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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# P2PXML - Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity"
],
"metadata": {
"id": "N47Ue-53WqAG"
}
},
{
"cell_type": "markdown",
"source": [
"List of resources:\n",
"\n",
"* Paper: https://www.biorxiv.org/content/10.1101/2024.06.09.598103v1\n",
"* Project Page: https://drug-discovery-entc.github.io/p2pxml/\n",
"* Codes: https://github.com/Drug-Discovery-ENTC/p2pxml/\n",
"* Our dataset: https://zenodo.org/records/11531319\n",
"\n",
"The codes, including this notebook, are released under MIT license (https://github.com/Drug-Discovery-ENTC/p2pxml/blob/main/LICENSE) and the dataset is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)."
],
"metadata": {
"id": "35qWIH9dWzu7"
}
},
{
"cell_type": "markdown",
"source": [
"Citation:\n",
"\n",
"```bibtex\n",
"@article{bandara2024deep,\n",
" title={Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity},\n",
" author={Bandara, Nuwan Sriyantha and Premathilaka, Dasun and Chandanayake, Sachini and Hettiarachchi, Sahan and Varenthirarajah, Vithurshan and Munasinghe, Aravinda and Madhawa, Kaushalya and Charles, Subodha},\n",
" journal={bioRxiv},\n",
" pages={2024--06},\n",
" year={2024},\n",
" publisher={Cold Spring Harbor Laboratory}\n",
"}\n",
"```"
],
"metadata": {
"id": "sA_FHFqAY25r"
}
},
{
"cell_type": "markdown",
"source": [
"*Disclaimer: This is a minimal working demonstration and the results may be inaccurate. Even though our models are trained on our curated dataset, which is the largest and most generalized publicly-available dataset for antibody-antigen binding affinity prediction to-date in the literature (to the best of our knowledge), it is still biased towards certain antigen variants such as HIV and SARS-CoV-2 due to their abundance in terms of number of data points in the dataset.*"
],
"metadata": {
"id": "VnvPPD74XZBK"
}
},
{
"cell_type": "markdown",
"source": [
"### Run all the cells"
],
"metadata": {
"id": "3Yam7nw1w2vB"
}
},
{
"cell_type": "code",
"source": [
"# @title\n",
"!pip install torchmetrics\n",
"!pip install torch_geometric\n",
"!pip install Biopandas\n",
"!pip install Bio\n",
"!pip install periodictable"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"id": "0LGtmIUycmB_",
"outputId": "2d00a0bd-9a18-4f2f-9b5d-5d4cd2dd481d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Installing collected packages: biopython, gprofiler-official, biothings-client, mygene, Bio\n",
"Successfully installed Bio-1.7.1 biopython-1.83 biothings-client-0.3.1 gprofiler-official-1.0.0 mygene-3.2.2\n",
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" Downloading periodictable-1.7.0.tar.gz (1.0 MB)\n",
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" Stored in directory: /root/.cache/pip/wheels/7e/19/a2/fef5d0ca2b1ad2b199e863a6e796ad9d5efc86563d80c91a0c\n",
"Successfully built periodictable\n",
"Installing collected packages: periodictable\n",
"Successfully installed periodictable-1.7.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# @title\n",
"import pandas as pd\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
"import math\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.optim import AdamW\n",
"from torch import Tensor\n",
"import torch.nn.functional as F\n",
"from torch.nn import Parameter\n",
"from torch.nn import Sequential, Linear, ReLU, MultiheadAttention, Dropout, LayerNorm, AvgPool1d\n",
"from torchmetrics.functional import mean_absolute_error\n",
