af/examples/af_cyc_design.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sokrypton/ColabDesign/blob/main/af/examples/af_cyc_design.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OA2k3sAYuiXe"
      },
      "source": [
        "#af_cyc_design\n",
        "\n",
        "**Cyclic peptide structure prediction and design using AlphaFold**\n",
        "\n",
        "Stephen Rettie, Katelyn Campbell, Asim Bera, Alex Kang, Simon Kozlov, Joshmyn De La Cruz, Victor Adebomi, Guangfeng Zhou, Frank DiMaio, Sergey Ovchinnikov, Gaurav Bhardwaj\n",
        "\n",
        "doi: https://doi.org/10.1101/2023.02.25.529956\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "-AXy0s_4cKaK"
      },
      "outputs": [],
      "source": [
        "#@title setup (~2 minutes)\n",
        "%%time\n",
        "import os\n",
        "if not os.path.isdir(\"params\"):\n",
        "  # get code\n",
        "  os.system(\"pip -q install git+https://github.com/sokrypton/ColabDesign.git@v1.1.1\")\n",
        "  # for debugging\n",
        "  os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/colabdesign colabdesign\")\n",
        "  # download params\n",
        "  os.system(\"mkdir params\")\n",
        "  os.system(\"apt-get install aria2 -qq\")\n",
        "  os.system(\"aria2c -q -x 16 https://storage.googleapis.com/alphafold/alphafold_params_2022-12-06.tar\")\n",
        "  os.system(\"tar -xf alphafold_params_2022-12-06.tar -C params\")\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "import os\n",
        "from colabdesign import mk_afdesign_model, clear_mem\n",
        "from IPython.display import HTML\n",
        "from google.colab import files\n",
        "import numpy as np\n",
        "\n",
        "import jax\n",
        "import jax.numpy as jnp\n",
        "from colabdesign.af.alphafold.common import residue_constants\n",
        "\n",
        "def get_pdb(pdb_code=\"\"):\n",
        "  if pdb_code is None or pdb_code == \"\":\n",
        "    upload_dict = files.upload()\n",
        "    pdb_string = upload_dict[list(upload_dict.keys())[0]]\n",
        "    with open(\"tmp.pdb\",\"wb\") as out: out.write(pdb_string)\n",
        "    return \"tmp.pdb\"\n",
        "  elif os.path.isfile(pdb_code):\n",
        "    return pdb_code\n",
        "  elif len(pdb_code) == 4:\n",
        "    os.system(f\"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb\")\n",
        "    return f\"{pdb_code}.pdb\"\n",
        "  else:\n",
        "    os.system(f\"wget -qnc https://alphafold.ebi.ac.uk/files/AF-{pdb_code}-F1-model_v3.pdb\")\n",
        "    return f\"AF-{pdb_code}-F1-model_v3.pdb\"\n",
        "\n",
        "def add_cyclic_offset(self, offset_type=2):\n",
        "  '''add cyclic offset to connect N and C term'''\n",
        "  def cyclic_offset(L):\n",
        "    i = np.arange(L)\n",
        "    ij = np.stack([i,i+L],-1)\n",
        "    offset = i[:,None] - i[None,:]\n",
        "    c_offset = np.abs(ij[:,None,:,None] - ij[None,:,None,:]).min((2,3))\n",
        "    if offset_type == 1:\n",
        "      c_offset = c_offset\n",
        "    elif offset_type >= 2:\n",
        "      a = c_offset < np.abs(offset)\n",
        "      c_offset[a] = -c_offset[a]\n",
        "    if offset_type == 3:\n",
        "      idx = np.abs(c_offset) > 2\n",
        "      c_offset[idx] = (32 * c_offset[idx] )/  abs(c_offset[idx])\n",
        "    return c_offset * np.sign(offset)\n",
        "  idx = self._inputs[\"residue_index\"]\n",
        "  offset = np.array(idx[:,None] - idx[None,:])\n",
        "\n",
        "  if self.protocol == \"binder\":\n",
        "    c_offset = cyclic_offset(self._binder_len)\n",
        "    offset[self._target_len:,self._target_len:] = c_offset\n",
        "\n",
        "  if self.protocol in [\"fixbb\",\"partial\",\"hallucination\"]:\n",
        "    Ln = 0\n",
        "    for L in self._lengths:\n",
        "      offset[Ln:Ln+L,Ln:Ln+L] = cyclic_offset(L)\n",
        "      Ln += L\n",
        "  self._inputs[\"offset\"] = offset\n",
        "\n",
        "def add_rg_loss(self, weight=0.1):\n",
        "  '''add radius of gyration loss'''\n",
        "  def loss_fn(inputs, outputs):\n",
