c054c",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -457,28 +450,24 @@
    "outputs": [],
    "source": [
     "import pathlib\n",
-    "import warnings\n",
     "import MDAnalysis as mda\n",
+    "from MDAnalysis.core.groups import AtomGroup\n",
     "from rdkit import Chem, rdBase\n",
     "from rdkit.Chem import AllChem\n",
     "\n",
-    "# don't show warnings that are expected and not an issue\n",
-    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
-    "\n",
     "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
     "\n",
     "output_dir = pathlib.Path(output_root, \"abfe_prep/\")\n",
     "output_dir.mkdir(exist_ok=True, parents=True)\n",
     "\n",
     "tmp_ligand_dir = pathlib.Path(output_root, \"temp_ligands\")\n",
-    "tmp_ligand_dir.mkdir(exist_ok=True)\n"
+    "tmp_ligand_dir.mkdir(exist_ok=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "5ec171f2-b162-4801-b3d2-204768770e55",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -490,10 +479,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
+   "id": "307df5ab-d79e-4592-b3ad-57334574249d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_protein_ligand(pdb_path:pathlib.Path)->(AtomGroup, AtomGroup):\n",
+    "    print(f\"Extracting protein and ligand from {pdb_path.name}\")\n",
+    "    pdb_universe = mda.Universe(pdb_path)\n",
+    "    protein = pdb_universe.select_atoms(\"protein\")\n",
+    "    ligand = pdb_universe.select_atoms(\"resname LIG\")\n",
+    "\n",
+    "    return (protein, ligand)\n",
+    "\n",
+    "def prep_ligand(sdf_in:pathlib.Path, sdf_out:pathlib.Path, name:str, template_smiles:str)->None:\n",
+    "    print(f\"Preparing {name}\")\n",
+    "    # load back into rdkit to add hydrogens\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(str(sdf_in), removeHs=True)\n",
+    "    \n",
+    "    # use the template to assign bond orders\n",
+    "    template_mol = Chem.MolFromSmiles(template_smiles)\n",
+    "    with rdBase.BlockLogs():  \n",
+    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "        \n",
+    "    # Add hydrogens\n",
+    "    rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
+    "    \n",
+    "    # Set the molecule name\n",
+    "    rdkit_mol.SetProp('_Name', f\"{name}\")\n",
+    "    \n",
+    "    # write to sdf\n",
+    "    Chem.MolToMolFile(rdkit_mol, sdf_out)\n",
+    "    print(f\"written to {sdf_out}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "id": "6a6a999f-f961-4909-add8-e8c3bc804c99",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -504,7 +528,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Extracting protein and ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n"
+      "Extracting protein and ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Preparing lig_jmc_27\n",
+      "written to openfold_to_openfe/abfe_prep/lig_jmc_27_prepped.sdf\n"
      ]
     }
    ],
@@ -512,37 +538,24 @@
     "ligand_name = 'lig_jmc_27'\n",
     "pdb_path = pathlib.Path(f\"assets/of3_tyk2_output/{ligand_name}/seed_42/{ligand_name}_seed_42_sample_1_model.pdb\")\n",
     "\n",
-    "pdb_universe = mda.Universe(pdb_path)\n",
-    "\n",
-    "print(f\"Extracting protein and ligand from {pdb_path.name}\")\n",
-    "aligned_ligand = pdb_universe.select_atoms(\"resname LIG\")\n",
-    "aligned_ligand.write(f'{output_dir}/{ligand_name}.pdb')\n",
+    "protein, ligand = extract_protein_ligand(pdb_path)\n",
     "\n",
-    "# load back into rdkit to add hydrogens\n",
-    "rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand_name}.pdb', removeHs=True)\n",
+    "protein.write(f'{output_dir}/protein.pdb')\n",
+    "ligand.write(f'{output_dir}/{ligand_name}.pdb')\n",
     "\n",
-    "# use the template to assign bond orders\n",
-    "template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
-    "with rdBase.BlockLogs():  \n",
-    "    rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
-    "# Add hydrogens\n",
-    "rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
-    "# Set the molecule name\n",
-    "rdkit_mol.SetProp('_Name', f\"{ligand_name}\")\n",
-    "# write to sdf\n",
-    "ligand_prepped_path = output_dir / f\"{ligand_name}_prepped.sdf\"\n",
-    "Chem.MolToMolFile(rdkit_mol, ligand_prepped_path)\n",
-    "\n",
-    "# save the protein alone\n",
-    "full_protein = pdb_universe.select_atoms(\"protein\")\n",
-    "full_protein.write(f'{output_dir}/protein.pdb')"
+    "prepped_ligand_path = output_dir / f\"{ligand_name}_prepped.sdf\"\n",
+    "prep_ligand(\n",
+    "    output_dir/f\"{ligand_name}.pdb\",\n",
+    "    prepped_ligand_path,\n",
+    "    ligand_name,\n",
+    "    ligand_smiles[ligand_name]\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "30816423-6c25-47bb-a90c-4870e85567c1",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -556,10 +569,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "ea82c47d-9794-424c-8d27-a9c6e69d29e8",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -575,7 +587,6 @@
    "cell_type": "markdown",
    "id": "3072cbd5-e273-4d44-aff4-e3c94bc53476",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -594,7 +605,6 @@
    "cell_type": "markdown",
    "id": "93d67ad4-6c06-4555-8760-06294e45f05f",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -608,7 +618,6 @@
    "cell_type": "markdown",
    "id": "eaa991f1-3ecf-4f15-b637-4538a0078b7e",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -620,10 +629,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "97da58c4-b4da-478a-918a-a487e5c7317b",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -638,7 +646,6 @@
    "cell_type": "markdown",
    "id": "ca1b4341-f636-45d8-9b20-ed313cc95213",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -650,10 +657,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "88bbbbca-4f6a-4496-bea9-f621ffd8183e",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -666,7 +672,7 @@
        "ProteinComponent(name=tyk2)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -680,7 +686,6 @@
    "cell_type": "markdown",
