PDB2FEATURES.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#ALPHAFOLD RESULTS TO FEATURES TO INPUT TO GNN"
      ],
      "metadata": {
        "id": "2isFUkbFXbyb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9ZtXAMbd4nN",
        "outputId": "b9b42e73-5808-402b-8048-9108578485ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n"
      ],
      "metadata": {
        "id": "EFPZTz2oeQmk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "import os\n",
        "\n",
        "\n",
        "directory = '/content/drive/MyDrive/alphafold_results'\n",
        " \n",
        "# iterate over files in that directory\n",
        "files = Path(directory).glob('*')\n",
        "for file in files:\n",
        "    # opening the zip file in READ mode\n",
        "    with ZipFile(file, 'r') as zip:\n",
        "      # storing all the contents of the zip file\n",
        "        zipped_files = zip.namelist()\n",
        "        # extracting only the pdb to a folder\n",
        "        for pdbfile in zipped_files:\n",
        "          if(pdbfile[-4:]==\".pdb\"):\n",
        "            print(pdbfile)\n",
        "            zip.extract(pdbfile,\"/content/drive/MyDrive/Extracted_PDBs\")\n",
        "            break\n",
        "    zip.close()\n",
        "      \n"
      ],
      "metadata": {
        "id": "Tbg2zujQdxTZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "068e19f1-da37-4459-eb0d-88d4153941d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_46b15_unrelaxed_rank_1_model_1.pdb\n",
            "test_f7da8_unrelaxed_rank_1_model_1.pdb\n",
            "test_85408_unrelaxed_rank_1_model_1.pdb\n",
            "test_d65b5_unrelaxed_rank_1_model_1.pdb\n",
            "test_35045_unrelaxed_rank_1_model_1.pdb\n",
            "test_342b4_unrelaxed_rank_1_model_1.pdb\n",
            "test_f0c24_unrelaxed_rank_1_model_1.pdb\n",
            "test_36d17_unrelaxed_rank_1_model_1.pdb\n",
            "test_c0e83_unrelaxed_rank_1_model_1.pdb\n",
            "test_9efd6_unrelaxed_rank_1_model_1.pdb\n",
            "test_9a340_unrelaxed_rank_1_model_1.pdb\n",
            "test_9e089_unrelaxed_rank_1_model_1.pdb\n",
            "test_f6e3a_unrelaxed_rank_1_model_1.pdb\n",
            "test_fb06a_unrelaxed_rank_1_model_1.pdb\n",
            "test_04206_unrelaxed_rank_1_model_1.pdb\n",
            "test_e12c8_unrelaxed_rank_1_model_1.pdb\n",
            "test_69d4b_unrelaxed_rank_1_model_1.pdb\n",
            "test_c77b9_unrelaxed_rank_1_model_1.pdb\n",
            "test_90296_unrelaxed_rank_1_model_1.pdb\n",
            "test_b667c_unrelaxed_rank_1_model_1.pdb\n",
            "test_2ff52_unrelaxed_rank_1_model_1.pdb\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def preprocess_to_feature_input(filename):\n",
        "  with open (filename, \"r\") as myfile:\n",
        "    data = myfile.read().splitlines()\n",
        "    \n",
        "  unique_atoms = np.unique([i.split()[0] for i in data] + [i.split()[1] for i in data])\n",
        "  atoms2id ={i:ind for ind, i in enumerate(unique_atoms)}\n",
        "  atoms2id\n",
        "  id2atoms = {ind:i for ind, i in enumerate(unique_atoms)}\n",
        "  deg_temp = np.unique([i.split()[0] for i in data] + [i.split()[1] for i in data], return_counts = True)\n",
        "  atom_degree = {atoms2id[deg_temp[0][i]]:deg_temp[1][i] for i in range(len(deg_temp[0]))}\n",
        "  edges = [[atoms2id[i.split()[0]], atoms2id[i.split()[1]]] for i in data]\n",
        "  bond_length = {str(atoms2id[i.split()[0]])+\"_\" + str(atoms2id[i.split()[1]]):float(i.split()[5][:-1]) for i in data}\n",
        "  id2atomName = {atoms2id[i]: i.split(\":\")[-1] for i in atoms2id}\n",
        "\n",
        "  res = {\"edges\": edges, \"features_degree\": atom_degree, \"bond_length\" : bond_length, \"atomName\":id2atomName, \"id2atoms\":id2atoms}\n",
        "  np.save(f\"{filename[:-4]}_out.npy\", res)\n",
        "\n",
        "  return res\n",
        "  \n"
      ],
      "metadata": {
        "id": "tZG6np-sBf3d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = str(file)\n",
        "x[-16:-11]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tho1e52xnuYC",
        "outputId": "6f51c804-076d-437c-bccf-a946e1a3fa76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2ff52'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install graphein"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "x7dcvuzA2HcI",
        "outputId": "9019482a-908a-440b-9d46-5721e5f80c40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "Successfully installed biopandas-0.4.1 biopython-1.79 bioservices-1.10.4 cattrs-22.2.0 colorama-0.4.6 colorlog-6.7.0 commonmark-0.9.1 deepdiff-6.2.1 easydev-0.12.0 exceptiongroup-1.0.1 fonttools-4.38.0 gevent-22.10.2 graphein-1.5.2 grequests-0.6.0 loguru-0.6.0 matplotlib-3.5.3 ordered-set-4.1.0 pyyaml-5.4.1 requests-cache-0.9.7 rich-12.6.0 rich-click-1.5.2 suds-community-1.1.2 url-normalize-1.4.3 urllib3-1.25.11 wget-3.2 xmltodict-0.13.0 zope.event-4.5.0 zope.interface-5.5.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from graphein.protein.graphs import construct_graph\n",
        "from graphein.protein.edges.atomic import add_atomic_edges\n",
        "from graphein.protein.config import ProteinGraphConfig\n",
        "import networkx as nx\n",
        "\n",
        "params_to_change = {\"granularity\": \"atom\", \"edge_construction_functions\": [add_atomic_edges]}\n",
        "\n",
        "config = ProteinGraphConfig(**params_to_change)\n",
        "config.dict()\n",
        "\n",
        "\n",
        "directory = '/content/drive/MyDrive/Extracted_PDBs'\n",
        " \n",
        "# iterate over files in that directory\n",
        "files = Path(directory).glob('*')\n",
        "for file in files:\n",
        "  # print(file)\n",
        "  if(str(file)[-4:]==\".pdb\"):\n",
        "    print(str(file)[-34:-29])\n",
        "    g = construct_graph(config=config, pdb_path=str(file))\n",
        "    txt_filename = 'pdb_text'+str(file)[-34:-29]+'.txt'\n",
        "    print(txt_filename)\n",
        "    nx.write_edgelist(g, txt_filename)\n",
        "    gh = preprocess_to_feature_input(txt_filename)\n",
        "    print(str(file)+\" Done\")"
      ],
      "metadata": {
        "id": "DskpFn5IyCT-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "97a3be84-5fe5-488f-ba31-d68cad04a7e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-28b1360c3660>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgraphein\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotein\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraphs\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconstruct_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgraphein\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotein\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matomic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0madd_atomic_edges\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgraphein\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotein\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mProteinGraphConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnetworkx\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'graphein'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    }
  ]
}