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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "AlphaFold_single.ipynb",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/sokrypton/af_backprop/blob/beta/examples/AlphaFold_single.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"#AlphaFold - single sequence input\n",
"- WARNING - For DEMO and educational purposes only. \n",
"- For natural proteins you often need more than a single sequence to accurately predict the structure. See [ColabFold](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) notebook if you want to predict the protein structure from a multiple-sequence-alignment. That being said, this notebook could potentially be useful for evaluating *de novo* designed proteins.\n"
],
"metadata": {
"id": "VpfCw7IzVHXv"
}
},
{
"cell_type": "code",
"source": [
"#@title Setup\n",
"from IPython.utils import io\n",
"import os,sys,re\n",
"import tensorflow as tf\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"\n",
"with io.capture_output() as captured:\n",
" if not os.path.isdir(\"af_backprop\"):\n",
" %shell git clone -b beta https://github.com/sokrypton/af_backprop.git\n",
" %shell pip -q install biopython dm-haiku ml-collections py3Dmol\n",
" %shell wget -qnc https://raw.githubusercontent.com/sokrypton/ColabFold/main/beta/colabfold.py\n",
" if not os.path.isdir(\"params\"):\n",
" %shell mkdir params\n",
" %shell curl -fsSL https://storage.googleapis.com/alphafold/alphafold_params_2021-07-14.tar | tar x -C params\n",
"\n",
"try:\n",
" # check if TPU is available\n",
" import jax.tools.colab_tpu\n",
" jax.tools.colab_tpu.setup_tpu()\n",
" print('Running on TPU')\n",
" DEVICE = \"tpu\"\n",
"except:\n",
" if jax.local_devices()[0].platform == 'cpu':\n",
" print(\"WARNING: no GPU detected, will be using CPU\")\n",
" DEVICE = \"cpu\"\n",
" else:\n",
" print('Running on GPU')\n",
" DEVICE = \"gpu\"\n",
" # disable GPU on tensorflow\n",
" tf.config.set_visible_devices([], 'GPU')\n",
"\n",
"sys.path.append('/content/af_backprop')\n",
"# import libraries\n",
"from utils import update_seq, update_aatype, get_plddt, get_pae\n",
"import colabfold as cf\n",
"from alphafold.common import protein\n",
"from alphafold.data import pipeline\n",
"from alphafold.model import data, config, model\n",
"from alphafold.common import residue_constants\n",
"\n",
"def clear_mem():\n",
" backend = jax.lib.xla_bridge.get_backend()\n",
" for buf in backend.live_buffers(): buf.delete()\n",
"\n",
"def setup_model(max_len, model_name=\"model_2_ptm\"):\n",
"\n",
" clear_mem()\n",
"\n",
" # setup model\n",
" cfg = config.model_config(\"model_5_ptm\")\n",
" cfg.model.num_recycle = 0\n",
" cfg.data.common.num_recycle = 0\n",
" cfg.data.eval.max_msa_clusters = 1\n",
" cfg.data.common.max_extra_msa = 1\n",
" cfg.data.eval.masked_msa_replace_fraction = 0\n",
" cfg.model.global_config.subbatch_size = None\n",
" model_params = data.get_model_haiku_params(model_name=model_name, data_dir=\".\")\n",
" model_runner = model.RunModel(cfg, model_params, is_training=False)\n",
"\n",
" seq = \"A\" * max_len\n",
" length = len(seq)\n",
" feature_dict = {\n",
" **pipeline.make_sequence_features(sequence=seq, description=\"none\", num_res=length),\n",
" **pipeline.make_msa_features(msas=[[seq]], deletion_matrices=[[[0]*length]])\n",
" }\n",