"import torch.optim as optim\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from torch.optim.lr_scheduler import LambdaLR\n",
"from tqdm import tqdm\n",
"\n",
"from torch.nn.init import zeros_,xavier_normal_\n",
"\n",
"import os\n",
"\n",
"import networkx as nx\n",
"import torch_geometric.data as Data\n",
"from torch_geometric.loader import DataLoader\n",
"from torch_geometric.nn import GCNConv, global_mean_pool, GATConv\n",
"from torch_geometric.transforms import NormalizeScale\n",
"from torch_geometric.data import Batch\n",
"from torchmetrics.functional import mean_absolute_error\n",
"from torch.utils.data import random_split\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from biopandas.pdb import PandasPdb\n",
"import periodictable\n",
"from Bio import SeqIO\n",
"from Bio.PDB import PDBParser\n",
"from Bio.SeqUtils import seq1\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import logging, sys"
],
"metadata": {
"cellView": "form",
"id": "z72fVm0ybIX5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Note**\n",
"\n",
"Here, input the **.pdb** files for both antibody and antigen. *Note that the current maximum FASTA sequence lengths for antibody and antigen are 669 and 3102 respectively.*\n",
"\n",
"Example PDB files for an antibody-antigen pair can be found at https://github.com/Drug-Discovery-ENTC/p2pxml/tree/main/data"
],
"metadata": {
"id": "Yyu_GMViwOen"
}
},
{
"cell_type": "code",
"source": [
"# @title\n",
"from google.colab import files\n",
"import os\n",
"import shutil\n",
"import pandas as pd\n",
"\n",
"os.makedirs(\"antibodies\", exist_ok=True)\n",
"os.makedirs(\"antigens\", exist_ok=True)\n",
"os.makedirs(\"graph_data\", exist_ok = True)\n",
"\n",
"print(\"Please upload the antibody .pdb file.\")\n",
"uploaded_antibody = files.upload()\n",
"\n",
"print(\" \")\n",
"print(\"Please upload the antigen .pdb file.\")\n",
"uploaded_antigen = files.upload()\n",
"\n",
"uploaded = {**uploaded_antibody, **uploaded_antigen}\n",
"\n",
"if len(uploaded) != 2:\n",
" print(\"Error: Please upload exactly two .pdb files.\")\n",
"else:\n",
" pdb_files = [filename for filename in uploaded.keys() if filename.endswith('.pdb')]\n",
"\n",
" if len(pdb_files) != 2:\n",
" print(\"Error: Please upload exactly two .pdb files.\")\n",
" else:\n",
" antibody_name = os.path.splitext(pdb_files[0])[0]\n",
" antigen_name = os.path.splitext(pdb_files[1])[0]\n",
"\n",
" test_df = pd.DataFrame({\n",
" \"Ab\": [antibody_name],\n",
" \"Ag\": [antigen_name],\n",
" \"log(IC50)\": 0.0\n",
" })\n",
"\n",
" shutil.move(pdb_files[0], os.path.join(\"antibodies\", pdb_files[0]))\n",
" shutil.move(pdb_files[1], os.path.join(\"antigens\", pdb_files[1]))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 184
},
"cellView": "form",
"id": "vJY-76IGngwE",
"outputId": "6da824c5-99a0-462c-cc6f-0f54752321c8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Please upload the antibody .pdb file.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-726cd675-eace-48d3-a066-db99c7b38db1\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-726cd675-eace-48d3-a066-db99c7b38db1\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
" inputElement.disabled = true;\n",
"\n",
" if (!files) {\n",
" return {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
" }\n",
"\n",
" for (const file of files) {\n",
" const li = document.createElement('li');\n",
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
" 'n/a'} - `));\n",
" const percent = span('0% done');\n",
" li.appendChild(percent);\n",
"\n",
" outputElement.appendChild(li);\n",
"\n",
