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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/sokrypton/ColabDesign/blob/v1.1.1/af/examples/af_pseudo_diffusion_recycle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VD9K5H2cnFxL"
},
"source": [
"#AF_pseudo_diffusion + proteinMPNN\n",
"Hacking AlphaFold to be a diffusion model (for backbone generation) via recycling mechanism. At each step add logits from proteinMPNN.\n",
"\n",
"\n",
"**WARNING**: This notebook is experimental, designed as a control. Not intended for practical use at this stage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "YCRzPGdTZfEe"
},
"outputs": [],
"source": [
"#@title setup\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, re\n",
"from colabdesign import mk_afdesign_model, clear_mem\n",
"from colabdesign.mpnn import mk_mpnn_model\n",
"from colabdesign.af.alphafold.common import residue_constants\n",
"from colabdesign.shared.protein import _np_get_cb\n",
"\n",
"from IPython.display import HTML\n",
"from google.colab import files\n",
"import numpy as np\n",
"import jax.numpy as jnp\n",
"import jax\n",
"from scipy.special import softmax, log_softmax\n",
"\n",
"import tqdm.notebook\n",
"TQDM_BAR_FORMAT = '{l_bar}{bar}| {n_fmt}/{total_fmt} [elapsed: {elapsed} remaining: {remaining}]'\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 sample_gumbel(shape, eps=1e-20): \n",
" \"\"\"Sample from Gumbel(0, 1)\"\"\"\n",
" U = np.random.uniform(size=shape)\n",
" return -np.log(-np.log(U + eps) + eps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "wxYMB4A9Zmrf"
},
"outputs": [],
"source": [
"#@title initialize the model\n",
"length = 100 #@param {type:\"integer\"}\n",
"#@markdown Provide a starting point (optional)\n",
"starting_seq = \"\" #@param {type:\"string\"}\n",
"starting_seq = re.sub(\"[^A-Z]\", \"\", starting_seq.upper())\n",
"#@markdown - if `starting_seq` provided the `length` option will be overwritten by length of starting sequence.\n",
"\n",
"if len(starting_seq) > 0:\n",
" length = len(starting_seq)\n",
"\n",
"clear_mem()\n",
"af_model = mk_afdesign_model(protocol=\"hallucination\")\n",
"af_model.prep_inputs(length=length)\n",
"mpnn_model = mk_mpnn_model()\n",
"\n",
"af_model_test = mk_afdesign_model(protocol=\"fixbb\", best_metric=\"rmsd\")\n",
"mpnn_model_test = mk_mpnn_model()\n",
"\n",
"print(\"lengths\",af_model._lengths)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3Dt8i00UbxtW",
"cellView": "form"
},
"outputs": [],
"source": [
"#@title run protocol\n",
"#@markdown Optimization options\n",
"iterations = 100 #@param [\"50\", \"100\", \"200\"] {type:\"raw\"}\n",
"\n",
"#AlphaFold options\n",
"use_dropout = True\n",
"num_recycles = 0\n",
"\n",
"#@markdown proteinMPNN options (set to `none` to disable)\n",
"mpnn_mode = \"conditional\" #@param [\"none\",\"conditional\", \"unconditional\"]\n",
"\n",
"#@markdown proteinMPNN contact map masking options\n",
"cmap_seqsep = 6 #@param {type:\"raw\"}\n",
"cmap_num = 1 #@param {type:\"integer\"}\n",
"cmap_cutoff = 8 #@param {type:\"raw\"}\n",
"\n",
"L = sum(af_model._lengths)\n",
"af_model.restart(mode=\"gumbel\")\n",
"af_model._args[\"clear_prev\"] = False\n",
"af_model.set_opt(cmap_cutoff=cmap_cutoff)\n",
"af_model.set_weights(helix=1e-8)\n",
"\n",
"# gather info about inputs\n",
"if \"offset\" in af_model._inputs:\n",
" offset = af_model._inputs\n",
"else:\n",
" idx = af_model._inputs[\"residue_index\"]\n",
" offset = idx[:,None] - idx[None,:]\n",
"\n",
"# initialize sequence\n",
"if len(starting_seq) > 1:\n",
" af_model.set_seq(seq=starting_seq)\n",
"\n",
"# initialize coordinates\n",
"af_model._inputs.pop(\"prev\",None)\n",
"af_model._inputs[\"batch\"] = {\"aatype\":np.zeros(L).astype(int),\n",
" \"all_atom_mask\":np.zeros((L,37)),\n",
" \"all_atom_positions\":np.zeros((L,37,3))}\n",
"\n",
"save_best = False\n",
"for k in range(iterations):\n",
"\n",
" if k > (iterations - 10):\n",
" use_dropout = False\n",
" save_best = True\n",
"\n",
"\n",
" # denoise\n",
" aux = af_model.predict(return_aux=True, verbose=False,\n",
" dropout=use_dropout,\n",