    "id": "b1b544a9-9b92-43f3-8b88-7af9205621b5",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -692,10 +697,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "c4dca30b-bafb-4d03-80c4-83684655ddcf",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -703,14 +707,13 @@
    },
    "outputs": [],
    "source": [
-    "ligand = openfe.SmallMoleculeComponent.from_sdf_file(ligand_prepped_path)"
+    "ligand = openfe.SmallMoleculeComponent.from_sdf_file(prepped_ligand_path)"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "f0cd4185-3491-4bcb-9b4d-d160d84ad40e",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -722,10 +725,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -736,7 +738,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.72it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.68it/s]\n"
      ]
     }
    ],
@@ -762,7 +764,6 @@
    "cell_type": "markdown",
    "id": "4d11803b-6d4b-4507-8c98-6ff40f75680e",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -774,10 +775,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": ""
     },
@@ -790,7 +790,7 @@
        "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -804,7 +804,6 @@
    "cell_type": "markdown",
    "id": "e1ef09d2-ec8b-4577-8510-c67cb89fc5b5",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -818,7 +817,6 @@
    "cell_type": "markdown",
    "id": "695c61c4-3fa6-4e1a-939f-5349e6829627",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -830,10 +828,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -875,7 +872,6 @@
    "cell_type": "markdown",
    "id": "e32fb4ef-dbf5-4a9a-90c4-b64d9e5af6ce",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -887,24 +883,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR! Session/line number was not unique in database. History logging moved to new session 13\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from openfe.protocols.openmm_afe import AbsoluteBindingProtocol\n",
     "\n",
@@ -917,7 +904,6 @@
    "cell_type": "markdown",
    "id": "6bf8af4c-9894-4af3-84f8-2ed3830120ad",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -929,10 +915,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -953,7 +938,6 @@
    "cell_type": "markdown",
    "id": "34ec9c8d-cb7f-4cad-9794-b73629a11246",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -967,7 +951,6 @@
    "cell_type": "markdown",
    "id": "df8cb66d-c201-4a22-b87a-a1e5501d4be4",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -981,7 +964,6 @@
    "cell_type": "markdown",
    "id": "9dd96548-7190-4851-b1ed-fb2d8c1bd87b",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1006,7 +988,6 @@
    "cell_type": "markdown",
    "id": "02a82dbd-5a01-43c5-af33-654b706a5721",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1022,10 +1003,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1081,7 +1061,7 @@
     "    # now load back with rdkit\n",
     "    rdkit_mol = Chem.MolFromPDBFile(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
     "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
-    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
+    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
     "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
     "    with rdBase.BlockLogs():  \n",
     "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
@@ -1098,7 +1078,6 @@
    "cell_type": "markdown",
    "id": "193dc76a-6e43-4e25-8b9d-5c0e4a79c3e9",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1112,10 +1091,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1162,7 +1140,7 @@
     "    # load back into rdkit to add hydrogens\n",
     "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand}.pdb', removeHs=True)\n",
     "    # use the template to assign bond orders\n",
-    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand])\n",
+    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand])\n",
     "    with rdBase.BlockLogs():  \n",
     "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
     "    # Add hydrogens\n",
@@ -1182,7 +1160,6 @@
    "cell_type": "markdown",
    "id": "6bf56d93-222a-4651-b8cf-f451f0360878",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1196,25 +1173,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/atravitz/micromamba/envs/openfe-notebooks/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=5400) is multi-threaded, use of forkpty() may lead to deadlocks in the child.\n",
-      "  pid, fd = os.forkpty()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# now run the reference protein through pdbfixer\n",
     "! pdbfixer openfold_to_openfe/p_aligned/protein.pdb --add-atoms=all --add-residues --output=openfold_to_openfe/p_aligned/protein_fixed.pdb"
@@ -1224,7 +1191,6 @@
    "cell_type": "markdown",
    "id": "1153dbd0-998a-4f3f-8e47-a950c806d1dd",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1236,10 +1202,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1250,7 +1215,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.78it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.73it/s]\n"
      ]
     }
    ],
@@ -1285,7 +1250,6 @@
    "cell_type": "markdown",
    "id": "cc023ddb-d32a-4f78-bea1-a4f9efddf902",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1299,7 +1263,6 @@
    "cell_type": "markdown",
    "id": "97f84495-88c8-49fe-987f-25ca25cdf210",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1313,7 +1276,6 @@
    "cell_type": "markdown",
    "id": "a90d2205-1c68-4349-95b1-e20f607d4a86",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1325,10 +1287,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1342,10 +1303,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1374,7 +1334,6 @@
    "cell_type": "markdown",
    "id": "54484f2f-347b-498e-b5e2-60e8c8db446a",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1386,10 +1345,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1406,10 +1364,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1430,7 +1387,6 @@
    "cell_type": "markdown",
    "id": "db4344c5-c300-4b6e-aa0a-a7cae9c6380f",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1444,7 +1400,6 @@