" inputs = model_runner.process_features(feature_dict,random_seed=0)\n",
"\n",
" def runner(seq, opt):\n",
" # update sequence\n",
" inputs = opt[\"inputs\"]\n",
" inputs.update(opt[\"prev\"])\n",
" update_seq(seq, inputs)\n",
" update_aatype(inputs[\"target_feat\"][...,1:], inputs)\n",
"\n",
" # mask prediction\n",
" mask = seq.sum(-1)\n",
" inputs[\"seq_mask\"] = inputs[\"seq_mask\"].at[:].set(mask)\n",
" inputs[\"msa_mask\"] = inputs[\"msa_mask\"].at[:].set(mask)\n",
" inputs[\"residue_index\"] = jnp.where(mask==1,inputs[\"residue_index\"],0)\n",
"\n",
" # get prediction\n",
" key = jax.random.PRNGKey(0)\n",
" outputs = model_runner.apply(opt[\"params\"], key, inputs)\n",
"\n",
" prev = {\"init_msa_first_row\":outputs['representations']['msa_first_row'][None],\n",
" \"init_pair\":outputs['representations']['pair'][None],\n",
" \"init_pos\":outputs['structure_module']['final_atom_positions'][None]}\n",
" \n",
" aux = {\"final_atom_positions\":outputs[\"structure_module\"][\"final_atom_positions\"],\n",
" \"final_atom_mask\":outputs[\"structure_module\"][\"final_atom_mask\"],\n",
" \"plddt\":get_plddt(outputs),\"pae\":get_pae(outputs),\n",
" \"inputs\":inputs, \"prev\":prev}\n",
" return aux\n",
"\n",
" return jax.jit(runner), {\"inputs\":inputs,\"params\":model_params}\n",
"\n",
"MAX_LEN = 50\n",
"RUNNER, OPT = setup_model(MAX_LEN)"
],
"metadata": {
"cellView": "form",
"id": "24ybo88aBiSU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%time\n",
"#@title Enter the amino acid sequence to fold ⬇️\n",
"\n",
"sequence = 'GGGGGGGGGGGGGGGGGGGG' #@param {type:\"string\"}\n",
"recycles = 0 #@param [\"0\", \"1\", \"2\", \"3\", \"6\", \"12\", \"24\"] {type:\"raw\"}\n",
"SEQ = re.sub(\"[^A-Z]\", \"\", sequence.upper())\n",
"LEN = len(SEQ)\n",
"if LEN > MAX_LEN:\n",
" print(\"recompiling...\")\n",
" MAX_LEN = LEN\n",
" RUNNER, OPT = setup_model(MAX_LEN)\n",
"\n",
"x = np.array([residue_constants.restype_order.get(aa,0) for aa in SEQ])\n",
"x = np.pad(x,[0,MAX_LEN-LEN],constant_values=-1)\n",
"x = jax.nn.one_hot(x,20)\n",
"\n",
"OPT[\"prev\"] = {'init_msa_first_row': np.zeros([1, MAX_LEN, 256]),\n",
" 'init_pair': np.zeros([1, MAX_LEN, MAX_LEN, 128]),\n",
" 'init_pos': np.zeros([1, MAX_LEN, 37, 3])}\n",
"\n",
"positions = []\n",
"plddts = []\n",
"for r in range(recycles+1):\n",
" outs = RUNNER(x, OPT)\n",
" outs = jax.tree_map(lambda x:np.asarray(x), outs)\n",
" positions.append(outs[\"prev\"][\"init_pos\"][0,:LEN])\n",
" plddts.append(outs[\"plddt\"][:LEN])\n",
" OPT[\"prev\"] = outs[\"prev\"]\n",
" if recycles > 0:\n",
" print(r, plddts[-1].mean())"
],
"metadata": {
"cellView": "form",
"id": "cAoC4ar8G7ZH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title Display 3D structure {run: \"auto\"}\n",
"color = \"lDDT\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n",
"show_sidechains = True #@param {type:\"boolean\"}\n",
"show_mainchains = False #@param {type:\"boolean\"}\n",
"#@markdown - TIP - hold mouse over aminoacid to get name and position number\n",
"\n",
"def save_pdb(outs, filename):\n",
" '''save pdb coordinates'''\n",
" p = {\"residue_index\":outs[\"inputs\"][\"residue_index\"][0][:LEN] + 1,\n",
" \"aatype\":outs[\"inputs\"][\"aatype\"].argmax(-1)[0][:LEN],\n",
" \"atom_positions\":outs[\"final_atom_positions\"][:LEN],\n",
" \"atom_mask\":outs[\"final_atom_mask\"][:LEN]}\n",