" const fileDataPromise = new Promise((resolve) => {\n",
" const reader = new FileReader();\n",
" reader.onload = (e) => {\n",
" resolve(e.target.result);\n",
" };\n",
" reader.readAsArrayBuffer(file);\n",
" });\n",
" // Wait for the data to be ready.\n",
" let fileData = yield {\n",
" promise: fileDataPromise,\n",
" response: {\n",
" action: 'continue',\n",
" }\n",
" };\n",
"\n",
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
" let position = 0;\n",
" do {\n",
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
"\n",
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
" yield {\n",
" response: {\n",
" action: 'append',\n",
" file: file.name,\n",
" data: base64,\n",
" },\n",
" };\n",
"\n",
" let percentDone = fileData.byteLength === 0 ?\n",
" 100 :\n",
" Math.round((position / fileData.byteLength) * 100);\n",
" percent.textContent = `${percentDone}% done`;\n",
"\n",
" } while (position < fileData.byteLength);\n",
" }\n",
"\n",
" // All done.\n",
" yield {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
"}\n",
"\n",
"scope.google = scope.google || {};\n",
"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving 0.5γ.pdb to 0.5γ.pdb\n",
" \n",
"Please upload the antigen .pdb file.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-dbe4ccc2-7c30-42c9-9d5c-74425ea6f0f7\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-dbe4ccc2-7c30-42c9-9d5c-74425ea6f0f7\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
" inputElement.disabled = true;\n",
"\n",
" if (!files) {\n",
" return {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
" }\n",
"\n",
" for (const file of files) {\n",
" const li = document.createElement('li');\n",
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
" 'n/a'} - `));\n",
" const percent = span('0% done');\n",
" li.appendChild(percent);\n",
"\n",
" outputElement.appendChild(li);\n",
"\n",
" const fileDataPromise = new Promise((resolve) => {\n",
" const reader = new FileReader();\n",
" reader.onload = (e) => {\n",
" resolve(e.target.result);\n",
" };\n",
" reader.readAsArrayBuffer(file);\n",
" });\n",
" // Wait for the data to be ready.\n",
" let fileData = yield {\n",
" promise: fileDataPromise,\n",
" response: {\n",
" action: 'continue',\n",
" }\n",
" };\n",
"\n",
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
" let position = 0;\n",
" do {\n",
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
"\n",
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
" yield {\n",
" response: {\n",
" action: 'append',\n",
" file: file.name,\n",
" data: base64,\n",
" },\n",
" };\n",
"\n",
" let percentDone = fileData.byteLength === 0 ?\n",
" 100 :\n",
" Math.round((position / fileData.byteLength) * 100);\n",
" percent.textContent = `${percentDone}% done`;\n",
"\n",
" } while (position < fileData.byteLength);\n",
" }\n",
"\n",
" // All done.\n",
" yield {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
"}\n",
"\n",
"scope.google = scope.google || {};\n",
"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving AC10_29.pdb to AC10_29.pdb\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "kigJrq_0XkxU"
},
"outputs": [],
"source": [
"# @title\n",
"max_antibody_sequence_length = 669\n",
"max_antigen_sequence_length = 3102\n",
"\n",
"class ProteinDataset(Dataset):\n",
" def __init__(self, df, max_antibody_sequence_length, max_antigen_sequence_length, save_dir='/content/graph_data'):\n",
" self.sequences = df['Ab'].values\n",
" self.viruses = df['Ag'].values\n",
" self.labels = df['log(IC50)'].values\n",
" self.max_antibody_sequence_length = max_antibody_sequence_length\n",
" self.max_antigen_sequence_length = max_antigen_sequence_length\n",
" self.save_dir = save_dir\n",
" os.makedirs(save_dir, exist_ok=True)\n",
"\n",
" def __len__(self):\n",
" return len(self.sequences)\n",
"\n",
" def __getitem__(self, idx):\n",
" sequence = self.sequences[idx]\n",
" virus = self.viruses[idx]\n",
" label = self.labels[idx]\n",
" label = torch.tensor(label, dtype=torch.float64)\n",
"\n",
" antibody = self.load_or_generate_graph(sequence, self.max_antibody_sequence_length, 'antibodies')\n",
" antigen = self.load_or_generate_graph(virus, self.max_antigen_sequence_length, 'antigens')\n",
"\n",
" if antibody is not None and antigen is not None:\n",
" return antibody, antigen, label\n",
" else:\n",
" return self.__getitem__((idx + 1) % len(self)) # Ensure idx is within bounds\n",
"\n",
" def load_or_generate_graph(self, pdb_file, max_sequence_length, graph_type):\n",
" graph_path = os.path.join(self.save_dir, f'{graph_type}_{pdb_file}.pt')\n",
"\n",
" if os.path.exists(graph_path):\n",
" return torch.load(graph_path)\n",
"\n",
" if graph_type == 'antigens':\n",
" graph_constructed = self.pdb_to_graph_virus(pdb_file, max_sequence_length)\n",
" else:\n",
" graph_constructed = self.pdb_to_graph_antibody(pdb_file, max_sequence_length)\n",
"\n",
" if graph_constructed is not None:\n",
" torch.save(graph_constructed, graph_path)\n",
" return graph_constructed\n",
"\n",
" def pdb_to_graph_virus(self, pdb_file, max_antigen_sequence_length):\n",
" return self.pdb_to_graph(pdb_file, max_antigen_sequence_length, 'antigens')\n",
"\n",
" def pdb_to_graph_antibody(self, pdb_file, max_antibody_sequence_length):\n",
" return self.pdb_to_graph(pdb_file, max_antibody_sequence_length, 'antibodies')\n",
"\n",
" def pdb_to_graph(self, pdb_file, max_sequence_length, graph_type):\n",
" seq_length = max_sequence_length\n",
" amino_acids = list(\"ACDEFGHIKLMNPQRSTVWY\")\n",
" aa_to_index = {aa: i for i, aa in enumerate(amino_acids)}\n",
" pdbparser = PDBParser()\n",
"\n",
" try:\n",
" structure = pdbparser.get_structure(pdb_file, os.path.join(f'/content/{graph_type}/'+pdb_file+'.pdb'))\n",
" chains = {chain.id: seq1(''.join(residue.resname for residue in chain)) for chain in structure.get_chains()}\n",
" full_sequence = ''\n",
" for value in chains.values():\n",
" full_sequence += value\n",
" full_sequence = full_sequence.replace(\"X\", \"\")\n",
"\n",
" if len(full_sequence) > seq_length:\n",
" print(f\"Exceeds max length {graph_type}\")\n",
" return None\n",
"\n",
" indices = [aa_to_index[aa] for aa in full_sequence]\n",
" encoded = F.one_hot(torch.tensor(indices), num_classes=len(amino_acids)).float()\n",
" padded_encoded = F.pad(encoded.flatten(), (0, max(seq_length * len(amino_acids) - encoded.flatten().shape[0], 0)))\n",
"\n",
" ppdb = PandasPdb()\n",
" ppdb.read_pdb(os.path.join(f'/content/{graph_type}/'+pdb_file+'.pdb'))\n",
" coords = ppdb.df['ATOM'][['x_coord', 'y_coord', 'z_coord']].values\n",
" atomic_nums = ppdb.df['ATOM']['element_symbol'].apply(lambda symbol: periodictable.elements.symbol(symbol).number).values\n",
"\n",
" graph = nx.Graph()\n",
" num_atoms = len(coords)\n",
" for i in range(num_atoms):\n",
" graph.add_node(i, x=coords[i][0], y=coords[i][1], z=coords[i][2], atomic_number=atomic_nums[i])\n",
" for i in range(num_atoms):\n",
" for j in range(i + 1, num_atoms):\n",
" dist = ((coords[i] - coords[j]) ** 2).sum() ** 0.5\n",
" if dist < 5:\n",
" bond_strength = 1 / dist\n",
" graph.add_edge(i, j, distance=dist, bond_strength=bond_strength)\n",
"\n",