    "cell_type": "markdown",
    "id": "6e16e781-c6dc-4a62-85a7-0bc59678e916",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1460,7 +1415,6 @@
    "cell_type": "markdown",
    "id": "884c5615-b2d4-4dde-b949-7760733ec10a",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1469,17 +1423,16 @@
    "source": [
     "Finally we create a Hybrid Topology RBFE Transformation.\n",
     "\n",
-    "In addition to aligning the protein structures, \n",
+    "In addition to aligning the protein structures, we must also align the ligands.\n",
     "\n",
     "Again, as per the SepTop example above, we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1529,7 +1482,7 @@
     "    # now load back with rdkit\n",
     "    rdkit_mol = Chem.MolFromPDBFile(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
     "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
-    "    template_mol = Chem.MolFromSmiles(ligands_dict[ligand_name])\n",
+    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
     "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
     "    with rdBase.BlockLogs():  \n",
     "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
@@ -1544,10 +1497,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1563,7 +1515,6 @@
    "cell_type": "markdown",
    "id": "dfbf86dd-1162-4cf5-998a-2b2c01e6080c",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1577,7 +1528,6 @@
    "cell_type": "markdown",
    "id": "eb09cf7b-e9e9-4f98-b9a5-7f6cfc9c146b",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1592,10 +1542,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1614,7 +1563,6 @@
    "cell_type": "markdown",
    "id": "d8617ab9-77c3-4103-a9fe-233262f19b74",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1626,10 +1574,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1659,7 +1606,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1680,7 +1627,6 @@
    "cell_type": "markdown",
    "id": "aeb15ac9-9999-4bb9-b5e3-bda5dda01dc9",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1692,10 +1638,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1722,10 +1667,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1752,7 +1696,6 @@
    "cell_type": "markdown",
    "id": "fe4f8dd1-ffdc-4167-97ca-a24b86002613",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1764,10 +1707,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1794,7 +1736,6 @@
    "cell_type": "markdown",
    "id": "9030db1c-35fe-4213-930c-b967832d9c7d",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1806,10 +1747,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1838,7 +1778,6 @@
    "cell_type": "markdown",
    "id": "f08aaff4-97d0-4f53-bb32-1c7e247432aa",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -1852,7 +1791,6 @@
    "cell_type": "markdown",
    "id": "6cae5156-f80f-4520-baee-4199774d441a",
    "metadata": {
-    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -1882,7 +1820,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.13"
+   "version": "3.11.15"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

From af0f12e1b9ebb723668ed6e699713c624bf27123 Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 17:31:29 +0200
Subject: [PATCH 13/15] cleaning up functions

---
 cookbook/openfold3_to_openfe.ipynb | 156 ++++++++++++++---------------
 1 file changed, 77 insertions(+), 79 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 5cc1f51..cf03a70 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -126,7 +126,8 @@
    "source": [
     "import warnings\n",
     "# don't show warnings that are expected and not an issue\n",
-    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") "
+    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
+    "from typing import Iterable"
    ]
   },
   {
@@ -485,7 +486,6 @@
    "outputs": [],
    "source": [
     "def extract_protein_ligand(pdb_path:pathlib.Path)->(AtomGroup, AtomGroup):\n",
-    "    print(f\"Extracting protein and ligand from {pdb_path.name}\")\n",
     "    pdb_universe = mda.Universe(pdb_path)\n",
     "    protein = pdb_universe.select_atoms(\"protein\")\n",
     "    ligand = pdb_universe.select_atoms(\"resname LIG\")\n",
@@ -510,7 +510,7 @@
     "    \n",
     "    # write to sdf\n",
     "    Chem.MolToMolFile(rdkit_mol, sdf_out)\n",
-    "    print(f\"written to {sdf_out}\")"
+    "    print(f\"Prepared ligand written to {sdf_out}\")"
    ]
   },
   {
@@ -528,9 +528,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Extracting protein and ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n",
       "Preparing lig_jmc_27\n",
-      "written to openfold_to_openfe/abfe_prep/lig_jmc_27_prepped.sdf\n"
+      "Prepared ligand written to openfold_to_openfe/abfe_prep/lig_jmc_27_prepped.sdf\n"
      ]
     }
    ],
@@ -697,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "c4dca30b-bafb-4d03-80c4-83684655ddcf",
    "metadata": {
     "slideshow": {
@@ -725,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "0b78ec14-9ef9-4068-84ab-3271e6780d40",
    "metadata": {
     "slideshow": {
@@ -738,7 +737,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.68it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.72it/s]\n"
      ]
     }
    ],
@@ -775,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "30373ee0-d777-4828-b89a-5a8fdbc56c41",
    "metadata": {
     "slideshow": {
@@ -790,7 +789,7 @@
        "SolventComponent(name=O, Na+, Cl-)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -828,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "987a1657-82d0-4310-9fe7-4eecd289632a",
    "metadata": {
     "slideshow": {
@@ -883,7 +882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "037cb959-8f0c-4a09-844a-30e39590eabf",
    "metadata": {
     "slideshow": {
@@ -915,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "ef7a9ebf-e0d7-4dda-aac5-d4428b120814",
    "metadata": {
     "slideshow": {
@@ -1003,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "c7b239c1-eaf3-466e-84ef-0700eab300e6",
    "metadata": {
     "slideshow": {