" b_factors = 100.0 * outs[\"plddt\"][:LEN,None] * p[\"atom_mask\"]\n",
" p = protein.Protein(**p,b_factors=b_factors)\n",
" pdb_lines = protein.to_pdb(p)\n",
" with open(filename, 'w') as f:\n",
" f.write(pdb_lines)\n",
"\n",
"save_pdb(outs,\"out.pdb\")\n",
"num_res = int(outs[\"inputs\"][\"aatype\"][0].sum())\n",
"\n",
"v = cf.show_pdb(\"out.pdb\", show_sidechains, show_mainchains, color,\n",
" color_HP=True, size=(800,480)) \n",
"v.setHoverable({},\n",
" True,\n",
" '''function(atom,viewer,event,container){if(!atom.label){atom.label=viewer.addLabel(\" \"+atom.resn+\":\"+atom.resi,{position:atom,backgroundColor:'mintcream',fontColor:'black'});}}''',\n",
" '''function(atom,viewer){if(atom.label){viewer.removeLabel(atom.label);delete atom.label;}}''')\n",
"v.show() \n",
"\n",
"if color == \"lDDT\":\n",
" cf.plot_plddt_legend().show() \n",
"if \"pae\" in outs:\n",
" cf.plot_confidence(outs[\"plddt\"][:LEN]*100, outs[\"pae\"][:LEN,:LEN]).show()\n",
"else:\n",
" cf.plot_confidence(outs[\"plddt\"][:LEN]*100).show()"
],
"metadata": {
"cellView": "form",
"id": "-KbUGG4ZOp0J"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title Animate\n",
"#@markdown - Animate trajectory if more than 0 recycle(s)\n",
"import matplotlib\n",
"from matplotlib import animation\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import HTML\n",
"\n",
"def make_animation(positions, plddts=None, line_w=2.0):\n",
"\n",
" def ca_align_to_last(positions):\n",
" def align(P, Q):\n",
" p = P - P.mean(0,keepdims=True)\n",
" q = Q - Q.mean(0,keepdims=True)\n",
" return p @ cf.kabsch(p,q)\n",
" \n",
" pos = positions[-1,:,1,:] - positions[-1,:,1,:].mean(0,keepdims=True)\n",
" best_2D_view = pos @ cf.kabsch(pos,pos,return_v=True)\n",
"\n",
" new_positions = []\n",
" for i in range(len(positions)):\n",
" new_positions.append(align(positions[i,:,1,:],best_2D_view))\n",
" return np.asarray(new_positions)\n",
"\n",
" # align all to last recycle\n",
" pos = ca_align_to_last(positions)\n",
"\n",
" fig, (ax1, ax2, ax3) = plt.subplots(1,3)\n",
" fig.subplots_adjust(top = 0.90, bottom = 0.10, right = 1, left = 0, hspace = 0, wspace = 0)\n",
" fig.set_figwidth(13)\n",
" fig.set_figheight(5)\n",
" fig.set_dpi(100)\n",
"\n",
" xy_min = pos[...,:2].min() - 1\n",
" xy_max = pos[...,:2].max() + 1\n",
"\n",
" for ax in [ax1,ax3]:\n",
" ax.set_xlim(xy_min, xy_max)\n",
" ax.set_ylim(xy_min, xy_max)\n",
" ax.axis(False)\n",
"\n",
" ims=[]\n",
" for k,(xyz,plddt) in enumerate(zip(pos,plddts)):\n",
" ims.append([])\n",
" im2 = ax2.plot(plddt, animated=True, color=\"black\")\n",
" tt1 = cf.add_text(\"colored by N->C\", ax1)\n",
" tt2 = cf.add_text(f\"recycle={k}\", ax2)\n",
" tt3 = cf.add_text(f\"pLDDT={plddt.mean():.3f}\", ax3)\n",
" ax2.set_xlabel(\"positions\")\n",
" ax2.set_ylabel(\"pLDDT\")\n",
" ax2.set_ylim(0,100)\n",
" ims[-1] += [cf.plot_pseudo_3D(xyz, ax=ax1, line_w=line_w)]\n",
" ims[-1] += [im2[0],tt1,tt2,tt3]\n",
" ims[-1] += [cf.plot_pseudo_3D(xyz, c=plddt, cmin=50, cmax=90, ax=ax3, line_w=line_w)]\n",
" \n",
" ani = animation.ArtistAnimation(fig, ims, blit=True, interval=120)\n",
" plt.close()\n",
" return ani.to_html5_video()\n",
"\n",
"HTML(make_animation(np.asarray(positions),\n",
" np.asarray(plddts) * 100.0))"
],
"metadata": {
"cellView": "form",
"id": "tdjdC0KFPjWw"
},
"execution_count": null,
"outputs": []
}
]
}
|