" edge_attrs = {}\n",
"\n",
" for u, v, data in graph.edges(data=True):\n",
" edge_attrs[(u, v)] = [data['distance'], data['bond_strength']]\n",
" edge_attrs[(v, u)] = [data['distance'], data['bond_strength']]\n",
"\n",
" data = Data.Data(\n",
" x=torch.tensor(list(nx.get_node_attributes(graph, 'x').values())).to(torch.float64),\n",
" y_coord=torch.tensor(list(nx.get_node_attributes(graph, 'y').values())).to(torch.float64),\n",
" z_coord=torch.tensor(list(nx.get_node_attributes(graph, 'z').values())).to(torch.float64),\n",
" pos=torch.tensor(coords).to(torch.float64),\n",
" edge_index=torch.tensor(list(graph.edges)).to(torch.float64).t().contiguous(),\n",
" edge_attr=torch.tensor([edge_attrs[e] for e in graph.edges()]).to(torch.float64),\n",
" z=torch.tensor(list(nx.get_node_attributes(graph, 'atomic_number').values())).to(torch.float64),\n",
" seq=padded_encoded.to(torch.float64),\n",
" y = torch.tensor([0.0]).to(torch.float64)\n",
" )\n",
" return data\n",
"\n",
" except Exception as e:\n",
" print(e)\n",
" return None\n",
"\n",
"test_ds = ProteinDataset(test_df, max_antibody_sequence_length, max_antigen_sequence_length)\n",
"test_loader = DataLoader(test_ds, batch_size=1, shuffle=False)"
]
},
{
"cell_type": "code",
"source": [
"# @title\n",
"class SelfAttention(nn.Module):\n",
" def __init__(self, embed_dim, num_heads=16):\n",
" super(SelfAttention, self).__init__()\n",
" self.embed_dim = embed_dim\n",
" self.num_heads = num_heads\n",
" if embed_dim % num_heads != 0:\n",
" raise ValueError(f\"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}\")\n",
" self.head_dim = embed_dim // num_heads\n",
" self.query_dense = nn.Linear(embed_dim, embed_dim)\n",
" self.key_dense = nn.Linear(embed_dim, embed_dim)\n",
" self.value_dense = nn.Linear(embed_dim, embed_dim)\n",
" self.combine_heads = nn.Linear(embed_dim, embed_dim)\n",
"\n",
" def forward(self, inputs):\n",
" query = self.query_dense(inputs)\n",
" key = self.key_dense(inputs)\n",
" value = self.value_dense(inputs)\n",
" query = query.view(-1, self.num_heads, self.head_dim)\n",
" key = key.view(-1, self.num_heads, self.head_dim)\n",
" value = value.view(-1, self.num_heads, self.head_dim)\n",
" query = query.permute(1, 0, 2)\n",
" key = key.permute(1, 0, 2)\n",
" value = value.permute(1, 0, 2)\n",
" dot_product = torch.matmul(query, key.permute(0, 2, 1))\n",
" scaled_dot_product = dot_product / torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32))\n",
" attention_weights = torch.softmax(scaled_dot_product, dim=-1)\n",
" output = torch.matmul(attention_weights, value)\n",
" output = output.permute(1, 0, 2)\n",
" output = output.view(-1, self.embed_dim)\n",
" output = self.combine_heads(output)\n",
" return output\n",
"\n",
"class TransformerBlock(nn.Module):\n",
" def __init__(self, embed_dim, num_heads, dense_dim=1024, dropout_rate=0.1):\n",
" super(TransformerBlock, self).__init__()\n",
" self.attention = SelfAttention(embed_dim, num_heads)\n",
" self.dropout1 = nn.Dropout(dropout_rate)\n",
" self.norm1 = nn.LayerNorm(embed_dim, eps=1e-6)\n",
" self.dense1 = nn.Linear(embed_dim, dense_dim)\n",
" self.dropout2 = nn.Dropout(dropout_rate)\n",
" self.norm2 = nn.LayerNorm(embed_dim, eps=1e-6)\n",
" self.dense2 = nn.Linear(dense_dim, embed_dim)\n",
"\n",
" def forward(self, inputs):\n",
" attention_output = self.attention(inputs)\n",
" attention_output = self.dropout1(attention_output)\n",
" output1 = self.norm1(inputs + attention_output)\n",
" dense_output = self.dense1(output1)\n",
" dense_output = self.dropout2(dense_output)\n",