        "    xyz = outputs[\"structure_module\"]\n",
        "    ca = xyz[\"final_atom_positions\"][:,residue_constants.atom_order[\"CA\"]]\n",
        "    rg = jnp.sqrt(jnp.square(ca - ca.mean(0)).sum(-1).mean() + 1e-8)\n",
        "    rg_th = 2.38 * ca.shape[0] ** 0.365\n",
        "    rg = jax.nn.elu(rg - rg_th)\n",
        "    return {\"rg\":rg}\n",
        "  self._callbacks[\"model\"][\"loss\"].append(loss_fn)\n",
        "  self.opt[\"weights\"][\"rg\"] = weight"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUfKrOzT0gOS"
      },
      "source": [
        "# fixed backbone design (fixbb) (~2 minutes)\n",
        "For a given protein backbone, generate/design a new sequence that AlphaFold thinks folds into that conformation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qLd1DsnKzxBJ"
      },
      "outputs": [],
      "source": [
        "clear_mem()\n",
        "af_model = mk_afdesign_model(protocol=\"fixbb\")\n",
        "af_model.prep_inputs(pdb_filename=get_pdb(\"7m28\"), chain=\"A\")\n",
        "add_cyclic_offset(af_model, offset_type=2)\n",
        "\n",
        "print(\"length\",  af_model._len)\n",
        "print(\"weights\", af_model.opt[\"weights\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(af_model._inputs[\"offset\"],cmap=\"bwr\")"
      ],
      "metadata": {
        "id": "xr6mw-CXl_o3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "af_model.restart()\n",
        "af_model.design_3stage()"
      ],
      "metadata": {
        "id": "u0AwskJ84NGx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "af_model.plot_traj()"
      ],
      "metadata": {
        "id": "8FB1v7dn1LL6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YEApO8YzBoS0"
      },
      "outputs": [],
      "source": [
        "af_model.save_pdb(f\"{af_model.protocol}.pdb\")\n",
        "af_model.plot_pdb(show_mainchains=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cW1KQiHKJpfp"
      },
      "outputs": [],
      "source": [
        "HTML(af_model.animate())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "af_model.get_seqs()"
      ],
      "metadata": {
        "id": "YDrChASGVUUx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# hallucination (~1 minute)\n",
        "For a given length, generate/hallucinate a protein sequence that AlphaFold thinks folds into a well structured protein (high plddt, low pae, many contacts)."
      ],
      "metadata": {
        "id": "qLwS2s_xcjRI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sZnYfCbfEvol"
      },
      "outputs": [],
      "source": [
        "clear_mem()\n",
        "af_model = mk_afdesign_model(protocol=\"hallucination\")\n",
        "af_model.prep_inputs(length=13, rm_aa=\"C\")\n",
        "add_cyclic_offset(af_model)\n",
        "# add_rg_loss(af_model)\n",
        "\n",
        "print(\"length\",af_model._len)\n",
        "print(\"weights\",af_model.opt[\"weights\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pre-design with gumbel initialization and softmax activation\n",
        "af_model.restart()\n",
        "af_model.set_seq(mode=\"gumbel\")\n",
        "af_model.set_opt(\"con\", binary=True, cutoff=21.6875, num=af_model._len, seqsep=0)\n",
        "af_model.set_weights(pae=1, plddt=1, con=0.5)\n",
        "af_model.design_soft(50)\n",
        "\n",
        "# three stage design\n",
        "af_model.set_seq(seq=af_model.aux[\"seq\"][\"pseudo\"])\n",
        "af_model.design_3stage(50,50,10)"
      ],
      "metadata": {
        "id": "f76xqCkw0vj9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A1GxeLZdTTya"
      },
      "outputs": [],
      "source": [
        "af_model.save_pdb(f\"{af_model.protocol}.pdb\")\n",
        "af_model.plot_pdb(show_mainchains=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L2E9Tn2Acchj"
      },
      "outputs": [],
      "source": [
        "HTML(af_model.animate())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YSKWYu0_GlUH"
      },
      "outputs": [],
      "source": [
        "af_model.get_seqs()"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "q4qiU9I0QHSz"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
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