@@ -1016,18 +1015,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Extracting ligand from lig_ejm_50_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_23_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_43_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_42_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_31_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_47_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_46_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_48_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_28_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_50_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_23_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_43_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_42_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_31_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_47_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_46_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_48_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_28_seed_42_sample_1_model.pdb\n",
       "\n",
-      "Largest ligand is lig_jmc_27 with volume 0.5209626577896912\n"
+      "Largest ligand_name is lig_jmc_27 with volume 0.5209626577896912\n"
      ]
     }
    ],
@@ -1044,34 +1043,33 @@
     "\n",
     "# gather all the complexes and align to a single structure\n",
     "# which has the largest ligand by volume \n",
-    "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
-    "\n",
-    "# load all the output pdbs and calculate ligand volumes\n",
-    "ligand_volumes = {}\n",
-    "\n",
-    "for output_file in of_results_root.glob(\"*/*/*.pdb\"):\n",
-    "    ligand_name = output_file.name.split('_seed')[0]\n",
-    "    print(f\"Extracting ligand from {output_file.name}\")\n",
-    "    with warnings.catch_warnings():   \n",
-    "        warnings.simplefilter(\"ignore\")\n",
-    "        u = mda.Universe(str(output_file))\n",
-    "    ligand = u.select_atoms(\"resname LIG\")\n",
-    "    # write the ligand to a temporary file\n",
-    "    ligand.write(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"))\n",
-    "    # now load back with rdkit\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
-    "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
-    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
-    "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
-    "    with rdBase.BlockLogs():  \n",
-    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
-    "    # calculate the volume\n",
-    "    vsa = Asphericity(rdkit_mol)\n",
-    "    ligand_volumes[ligand_name] = vsa\n",
-    "\n",
-    "# find the ligand with the largest volume\n",
-    "largest_ligand = max(ligand_volumes, key=ligand_volumes.get)\n",
-    "print(f\"\\nLargest ligand is {largest_ligand} with volume {ligand_volumes[largest_ligand]}\")"
+    "of3_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
+    "\n",
+    "\n",
+    "def find_largest_ligand(ligand_sdfs:Iterable[pathlib.Path])->(str,float):\n",
+    "    # load all the output pdbs and calculate ligand volumes\n",
+    "    ligand_volumes = {}\n",
+    "    for lig_sdf in ligand_sdfs:\n",
+    "        ligand_name = lig_sdf.name.split('_seed')[0]\n",
+    "        print(f\"Calculating volume of {lig_sdf.name}\")\n",
+    "        _, ligand = extract_protein_ligand(lig_sdf)\n",
+    "        # write the ligand to a temporary file\n",
+    "        ligand.write(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"))\n",
+    "        # now load back with rdkit\n",
+    "        rdkit_mol = Chem.MolFromPDBFile(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
+    "        # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
+    "        template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
+    "        # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
+    "        with rdBase.BlockLogs():  \n",
+    "            rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "        # calculate the volume\n",
+    "        vsa = Asphericity(rdkit_mol)\n",
+    "        ligand_volumes[ligand_name] = vsa\n",
+    "        largest_ligand = max(ligand_volumes, key=ligand_volumes.get)\n",
+    "    return largest_ligand, ligand_volumes[largest_ligand]\n",
+    "    \n",
+    "largest_ligand, largest_vol= find_largest_ligand(of3_results_root.glob(\"*/*/*.pdb\"))\n",
+    "print(f\"\\nLargest ligand_name is {largest_ligand} with volume {largest_vol}\")"
    ]
   },
   {
@@ -1091,7 +1089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
    "metadata": {
     "slideshow": {
@@ -1118,15 +1116,15 @@
     }
    ],
    "source": [
-    "reference_pdb = pathlib.Path(of_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")\n",
-    "other_pdbs = of_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
+    "reference_pdb = pathlib.Path(of3_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")\n",
+    "other_pdbs = of3_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
     "output_dir = pathlib.Path(\"openfold_to_openfe/p_aligned/\")\n",
     "output_dir.mkdir(exist_ok=True)\n",
     "\n",
     "ref = mda.Universe(reference_pdb)\n",
     "for pdb in other_pdbs:\n",
     "    print(f\"Aligning {pdb.name} to {reference_pdb.name}\")\n",
-    "    ligand = pdb.name.split('_seed')[0]\n",
+    "    ligand_name = pdb.name.split('_seed')[0]\n",
     "    mob = mda.Universe(pdb)\n",
     "\n",
     "    # Align mobile → reference using protein backbone\n",
@@ -1136,19 +1134,19 @@
     "    # Perform alignment in-place\n",
     "    align.alignto(mob_prot, ref_prot)\n",
     "    aligned_ligand = mob.select_atoms(\"resname LIG\")\n",
-    "    aligned_ligand.write(f'{output_dir}/{ligand}.pdb')\n",
+    "    aligned_ligand.write(f'{output_dir}/{ligand_name}.pdb')\n",
     "    # load back into rdkit to add hydrogens\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand}.pdb', removeHs=True)\n",
+    "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand_name}.pdb', removeHs=True)\n",
     "    # use the template to assign bond orders\n",
