IgFold.ipynb

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        "# **IgFold**: Fast, accurate antibody structure prediction\n",
        "\n",
        "Official notebook for [IgFold](https://www.biorxiv.org/content/10.1101/2022.04.20.488972): Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.  The code, data, and weights for this work are made available for non-commercial use. For commercial inquiries, please contact `jruffolo[at]jhu.edu`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
      "outputs": [],
      "source": [
        "#@title Input antibody Fv sequences then press `Runtime` -> `Run all`\n",
        "\n",
        "import os\n",
        "import sys\n",
        "\n",
        "python_version = f\"{sys.version_info.major}.{sys.version_info.minor}\"\n",
        "\n",
        "name = \"my_antibody\" #@param {type:\"string\"}\n",
        "pred_dir = name\n",
        "os.makedirs(pred_dir, exist_ok=True)\n",
        "\n",
        "#@markdown Enter antibody sequences for structure prediction. To predict a nanobody structure (or an individual heavy or light chain), simply provide one sequence.\n",
        "heavy_sequence = \"EVQLVQSGPEVKKPGTSVKVSCKASGFTFMSSAVQWVRQARGQRLEWIGWIVIGSGNTNYAQKFQERVTITRDMSTSTAYMELSSLRSEDTAVYYCAAPYCSSISCNDGFDIWGQGTMVTVS\" #@param {type:\"string\"}\n",
        "light_sequence = \"DVVMTQTPFSLPVSLGDQASISCRSSQSLVHSNGNTYLHWYLQKPGQSPKLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPYTFGGGTKLEIK\" #@param {type:\"string\"}\n",
        "\n",
        "sequences = {}\n",
        "if len(heavy_sequence) > 0:\n",
        "    sequences[\"H\"] = heavy_sequence\n",
        "if len(light_sequence) > 0:\n",
        "    sequences[\"L\"] = light_sequence\n",
        "\n",
        "#@markdown Perform structural refinement with OpenMM\n",
        "do_refine = True #@param {type:\"boolean\"}\n",
        "# #@markdown Use OpenMM for refinement (instead of PyRosetta)\n",
        "use_openmm = True\n",
        "#@markdown Renumber predicted antibody structure (Chothia) with AbNumber\n",
        "do_renum = True #@param {type:\"boolean\"}\n",
        "#@markdown Use only a single model for predictions (instead of model ensemble)\n",
        "single_model = False #@param {type:\"boolean\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "LsJNdVE87Go2"
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      "outputs": [],
      "source": [
        "#@title Download dependencies\n",
        "\n",
        "%%capture\n",
        "%%bash -s $python_version\n",
        "\n",
        "PYTHON_VERSION=$1\n",
        "set -e\n",
        "\n",
        "if [ ! -f CODE_READY ]; then\n",
        "  # install dependencies\n",
        "  pip install igfold>=0.3.0\n",
        "  pip install py3Dmol==1.8.0 matplotlib seaborn\n",
        "  touch CODE_READY\n",
        "fi\n",
        "\n",
        "# setup conda\n",
        "if [ ! -f CONDA_READY ]; then\n",
        "  wget -qnc https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
        "  bash Miniconda3-latest-Linux-x86_64.sh -bfp /usr/local\n",
        "  rm Miniconda3-latest-Linux-x86_64.sh\n",
        "  touch CONDA_READY\n",
        "fi\n",
        "\n",
        "# setup openmm for amber refinement\n",
        "if [ ! -f OPENMM_READY ]; then\n",
        "  pip install chardet\n",
        "  conda install -y -q -c conda-forge openmm=7.7.0 python=\"${PYTHON_VERSION}\" pdbfixer\n",
        "  touch OPENMM_READY\n",
        "fi\n",
        "\n",
        "# setup anarci\n",
        "if [ ! -f ABNUMBER_READY ]; then\n",
        "  conda install -y -q -c bioconda abnumber python=\"${PYTHON_VERSION}\"\n",
        "  touch ABNUMBER_READY\n",
        "fi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "a2a3BsiE9AXI"
      },
      "outputs": [],
      "source": [
        "#@title Predict antibody structure with IgFold\n",
        "\n",
        "if f\"/usr/local/lib/python{python_version}/site-packages/\" not in sys.path:\n",
        "    sys.path.insert(0, f\"/usr/local/lib/python{python_version}/site-packages/\")\n",
        "\n",
        "from igfold.utils.visualize import *\n",
        "from igfold import IgFoldRunner\n",
        "\n",
        "num_models = 1 if single_model else 4\n",
        "igfold = IgFoldRunner(num_models=num_models)\n",
        "\n",
        "pred_pdb = os.path.join(pred_dir, f\"{name}.pdb\")\n",
        "pred = igfold.fold(\n",
        "    pred_pdb,\n",
        "    sequences=sequences,\n",
        "    do_refine=do_refine,\n",
        "    use_openmm=True,\n",
        "    do_renum=do_renum,\n",
        ")\n",
        "show_pdb(pred_pdb, len(sequences), bb_sticks=False, sc_sticks=True, color=\"rainbow\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "xFOTYxsP9Cz1"
      },
      "outputs": [],
      "source": [
        "#@title Plot per-residue predicted RMSD\n",
        "\n",
        "prmsd_fig_file = os.path.join(pred_dir, f\"{name}_prmsd.png\")\n",
        "plot_prmsd(sequences, pred.prmsd, prmsd_fig_file, shade_cdr=do_renum, pdb_file=pred_pdb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "ajyElWbZ9EFF"
      },
      "outputs": [],
      "source": [
        "#@title Show predicted structure with predicted RMSD\n",
        "\n",
        "#@markdown Structure is colored from low (blue) to high (red) pRMSD.\n",
        "\n",
        "show_pdb(pred_pdb, len(sequences), bb_sticks=False, sc_sticks=True, color=\"b\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "gZBzjpMdJ77q"
      },
      "outputs": [],
      "source": [
        "#@title Download results\n",
        "\n",
        "#@markdown Download zip file containing structure prediction and annotation results. If download fails, results are also accessible from file explorer on the left panel of the notebook.\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "!zip -FSr $name\".result.zip\" $pred_dir/ &> /dev/null\n",
        "files.download(f\"{name}.result.zip\")"
      ]
    }
  ],
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