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__osx >=11.0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + purls: [] + size: 433413 + timestamp: 1764777166076 diff --git a/pyproject.toml b/pyproject.toml index 98e28b1..834c1e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "openprotein-docs" version = "0.1.0" description = "Documentation for OpenProtein" readme = "README.md" -requires-python = ">=3.8,<3.13" +requires-python = ">=3.8,<3.15" classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", @@ -57,7 +57,7 @@ dev = [ nb = [ "molviewspec", "ipykernel", - # "openprotein-python @ git+https://github.com/OpenProteinAI/openprotein-python-private.git", + # "openprotein-python @ git+https://github.com/OpenProteinAI/openprotein-python-private.git@6e6214088bee247f20f72afe0bec4e8f686f08d9", "seaborn", "scipy", ] diff --git a/source/walkthroughs/Nanobody_binder_design_with_BoltzGen.ipynb b/source/walkthroughs/Nanobody_binder_design_with_BoltzGen.ipynb new file mode 100644 index 0000000..c168d92 --- /dev/null +++ b/source/walkthroughs/Nanobody_binder_design_with_BoltzGen.ipynb @@ -0,0 +1,1635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro", + "metadata": {}, + "source": [ + "# Nanobody binder design with BoltzGen\n", + "*Designing de novo nanobody binders to PDL1 using BoltzGen and ProteinMPNN*\n", + "\n", + "In this tutorial, we'll demonstrate how to use the OpenProtein.AI Python client to\n", + "design a nanobody that binds to a target protein. We refer to the designed protein as the\n", + "**binder** and the protein being bound as the **target**.\n", + "\n", + "Unlike general protein binder design, for nanobody design we utilize a **scaffold-based approach**. We will start with an existing nanobody framework and essentially \"graft\" new Complementarity-Determining Regions (CDRs) onto it. This ensures that our designed binder retains the stable, expressible framework regions of a natural nanobody while tailoring the binding loops (CDRs) to our specific target. The design process consists of four main steps:\n", + "\n", + "1. **Query Specification**: Specify the design problem as a \"query\", including\n", + " 1. the target protein (PDL1)\n", + " 2. the nanobody scaffold (framework regions)\n", + " 3. the lengths of the CDR loops to be designed\n", + "\n", + "2. **Structure Generation**: Generate plausible structures for the nanobody binder CDRs using\n", + " **BoltzGen** ([Stark et al., 2025](https://www.biorxiv.org/content/10.1101/2025.11.20.689494v1)), a generative model capable of designing backbone structures using scaffolds.\n", + "\n", + "3. **Sequence Design**: Design sequences for the generated CDRs using **ProteinMPNN** ([Dauparas et al., 2022](https://doi.org/10.1126/science.add2187)),\n", + " an inverse folding model for generating the binder sequence conditioned on the generated structure.\n", + "\n", + "4. **In Silico Validation**: Validate the designs by predicting their structures with **Boltz-2** ([Passaro et al., 2025](https://www.biorxiv.org/content/10.1101/2025.06.14.659707v1))\n", + " and computing metrics to select the best candidates for experimental testing.\n", + "\n", + "# Prerequisites\n", + "\n", + "To run this tutorial, you'll need a Python environment containing the following\n", + "packages:\n", + "\n", + "- `openprotein_python>=0.10.1`\n", + "- `molviewspec` (for structure visualization)\n", + "\n", + "See the Python client [installation instructions](../python-api/installation.html) for more info.