-    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand])\n",
+    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
     "    with rdBase.BlockLogs():  \n",
     "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
     "    # Add hydrogens\n",
     "    rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
     "    # Set the molecule name\n",
-    "    rdkit_mol.SetProp('_Name', f\"{ligand}\")\n",
+    "    rdkit_mol.SetProp('_Name', f\"{ligand_name}\")\n",
     "    # write to sdf\n",
-    "    Chem.MolToMolFile(rdkit_mol, output_dir / f\"{ligand}_prepped.sdf\")\n",
+    "    Chem.MolToMolFile(rdkit_mol, output_dir / f\"{ligand_name}_prepped.sdf\")\n",
     "\n",
     "    # For the reference pdb, save the protein alone\n",
     "    if pdb.name.split('_seed')[0] == largest_ligand:\n",
@@ -1173,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
    "metadata": {
     "slideshow": {
@@ -1202,7 +1200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
    "metadata": {
     "slideshow": {
@@ -1215,7 +1213,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.73it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.79it/s]\n"
      ]
     }
    ],
@@ -1287,7 +1285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {
     "slideshow": {
@@ -1303,7 +1301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {
     "slideshow": {
@@ -1345,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {
     "slideshow": {
@@ -1364,7 +1362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {
     "slideshow": {
@@ -1430,7 +1428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {
     "slideshow": {
@@ -1464,13 +1462,13 @@
     "\n",
     "# gather all the complexes and align to a single structure\n",
     "# which has the largest ligand by volume \n",
-    "of_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
+    "of3_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
     "tmp_ligand_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
     "tmp_ligand_dir.mkdir(exist_ok=True,parents=True)\n",
     "# load all the output pdbs and calculate ligand volumes\n",
     "ligand_volumes = {}\n",
     "\n",
-    "for output_file in of_results_root.glob(\"*/*/*.pdb\"):\n",
+    "for output_file in of3_results_root.glob(\"*/*/*.pdb\"):\n",
     "    ligand_name = output_file.name.split('_seed')[0]\n",
     "    print(f\"Extracting ligand from {output_file.name}\")\n",
     "    with warnings.catch_warnings():   \n",
@@ -1497,7 +1495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {
     "slideshow": {
@@ -1542,7 +1540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {
     "slideshow": {
@@ -1574,7 +1572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {
     "slideshow": {
@@ -1638,7 +1636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {
     "slideshow": {
@@ -1667,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {
     "slideshow": {
@@ -1707,7 +1705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {
     "slideshow": {
@@ -1747,7 +1745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {
     "slideshow": {

From 982322a200c7326404b7e6900f850e8e63d93899 Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 17:49:23 +0200
Subject: [PATCH 14/15] protein alignment function

---
 cookbook/openfold3_to_openfe.ipynb | 219 +++++++++++++++++------------
 1 file changed, 126 insertions(+), 93 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index cf03a70..7f7efab 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -458,8 +458,8 @@
     "\n",
     "output_root = pathlib.Path(\"openfold_to_openfe\")\n",
     "\n",
-    "output_dir = pathlib.Path(output_root, \"abfe_prep/\")\n",
-    "output_dir.mkdir(exist_ok=True, parents=True)\n",
+    "abfe_out_dir = pathlib.Path(output_root, \"abfe_prep/\")\n",
+    "abfe_out_dir.mkdir(exist_ok=True, parents=True)\n",
     "\n",
     "tmp_ligand_dir = pathlib.Path(output_root, \"temp_ligands\")\n",
     "tmp_ligand_dir.mkdir(exist_ok=True)"
@@ -539,12 +539,12 @@
     "\n",
     "protein, ligand = extract_protein_ligand(pdb_path)\n",
     "\n",
-    "protein.write(f'{output_dir}/protein.pdb')\n",
-    "ligand.write(f'{output_dir}/{ligand_name}.pdb')\n",
+    "protein.write(f'{abfe_out_dir}/protein.pdb')\n",
+    "ligand.write(f'{abfe_out_dir}/{ligand_name}.pdb')\n",
     "\n",
-    "prepped_ligand_path = output_dir / f\"{ligand_name}_prepped.sdf\"\n",
+    "prepped_ligand_path = abfe_out_dir / f\"{ligand_name}_prepped.sdf\"\n",
     "prep_ligand(\n",
-    "    output_dir/f\"{ligand_name}.pdb\",\n",
+    "    abfe_out_dir/f\"{ligand_name}.pdb\",\n",
     "    prepped_ligand_path,\n",
     "    ligand_name,\n",
     "    ligand_smiles[ligand_name]\n",
@@ -737,7 +737,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.72it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.80it/s]\n"
      ]
     }
    ],
@@ -973,12 +973,12 @@
     "\n",
     "a) Choosing a reference protein structure which will be used for all binding affinity predictions\n",
     "\n",
-    "  - This is necessary for RBFE simulations, where a single structure must be kept constant throughout the transformations. Note that for ABFE simulations, this step can be avoided.\n",
+    "  - This is necessary for RBFE simulations, where a single structure must be kept constant throughout the transformations.\n",
     "  - For the sake of this demonstration, we will be choosing the predicted structure that is bound to the ligand occupying the largest volume. The idea here being that hopefully the binding site of that structure can accomodate all the other ligands. We note that in practice this is not always true.\n",
     "\n",
     "b) Aligning the structures to the reference & extracting the necessary structures\n",
     "\n",
-    "  - This is so that we can extract the ligands and use them with the chosen structure.\n",
+    "  - This is so that we can extract the ligands from all predictions and use them with the chosen reference structure.\n",
     "\n",
     "c) Fixing & protonating the reference protein structure, as we did above for the ABFE protein structure.\n"
