![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"\n",
"# @title **Install Python Packages** { run: \"auto\", display-mode: \"form\" }\n",
"\n",
"import os\n",
"from sys import version_info\n",
"PYTHON_VERSION = f\"{version_info.major}.{version_info.minor}\"\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",
"\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/Miniforge3-Linux-x86_64.sh\")\n",
" os.system(\"bash Miniforge3-Linux-x86_64.sh -bfp /usr/local\")\n",
" os.system(\"conda config --set auto_update_conda false\")\n",
" os.system(\"touch CONDA_READY\")\n",
"\n",
"\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",
"\n",
"if not os.path.isfile(\"HH_READY\") 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 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 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"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "CX768sUrS7lV",
"outputId": "1d72ced4-56b6-473c-843c-97e232c1ab6f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"installing colabfold...\n",
"installing conda...\n",
"installing hhsuite and amber...\n",
"CPU times: user 269 ms, sys: 52.3 ms, total: 321 ms\n",
"Wall time: 1min 24s\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uTVzPTKjTjXX"
},
"source": [
"# 1. **ColabFold Tutorial:** Predicting the structure of Bax protein\n",
"\n",
"Let's get familar with the workflow for predicting a monomer structure. This example looks at Bax, an apoptosis regulating protein.\n",
"\n",
"We will only use the default options set by Steinegger *et al.* which excludes Amber minimization and structure templates.\n",
"\n",
"**a. Background**\n",
" - Encoded by the BAX gene in the Bcl-2 family\n",
" - Highly conserved DNA sequence and structural motifs\n",
" - Different regulatory function in anti-apoptosis or pro-apoptosis\n",
" - Permeation of the mitochondrial outer membrane\n",
"\n",
"\n",
"**b. Pro-death signals triggers conformational shift**\n",
" - Bax adopts a globular state when inactive\n",
" - Apoptotic signals displaces N-terminal $\\alpha$-helicies\n",
" - Becomes membrane-associated\n",
" - Oligomers form, resulting in a pore and cell death\n",
"\n",
"Dysregulation of Bax is implicated in cancer progression, and suggests some contributions towards in Alzheimer's and Parkinson's disease.\n",
"\n",
"Experiments have shown point-mutations near opposite of the binding site region, has been shown to affect activity. This is also near the loop region between $\\alpha$-helix1 and $\\alpha$-helicies2, which has been difficult to structurally resolve. Does this loop play a role in membrane insertion mechanism?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kOblAo-xetgx"
},
"outputs": [],
"source": [
"#@title # **2. Notebook Setup** { display-mode: \"form\" }\n",
"\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",
"#@markdown \\\n",
"#@markdown ##a. Protein Sequence for Structure Prediction\n",
"#@markdown For bax (human), the amino acid sequence was obtained from\n",
"#@markdown UniProt ([Q07812](https://www.uniprot.org/uniprotkb/Q07812/entry))\n",
"query_sequence = 'MDGSGEQPRGGGPTSSEQIMKTGALLLQGFIQDRAGRMGGEAPELALDPVPQDASTKKLSECLKRIGDELDSNMELQRMIAAVDTDSPREVFFRVAADMFSDGNFNWGRVVALFYFASKLVLKALCTKVPELIRTIMGWTLDFLRERLLGWIQDQGGWDGLLSYFGTPTWQTVTIFVAGVLTASLTIWKKMG' #@param {type:\"string\"}\n",
"#@markdown **Note:** For modeling complexes, use `:` to specify inter-protein chainbreaks\n",
"#@markdown (supports homo- and hetro-oligomers).\n",
"#@markdown For example, **PI...SK:PI...SK** for a homodimer.\n",
"\n",
"#@markdown \\\n",
"#@markdown ## b. Job Name (also name of saving directory)\n",
"jobname = 'colabfold-bax-1f16' #@param {type:\"string\"}\n",
"\n",
"#@markdown \\\n",
"#@markdown ## c. Number of predicted models to relax using Amber\n",
"num_relax = 0 #@param [0, 1, 5] {type:\"raw\"}\n",
"\n",
"#@markdown \\\n",
"#@markdown ## **d. Option for Structure Template**\n",
"#@markdown Search for PDB structure (xyz-coordinates)\n",
"template_mode = \"none\" #@param [\"none\", \"pdb100\",\"custom\"]\n",