" num_recycles=num_recycles)\n",
" af_model._inputs[\"prev\"] = aux[\"prev\"]\n",
" af_model._inputs[\"prev\"][\"prev_msa_first_row\"] *= 0\n",
" af_model._inputs[\"prev\"][\"prev_pos\"] *= 0\n",
"\n",
" # per position confidence\n",
" cmap = aux[\"cmap\"] * (np.abs(offset) > cmap_seqsep)\n",
" conf = np.sort(cmap)[:,-cmap_num:].mean(-1)\n",
"\n",
" # gather features\n",
" plddt = aux[\"plddt\"]\n",
" seq = aux[\"seq\"][\"hard\"][0].argmax(-1)\n",
" xyz = aux[\"atom_positions\"].copy()\n",
" \n",
" # update inputs \n",
" af_model._inputs[\"batch\"][\"aatype\"] = seq\n",
" af_model._inputs[\"batch\"][\"all_atom_positions\"] = xyz\n",
"\n",
" # add logits from proteinmpnn at each stage\n",
" if mpnn_mode != \"none\": \n",
" mpnn_model.get_af_inputs(af_model)\n",
" opt = {\"mask\":np.sqrt(conf)} \n",
" if mpnn_mode == \"unconditional\":\n",
" opt[\"ar_mask\"] = np.zeros((L,L))\n",
" mpnn_out = mpnn_model.score(**opt)\n",
" mpnn_logits = mpnn_out[\"logits\"][:,:20]\n",
" aux[\"log\"][\"mpnn\"] = mpnn_out[\"score\"]\n",
" \n",
" c = conf[:,None]\n",
" new_logits = (1 - c) * sample_gumbel(mpnn_logits.shape) + c * mpnn_logits\n",
" af_model._params[\"seq\"] = 0.9 * af_model._params[\"seq\"] + 0.1 * new_logits\n",
"\n",
" # save results\n",
" af_model._save_results(aux, save_best=save_best)\n",
" af_model._k += 1\n",
"\n",
"af_model.save_pdb(\"init.pdb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YeYD4KF8MUA_"
},
"outputs": [],
"source": [
"af_model.plot_pdb()\n",
"af_model.get_seqs()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6cJhizcYcUxm"
},
"outputs": [],
"source": [
"HTML(af_model.animate(dpi=100))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "3H_B0AoYIiWH"
},
"outputs": [],
"source": [
"#@title sample new sequences using proteinMPNN and rescore with alphafold (w/o template)\n",
"#@markdown #### MPNN Options\n",
"num_seqs = 16 #@param [\"8\", \"16\", \"32\", \"64\", \"128\", \"256\", \"512\", \"1024\"] {type:\"raw\"}\n",
"sampling_temp = 0.1 \n",
"#@markdown #### AlphaFold Options\n",
"alphafold_model = \"model_4_ptm\" #@param [\"model_1_ptm\", \"model_2_ptm\", \"model_3_ptm\", \"model_4_ptm\", \"model_5_ptm\"]\n",
"num_recycles = 3 #@param [\"0\", \"1\", \"2\", \"3\"] {type:\"raw\"}\n",
"import pandas as pd\n",
"\n",
"# zero out template inputs\n",
"af_model_test.prep_inputs(\"init.pdb\")\n",
"mpnn_model_test.get_af_inputs(af_model_test)\n",
"out = mpnn_model_test.sample(num=num_seqs//8, batch=8,\n",
" temperature=sampling_temp)\n",
"af_terms = [\"plddt\",\"ptm\",\"pae\",\"rmsd\",\"dgram_cce\"]\n",
"for k in af_terms: out[k] = []\n",
"os.system(\"mkdir -p output/all_pdb\")\n",
"\n",
"with tqdm.notebook.tqdm(total=out[\"S\"].shape[0], bar_format=TQDM_BAR_FORMAT) as pbar:\n",
" with open(\"design.fasta\",\"w\") as fasta:\n",
" for n in range(num_seqs):\n",
" seq = out[\"seq\"][n]\n",
" af_model_test.predict(seq=seq,\n",
" num_recycles=num_recycles,\n",
" num_models=1,\n",
" verbose=False,\n",
" models=alphafold_model)\n",
"\n",
" for t in af_terms:\n",
" out[t].append(af_model_test.aux[\"log\"][t])\n",
" out[\"pae\"][-1] = out[\"pae\"][-1] * 31\n",
" af_model_test._save_results(save_best=True, verbose=False)\n",
" af_model_test.save_current_pdb(f\"output/all_pdb/n{n}.pdb\")\n",
" af_model_test._k += 1\n",
"\n",
" line = f'>mpnn:{out[\"score\"][n]:.3f}_plddt:{out[\"plddt\"][n]:.3f}_ptm:{out[\"ptm\"][n]:.3f}_pae:{out[\"pae\"][n]:.3f}\\n{out[\"seq\"][n]}'\n",
" fasta.write(line+\"\\n\")\n",
" pbar.update(1)\n",
"\n",
"af_model_test.save_pdb(\"final.pdb\")\n",
"\n",
"labels = [\"score\"] + af_terms + [\"seq\"]\n",
"data = [[out[k][n] for k in labels] for n in range(num_seqs)]\n",
"labels[0] = \"mpnn\"\n",
"\n",
"df = pd.DataFrame(data, columns=labels)\n",
"df.to_csv('output/mpnn_results.csv')\n",
"df.round(3).sort_values(\"rmsd\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "v3dPQeEQcAcT"
},
"outputs": [],
"source": [
"af_model_test.plot_pdb()\n",
"af_model_test.get_seqs()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": [],
"include_colab_link": true
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |