{"repo":"sokrypton/ColabFold","free":true,"listed":false,"github":"https://github.com/sokrypton/ColabFold","clone":"git clone https://github.com/sokrypton/ColabFold.git","description":"Making Protein folding accessible to all!","language":"Jupyter Notebook","stars":2879,"topics":["bioinformatics","multiple-sequence-alignment","protein-structure","structure-prediction"],"license":"MIT","category":"bioinformatics_tool","readme_excerpt":"# ColabFold - v1.6.2\n\nFor details of what was changed in v1.6.2, see [change log](https://github.com/sokrypton/ColabFold/wiki/v1.6.2)!\n\n<p align=\"center\"><img src=\"https://github.com/sokrypton/ColabFold/raw/main/.github/ColabFold_Marv_Logo.png\" height=\"250\"/></p>\n\n### Making Protein folding accessible to all via Google Colab!\n\n| Notebooks                                                                                                                                        | monomers | complexes | mmseqs2 | jackhmmer | templates |\n| :----------------------------------------------------------------------------------------------------------------------------------------------- | -------- | --------- | ------- | --------- | --------- |\n| [AlphaFold2_mmseqs2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb)                                    | Yes      | Yes       | Yes     | No        | Yes       |\n| [AlphaFold3 (OpenFold3)](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold3_of3.ipynb)                            | Yes      | Yes       | Yes     | No        | No        |\n| [AlphaFold2_batch](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/batch/AlphaFold2_batch.ipynb)                          | Yes      | Yes       | Yes     | No        | Yes       |\n| [AlphaFold2](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb) (from Deepmind)                    | Yes      | Yes       | No      | Yes       | No        |\n| [relax_amber](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/relax_amber.ipynb) (relax input structure)             |          |           |         |           |           |\n| [ESMFold](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/ESMFold.ipynb)                                                  | Yes      | Maybe     | No      | No        | No        |\n|                                                                                                                                                  |\n| **BETA (in development) notebooks**                                                                                                              |          |           |         |           |           |\n| [RoseTTAFold2](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/RoseTTAFold2.ipynb)                                        | Yes      | Yes       | Yes     | No        | WIP       |\n| [Boltz](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/Boltz1.ipynb)                                        | Yes      | Yes       | Yes     | No        | No       |\n| [BioEmu](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/BioEmu.ipynb)                                        | Yes      | No       | Yes     | No        | No       |\n| [OmegaFold](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/omegafold.ipynb)                                         | Yes      | Maybe     | No      | No        | No        |\n| [AlphaFold2_advanced_v2](https://colab.research.google.com/github/sokrypton/ColabDesign/blob/gamma/af/examples/predict.ipynb) (new experimental notebook)                  | Yes      | Yes       | Yes     | No        | Yes       |\n\nCheck the wiki page [old retired notebooks](https://github.com/sokrypton/ColabFold/wiki/Old-retired-notebooks) for unsupported notebooks.\n\n### FAQ\n- Where can I chat with other ColabFold users?\n  - See our [Discord](https://discord.gg/gna8maru7d) channel!\n- Can I use the models for **Molecular Replacement**?\n  - Yes, but be **CAREFUL**, the bfactor column is populated with pLDDT confidence values (higher = better). Phenix.phaser expects a \"real\" bfactor, where (lower = better). See [post](https://twitter.com/cheshireminima/status/1423929241675120643) from Claudia Millán.\n- What is the maximum length?\n  - Limits depends on free GPU provided by Google Colab `fingers-crossed`\n  - For GPUs with ~16G the max length is ~2000\n- Is it okay to use the MMseqs2 MSA server on a local computer?\n  - You can access the server from a local computer if you queries are serial from a single IP. Please do not use multiple computers to query the server.\n- Where can I download the databases used by ColabFold?\n  - The databases are available at [colabfold.mmseqs.com](https://colabfold.mmseqs.com)\n- I want to render my own images of the predicted structures, how do I color by pLDDT?