"#@markdown `none` = no template information is used. \\\n",
"#@markdown `pdb100` = detect templates in pdb100 (see [notes](#pdb100)). \\\n",
"#@markdown `custom` - upload and search own templates (PDB or mmCIF format, see [notes](#custom_templates))\n",
"\n",
"use_amber = num_relax > 0\n",
"\n",
"query_sequence = \"\".join(query_sequence.split()) # remove whitespaces\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "C2_sh2uAonJH",
"cellView": "form"
},
"outputs": [],
"source": [
"#@title # **3. Multiple Sequence Alignment (MSA)**\n",
"\n",
"#@markdown ## a. MSA options\n",
"msa_mode = \"mmseqs2_uniref\" #@param [\"mmseqs2_uniref_env\", \"mmseqs2_uniref\",\"single_sequence\",\"custom\"]\n",
"#@markdown - pairing mode\n",
"#@markdown - single sequence\n",
"#@markdown - custom MSA upload\n",
"\n",
"#@markdown \\\n",
"pair_mode = \"unpaired_paired\" #@param [\"unpaired_paired\",\"unpaired\",\"paired\"] {type:\"string\"}\n",
"#@markdown - \"unpaired_paired\" - pair sequences from same species + unpaired MSA\n",
"#@markdown - \"unpaired\" - seperate MSA for each chain\n",
"#@markdown - \"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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ADDuaolKmjGW"
},
"outputs": [],
"source": [
"# @title # **4. Advanced Settings** { run: \"auto\", display-mode: \"form\" }\n",
"\n",
"#@markdown ## a. ML Model\n",
"model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\", \"alphafold2_multimer_v1\", \"alphafold2_multimer_v2\", \"alphafold2_multimer_v3\"]\n",
"#@markdown **Note:** If `auto` selected, will use `alphafold2_ptm` for monomer prediction\n",
"#@markdown and `alphafold2_multimer_v3` for complex prediction.\n",
"#@markdown Any of the `mode_types` can be used (regardless if input is monomer or complex).\n",
"\n",
"#@markdown \\\n",
"#@markdown ## b. Re-Train with Predicted Structures\n",
"num_recycles = \"3\" #@param [\"auto\", \"0\", \"1\", \"3\", \"6\", \"12\", \"24\", \"48\"]\n",
"#@markdown **Note:**\n",
"#@markdown - If `auto` selected, will use `num_recycles=20`,\n",
"#@markdown - If `model_type=alphafold2_multimer_v3`, else `num_recycles=3`\n",
"\n",
"#@markdown \\\n",
"recycle_early_stop_tolerance = \"auto\" #@param [\"auto\", \"0.0\", \"0.5\", \"1.0\"]\n",
"#@markdown - if `auto` selected, will use `tol=0.5`\n",
"#@markdown - If `model_type=alphafold2_multimer_v3`, else `tol=0.0`.\n",
"\n",
"#@markdown \\\n",
"#@markdown ## c. Amber Relaxation\n",
"relax_max_iterations = 200 #@param [0, 200, 2000] {type:\"raw\"}\n",
"#@markdown - max amber relax iterations\n",
"#@markdown - `0` = unlimited (AlphaFold2 default, can take very long)\n",
"\n",
"#@markdown \\\n",
"#@markdown ## d. MSA Pairing\n",
"pairing_strategy = \"greedy\" #@param [\"greedy\", \"complete\"] {type:\"string\"}\n",
"#@markdown - `greedy` = pair any taxonomically matching subsets,\n",
"#@markdown - `complete` = all sequences have to match in one line.\n",
"\n",
"#@markdown \\\n",
"#@markdown ## e. 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 #@param [1,2,4,8,16] {type:\"raw\"}\n",
"use_dropout = False #@param {type:\"boolean\"}\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 \\\n",
"#@markdown # **5. Save settings**\n",
"#@markdown This will ask for your credentials, to save the outputs.\n",
"save_all = False #@param {type:\"boolean\"}\n",
"save_recycles = False #@param {type:\"boolean\"}\n",
"save_to_google_drive = False #@param {type:\"boolean\"}\n",
"#@markdown - if the save_to_google_drive option was selected, the result zip will be uploaded to your Google Drive\n",
"dpi = 200 #@param {type:\"integer\"}\n",
"#@markdown - set dpi for image resolution\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mbaIO9pWjaN0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"collapsed": true,
"outputId": "78730d97-e9e1-424a-e392-2ef0241e13e3"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [01:33<00:00, 39.9MB/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2023-12-08 01:03:26,095 Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: \"rocm\". Available platform names are: CUDA\n",