    ]
@@ -1010,26 +1010,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calculating volume of lig_ejm_50_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_jmc_23_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_ejm_43_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_ejm_42_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_ejm_31_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_ejm_47_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_ejm_46_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_ejm_48_seed_42_sample_1_model.pdb\n",
-      "Calculating volume of lig_jmc_28_seed_42_sample_1_model.pdb\n",
-      "\n",
-      "Largest ligand_name is lig_jmc_27 with volume 0.5209626577896912\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pathlib\n",
     "import MDAnalysis as mda\n",
@@ -1038,16 +1019,8 @@
     "from rdkit.Chem.Descriptors3D import Asphericity\n",
     "import warnings\n",
     "\n",
-    "# don't show warnings that are expected and not an issue\n",
-    "warnings.filterwarnings(action=\"ignore\", message=r\"Unit cell dimensions not found\") \n",
-    "\n",
-    "# gather all the complexes and align to a single structure\n",
-    "# which has the largest ligand by volume \n",
-    "of3_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
-    "\n",
-    "\n",
     "def find_largest_ligand(ligand_sdfs:Iterable[pathlib.Path])->(str,float):\n",
-    "    # load all the output pdbs and calculate ligand volumes\n",
+    "    \"\"\"gather all the complexes and align to a single structure which has the largest ligand by volume\"\"\"\n",
     "    ligand_volumes = {}\n",
     "    for lig_sdf in ligand_sdfs:\n",
     "        ligand_name = lig_sdf.name.split('_seed')[0]\n",
@@ -1066,10 +1039,56 @@
     "        vsa = Asphericity(rdkit_mol)\n",
     "        ligand_volumes[ligand_name] = vsa\n",
     "        largest_ligand = max(ligand_volumes, key=ligand_volumes.get)\n",
-    "    return largest_ligand, ligand_volumes[largest_ligand]\n",
-    "    \n",
+    "    return largest_ligand, ligand_volumes[largest_ligand]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "eb57e727-60f0-4945-861a-56032817f309",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating volume of lig_ejm_50_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_23_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_43_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_42_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_31_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_47_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_46_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_48_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_28_seed_42_sample_1_model.pdb\n",
+      "\n",
+      "Largest ligand_name is lig_jmc_27 with volume 0.521\n"
+     ]
+    }
+   ],
+   "source": [
+    "of3_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
     "largest_ligand, largest_vol= find_largest_ligand(of3_results_root.glob(\"*/*/*.pdb\"))\n",
-    "print(f\"\\nLargest ligand_name is {largest_ligand} with volume {largest_vol}\")"
+    "print(f\"\\nLargest ligand_name is {largest_ligand} with volume {largest_vol:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c7639ea-8081-4bc1-bf67-2afc1f495230",
+   "metadata": {},
+   "source": [
+    "Choose the corresponding protein as the reference protein"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a18f49cb-a504-410b-8d49-481d272666f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_pdb = pathlib.Path(of3_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")"
    ]
   },
   {
@@ -1089,7 +1108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "id": "26c10e11-f001-44e5-87a4-2939ead048a8",
    "metadata": {
     "slideshow": {
@@ -1097,6 +1116,49 @@
     },
     "tags": []
    },
+   "outputs": [],
+   "source": [
+    "def align_proteins_to_ref(all_pdbs, ref_pdb, out_dir):\n",
+    "    ref_u = mda.Universe(reference_pdb)\n",
+    "    ref_prot = ref_u.select_atoms(\"protein and backbone\")\n",
+    "    for pdb in all_pdbs:\n",
+    "        print(f\"Aligning {pdb.name} to {reference_pdb.name}\")\n",
+    "        ligand_name = pdb.name.split('_seed')[0]\n",
+    "        mobile_u = mda.Universe(pdb)\n",
+    "    \n",
+    "        # Align mobile → reference using protein backbone\n",
+    "        mobile_protein = mobile_u.select_atoms(\"protein and backbone\")\n",
+    "        \n",
+    "        # Perform alignment in-place\n",
+    "        align.alignto(mobile_protein, ref_prot)\n",
+    "        aligned_ligand = mobile_u.select_atoms(\"resname LIG\")\n",
+    "        aligned_ligand.write(f'{out_dir}/{ligand_name}.pdb')\n",
+    "        # load back into rdkit to add hydrogens\n",
+    "        rdkit_mol = Chem.MolFromPDBFile(f'{out_dir}/{ligand_name}.pdb', removeHs=True)\n",
+    "        # use the template to assign bond orders\n",
+    "        template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
+    "        with rdBase.BlockLogs():  \n",
+    "            rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
+    "        # Add hydrogens\n",
+    "        rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
+    "        # Set the molecule name\n",
+    "        rdkit_mol.SetProp('_Name', f\"{ligand_name}\")\n",
+    "        # write to sdf\n",
+    "        Chem.MolToMolFile(rdkit_mol, out_dir / f\"{ligand_name}_prepped.sdf\")\n",
+    "    \n",
+    "        # For the reference pdb, save the protein alone\n",
+    "        if pdb.name.split('_seed')[0] == largest_ligand:\n",
+    "            full_protein = mobile_u.select_atoms(\"protein\")\n",
+    "            full_protein.write(f'{out_dir}/protein.pdb')\n",
+    "            \n",
+    "    print(f\"\\nStructures aligned and written to {out_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "73573473-ad11-423c-9b9f-bef57a5046ac",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1111,47 +1173,18 @@
       "Aligning lig_jmc_27_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
