![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
]
},
{
"cell_type": "code",
"source": [
"#@title Run this cell for mounting google drive\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"id": "QBbOBkzuJsAE",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f1a2771b-1b6e-4a10-edfd-991d25189a22",
"cellView": "form"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@title Install dependencies\n",
"%%time\n",
"from google.colab import files\n",
"import os\n",
"import re\n",
"import hashlib\n",
"import random\n",
"\n",
"!mkdir colabfold\n",
"%cd /content/colabfold\n",
"\n",
"from sys import version_info\n",
"python_version = f\"{version_info.major}.{version_info.minor}\"\n",
"\n",
"\n",
"import os\n",
"USE_AMBER = False\n",
"USE_TEMPLATES = \"none\"\n",
"PYTHON_VERSION = python_version\n",
"\n",
"if not os.path.isfile(\"COLABFOLD_READY\"):\n",
" print(\"installing colabfold...\")\n",
" os.system(\"pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold'\")\n",
" os.system(\"pip install --upgrade dm-haiku\")\n",
" os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold\")\n",
" os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold\")\n",
" # patch for jax > 0.3.25\n",
" os.system(\"sed -i 's/weights = jax.nn.softmax(logits)/logits=jnp.clip(logits,-1e8,1e8);weights=jax.nn.softmax(logits)/g' alphafold/model/modules.py\")\n",
" os.system(\"touch COLABFOLD_READY\")\n",
"\n",
"if USE_AMBER or USE_TEMPLATES:\n",
" if not os.path.isfile(\"CONDA_READY\"):\n",
" print(\"installing conda...\")\n",
" os.system(\"wget -qnc https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\")\n",
" os.system(\"bash Mambaforge-Linux-x86_64.sh -bfp /usr/local\")\n",
" os.system(\"mamba config --set auto_update_conda false\")\n",
" os.system(\"touch CONDA_READY\")\n",
"\n",
"if USE_TEMPLATES and not os.path.isfile(\"HH_READY\") and USE_AMBER and not os.path.isfile(\"AMBER_READY\"):\n",
" print(\"installing hhsuite and amber...\")\n",
" os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n",
" os.system(\"touch HH_READY\")\n",
" os.system(\"touch AMBER_READY\")\n",
"else:\n",
" if USE_TEMPLATES and not os.path.isfile(\"HH_READY\"):\n",
" print(\"installing hhsuite...\")\n",
" os.system(f\"mamba install -y -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 python='{PYTHON_VERSION}'\")\n",
" os.system(\"touch HH_READY\")\n",
" if USE_AMBER and not os.path.isfile(\"AMBER_READY\"):\n",
" print(\"installing amber...\")\n",
" os.system(f\"mamba install -y -c conda-forge openmm=7.7.0 python='{PYTHON_VERSION}' pdbfixer\")\n",
" os.system(\"touch AMBER_READY\")"
],
"metadata": {
"id": "AzIKiDiCaHAn",
"outputId": "c17e4366-41c6-4878-bb58-17ba1041fe83",
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/colabfold\n",
"installing colabfold...\n",
"installing conda...\n",
"installing hhsuite...\n",
"CPU times: user 345 ms, sys: 55 ms, total: 400 ms\n",
"Wall time: 1min 26s\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kOblAo-xetgx",
"cellView": "form",
"outputId": "1df05123-7899-42aa-ea45-e0c8f35c2cc2",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"#@title Input protein sequence(s) and MSA parameters\n",
"%cd /content/colabfold\n",
"\n",
"query_sequence = 'MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG' #@param {type:\"string\"}\n",
"#@markdown - Use `:` to specify inter-protein chainbreaks for **modeling complexes** (supports homo- and hetro-oligomers). For example **PI...SK:PI...SK** for a homodimer\n",
"max_msa = \"4:8\" #@param [\"1024:2048\", \"512:1024\", \"256:512\",\"128:256\", \"64:128\", \"32:64\", \"16:32\",\"8:16\",\"4:8\", \"2:4\",\"1:2\"]\n",
"#@markdown - Choose `max_msa_cluster:max_extra_msa` to reduce the length of MSA used in initialization of AF2 (prescribed 16:32 and/or 8:16)\n",
"num_seeds = 4 #@param [4,8,16,32,64,128] {type:\"raw\"}\n",
