{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "AF2Rank.ipynb", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "gpuClass": "standard" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "#AF2Rank\n", "[AF2Rank](https://github.com/jproney/AF2Rank) implemented using ColabDesign. \n", "\n", "If you find useful, please cite:\n", "- Roney, J.P. and Ovchinnikov, S., 2022. **State-of-the-Art estimation of protein model accuracy using AlphaFold**. [BioRxiv](https://www.biorxiv.org/content/10.1101/2022.03.11.484043v3.full)." ], "metadata": { "id": "lN62y-y2VHUX" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id": "zk6_tVpg9Bdi" }, "outputs": [], "source": [ "#@title ## setup\n", "%%bash\n", "if [ ! -d params ]; then\n", " # get code\n", " pip -q install git+https://github.com/sokrypton/ColabDesign.git@v1.1.1\n", " # for debugging\n", " ln -s /usr/local/lib/python3.*/dist-packages/colabdesign colabdesign\n", "\n", " # alphafold params\n", " mkdir params\n", " curl -fsSL https://storage.googleapis.com/alphafold/alphafold_params_2022-12-06.tar | tar x -C params\n", "\n", " wget -qnc https://zhanggroup.org/TM-score/TMscore.cpp\n", " g++ -static -O3 -ffast-math -lm -o TMscore TMscore.cpp\n", "fi" ] }, { "cell_type": "code", "source": [ "#@title import libraries\n", "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "from colabdesign import clear_mem, mk_af_model\n", "from colabdesign.shared.utils import copy_dict\n", "\n", "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import spearmanr\n", "import jax\n", "\n", "def tmscore(x,y):\n", " # save to dumpy pdb files\n", " for n,z in enumerate([x,y]): \n", " out = open(f\"{n}.pdb\",\"w\")\n", " for k,c in enumerate(z):\n", " out.write(\"ATOM %5d %-2s %3s %s%4d %8.3f%8.3f%8.3f %4.2f %4.2f\\n\" \n", " % (k+1,\"CA\",\"ALA\",\"A\",k+1,c[0],c[1],c[2],1,0))\n", " out.close()\n", " # pass to TMscore\n", " output = os.popen('./TMscore 0.pdb 1.pdb')\n", "\n", " # parse outputs\n", " parse_float = lambda x: float(x.split(\"=\")[1].split()[0])\n", " o = {}\n", " for line in output:\n", " line = line.rstrip()\n", " if line.startswith(\"RMSD\"): o[\"rms\"] = parse_float(line)\n", " if line.startswith(\"TM-score\"): o[\"tms\"] = parse_float(line)\n", " if line.startswith(\"GDT-TS-score\"): o[\"gdt\"] = parse_float(line)\n", " \n", " return o\n", " \n", "def plot_me(scores, x=\"tm_i\", y=\"composite\", \n", " title=None, diag=False, scale_axis=True, dpi=100, **kwargs):\n", " def rescale(a,amin=None,amax=None): \n", " a = np.copy(a)\n", " if amin is None: amin = a.min()\n", " if amax is None: amax = a.max()\n", " a[a < amin] = amin\n", " a[a > amax] = amax\n", " return (a - amin)/(amax - amin)\n", "\n", " plt.figure(figsize=(5,5), dpi=dpi)\n", " if title is not None: plt.title(title)\n", " x_vals = np.array([k[x] for k in scores])\n", " y_vals = np.array([k[y] for k in scores])\n", " c = rescale(np.array([k[\"plddt\"] for k in scores]),0.5,0.9)\n", " plt.scatter(x_vals, y_vals, c=c*0.75, s=5, vmin=0, vmax=1, cmap=\"gist_rainbow\",\n", " **kwargs)\n", " if diag:\n", " plt.plot([0,1],[0,1],color=\"black\")\n", " \n", " labels = {\"tm_i\":\"TMscore of Input\",\n", " \"tm_o\":\"TMscore of Output\",\n", " \"tm_io\":\"TMscore between Input and Output\",\n", " \"ptm\":\"Predicted TMscore (pTM)\",\n", " \"i_ptm\":\"Predicted interface TMscore (ipTM)\",\n", " \"plddt\":\"Predicted LDDT (pLDDT)\",\n", " \"composite\":\"Composite\"}\n", "\n", " plt.xlabel(labels.get(x,x)); plt.ylabel(labels.get(y,y))\n", " if scale_axis:\n", " if x in labels: plt.xlim(-0.1, 