       "Aligning lig_ejm_46_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
       "Aligning lig_ejm_48_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Aligning lig_jmc_28_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n"
+      "Aligning lig_jmc_28_seed_42_sample_1_model.pdb to lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "\n",
+      "Structures aligned and written to openfold_to_openfe/p_aligned\n"
      ]
     }
    ],
    "source": [
-    "reference_pdb = pathlib.Path(of3_results_root, largest_ligand, \"seed_42\", f\"{largest_ligand}_seed_42_sample_1_model.pdb\")\n",
-    "other_pdbs = of3_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
-    "output_dir = pathlib.Path(\"openfold_to_openfe/p_aligned/\")\n",
-    "output_dir.mkdir(exist_ok=True)\n",
-    "\n",
-    "ref = mda.Universe(reference_pdb)\n",
-    "for pdb in other_pdbs:\n",
-    "    print(f\"Aligning {pdb.name} to {reference_pdb.name}\")\n",
-    "    ligand_name = pdb.name.split('_seed')[0]\n",
-    "    mob = mda.Universe(pdb)\n",
-    "\n",
-    "    # Align mobile → reference using protein backbone\n",
-    "    ref_prot = ref.select_atoms(\"protein and backbone\")\n",
-    "    mob_prot = mob.select_atoms(\"protein and backbone\")\n",
-    "    \n",
-    "    # Perform alignment in-place\n",
-    "    align.alignto(mob_prot, ref_prot)\n",
-    "    aligned_ligand = mob.select_atoms(\"resname LIG\")\n",
-    "    aligned_ligand.write(f'{output_dir}/{ligand_name}.pdb')\n",
-    "    # load back into rdkit to add hydrogens\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(f'{output_dir}/{ligand_name}.pdb', removeHs=True)\n",
-    "    # use the template to assign bond orders\n",
-    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
-    "    with rdBase.BlockLogs():  \n",
-    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
-    "    # Add hydrogens\n",
-    "    rdkit_mol = AllChem.AddHs(rdkit_mol, addCoords=True)\n",
-    "    # Set the molecule name\n",
-    "    rdkit_mol.SetProp('_Name', f\"{ligand_name}\")\n",
-    "    # write to sdf\n",
-    "    Chem.MolToMolFile(rdkit_mol, output_dir / f\"{ligand_name}_prepped.sdf\")\n",
+    "p_aligned_dir = pathlib.Path(\"openfold_to_openfe/p_aligned/\")\n",
+    "p_aligned_dir.mkdir(exist_ok=True)\n",
     "\n",
-    "    # For the reference pdb, save the protein alone\n",
-    "    if pdb.name.split('_seed')[0] == largest_ligand:\n",
-    "        full_protein = mob.select_atoms(\"protein\")\n",
-    "        full_protein.write(f'{output_dir}/protein.pdb')"
+    "all_pdbs = of3_results_root.glob(\"*/*/*_seed_42_sample_1_model.pdb\")\n",
+    "align_proteins_to_ref(all_pdbs, reference_pdb, p_aligned_dir)"
    ]
   },
   {
@@ -1171,7 +1204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "fdfd428e-42af-49d3-9857-7a1b086f9ab2",
    "metadata": {
     "slideshow": {
@@ -1200,7 +1233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "992d53f3-d086-4283-9d6a-055be01588ad",
    "metadata": {
     "slideshow": {
@@ -1213,7 +1246,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.79it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.78it/s]\n"
      ]
     }
    ],
@@ -1285,7 +1318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "7ad01abd-097c-466d-9a3f-74a67e4d9015",
    "metadata": {
     "slideshow": {
@@ -1301,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "71d4ff18-a837-443d-bc02-69554e7d76cf",
    "metadata": {
     "slideshow": {
@@ -1343,7 +1376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "3e343c11-b693-4700-b1ea-2fa7951afa88",
    "metadata": {
     "slideshow": {
@@ -1362,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "a531a0cb-3eda-4d25-89d8-347f470d40ea",
    "metadata": {
     "slideshow": {
@@ -1428,7 +1461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "25ef33bf-fb1a-45da-96e4-004f77ef7d34",
    "metadata": {
     "slideshow": {
@@ -1495,7 +1528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "31d18f6d-b938-4de3-9b2f-3c6e9bedd827",
    "metadata": {
     "slideshow": {
@@ -1540,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "b5186710-f89e-4c86-998f-4e9fe076a2d8",
    "metadata": {
     "slideshow": {
@@ -1572,7 +1605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "c8db1e94-c146-4daf-bdb2-4383b4fcd0d6",
    "metadata": {
     "slideshow": {
@@ -1636,7 +1669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "7d3c436c-fd93-4a3e-9933-e43b00783931",
    "metadata": {
     "slideshow": {
@@ -1665,7 +1698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "b161b5b6-7718-4a47-b834-e3d88082bb31",
    "metadata": {
     "slideshow": {
@@ -1705,7 +1738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "id": "3bbd3d6d-35f0-490f-8fdd-4debc7874e23",
    "metadata": {
     "slideshow": {
@@ -1745,7 +1778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "id": "8b374e77-64fb-48a1-b9ae-9b1dba74ebb5",
    "metadata": {
     "slideshow": {

From 744b3ae26a82b9dcbd765e8f4558994268b61c5b Mon Sep 17 00:00:00 2001
From: Alyssa Travitz <alyssa.travitz@omsf.io>
Date: Wed, 6 May 2026 17:54:16 +0200
Subject: [PATCH 15/15] clean up hybrid top cells

---
 cookbook/openfold3_to_openfe.ipynb | 71 ++++++++++--------------------
 1 file changed, 24 insertions(+), 47 deletions(-)

diff --git a/cookbook/openfold3_to_openfe.ipynb b/cookbook/openfold3_to_openfe.ipynb
index 7f7efab..2888ccc 100644
--- a/cookbook/openfold3_to_openfe.ipynb
+++ b/cookbook/openfold3_to_openfe.ipynb
@@ -737,7 +737,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.80it/s]\n"
+      "Generating charges: 100%|█████████████████████████| 1/1 [00:00<00:00,  1.84it/s]\n"
      ]
     }
    ],
@@ -1246,7 +1246,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.78it/s]\n"
+      "Generating charges: 100%|███████████████████████| 10/10 [00:05<00:00,  1.81it/s]\n"
      ]
     }
    ],
@@ -1456,7 +1456,7 @@
     "\n",
     "In addition to aligning the protein structures, we must also align the ligands.\n",
     "\n",
-    "Again, as per the SepTop example above, we will do the transformation between `lig_jmc_27` and `lig_ejm_46`."
