![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
]
},
{
"cell_type": "markdown",
"source": [
"# Protein conservation to 3D structure generator.\n",
"\n",
"\n",
"\n",
"This colab workspace allows for automatic conservation analysis, in the absence of the updated Weblogo and Consurf servers. In order to analyse a protein, type in the amino acid sequence and name, then on the main bar of the notebook under 'Runtime' check run all. After 10 minutes you will download the weblogo.png and .txt files of your protein of interest to understand the most conserved residues. This notebook then also adds these values to the b-factor of an alphafold generated model, to show this in a 3D context.\n",
"\n",
"\n",
"It uses the weblogo plugin from this gitlab - https://github.com/WebLogo/weblogo as well as Alphafold2 Colabfold servers that host MMSEQ2, to generate the a3m files for alignment and sequence analysis.\n",
"\n",
"This workbook is based greatly off the Alphafold colab notebook here: https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb#scrollTo=G4yBrceuFbf3 and published here:\n",
"\n",
"[Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all.\n",
"*Nature Methods*, 2022](https://www.nature.com/articles/s41592-022-01488-1)\n",
"\n",
"\n",
" Make sure to note down the MSA settings for reproducibility. The.a3m files can be downloaded from the colab notebook, if you require them for downstream use. See end of documentation for more details on citation of this specific software.\n"
],
"metadata": {
"id": "0npxgmjkLX7I"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kOblAo-xetgx",
"outputId": "bc9c2af1-b72d-4d96-cbac-70217cb3ffb6",
"cellView": "form"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"jobname SpoIIP_7bf0e_0\n",
"sequence MRNKRRNRQIVVAVNGGKAVKAIFLFIVSLIVIFVLSGVLTSLRPELRPSSDSFYGIAEELPGDVFAHLLQMENHYFASDLSQTDSSFHLSRLSLKLATSINLEDPRSFLGRELPGFAQFDTEILLAGQGTDYTNMPAESPPPSKVMEEEREANLAEIEKQQTQSDNAQKDPPKQTTGDKKVVFIYHTHNTESYLPLLKGETDPDMARHSKANVTLVGDMFGQALESQGIGATVNKTDIQSKLNKKGLNYARSYDESRPVVKDALASNKNLQYIIDIHRDSRRKKDTTATIKGKSYARVAFVVGKKSKNFEENYKIASELHKLMEKKYPGLSTGVFSKGSPGDNGVYNQDLTDRALLLEFGGVDNNLEELQRAANAAADVFSEMYWDAEKVNAASGETKKQ\n",
"length 401\n"
]
}
],
"source": [
"#@title Input protein sequence(s), then hit `Runtime` -> `Run all`\n",
"from google.colab import files\n",
"import os\n",
"import re\n",
"import hashlib\n",
"import random\n",
"\n",
"from sys import version_info\n",
"python_version = f\"{version_info.major}.{version_info.minor}\"\n",
"\n",
"def add_hash(x,y):\n",
" return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n",
"\n",
"query_sequence = 'MRNKRRNRQI VVAVNGGKAV KAIFLFIVSL IVIFVLSGVL TSLRPELRPS SDSFYGIAEE LPGDVFAHLL QMENHYFASD LSQTDSSFHL SRLSLKLATS INLEDPRSFL GRELPGFAQF DTEILLAGQG TDYTNMPAES PPPSKVMEEE REANLAEIEK QQTQSDNAQK DPPKQTTGDK KVVFIYHTHN TESYLPLLKG ETDPDMARHS KANVTLVGDM FGQALESQGI GATVNKTDIQ SKLNKKGLNY ARSYDESRPV VKDALASNKN LQYIIDIHRD SRRKKDTTAT IKGKSYARVA FVVGKKSKNF EENYKIASEL HKLMEKKYPG LSTGVFSKGS PGDNGVYNQD LTDRALLLEF GGVDNNLEEL QRAANAAADV FSEMYWDAEK VNAASGETKK Q' #@param {type:\"string\"}\n",
"\n",
"jobname = 'SpoIIP' #@param {type:\"string\"}\n",
"# number of models to use\n",
"num_relax = 0\n",
"\n",
"template_mode = \"pdb100\"\n",
"\n",
"\n",
"use_amber = num_relax > 0\n",
"\n",
"# remove whitespaces\n",
"query_sequence = \"\".join(query_sequence.split())\n",
"\n",
"basejobname = \"\".join(jobname.split())\n",
"basejobname = re.sub(r'\\W+', '', basejobname)\n",
"jobname = add_hash(basejobname, query_sequence)\n",
"\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",
"\n",
"# make directory to save results\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",
"if template_mode == \"pdb100\":\n",
" use_templates = True\n",
" custom_template_path = None\n",
"elif template_mode == \"custom\":\n",
" custom_template_path = os.path.join(jobname,f\"template\")\n",
" os.makedirs(custom_template_path, exist_ok=True)\n",
" uploaded = files.upload()\n",
" use_templates = True\n",