1.1)\n", " if y in labels: plt.ylim(-0.1, 1.1)\n", " \n", " print(spearmanr(x_vals,y_vals).correlation)\n", "\n", "class af2rank:\n", " def __init__(self, pdb, chain=None, model_name=\"model_1_ptm\", model_names=None):\n", " self.args = {\"pdb\":pdb, \"chain\":chain,\n", " \"use_multimer\":(\"multimer\" in model_name),\n", " \"model_name\":model_name,\n", " \"model_names\":model_names}\n", " self.reset()\n", "\n", " def reset(self):\n", " self.model = mk_af_model(protocol=\"fixbb\",\n", " use_templates=True,\n", " use_multimer=self.args[\"use_multimer\"],\n", " debug=False,\n", " model_names=self.args[\"model_names\"])\n", " \n", " self.model.prep_inputs(self.args[\"pdb\"], chain=self.args[\"chain\"])\n", " self.model.set_seq(mode=\"wildtype\")\n", " self.wt_batch = copy_dict(self.model._inputs[\"batch\"])\n", " self.wt = self.model._wt_aatype\n", "\n", " def set_pdb(self, pdb, chain=None):\n", " if chain is None: chain = self.args[\"chain\"]\n", " self.model.prep_inputs(pdb, chain=chain)\n", " self.model.set_seq(mode=\"wildtype\")\n", " self.wt = self.model._wt_aatype\n", "\n", " def set_seq(self, seq):\n", " self.model.set_seq(seq=seq)\n", " self.wt = self.model._params[\"seq\"][0].argmax(-1)\n", "\n", " def _get_score(self):\n", " score = copy_dict(self.model.aux[\"log\"])\n", "\n", " score[\"plddt\"] = score[\"plddt\"]\n", " score[\"pae\"] = 31.0 * score[\"pae\"]\n", " score[\"rmsd_io\"] = score.pop(\"rmsd\",None)\n", "\n", " i_xyz = self.model._inputs[\"batch\"][\"all_atom_positions\"][:,1]\n", " o_xyz = np.array(self.model.aux[\"atom_positions\"][:,1])\n", "\n", " # TMscore to input/output\n", " if hasattr(self,\"wt_batch\"):\n", " n_xyz = self.wt_batch[\"all_atom_positions\"][:,1]\n", " score[\"tm_i\"] = tmscore(n_xyz,i_xyz)[\"tms\"]\n", " score[\"tm_o\"] = tmscore(n_xyz,o_xyz)[\"tms\"]\n", "\n", " # TMscore between input and output\n", " score[\"tm_io\"] = tmscore(i_xyz,o_xyz)[\"tms\"]\n", "\n", " # composite score\n", " score[\"composite\"] = score[\"ptm\"] * score[\"plddt\"] * score[\"tm_io\"]\n", " return score\n", " \n", " def predict(self, pdb=None, seq=None, chain=None, \n", " input_template=True, model_name=None,\n", " rm_seq=True, rm_sc=True, rm_ic=False,\n", " recycles=1, iterations=1,\n", " output_pdb=None, extras=None, verbose=True):\n", " \n", " if model_name is not None:\n", " self.args[\"model_name\"] = model_name\n", " if \"multimer\" in model_name: \n", " if not self.args[\"use_multimer\"]:\n", " self.args[\"use_multimer\"] = True\n", " self.reset()\n", " else:\n", " if self.args[\"use_multimer\"]:\n", " self.args[\"use_multimer\"] = False\n", " self.reset()\n", " \n", " if pdb is not None: self.set_pdb(pdb, chain)\n", " if seq is not None: self.set_seq(seq)\n", "\n", " # set template sequence\n", " self.model._inputs[\"batch\"][\"aatype\"] = self.wt\n", "\n", " # set other options\n", " self.model.set_opt(\n", " template=dict(rm_ic=rm_ic),\n", " num_recycles=recycles)\n", " self.model._inputs[\"rm_template\"][:] = not input_template\n", " self.model._inputs[\"rm_template_sc\"][:] = rm_sc\n", " self.model._inputs[\"rm_template_seq\"][:] = rm_seq\n", " \n", " # \"manual\" recycles using templates\n", " ini_atoms = self.model._inputs[\"batch\"][\"all_atom_positions\"].copy()\n", " for i in range(iterations):\n", " self.model.predict(models=self.args[\"model_name\"], verbose=False)\n", " if i < iterations - 1:\n", " self.model._inputs[\"batch\"][\"all_atom_positions\"] = self.model.aux[\"atom_positions\"]\n", " else:\n", " self.model._inputs[\"batch\"][\"all_atom_positions\"] = ini_atoms\n", " \n", " score = self._get_score()\n", " if extras is not None:\n", " score.update(extras)\n", "\n", " if output_pdb is not None:\n", " self.model.save_pdb(output_pdb)\n", " \n", " if verbose:\n", " print_list = [\"tm_i\",\"tm_o\",\"tm_io\",\"composite\",\"ptm\",\"i_ptm\",\"plddt\",\"fitness\",\"id\"]\n", " print_score = lambda k: f\"{k} {score[k]:.4f}\" if isinstance(score[k],float) else f\"{k} {score[k]}\"\n", " print(*[print_score(k) for k in print_list if k in score])\n", " \n", " return score" ], "metadata": { "cellView": "form", "id": "1o-_Rl4hFfkR" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "#@markdown ### **settings**\n", "recycles = 1 #@param [\"0\", \"1\", \"2\", \"3\", \"4\"] {type:\"raw\"}\n", "iterations = 1 \n", "\n", "# decide what model to use\n", "model_mode = \"alphafold\" #@param [\"alphafold\", \"alphafold-multimer\"]\n", "model_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n", "\n", "if model_mode == \"alphafold\":\n", " model_name = f\"model_{model_num}_ptm\"\n", "if model_mode == \"alphafold-multimer\":\n", " model_name = f\"model_{model_num}_multimer_v3\"\n", "\n", "save_output_pdbs = False #@param {type:\"boolean\"}\n", "\n", "#@markdown ### **advanced**\n", "mask_sequence = True #@param {type:\"boolean\"}\n", "mask_sidechains = True #@param {type:\"boolean\"}\n", "mask_interchain = False #@param {type:\"boolean\"}\n", "\n", "SETTINGS = {\"rm_seq\":mask_sequence,\n", " \"rm_sc\":mask_sidechains,\n", " \"rm_ic\":mask_interchain,\n", " \"recycles\":int(recycles),\n", " \"iterations\":int(iterations),\n", " \"model_name\":model_name}" ], "metadata": { "cellView": "form", "id": "6G7XWsStB1sB" }, "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "source": [ "## rank structures" ], "metadata": { "id": "iCsF7ceG9QCO" } }, { "cell_type": "code", "source": [ "NAME = \"1mjc\"\n", "CHAIN = \"A\" # this can be multiple chains\n", "NATIVE_PATH = f\"{NAME}.pdb\"\n", "DECOY_DIR = f\"{NAME}\"\n", "\n", "if save_output_pdbs:\n", " os.makedirs(f\"{NAME}_output\",ok_exists=True)\n", "\n", "\n", "# get data\n", "%shell wget -qnc https://files.ipd.uw.edu/pub/decoyset/natives/{NAME}.pdb\n", "%shell wget -qnc https://files.ipd.uw.edu/pub/decoyset/decoys/{NAME}.zip\n", "%shell unzip -qqo {NAME}.zip\n", "\n", "# setup model\n", "clear_mem()\n", "af = af2rank(NATIVE_PATH, CHAIN, model_name=SETTINGS[\"model_name\"])" ], "metadata": { "id": "iDCRJjdSIG0g" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "# score no structure\n", "_ = af.predict(pdb=NATIVE_PATH, input_template=False, **SETTINGS)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UCUZxJdbBjZt", "outputId": "bb0440ad-cb54-4059-fd90-4198dcfd5f7b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "tm_i 1.0000 tm_o 0.6650 tm_io 0.6650 composite 0.2399 ptm 0.5467 i_ptm 0.0000 plddt 0.6599\n" ] } ] }, { "cell_type": "code", "source": [ "SCORES = []\n", "\n", "# score native structure\n", "SCORES.append(af.predict(pdb=NATIVE_PATH, **SETTINGS, extras={\"id\":NATIVE_PATH}))\n", "\n", "# score the decoy sctructures\n", "for decoy_pdb in os.listdir(DECOY_DIR):\n", " input_pdb = os.path.join(DECOY_DIR, decoy_pdb)\n", " if save_output_pdbs:\n", " output_pdb = os.path.join(f\"{NAME}_output\",decoy_pdb)\n", " else:\n", " output_pdb = None\n", " SCORES.append(af.predict(pdb=input_pdb, output_pdb=output_pdb,\n", " **SETTINGS, extras={\"id\":decoy_pdb}))" ], "metadata": { "id": "ChgI637YCArk" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "plot_me(SCORES, x=\"tm_i\", y=\"composite\",\n", " title=f\"{NAME}: ranking INPUT decoys using composite score\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 497 }, "id": "ZUEaAlP5CR8h", "outputId": "7322efa2-96d6-4866-d1ad-8c4d46a771f8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.9286667300616703\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "plot_me(SCORES, x=\"tm_o\", y=\"ptm\",\n", " title=f\"{NAME}: ranking OUTPUT decoys using predicted TMscore\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 497 }, "id": "yi_ztCc3kRXy", "outputId": "6d6cd607-5818-4ed8-be63-f192816030aa" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.9143006449691364\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "plot_me(SCORES, x=\"tm_i\", y=\"tm_o\", diag=True,\n", " title=f\"{NAME}: improvements over input structure\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 497 }, "id": "_Ix9KPR3kuLu", "outputId": "2ed55002-b417-4526-88fd-ea844544d7a5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.8287990333595944\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "## rank sequences\n", "Example: ParD and ParE are an example of a toxin and antitoxin pair of proteins. If the pair of proteins bind, the organism survives, if they do not, organism does not! Mike Laub et al. created a library of mutants that targets this interface and their measured \"fitness\". Let's see how well AlphaFold can predict this, using the template trick." ], "metadata": { "id": "vD1QKR0DDdQy" } }, { "cell_type": "code", "source": [ "# get data\n", "%shell wget -qnc https://files.ipd.uw.edu/krypton/5CEG_AD_trim.pdb\n", "%shell wget -qnc https://files.ipd.uw.edu/krypton/design/Library_fitness_vs_parE3_replicate_A.csv\n", "%shell wget -qnc https://files.ipd.uw.edu/krypton/design/Library_fitness_vs_parE3_replicate_B.csv\n", "\n", "# lets parse the data\n", "lib_a = dict([line.rstrip().split(\",\") for line in open(\"Library_fitness_vs_parE3_replicate_A.csv\")])\n", "lib_b = dict([line.rstrip().split(\",\") for line in open(\"Library_fitness_vs_parE3_replicate_B.csv\")])\n", "lib_ab = jax.tree_map(lambda a,b:(float(a)+float(b))/2,lib_a,lib_b)\n", "\n", "# get sequences\n", "seqs = {}\n", "for mut,sco in lib_ab.items():\n", " seq = list(\"RHDDIRRLRQLWDEGKASGRPEPVDFDALRKEARQKLTEVRLVWSPTAKADLIDIYVMIGSENIRAADRYYDQLEARALQLADQPRMGVRRPDIRPSARMLVEAPFVLLYETVPDTDDGPVEWVEIVRVVDGRRDLNRLF\")\n", " # mutate seq\n", " for i,m in zip([10,11,12,15],list(mut)): seq[i] = m\n", " seq = \"\".join(seq)\n", " seqs[mut] = {\"seq\":seq, \"sco\":sco}\n", "\n", "NAME = \"toxin\"\n", "if save_output_pdbs:\n", " os.makedirs(f\"{NAME}_output\",ok_exists=True)\n" ], "metadata": { "id": "Ed3SBjR0Defv" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# setup model\n", "clear_mem()\n", "af = af2rank(\"5CEG_AD_trim.pdb\", chain=\"A,B\", model_name=SETTINGS[\"model_name\"])\n", "SCORES,LABELS = [],[]" ], "metadata": { "id": "lySWA526TtUQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "for label,x in seqs.items():\n", " if label not in LABELS:\n", "\n", " if save_output_pdbs:\n", " output_pdb = os.path.join(f\"{NAME}_output\",f\"{label}.pdb\")\n", " else:\n", " output_pdb = None\n", "\n", " score = af.predict(seq=x[\"seq\"], **SETTINGS, output_pdb=output_pdb,\n", " extras={\"fitness\":x[\"sco\"], \"id\":label})\n", " SCORES.append(score)\n", " LABELS.append(label)" ], "metadata": { "id": "fYUmIaJBL4j7" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "plot_me(SCORES, x=\"fitness\", y=\"composite\", scale_axis=False)" ], "metadata": { "id": "mpBcceKdSlOG" }, "execution_count": null, "outputs": [] } ] }