"2023-12-08 01:03:26,097 Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
"2023-12-08 01:03:28,068 Running on GPU\n",
"2023-12-08 01:03:28,251 Found 3 citations for tools or databases\n",
"2023-12-08 01:03:28,252 Query 1/1: colabfoldbax1f16_f85cf (length 192)\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"COMPLETE: 100%|██████████| 150/150 [elapsed: 00:02 remaining: 00:00]\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
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
![](\"https://raw.githubusercontent.com/sokrypton/ColabFold/main/.github/ColabFold_Marv_Logo_Small.png\"){kind=logo}
\n",
">\n",
"> ## ColabFold v1.5.3: AlphaFold2 using MMseqs2\n",
">\n",
"> Easy to use protein structure and complex prediction using [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2) and [Alphafold2-multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1). Sequence alignments/templates are generated through [MMseqs2](mmseqs.com) and [HHsearch](https://github.com/soedinglab/hh-suite). For more details, see bottom of the notebook, checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold) and read our manuscript.\n",
">\n",
"> Old versions: [v1.4](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.4.0/AlphaFold2.ipynb), [v1.5.1](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.1/AlphaFold2.ipynb), [v1.5.2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.2/AlphaFold2.ipynb)\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",
">\n",
"> ### News\n",
"> - 2023/07/31: The ColabFold MSA server is back to normal. It was using older DB (UniRef30 2202/PDB70 220313) from 27th ~8:30 AM CEST to 31st ~11:10 AM CEST.\n",
"> - 2023/06/12: New databases! UniRef30 updated to 2023_02 and PDB to 230517. We now use PDB100 instead of PDB70 (see [notes](#pdb100)).\n",
"> - 2023/06/12: We introduced a new default pairing strategy: Previously, for multimer predictions with more than 2 chains, we only pair if all sequences taxonomically match (\"complete\" pairing). The new default \"greedy\" strategy pairs any taxonomically matching subsets.\n",
">\n",
"># Instructions \n",
">\n",
"> **Quick start**\n",
"> 1. Paste your protein sequence(s) in the input field.\n",
"> 2. Press \"Runtime\" -> \"Run all\".\n",
"> 3. The pipeline consists of 5 steps. The currently running step is indicated by a circle with a stop sign next to it.\n",
">\n",
"> **Result zip file contents**\n",
">\n",
"> 1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n",
"> 2. Plots of the model quality.\n",
"> 3. Plots of the MSA coverage.\n",
"> 4. Parameter log file.\n",
"> 5. A3M formatted input MSA.\n",
"> 6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n",
"> 7. BibTeX file with citations for all used tools and databases.\n",
">\n",
"> At the end of the job a download modal box will pop up with a `jobname.result.zip` file. Additionally, if the `save_to_google_drive` option was selected, the `jobname.result.zip` will be uploaded to your Google Drive.\n",
"> \n",
">\n",
"> **License**\n",
"> githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this > notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) > and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/> alphafold).\n",
">\n",
"> **Acknowledgments**\n",
"> - We thank the AlphaFold team for developing an excellent model and open > sourcing the software.\n",
">\n",
"> - [KOBIC](https://kobic.re.kr) and [Söding Lab](https://www.mpinat.mpg.de/soeding) for providing the computational resources for the MMseqs2 MSA server.\n",
">\n",
"> - Richard Evans for helping to benchmark the ColabFold's Alphafold-multimer support.\n",
">\n",
"> - [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring!\n",
">\n",
"> - Do-Yoon Kim for creating the ColabFold logo.\n",
">\n",
"> - A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)).\n",
">\n",
"\n",
"\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": [],
"gpuType": "T4",
"cell_execution_strategy": "setup",
"machine_shape": "hm",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}