" for fn in uploaded.keys():\n",
" os.rename(fn,os.path.join(custom_template_path,fn))\n",
"else:\n",
" custom_template_path = None\n",
" use_templates = False\n",
"\n",
"print(\"jobname\",jobname)\n",
"print(\"sequence\",query_sequence)\n",
"print(\"length\",len(query_sequence.replace(\":\",\"\")))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "iccGdbe_Pmt9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"outputId": "34748063-a4bd-4ed2-842e-04fdf8c366f5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"Reading package lists... Done\n",
"Building dependency tree... Done\n",
"Reading state information... Done\n",
"pymol is already the newest version (2.5.0+dfsg-1build1).\n",
"0 upgraded, 0 newly installed, 0 to remove and 19 not upgraded.\n",
"CPU times: user 62.5 ms, sys: 9.17 ms, total: 71.7 ms\n",
"Wall time: 5.77 s\n"
]
}
],
"source": [
"#@markdown ### Install Dependencies\n",
"\n",
"%%time\n",
"import os\n",
"USE_AMBER = use_amber\n",
"USE_TEMPLATES = use_templates\n",
"PYTHON_VERSION = python_version\n",
"\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\")\n",
"\n",
"!python -m pip install --upgrade pip -q\n",
"!pip install tr-rosetta-pytorch -q\n",
"!apt-get install pymol\n",
"!touch TRROSETTA_READY"
]
},
{
"cell_type": "code",
"source": [
"#@markdown ### MSA options (custom MSA upload, single sequence, pairing mode)\n",
"msa_mode = \"mmseqs2_uniref_env\" #@param [\"mmseqs2_uniref_env\", \"mmseqs2_uniref\",\"single_sequence\",\"custom\"]\n",
"pair_mode = \"unpaired_paired\" #@param [\"unpaired_paired\",\"paired\",\"unpaired\"] {type:\"string\"}\n",
"#@markdown - \"unpaired_paired\" = pair sequences from same species + unpaired MSA, \"unpaired\" = seperate MSA for each chain, \"paired\" - only use paired sequences.\n",
"\n",
"# decide which a3m to use\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",
"\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",
"#@markdown ### Advanced settings\n",
"model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\"]\n",
"#@markdown - if `auto` selected, will use `alphafold2_ptm` for monomer prediction.\n",
"num_recycles = \"1\" #@param [\"auto\", \"0\", \"1\", \"3\", \"6\", \"12\", \"24\", \"48\"]\n",
"recycle_early_stop_tolerance = \"auto\" #@param [\"auto\", \"0.0\", \"0.5\", \"1.0\"]\n",
"#@markdown - if `auto` selected, will use 20 recycles if `model_type=alphafold2_multimer_v3` (with tol=0.5), all else 3 recycles (with tol=0.0).\n",
"pairing_strategy = \"greedy\" #@param [\"greedy\", \"complete\"] {type:\"string\"}\n",
"#@markdown - `greedy` = pair any taxonomically matching subsets, `complete` = all sequences have to match in one line.\n",
"\n",
"\n",
"#@markdown #### Sample settings\n",
"#@markdown - enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.\n",
"#@markdown - decrease `max_msa` to increase uncertainity\n",
"max_msa = \"auto\" #@param [\"auto\", \"512:1024\", \"256:512\", \"64:128\", \"32:64\", \"16:32\"]\n",
"num_seeds = 1\n",
"use_dropout = False\n",
"\n",
"num_recycles = None if num_recycles == \"auto\" else int(num_recycles)\n",
"recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == \"auto\" else float(recycle_early_stop_tolerance)\n",
"if max_msa == \"auto\": max_msa = None\n",
"\n",
"#@markdown #### Save settings\n",
"save_all = False\n",
"save_recycles = False\n",
"save_to_google_drive = False\n",
"#@markdown - if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive\n",
"dpi = 300\n",
"\n",
"\n",
"if save_to_google_drive:\n",
" from pydrive.drive import GoogleDrive\n",
" from pydrive.auth import GoogleAuth\n",
" from google.colab import auth\n",
" from oauth2client.client import GoogleCredentials\n",
" auth.authenticate_user()\n",
" gauth = GoogleAuth()\n",
" gauth.credentials = GoogleCredentials.get_application_default()\n",
" drive = GoogleDrive(gauth)\n",
" print(\"You are logged into Google Drive and are good to go!\")\n",
"\n",
"#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form."