" output2 = self.norm2(output1 + dense_output)\n",
" output = self.dense2(output2)\n",
" return output\n",
"\n",
"class CrossAttention(nn.Module):\n",
" def __init__(self, dim, input_shape):\n",
" super(CrossAttention, self).__init__()\n",
" self.dim = dim\n",
" self.input_shape = input_shape\n",
"\n",
" self.Wq = nn.Parameter(torch.Tensor(input_shape[0][-1], self.dim))\n",
" self.Wk = nn.Parameter(torch.Tensor(input_shape[1][-1], self.dim))\n",
" self.Wv = nn.Parameter(torch.Tensor(input_shape[1][-1], self.dim))\n",
"\n",
" nn.init.xavier_uniform_(self.Wq)\n",
" nn.init.xavier_uniform_(self.Wk)\n",
" nn.init.xavier_uniform_(self.Wv)\n",
"\n",
" def forward(self, inputs):\n",
" x, y = inputs\n",
"\n",
" Q = torch.matmul(x, self.Wq)\n",
" K = torch.matmul(y, self.Wk)\n",
" V = torch.matmul(y, self.Wv)\n",
"\n",
" attn_weights = torch.matmul(Q, K.t()) / torch.sqrt(torch.tensor(self.dim, dtype=torch.float64))\n",
" attn_weights = F.softmax(attn_weights, dim=-1)\n",
" attn_output = attn_weights * V\n",
" output = torch.cat([x, attn_output], dim=-1)\n",
"\n",
" return output\n",
"\n",
"class CombinedModel(nn.Module):\n",
" def __init__(self, hidden_channels=128, num_layers=16):\n",
" super(CombinedModel, self).__init__()\n",
"\n",
" self.cross_attn_1 = CrossAttention(128, [(1024,),(1024,)])\n",
" self.cross_attn_2 = CrossAttention(128,[(1024,),(1024,)])\n",
" self.cross_attn = CrossAttention(128,[(1152,),(1152,)])\n",
" self.self_atten_1 = nn.AdaptiveAvgPool1d(1)\n",
" self.self_atten_2 = nn.AdaptiveAvgPool1d(1)\n",
" self.cross_pooling = nn.AdaptiveAvgPool1d(1)\n",
" self.dense = nn.Linear(1280, 256)\n",
" self.output_layer1 = nn.Linear(2816, 128)\n",
" self.output_layer = nn.Linear(128, 1)\n",
"\n",
" self.input_1 = nn.Linear(input_shape_1[0], 1024)\n",
" self.self_attn_1 = SelfAttention(1024)\n",
" self.transformer_1 = TransformerBlock(1024, 4)\n",
" self.pooling_1 = nn.AdaptiveAvgPool1d(1)\n",
" self.dense_1 = nn.Linear(1024, 1024)\n",
" self.dropout_1 = nn.Dropout(p=0.05)\n",
"\n",
" self.input_2 = nn.Linear(input_shape_2[0], 1024)\n",
" self.self_attn_2 = SelfAttention(1024)\n",
" self.transformer_2 = TransformerBlock(1024, 4)\n",
" self.pooling_2 = nn.AdaptiveAvgPool1d(1)\n",
" self.dense_2 = nn.Linear(1024, 1024)\n",
" self.dropout_2 = nn.Dropout(p=0.05)\n",
"\n",
" self.num_layers = num_layers\n",
"\n",
" self.convs1 = nn.ModuleList()\n",
" self.convs1.append(GCNConv(4, hidden_channels))\n",
" for _ in range(num_layers - 1):\n",
" self.convs1.append(GCNConv(hidden_channels, hidden_channels))\n",
"\n",
" self.convs2 = nn.ModuleList()\n",
" self.convs2.append(GCNConv(4, hidden_channels))\n",
" for _ in range(num_layers - 1):\n",
" self.convs2.append(GCNConv(hidden_channels, hidden_channels))\n",
"\n",
" self.cross_att = GATConv(hidden_channels, hidden_channels, heads=2)\n",
"\n",
" self.lin1 = nn.Linear(2816, hidden_channels)\n",
" self.lin2 = nn.Linear(hidden_channels, 1)\n",
"\n",
" self.transform = NormalizeScale()\n",
"\n",
" def forward(self, data_batch_1, data_batch_2):\n",
" x1 = data_batch_1.x.double()\n",
" edge_index_1 = data_batch_1.edge_index\n",
" z1 = data_batch_1.z\n",
" y1_coord = data_batch_1.y_coord\n",
" z1_coord = data_batch_1.z_coord\n",
"\n",
" concatenated_x1 = torch.stack([x1, z1, y1_coord, z1_coord], dim=1)\n",
"\n",
" # Apply graph normalization\n",
" data_batch_1 = self.transform(data_batch_1)\n",
" x1 = data_batch_1.x\n",
"\n",
" for i in range(self.num_layers):\n",
" concatenated_x1 = self.convs1[i](concatenated_x1, edge_index_1.to(torch.int64))\n",