"#@markdown - For a `num_seeds` value rMSA give `num_seeds*5` structures (prescribed 128)\n",
"msa_mode = \"mmseqs2\" #@param [\"mmseqs2\", \"no_msa\",\"custom\"]\n",
"#@markdown - Choose the type of MSA input `[mmseqs2, custom, no msa]`\n",
"\n",
"# Initializations and creating working dir\n",
"jobname = 'test'\n",
"custom_template_path = None\n",
"use_templates = False\n",
"pair_mode = \"unpaired_paired\"\n",
"query_sequence = \"\".join(query_sequence.split())\n",
"basejobname = \"\".join(jobname.split())\n",
"basejobname = re.sub(r'\\W+', '', basejobname)\n",
"model_type = \"auto\"\n",
"num_recycles = 1\n",
"recycle_early_stop_tolerance = None\n",
"relax_max_iterations = 200\n",
"pairing_strategy = \"greedy\"\n",
"use_dropout = True\n",
"save_all = True\n",
"save_recycles = False\n",
"save_to_google_drive = False\n",
"dpi = 200\n",
"def add_hash(x,y):\n",
" return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n",
"jobname = add_hash(basejobname, query_sequence)\n",
"# check if directory with jobname exists\n",
"def check(folder):\n",
" if os.path.exists(folder):\n",
" return False\n",
" else:\n",
" return True\n",
"if not check(jobname):\n",
" n = 0\n",
" while not check(f\"{jobname}_{n}\"): n += 1\n",
" jobname = f\"{jobname}_{n}\"\n",
"os.makedirs(jobname, exist_ok=True)\n",
"\n",
"#save queries\n",
"queries_path = os.path.join(jobname, f\"{jobname}.csv\")\n",
"with open(queries_path, \"w\") as text_file:\n",
" text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n",
"\n",
"# MSA decision\n",
"if \"mmseqs2\" in msa_mode:\n",
" a3m_file = os.path.join(jobname,f\"{jobname}.a3m\")\n",
"\n",
"elif msa_mode == \"custom\":\n",
" a3m_file = os.path.join(jobname,f\"{jobname}.custom.a3m\")\n",
" if not os.path.isfile(a3m_file):\n",
" custom_msa_dict = files.upload()\n",
" custom_msa = list(custom_msa_dict.keys())[0]\n",
" header = 0\n",
" import fileinput\n",
" for line in fileinput.FileInput(custom_msa,inplace=1):\n",
" if line.startswith(\">\"):\n",
" header = header + 1\n",
" if not line.rstrip():\n",
" continue\n",
" if line.startswith(\">\") == False and header == 1:\n",
" query_sequence = line.rstrip()\n",
" print(line, end='')\n",
"\n",
" os.rename(custom_msa, a3m_file)\n",
" queries_path=a3m_file\n",
" print(f\"moving {custom_msa} to {a3m_file}\")\n",
"else:\n",
" a3m_file = os.path.join(jobname,f\"{jobname}.single_sequence.a3m\")\n",
" with open(a3m_file, \"w\") as text_file:\n",
" text_file.write(\">1\\n%s\" % query_sequence)\n",
"\n",
"print(\"sequence\",query_sequence)\n",
"print(\"length\",len(query_sequence.replace(\":\",\"\")))\n",
"\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/colabfold\n",
"sequence MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG\n",
"length 76\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@title Run Prediction\n",
"display_images = True\n",
"\n",
"import sys\n",
"import warnings\n",
"warnings.simplefilter(action='ignore', category=FutureWarning)\n",
"from Bio import BiopythonDeprecationWarning\n",
"warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)\n",
"from pathlib import Path\n",
"from colabfold.download import download_alphafold_params, default_data_dir\n",
"from colabfold.utils import setup_logging\n",
"from colabfold.batch import get_queries, run, set_model_type\n",
"from colabfold.plot import plot_msa_v2\n",
"\n",
"import os\n",
"import numpy as np\n",
"try:\n",
" K80_chk = os.popen('nvidia-smi | grep \"Tesla K80\" | wc -l').read()\n",
"except:\n",
" K80_chk = \"0\"\n",
" pass\n",
"if \"1\" in K80_chk:\n",
" print(\"WARNING: found GPU Tesla K80: limited to total length < 1000\")\n",
" if \"TF_FORCE_UNIFIED_MEMORY\" in os.environ:\n",
" del os.environ[\"TF_FORCE_UNIFIED_MEMORY\"]\n",
" if \"XLA_PYTHON_CLIENT_MEM_FRACTION\" in os.environ:\n",
" del os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]\n",