],
"metadata": {
"id": "F6CEk9N1v-Oi",
"cellView": "form"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "QU6WYAzZCPLl"
}
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "_sztQyz29DIC",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f81b7d63-08f0-49f4-ab5f-7c79ec752d62"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2023-11-13 18:52:17,533 Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: \"rocm\". Available platform names are: CUDA\n",
"2023-11-13 18:52:17,546 Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
"2023-11-13 18:52:23,259 Running on GPU\n",
"2023-11-13 18:52:23,807 Found 7 citations for tools or databases\n",
"2023-11-13 18:52:23,820 Query 1/1: SpoIIP_7bf0e_0 (length 401)\n",
"2023-11-13 18:52:23,821 No user agent specified. Please set a user agent (e.g., 'toolname/version contact@email') to help us debug in case of problems. This warning will become an error in the future.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"COMPLETE: 100%|██████████| 150/150 [elapsed: 00:02 remaining: 00:00]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2023-11-13 18:52:25,968 Sequence 0 found no templates\n",
"2023-11-13 18:52:25,973 No user agent specified. Please set a user agent (e.g., 'toolname/version contact@email') to help us debug in case of problems. This warning will become an error in the future.\n",
"2023-11-13 18:52:28,181 Setting max_seq=512, max_extra_seq=4577\n",
"2023-11-13 18:54:34,142 alphafold2_ptm_model_1_seed_000 recycle=0 pLDDT=81.9 pTM=0.716\n",
"2023-11-13 18:55:22,571 alphafold2_ptm_model_1_seed_000 recycle=1 pLDDT=83.7 pTM=0.738 tol=0.777\n",
"2023-11-13 18:55:22,572 alphafold2_ptm_model_1_seed_000 took 148.0s (1 recycles)\n",
"2023-11-13 18:55:22,777 reranking models by 'plddt' metric\n",
"2023-11-13 18:55:22,777 rank_001_alphafold2_ptm_model_1_seed_000 pLDDT=83.7 pTM=0.738\n",
"2023-11-13 18:55:23,710 Done\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0"
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"# @title Run Alignment { display-mode: \"form\" }\n",
"display_images = False\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",
"# For some reason we need that to get pdbfixer to import\n",
"if use_amber and f\"/usr/local/lib/python{python_version}/site-packages/\" not in sys.path:\n",
" sys.path.insert(0, f\"/usr/local/lib/python{python_version}/site-packages/\")\n",
"\n",
"def input_features_callback(input_features):\n",
" if display_images:\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 not relaxed:\n",
" if display_images:\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",
"\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=1,\n",
" num_recycles=num_recycles,\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=True,\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",
")\n",
"results_zip = f\"{jobname}.result.zip\"\n",
"os.system(f\"zip -r {results_zip} {jobname}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "k9tlty5uz39u",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "ccc8fb88-0f6b-42b1-d080-1f9a9f6a33c1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m2023-11-13 18:55:25,385 init\n",
"2023-11-13 18:55:25,387 reset context of device 0\n",
"predictions for paired.aln saved to paired.npz\n",
"Collecting package metadata (current_repodata.json): ...working... done\n",
"Solving environment: ...working... done\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Image loaded successfully. Dimensions: 627x1040, Mode: RGB\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol
You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n",
" jupyter labextension install jupyterlab_3dmol