" concatenated_x1 = F.relu(concatenated_x1.double())\n",
"\n",
" # Process second graph\n",
" x2 = data_batch_2.x\n",
" edge_index_2 = data_batch_2.edge_index\n",
" z2 = data_batch_2.z\n",
" y2_coord = data_batch_2.y_coord\n",
" z2_coord = data_batch_2.z_coord\n",
"\n",
" concatenated_x2 = torch.stack([x2, z2, y2_coord, z2_coord], dim=1)\n",
"\n",
" # Apply graph normalization\n",
" data_batch_2 = self.transform(data_batch_2)\n",
" x2 = data_batch_2.x\n",
"\n",
" for i in range(self.num_layers):\n",
" concatenated_x2 = self.convs2[i](concatenated_x2, edge_index_2.to(torch.int64))\n",
" concatenated_x2 = F.relu(concatenated_x2)\n",
"\n",
" # Cross-attention block\n",
" x1 = self.cross_att(concatenated_x1, edge_index_1.to(torch.int64))\n",
" x2 = self.cross_att(concatenated_x2, edge_index_2.to(torch.int64))\n",
"\n",
" # Concatenate all tensors along the last dimension\n",
" x = torch.cat([\n",
" global_mean_pool(x1, data_batch_1.batch),\n",
" global_mean_pool(x2, data_batch_2.batch)], dim=1)\n",
"\n",
" input_11 = self.input_1(data_batch_1.seq)\n",
" self_attn_1 = self.self_attn_1(input_11)\n",
" transformer_1 = self.transformer_1(self_attn_1)\n",
" pooling_1 = self.pooling_1(transformer_1.transpose(0, 1)).squeeze(dim=1)\n",
" dense_1 = self.dense_1(pooling_1)\n",
" dropout_1 = self.dropout_1(dense_1)\n",
"\n",
" input_22 = self.input_2(data_batch_2.seq)\n",
" self_attn_2 = self.self_attn_2(input_22)\n",
" transformer_2 = self.transformer_2(self_attn_2)\n",
" pooling_2 = self.pooling_2(transformer_2.transpose(0, 1)).squeeze(dim=1)\n",
" dense_2 = self.dense_2(pooling_2)\n",
" dropout_2 = self.dropout_2(dense_2)\n",
"\n",
" input_shape = [(dropout_1.shape[-1],), (dropout_2.shape[-1],)]\n",
" cross_attn_1 = self.cross_attn_1([dropout_1, dropout_2])\n",
" cross_attn_2 = self.cross_attn_2([self.self_attn_1(input_11), self.self_attn_2(input_22)])\n",
" cross_pooling = self.cross_pooling(cross_attn_2.transpose(0, 1)).squeeze(dim=1)\n",
" cross_attn = self.cross_attn([cross_attn_1, cross_pooling])\n",
" cross_atten = F.tanh(self.dense(cross_attn))\n",
"\n",
" self_atten_1 = self.self_atten_1(self_attn_1.transpose(0, 1)).squeeze(dim=1)\n",
" self_atten_2 = self.self_atten_2(self_attn_2.transpose(0, 1)).squeeze(dim=1)\n",
" attention_scores = torch.cat([self_atten_1, self_atten_2, cross_atten], dim=-1)\n",
"\n",
" x_2 = torch.cat([self.pooling_2(x.transpose(0, 1)).squeeze(dim=1), attention_scores], dim = -1)\n",
"\n",
" attention_scores = torch.cat([attention_scores, self.pooling_2(x.transpose(0, 1)).squeeze(dim=1)], dim = -1)\n",
"\n",
" output_layer1 = F.tanh(self.output_layer1(attention_scores)) #transformer\n",
" output_layer = self.output_layer(output_layer1)\n",
"\n",
" x = F.relu(self.lin1(x_2))\n",
" x = self.lin2(x)\n",
"\n",
" return x, output_layer"
],
"metadata": {
"cellView": "form",
"id": "apGATVycfjcM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title\n",
"!wget \"https://uniofmora-my.sharepoint.com/:u:/g/personal/180066f_uom_lk/EY2Q0k-ghI1Hs6iZfCUwF6gBJZacOak9XEwzmPd6SK9uAQ?e=yHk23u&download=1\"\n",
"old_name = r\"/content/EY2Q0k-ghI1Hs6iZfCUwF6gBJZacOak9XEwzmPd6SK9uAQ?e=yHk23u&download=1\"\n",
"new_name = r\"/content/model_weights.pth\"\n",
"os.rename(old_name, new_name)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"id": "wWCGMOsynHU8",
"outputId": "dc83398f-ea31-4e0e-93ce-c0460ebd7fe5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2024-06-20 14:59:31-- https://uniofmora-my.sharepoint.com/:u:/g/personal/180066f_uom_lk/EY2Q0k-ghI1Hs6iZfCUwF6gBJZacOak9XEwzmPd6SK9uAQ?e=yHk23u&download=1\n",