\n  - In pymol for AlphaFold structures: `spectrum b, red_yellow_green_cyan_blue, minimum=50, maximum=90`\n  - If you want to use AlphaFold Colours (credit: Konstantin Korotkov)\n    ```python\n    set_color n0, [0.051, 0.341, 0.827]\n    set_color n1, [0.416, 0.796, 0.945]\n    set_color n2, [0.996, 0.851, 0.212]\n    set_color n3, [0.992, 0.490, 0.302]\n    color n0, b < 100; color n1, b < 90\n    color n2, b < 70;  color n3, b < 50\n    ```\n  - In pymol for RoseTTAFold structures: `spectrum b, red_yellow_green_cyan_blue, minimum=0.5, maximum=0.9`\n- What is the difference between the AlphaFold2_advanced and AlphaFold2_mmseqs2 (_batch) notebook for complex prediction?\n  - We currently have two different ways to predict protein complexes: (1) using the AlphaFold2 model with residue index jump and (2) using the AlphaFold2-multimer model. AlphaFold2_advanced supports (1) and AlphaFold2_mmseqs2 (_batch) (2).\n- What is the difference between localcolabfold and the pip installable colabfold_batch?\n  -  [LocalColabFold](https://github.com/YoshitakaMo/localcolabfold) is an installer script designed to make ColabFold functionality available on local users' machines. It supports wide range of operating systems, such as Windows 10 or later (using Windows Subsystem for Linux 2), macOS, and Linux.\n- Is there a way to amber-relax structures without having to rerun alphafold/colabfold from scratch?\n  - Yes, see this [notebook](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/relax_amber.ipynb).\n- Where can I find the old notebooks that were previously developed and are now retired?\n  - You can find the list of retired notebooks in the [old retired notebooks](https://github.com/sokrypton/ColabFold/wiki/Old-retired-notebooks) wiki page.\n- Where can I find the history of MSA Server Databases used in ColabFold?\n  - You can view the database version history on the [MSA Server Database History](https://github.com/sokrypton/ColabFold/wiki/MSA-Server-Database-History) wiki page.\n\n### Installation\n\nFor a one-step installer script that supports Linux, macOS, and Windows (WSL2), see [LocalColabFold](https://github.com/YoshitakaMo/localcolabfold).\n\nAlternatively, ColabFold can be installed directly with `conda` and `pip`:\n\n```shell\nconda create -n colabfold -c conda-forge -c bioconda python=3.13 kalign2=2.04 hhsuite=3.3.0 mmseqs2=18.8cc5c\nconda activate colabfold\n# With CUDA GPU support\npip install colabfold[alphafold,openmm] jax[cuda12] openmm[cuda12]\n# CUDA 13 is recommended if you have a Blackwell GPU or newer\npip install colabfold[alphafold,openmm] jax[cuda13] openmm[cuda13]\n# CPU only\npip install colabfold[alphafold,openmm]\n# For colabfold_search only (no structure prediction)\npip install colabfold\n```\n\nA Docker image is also available:\n\n```shell\ndocker pull ghcr.io/sokrypton/colabfold:1.6.2-cuda12\n```\n\nSee our [wiki](https://github.com/sokrypton/ColabFold/wiki/Running-ColabFold-in-Docker) for more details on running ColabFold in Docker.\n\n### Generating MSAs for small scale local structure/complex predictions using the MSA server\n\nWhen you pass a FASTA or CSV file containing your sequences to `colabfold_batch` it will automatically query the public MSA server to generate MSAs. You might want to split this into two steps for better GPU resource utilization:\n\n```\n# Query the MSA server and predict the structure on local GPU in one go:\ncolabfold_batch input_sequences.fasta out_dir\n\n# Split querying MSA server and GPU predictions into two steps\ncolabfold_batch input_sequences.fasta out_dir --msa-only\ncolabfold_batch input_sequences.fasta out_dir\n```\n\n### Generating MSAs for large scale structure/complex predictions\n\nFirst create a directory for the databases on a disk with sufficient storage (940GB (!)). Depending on where you are, this will take a couple of hours:\n\nNote: [MMseqs2 Release 18](https://github.com/soedinglab/MMseqs2/releases/tag/18-8cc5c) is used to create the databases and perform sequece search in the ColabFold MSA server. Please use this version if you want to obtain the same MSAs as the server.\n\n```shell\nMMSEQS_NO_INDEX=1 ./setup_databases.sh /path/to/db_folder\n```\n\nIf MMseqs2 is not installed in your `PATH`, add `--mmseqs <path to mmseqs>` to your `mmseqs` in `colabfold_search`:\n\n```shell\n# This needs a lot of CPU\ncolabfold_search --mmseqs /path/to/bin/mmseqs input_sequences.fasta /path/to/db_folder msas\n# This needs a GPU\ncolabfold_batch msas predictions\n```\n\nThis will create intermediate folder `msas` that contains all input multiple sequence alignments formated as a3m files and a `predictions` folder with all predicted pdb,json and png files.