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 1/4] 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 2/4] 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 3/4] 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 04a1fdd61191550c01e8917e54da8e3fc96bfc6d 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 4/4] 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..dce7eea 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=\"200\"></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"
      ]
     }
    ],