\n", + "\n", + "Additionally, you should have your [credentials set up](../python-api/quickstart.html) in `~/.openprotein/config.toml` to\n", + "authenticate with the OpenProtein.AI API." + ] + }, + { + "cell_type": "markdown", + "id": "imports", + "metadata": {}, + "source": [ + "## Import necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "imports_code", + "metadata": {}, + "outputs": [], + "source": [ + "from dataclasses import dataclass\n", + "\n", + "import numpy as np\n", + "import numpy.typing as npt\n", + "import pandas as pd\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "import molviewspec as mvs\n", + "from molviewspec.nodes import RepresentationTypeT\n", + "\n", + "import openprotein\n", + "from openprotein.molecules import Protein, Complex, Structure" + ] + }, + { + "cell_type": "markdown", + "id": "connect", + "metadata": {}, + "source": [ + "## Connect to OpenProtein.AI" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "connect_code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Successfully connected to the OpenProtein.AI API!\n" + ] + } + ], + "source": [ + "session = openprotein.connect()\n", + "print(\"✅ Successfully connected to the OpenProtein.AI API!\")" + ] + }, + { + "cell_type": "markdown", + "id": "step1", + "metadata": {}, + "source": [ + "# Step 1: Binder design problem specification\n", + "*Specify the nanobody binder design problem*\n", + "\n", + "In this tutorial, we will design nanobody binders against **Programmed Death-Ligand 1 (PDL1)**. This design problem is adapted from the BoltzGen study ([Stark et al., 2025](https://www.biorxiv.org/content/10.1101/2025.11.20.689494v1)).\n", + "\n", + "We will design a **nanobody binder**, which is a single-domain antibody fragment derived from heavy-chain-only antibodies found in camelids. To restrict the BotlzGen structure generator to specifically design nanobody binders, we will use a **scaffold**. The scaffold defines an overall framework structure and specific designable regions for binding to the target. This means we will keep the framework regions of an existing, well-behaved nanobody constant, while redesigning the Complementarity Determining Regions (CDRs) to bind our specific target. Later, we will use ProteinMPNN to fill in the CDR sequences conditioned on the generated binder CDR structures.\n", + "\n", + "The **scaffold** provides convenient ways to generate binders of other types such as scFvs for FAbs.\n", + "\n", + "_Note_: We are using ProteinMPNN for inverse folding the CDRs here, but we could use other generative models such as **PoET-2** instead. PoET-2 is unique in its ability to use **prompt context** sequences that define specific families of proteins, e.g., human VHH domains, that can be used to guide the generated proteins towards specific characteristics. This is especially useful if we want to redesign the whole binder sequence (framework regions and CDRs). Because we are only redesigning the CDRs here, we'll use ProteinMPNN for simplicity. [Learn more about PoET-2](https://www.openprotein.ai/a-multimodal-foundation-model-for-controllable-protein-generation-and-representation-learning)." + ] + }, + { + "cell_type": "markdown", + "id": "step1_1", + "metadata": {}, + "source": [ + "## Step 1.1: Define and visualize the target\n", + "\n", + "We fetch the structure of the PDL1 target from PDB." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b5a8277", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unknown amino acid at position 1: ACE\n", + "Residue at position 1 missing backbone atom=N\n", + "Residue at position 1 missing backbone atom=CA\n", + "Unknown amino acid at position 1: ACE\n", + "Residue at position 1 missing backbone atom=N\n", + "Residue at position 1 missing backbone atom=CA\n", + "0 SEQUENCE MAFTVTVPKDLYVVEYGSNMTIECKFPVEKQLDLAALIVYWEMEDKNIIQFVHGEEDLKV\n", + "0 STRUCTURE_MASK ^ \n", + "\n", + "60 SEQUENCE QHSSYRQRARLLKDQLSLGNAALQITDVKLQDAGVYRCMISYGGADYKRITVKVNAPYAA\n", + "60 STRUCTURE_MASK \n", + "\n", + "120 SEQUENCE ALEHHHHHH\n", + "120 STRUCTURE_MASK ^\n" + ] + } + ], + "source": [ + "structure = Structure.from_pdb_id(\"7uxq\") # PDL1\n", + "first_complex = structure[0]\n", + "target = first_complex.get_protein(chain_id=\"A\")\n", + "print(target.formatted(include=(\"sequence\", \"structure_mask\")))" + ] + }, + { + "cell_type": "markdown", + "id": "af16e90c", + "metadata": {}, + "source": [ + "Remove the His tag and linker since we don't want to bind that." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dbceee6d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 SEQUENCE MAFTVTVPKDLYVVEYGSNMTIECKFPVEKQLDLAALIVYWEMEDKNIIQFVHGEEDLKV\n", + "0 STRUCTURE_MASK ^ \n", + "\n", + "60 SEQUENCE QHSSYRQRARLLKDQLSLGNAALQITDVKLQDAGVYRCMISYGGADYKRITVKVNAPYAA\n", + "60 STRUCTURE_MASK \n", + "\n", + "120 SEQUENCE A\n", + "120 STRUCTURE_MASK \n" + ] + } + ], + "source": [ + "target = target[:len(target) - 8]\n", + "print(target.formatted(include=(\"sequence\", \"structure_mask\")))" + ] + }, + { + "cell_type": "markdown", + "id": "step1_2", + "metadata": {}, + "source": [ + "## Step 1.2: Define the nanobody scaffold\n", + "\n", + "We will use the structure from PDB ID `7eow` as our scaffold. Thie scaffold is from *caplacizumab*, a humanized VHH. We'll use the structure and framework region of this VHH as the scaffold for our binder, but design new CDRs for binding to our target.\n", + "\n", + "First, we load the protein." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "load_scaffold", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 SEQUENCE MEVQLVESGGGLVQPGGSLRLSCAASGRTFSYNPMGWFRQAPGKGRELVAAISRTGGSTY\n", + "0 STRUCTURE_MASK ^ \n", + "\n", + "60 SEQUENCE YPDSVEGRFTISRDNAKRMVYLQMNSLRAEDTAVYYCAAAGVRAEDGRVRTLPSEYTFWG\n", + "60 STRUCTURE_MASK \n", + "\n", + "120 SEQUENCE QGTQVTVSSLEHHHHHH\n", + "120 STRUCTURE_MASK ^^^^^^^^\n" + ] + } + ], + "source": [ + "structure = Structure.from_pdb_id(\"7eow\")\n", + "first_complex = structure[0]\n", + "binder_scaffold = first_complex.get_protein(chain_id=\"B\")\n", + "print(binder_scaffold.formatted(include=(\"sequence\", \"structure_mask\")))" + ] + }, + { + "cell_type": "markdown", + "id": "clean_scaffold", + "metadata": {}, + "source": [ + "### Clean the scaffold\n", + "\n", + "We remove the leading Methionine (M) and the trailing Histidine tag (His-tag) because they are expression artifacts. The structure mask above confirms these residues have no defined structure." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "remove_artifacts", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 SEQUENCE EVQLVESGGGLVQPGGSLRLSCAASGRTFSYNPMGWFRQAPGKGRELVAAISRTGGSTYY\n", + "0 STRUCTURE_MASK \n", + "\n", + "60 SEQUENCE PDSVEGRFTISRDNAKRMVYLQMNSLRAEDTAVYYCAAAGVRAEDGRVRTLPSEYTFWGQ\n", + "60 STRUCTURE_MASK \n", + "\n", + "120 SEQUENCE GTQVTVSS\n", + "120 STRUCTURE_MASK \n" + ] + } + ], + "source": [ + "binder_scaffold = binder_scaffold[~binder_scaffold.get_structure_mask()]\n", + "print(binder_scaffold.formatted(include=(\"sequence\", \"structure_mask\")))" + ] + }, + { + "cell_type": "markdown", + "id": "extract_frameworks", + "metadata": {}, + "source": [ + "### Define the framework and binding regions\n", + "\n", + "We want to use the nanobody structure as a framework, but design new CDRs for binding to our target. To do this, we keep the framework regions (FWRs) constant but replace the CDRs with designable regions.