"\n",
"from colabfold.colabfold import plot_protein\n",
"from pathlib import Path\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def input_features_callback(input_features):\n",
" plot_msa_v2(input_features)\n",
" plt.show()\n",
" plt.close()\n",
"\n",
"def prediction_callback(protein_obj, length,\n",
" prediction_result, input_features, mode):\n",
" model_name, relaxed = mode\n",
" if False:\n",
" fig = plot_protein(protein_obj, Ls=length, dpi=150)\n",
" plt.show()\n",
" plt.close()\n",
"\n",
"result_dir = jobname\n",
"log_filename = os.path.join(jobname,\"log.txt\")\n",
"if not os.path.isfile(log_filename) or 'logging_setup' not in globals():\n",
" setup_logging(Path(log_filename))\n",
" logging_setup = True\n",
"\n",
"queries, is_complex = get_queries(queries_path)\n",
"model_type = set_model_type(is_complex, model_type)\n",
"\n",
"if \"multimer\" in model_type and max_msa is not None:\n",
" use_cluster_profile = False\n",
"else:\n",
" use_cluster_profile = True\n",
"num_relax=0\n",
"download_alphafold_params(model_type, Path(\".\"))\n",
"results = run(\n",
" queries=queries,\n",
" result_dir=result_dir,\n",
" use_templates=use_templates,\n",
" custom_template_path=custom_template_path,\n",
" num_relax=num_relax,\n",
" msa_mode=msa_mode,\n",
" model_type=model_type,\n",
" num_models=5,\n",
" num_recycles=num_recycles,\n",
" relax_max_iterations=relax_max_iterations,\n",
" recycle_early_stop_tolerance=recycle_early_stop_tolerance,\n",
" num_seeds=num_seeds,\n",
" use_dropout=use_dropout,\n",
" model_order=[1,2,3,4,5],\n",
" is_complex=is_complex,\n",
" data_dir=Path(\".\"),\n",
" keep_existing_results=False,\n",
" rank_by=\"auto\",\n",
" pair_mode=pair_mode,\n",
" pairing_strategy=pairing_strategy,\n",
" stop_at_score=float(100),\n",
" prediction_callback=prediction_callback,\n",
" dpi=dpi,\n",
" zip_results=False,\n",
" save_all=save_all,\n",
" max_msa=max_msa,\n",
" use_cluster_profile=use_cluster_profile,\n",
" input_features_callback=input_features_callback,\n",
" save_recycles=save_recycles,\n",
" user_agent=\"colabfold/google-colab-main\",\n",
")\n",
"\n",
"\n",
"## save the results\n",
"os.chdir('/content/colabfold/')\n",
"if not os.path.isdir('/content/structures'):\n",
" os.mkdir('/content/structures')\n",
"else:\n",
" print('Already exists!!!')\n",
" os.system('mv /content/structures /content/backup')\n",
" os.mkdir('/content/structures')\n",
"\n",
"for i in range(num_seeds*5):\n",
" tag = results[\"rank\"][0][i]\n",
" jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
" pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n",
" json_filename = f\"{jobname}/{jobname}{jobname_prefix}_scores_{tag}.json\"\n",
" os.system(f'cp {pdb_filename} /content/structures/pred_{i+1}.pdb')\n",
" os.system(f'cp {json_filename} /content/structures/pred_{i+1}.json')\n",
"os.system(f'cp {jobname}/config.json /content/structures/.')\n",
"os.system(f'cp {jobname}/{jobname}.a3m /content/structures/msa.a3m')\n",
"os.system(f'rm -r {jobname}')\n",
"os.chdir('/content/')\n",
"os.system('zip -r structures.zip structures')\n",
"\n",
"print('All structures are saved in `/content/structures.zip`')"
],
"metadata": {
"cellView": "form",
"id": "mbaIO9pWjaN0",
"outputId": "d70d51b4-f5a3-43c9-b028-173eeda4ebcb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-25 20:01:38,894 Running on GPU\n",
"2024-04-25 20:01:38,897 Found 4 citations for tools or databases\n",
"2024-04-25 20:01:38,897 Query 1/1: test_7686d (length 76)\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"COMPLETE: 100%|██████████| 150/150 [elapsed: 00:03 remaining: 00:00]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"3Dmol.js failed to load for some reason. Please check your browser console for error messages.
3Dmol.js failed to load for some reason. Please check your browser console for error messages.