"Resolving uniofmora-my.sharepoint.com (uniofmora-my.sharepoint.com)... 13.107.136.10, 13.107.138.10, 2620:1ec:8f8::10, ...\n",
"Connecting to uniofmora-my.sharepoint.com (uniofmora-my.sharepoint.com)|13.107.136.10|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: /personal/180066f_uom_lk/Documents/FYP/model_weights/28_best_model.pth?ga=1 [following]\n",
"--2024-06-20 14:59:31-- https://uniofmora-my.sharepoint.com/personal/180066f_uom_lk/Documents/FYP/model_weights/28_best_model.pth?ga=1\n",
"Reusing existing connection to uniofmora-my.sharepoint.com:443.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 2475531770 (2.3G) [application/octet-stream]\n",
"Saving to: ‘EY2Q0k-ghI1Hs6iZfCUwF6gBJZacOak9XEwzmPd6SK9uAQ?e=yHk23u&download=1’\n",
"\n",
"EY2Q0k-ghI1Hs6iZfCU 100%[===================>] 2.30G 142MB/s in 24s \n",
"\n",
"2024-06-20 14:59:55 (98.5 MB/s) - ‘EY2Q0k-ghI1Hs6iZfCUwF6gBJZacOak9XEwzmPd6SK9uAQ?e=yHk23u&download=1’ saved [2475531770/2475531770]\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Running on GPU (\"cuda\") device is advised"
],
"metadata": {
"id": "FtQaxzI349rF"
}
},
{
"cell_type": "code",
"source": [
"# @title\n",
"input_shape_1 = (int(max_antibody_sequence_length*20),)\n",
"input_shape_2 = (int(max_antigen_sequence_length*20),)\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"com_model = CombinedModel().to(device)\n",
"com_model = com_model.to(torch.float64)\n",
"\n",
"alpha = 0.45\n",
"beta = 0.55\n",
"gamma = 0.05\n",
"\n",
"com_model_cp = torch.load('/content/model_weights.pth')\n",
"com_model_epoch = com_model_cp['epoch']\n",
"com_model.load_state_dict(com_model_cp['model_state_dict'])\n",
"\n",
"# Evaluate the model\n",
"com_model.eval()\n",
"test_loss = 0.0\n",
"test_mae = 0.0\n",
"total_samples = 0\n",
"\n",
"with torch.no_grad():\n",
" for input in test_loader:\n",
" input_1 = input[0].to(device)\n",
" input_2 = input[1].to(device)\n",
" target = input[2].to(device)\n",
" batch_size = input_1.size(0)\n",
"\n",
" if input_1.pos is not None:\n",
" input_1 = NormalizeScale()(input_1)\n",
" else:\n",
" print(\"Data does not have position information, skipping normalization.\")\n",
"\n",
" if input_2.pos is not None:\n",
" input_2 = NormalizeScale()(input_2)\n",
" else:\n",
" print(\"Data does not have position information, skipping normalization.\")\n",
"\n",
" output_gnn, output_tranf = com_model(input_1, input_2)\n",
" print(f\"Predicted binding affinity for the uploaded antibody-antigen pair (in IC50): {10**(alpha*output_gnn.item()+beta*output_tranf.item()+gamma*np.abs(output_gnn.item() - output_tranf.item()))}\") #"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"id": "Sdn3Mq5PbxRb",
"outputId": "66b69bc7-fe5b-424c-ebae-4995c9cf2011"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Predicted binding affinity for the uploaded antibody-antigen pair (in IC50): 22.93421825850764\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**Notes:**\n",
"\n",
"- Check that the runtime type is set to GPU at \"Runtime\" -> \"Change runtime type\".\n",
"- Try to restart the session \"Runtime\" -> \"Restart session and run all\".\n",
"- Check your input PDB files.\n",
"- Our current model only supports proteins with amino-acid sequence lengths upto 669 (for antibodies) and 3102 (for antigens).\n",
"- If you encounter any bugs, please report the issue to https://github.com/Drug-Discovery-ENTC/p2pxml/issues or email the corresponding author (Nuwan) at pmnsribandara@gmail.com"
],
"metadata": {
"id": "SUyJQvdLMI3j"
}
}
]
} |