\n", + "\n", + "In this example, we will set CDR1 length to 10 (increased from 9), CDR2 to 8 (same as scaffold), and CDR3 to 20 (decreased from 21). The `X` characters represent residues to be designed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "extract_fw", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FWR1: EVQLVESGGGLVQPGGSLRLSCAAS\n", + "FWR2: GWFRQAPGKGRELVAAI\n", + "FWR3: YPDSVEGRFTISRDNAKRMVYLQMNSLRAEDTAVYYCA\n", + "FWR4: GQGTQVTVSS\n" + ] + } + ], + "source": [ + "# Extract framework regions and print their sequences for verification\n", + "fwr1 = binder_scaffold[:25]\n", + "fwr2 = binder_scaffold[34:51]\n", + "fwr3 = binder_scaffold[59:97]\n", + "fwr4 = binder_scaffold[118:]\n", + "print(\"FWR1:\", fwr1.sequence.decode())\n", + "print(\"FWR2:\", fwr2.sequence.decode())\n", + "print(\"FWR3:\", fwr3.sequence.decode())\n", + "print(\"FWR4:\", fwr4.sequence.decode())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "insert_cdrs", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 SEQUENCE EVQLVESGGGLVQPGGSLRLSCAASXXXXXXXXXXGWFRQAPGKGRELVAAIXXXXXXXX\n", + "0 STRUCTURE_MASK ^^^^^^^^^^ ^^^^^^^^\n", + "\n", + "60 SEQUENCE YPDSVEGRFTISRDNAKRMVYLQMNSLRAEDTAVYYCAXXXXXXXXXXXXXXXXXXXXGQ\n", + "60 STRUCTURE_MASK ^^^^^^^^^^^^^^^^^^^^ \n", + "\n", + "120 SEQUENCE GTQVTVSS\n", + "120 STRUCTURE_MASK \n" + ] + } + ], + "source": [ + "# Create the binder scaffold by combining the frameworks and cdrs of the required length\n", + "cdr1_length = 10\n", + "cdr2_length = 8\n", + "cdr3_length = 20\n", + "binder_scaffold = (\n", + " fwr1\n", + " + \"X\" * cdr1_length\n", + " + fwr2\n", + " + \"X\" * cdr2_length\n", + " + fwr3\n", + " + \"X\" * cdr3_length\n", + " + fwr4\n", + ")\n", + "print(binder_scaffold.formatted(include=(\"sequence\", \"structure_mask\")))" + ] + }, + { + "cell_type": "markdown", + "id": "step1_3", + "metadata": {}, + "source": [ + "## Step 1.3: Configure relative positioning (Groups)\n", + "\n", + "By default, all residues are in \"group 0\", which implies their relative positions are fixed. Since we want the nanobody to dock against the target (i.e., its position relative to the target is not fixed), we assign the scaffold to a different group (group 1)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "set_groups", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Visualize target groups:\n", + "0 SEQUENCE MAFTVTVPKDLYVVEYGSNMTIECKFPVEKQLDLAALIVYWEMEDKNIIQFVHGEEDLKV\n", + "0 GROUP 000000000000000000000000000000000000000000000000000000000000\n", + "\n", + "60 SEQUENCE QHSSYRQRARLLKDQLSLGNAALQITDVKLQDAGVYRCMISYGGADYKRITVKVNAPYAA\n", + "60 GROUP 000000000000000000000000000000000000000000000000000000000000\n", + "\n", + "120 SEQUENCE A\n", + "120 GROUP 0\n", + "\n", + "Visualize binder scaffold groups:\n", + "0 SEQUENCE EVQLVESGGGLVQPGGSLRLSCAASXXXXXXXXXXGWFRQAPGKGRELVAAIXXXXXXXX\n", + "0 GROUP 000000000000000000000000000000000000000000000000000000000000\n", + "\n", + "60 SEQUENCE YPDSVEGRFTISRDNAKRMVYLQMNSLRAEDTAVYYCAXXXXXXXXXXXXXXXXXXXXGQ\n", + "60 GROUP 000000000000000000000000000000000000000000000000000000000000\n", + "\n", + "120 SEQUENCE GTQVTVSS\n", + "120 GROUP 00000000\n" + ] + } + ], + "source": [ + "# Visualize current groups (all 0)\n", + "print(\"\\nVisualize target groups:\")\n", + "print(target.formatted((\"sequence\", \"group\")))\n", + "print(\"\\nVisualize binder scaffold groups:\")\n", + "print(binder_scaffold.formatted((\"sequence\", \"group\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5f0f1bbb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Updated binder scaffold groups:\n", + "0 SEQUENCE EVQLVESGGGLVQPGGSLRLSCAASXXXXXXXXXXGWFRQAPGKGRELVAAIXXXXXXXX\n", + "0 GROUP 111111111111111111111111111111111111111111111111111111111111\n", + "\n", + "60 SEQUENCE YPDSVEGRFTISRDNAKRMVYLQMNSLRAEDTAVYYCAXXXXXXXXXXXXXXXXXXXXGQ\n", + "60 GROUP 111111111111111111111111111111111111111111111111111111111111\n", + "\n", + "120 SEQUENCE GTQVTVSS\n", + "120 GROUP 11111111\n" + ] + } + ], + "source": [ + "# Set scaffold to group 1 to unfix relative position\n", + "binder_scaffold = binder_scaffold.set_group(1)\n", + "print(\"\\nUpdated binder scaffold groups:\")\n", + "print(binder_scaffold.formatted((\"sequence\", \"group\")))" + ] + }, + { + "cell_type": "markdown", + "id": "combine_query", + "metadata": {}, + "source": [ + "Finally, we combine the target and the binder scaffold into a single `Complex` query." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "create_query", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query type \n", + "Chains in query: ['A', 'B']\n", + "\n", + "Visualize target (Chain A):\n", + "0 SEQUENCE MAFTVTVPKDLYVVEYGSNMTIECKFPVEKQLDLAALIVYWEMEDKNIIQFVHGEEDLKV\n", + "0 STRUCTURE_MASK ^ \n", + "\n", + "60 SEQUENCE QHSSYRQRARLLKDQLSLGNAALQITDVKLQDAGVYRCMISYGGADYKRITVKVNAPYAA\n", + "60 STRUCTURE_MASK \n", + "\n", + "120 SEQUENCE A\n", + "120 STRUCTURE_MASK \n", + "\n", + "Visualize binder scaffold (Chain B):\n", + "0 SEQUENCE EVQLVESGGGLVQPGGSLRLSCAASXXXXXXXXXXGWFRQAPGKGRELVAAIXXXXXXXX\n", + "0 STRUCTURE_MASK ^^^^^^^^^^ ^^^^^^^^\n", + "\n", + "60 SEQUENCE YPDSVEGRFTISRDNAKRMVYLQMNSLRAEDTAVYYCAXXXXXXXXXXXXXXXXXXXXGQ\n", + "60 STRUCTURE_MASK ^^^^^^^^^^^^^^^^^^^^ \n", + "\n", + "120 SEQUENCE GTQVTVSS\n", + "120 STRUCTURE_MASK \n" + ] + } + ], + "source": [ + "query = target & binder_scaffold\n", + "print(\"Query type\", type(query))\n", + "print(\"Chains in query:\", list(query.get_chains().keys()))\n", + "print(\"\\nVisualize target (Chain A):\")\n", + "print(query.get_protein(chain_id=\"A\").formatted(include=(\"sequence\", \"structure_mask\")))\n", + "print(\"\\nVisualize binder scaffold (Chain B):\")\n", + "print(query.get_protein(chain_id=\"B\").formatted(include=(\"sequence\", \"structure_mask\")))" + ] + }, + { + "cell_type": "markdown", + "id": "bf083d77", + "metadata": {}, + "source": [ + "## Visualize the structure of the query\n", + "\n", + "It's always a good idea to visualize the 3D structures of our `query` to ensure that\n", + "we've created it correctly. First, let's define a helper function using the\n", + "`molviewspec` package to help us create the visualization. It's not necessary to\n", + "understand the implementation details." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2fdd48a9", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass(frozen=True)\n", + "class ColorSpec:\n", + " chain_id: str\n", + " color: str\n", + " positions: list[int] | None = None\n", + " rep_type: RepresentationTypeT = \"cartoon\"\n", + "\n", + "\n", + "def visualize_cif(cif_string: str, colors: list[ColorSpec]):\n", + " builder = mvs.create_builder()\n", + " model = (\n", + " builder.download(url=\"structure.cif\").parse(format=\"mmcif\").model_structure()\n", + " )\n", + " for color_spec in colors:\n", + " component = model.component(\n", + " selector=(\n", + " mvs.ComponentExpression(label_asym_id=color_spec.chain_id)\n", + " if color_spec.positions is None\n", + " else [\n", + " mvs.ComponentExpression(\n", + " label_asym_id=color_spec.chain_id, label_seq_id=i\n", + " )\n", + " for i in color_spec.positions\n", + " ]\n", + " )\n", + " )\n", + " rep = component.representation(type=color_spec.rep_type)\n", + " rep.color(color=color_spec.color)\n", + " builder.molstar_notebook(\n", + " data={\"structure.cif\": cif_string},\n", + " width=600,\n", + " height=500,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "62d168f0", + "metadata": {}, + "source": [ + "Now, let's use the helper function, `visualize_cif`, to visualize our `query`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1f8d4677", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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