\n\nThe procedure above disables MMseqs2 preindexing of the various ColabFold databases by setting the `MMSEQS_NO_INDEX=1` environment variable before calling the database setup script. For most use-cases of `colabfold_search` precomputing the index is not required and might hurt search speed. The precomputed index is necessary for fast response times of the ColabFold server, where the whole database is permamently kept in memory. In any case the batch searches will require a machine with about 128GB RAM or, if the databases are to be kept permamently in RAM, with over 1TB RAM.\n\nIn some cases using precomputed database can still be useful. For the following cases, call the `setup_databases.sh` script without the `MMSEQS_NO_INDEX` environment variable:\n\n(0) As mentioned above, if you want to set-up a server.\n\n(1","default_branch":"main","files":121,"tree":[".gitattributes",".github/ColabFold_Marv_Logo.png",".github/ColabFold_Marv_Logo_Small.png",".github/ISSUE_TEMPLATE.md",".github/workflows/docker.yml",".github/workflows/publish.yml",".github/workflows/test.yml",".gitignore","AlphaFold2.ipynb","AlphaFold3_of3.ipynb","BioEmu.ipynb","Boltz1.ipynb","Contributing.md","Dockerfile","ESMFold.ipynb","LICENSE","MsaServer/README.md","MsaServer/config.json","MsaServer/restart-systemd.sh","MsaServer/setup-and-start-local.sh","MsaServer/systemd-example-mmseqs-server.service","README.md","RoseTTAFold.ipynb","RoseTTAFold2.ipynb","batch/AlphaFold2_batch.ipynb","beta/AlphaFold2_advanced.ipynb","beta/AlphaFold2_advanced_beta.ipynb","beta/AlphaFold2_advanced_old.ipynb","beta/AlphaFold2_complexes.ipynb","beta/AlphaFold_wJackhmmer.ipynb","beta/Alphafold_single.ipynb","beta/ESMFold.ipynb","beta/ESMFold_advanced.ipynb","beta/ESMFold_api.ipynb","beta/RoseTTAFold.ipynb","beta/RoseTTAFold__network__Refine_module.patch","beta/RoseTTAFold_install.sh","beta/RoseTTAFold_run.sh","beta/alphafold_output_at_each_recycle.ipynb","beta/colabfold.py","beta/colabfold_alphafold.py","beta/config.patch","beta/convert_256_to_384_rep.ipynb","beta/folding.patch","beta/mapping.patch","beta/model.patch","beta/modules.patch","beta/omegafold.ipynb","beta/omegafold_hacks.ipynb","beta/pairmsa.py","beta/protein.patch","beta/recycle_0.patch","beta/relax_amber.ipynb","colabfold/__init__.py","colabfold/alphafold/__init__.py","colabfold/alphafold/extra_ptm.py","colabfold/alphafold/ipsae.py","colabfold/alphafold/models.py","colabfold/alphafold/msa.py","colabfold/batch.py","colabfold/citations.py","colabfold/colabfold.py","colabfold/download.py","colabfold/input.py","colabfold/mmseqs/__init__.py","colabfold/mmseqs/merge_and_split_msas.py","colabfold/mmseqs/search.py","colabfold/mmseqs/split_msas.py","colabfold/openstructure/LGPL.txt","colabfold/openstructure/README.md","colabfold/openstructure/stereo_chemical_props.txt","colabfold/pdb.py","colabfold/plot.py","colabfold/relax.py","colabfold/utils.py","colabfold_search.sh","poetry.lock","pyproject.toml","setup_databases.sh","test-data/ERR550519_2213899_unrelaxed_model_1.cif","test-data/ERR550519_2213899_unrelaxed_model_1.pdb","test-data/P54025.fasta","test-data/a3m/5AWL1.a3m","test-data/a3m/6A5J.a3m","test-data/a3m/empty.a3m","test-data/batch/5AWL_1/model_feat.pkl.xz","test-data/batch/5AWL_1/model_pred.pkl.xz","test-data/batch/6A5J/model_feat.pkl.xz","test-data/batch/6A5J/model_pred.pkl.xz","test-data/batch/input/5AWL_1.fasta","test-data/batch/input/6A5J.fasta","test-data/batch/input/empty.fasta","test-data/complex/3G5O_A_3G5O_B/model_feat.pkl.xz","test-data/complex/3G5O_A_3G5O_B/model_pred.pkl.xz","test-data/complex/input.csv","test-data/complex_monomer/A_A/model_feat.pkl.xz","test-data/complex_monomer/A_A/model_pred.pkl.xz","test-data/complex_monomer_ptm/A_A/model_feat.pkl.xz","test-data/complex_monomer_ptm/A_A/model_pred.pkl.xz","test-data/complex_ptm/3G5O_A_3G5O_B/model_feat.pkl.xz","test-data/complex_ptm/3G5O_A_3G5O_B/model_pred.pkl.xz","test-data/mmseqs-api-reponses/batch.json","test-data/mmseqs-api-reponses/complex.json","test-data/mmseqs-api-reponses/complex_monomer.json","test-data/mmseqs-api-reponses/get_msa_single_sequence.json","test-data/mmseqs-api-reponses/get_msa_uniref.json","test-data/mmseqs-api-reponses/get_msa_uniref_env.json","test-data/mmseqs-api-reponses/single.json","test-data/single/5AWL_1/model_feat.pkl.xz","test-data/single/5AWL_1/model_pred.pkl.xz","tests/__init__.py","tests/mock.py","tests/reindent_ipynb.py","tests/test_colabfold.py","tests/test_msa.py","tests/test_utils.py","utils/3G5O_A_3G5O_B_unrelaxed_rank_1_model_1_scores.json","utils/convert_deepfold_weights.py","utils/plot_scores.ipynb","verbose/alphafold_noTemplates_noMD.ipynb","verbose/alphafold_noTemplates_yesMD.ipynb"],"storefront":"/r/sokrypton","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sokrypton/ColabFold/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}