+    "We will again use the transformation between `lig_jmc_27` and `lig_ejm_46` as an example."
    ]
   },
   {
@@ -1474,56 +1474,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Extracting ligand from lig_ejm_50_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_23_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_43_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_42_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_31_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_47_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_27_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_46_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_ejm_48_seed_42_sample_1_model.pdb\n",
-      "Extracting ligand from lig_jmc_28_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_50_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_23_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_43_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_42_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_31_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_47_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_27_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_46_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_ejm_48_seed_42_sample_1_model.pdb\n",
+      "Calculating volume of lig_jmc_28_seed_42_sample_1_model.pdb\n",
       "\n",
-      "Largest ligand is lig_jmc_27 with volume 0.5209626577896912\n"
+      "Largest ligand_name is lig_jmc_27 with volume 0.521\n"
      ]
     }
    ],
    "source": [
-    "from MDAnalysis.analysis import align\n",
-    "from rdkit.Chem.Descriptors3D import Asphericity\n",
-    "\n",
-    "# gather all the complexes and align to a single structure\n",
-    "# which has the largest ligand by volume \n",
     "of3_results_root = pathlib.Path(\"assets/of3_tyk2_output/\")\n",
-    "tmp_ligand_dir = pathlib.Path(output_root,\"temp_ligands\")\n",
-    "tmp_ligand_dir.mkdir(exist_ok=True,parents=True)\n",
-    "# load all the output pdbs and calculate ligand volumes\n",
-    "ligand_volumes = {}\n",
-    "\n",
-    "for output_file in of3_results_root.glob(\"*/*/*.pdb\"):\n",
-    "    ligand_name = output_file.name.split('_seed')[0]\n",
-    "    print(f\"Extracting ligand from {output_file.name}\")\n",
-    "    with warnings.catch_warnings():   \n",
-    "        warnings.simplefilter(\"ignore\")\n",
-    "        u = mda.Universe(str(output_file))\n",
-    "    ligand = u.select_atoms(\"resname LIG\")\n",
-    "    # write the ligand to a temporary file\n",
-    "    ligand.write(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"))\n",
-    "    # now load back with rdkit\n",
-    "    rdkit_mol = Chem.MolFromPDBFile(str(tmp_ligand_dir / f\"{ligand_name}.pdb\"), removeHs=True)\n",
-    "    # now use a template from the SMILES definitions to make sure the bond orders are correct\n",
-    "    template_mol = Chem.MolFromSmiles(ligand_smiles[ligand_name])\n",
-    "    # don't show rdkit's \"WARNING: More than one matching pattern found - picking one\" for every ligand\n",
-    "    with rdBase.BlockLogs():  \n",
-    "        rdkit_mol = AllChem.AssignBondOrdersFromTemplate(template_mol, rdkit_mol)\n",
-    "    # calculate the volume\n",
-    "    vsa = Asphericity(rdkit_mol)\n",
-    "    ligand_volumes[ligand_name] = vsa\n",
-    "\n",
-    "# find the ligand with the largest volume\n",
-    "largest_ligand = max(ligand_volumes, key=ligand_volumes.get)\n",
-    "print(f\"\\nLargest ligand is {largest_ligand} with volume {ligand_volumes[largest_ligand]}\")"
+    "largest_ligand, largest_vol= find_largest_ligand(of3_results_root.glob(\"*/*/*.pdb\"))\n",
+    "print(f\"\\nLargest ligand_name is {largest_ligand} with volume {largest_vol:.3f}\")\n"
    ]
   },
   {
@@ -1833,6 +1802,14 @@
     "In practice, one would create entire network of Transformations, and then run all the simulations.\n",
     "This notebook will not cover these aspects. To find out more about this, please see the [OpenFE Tutorials](https://docs.openfree.energy/en/latest/tutorials/index.html)."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fab60c32-e717-43b3-b8e7-81d2e2aaa9c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {