{"id":13752507,"url":"https://github.com/soedinglab/CCMpred","last_synced_at":"2025-05-09T19:32:17.631Z","repository":{"id":31468427,"uuid":"35032428","full_name":"soedinglab/CCMpred","owner":"soedinglab","description":"Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.","archived":false,"fork":false,"pushed_at":"2023-11-08T07:51:35.000Z","size":142,"stargazers_count":107,"open_issues_count":22,"forks_count":28,"subscribers_count":12,"default_branch":"master","last_synced_at":"2025-04-27T14:39:22.198Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://www.ncbi.nlm.nih.gov/pubmed/25064567","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/soedinglab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2015-05-04T12:03:56.000Z","updated_at":"2024-12-03T01:10:43.000Z","dependencies_parsed_at":"2024-08-03T09:13:55.624Z","dependency_job_id":null,"html_url":"https://github.com/soedinglab/CCMpred","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FCCMpred","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FCCMpred/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FCCMpred/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soedinglab%2FCCMpred/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/soedinglab","download_url":"https://codeload.github.com/soedinglab/CCMpred/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253312340,"owners_count":21888624,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T09:01:06.812Z","updated_at":"2025-05-09T19:32:17.232Z","avatar_url":"https://github.com/soedinglab.png","language":"C","funding_links":[],"categories":["Ranked by starred repositories"],"sub_categories":[],"readme":"# CCMpred\n\n[![Travis](https://img.shields.io/travis/soedinglab/CCMpred.svg)](https://travis-ci.org/soedinglab/CCMpred)\n[![Codeship](https://img.shields.io/codeship/c2512a40-d488-0132-75f3-623d5159f317.svg)](https://codeship.com/projects/77807)\n\nProtein Residue-Residue **C**ontacts from **C**orrelated **M**utations **pred**icted quickly and accurately.\n\nCCMpred is a C implementation of a Markov Random Field pseudo-likelihood maximization for learning protein residue-residue contacts as made popular by Ekeberg et al. [1] and Balakrishnan and Kamisetty [2]. While predicting contacts with comparable accuracy to the referenced methods, however, CCMpred is written in C / CUDA C, performance-tuned and therefore much faster.\n\n## Requirements\n\nTo compile from source, you will need:\n\n  * a recent C compiler (we suggest GCC 4.4 or later)\n  * [CMake](http://cmake.org/) 2.8 or later\n  * Optional: [NVIDIA CUDA SDK](https://developer.nvidia.com/cuda-downloads) 5.0 or later (if you want to compile for the GPU)\n\nTo run CUDA-accelerated computations, you will need an NVIDIA GPU with a Compute Capability of 2.0 or later and the proprietary NVIDIA drivers installed. See the [NVIDIA CUDA GPU Overview](https://developer.nvidia.com/cuda-gpus) for details on your graphics card.\n\n### Memory Requirement on the GPU\nWhen doing computations on the GPU, the available memory limits the size of the model you will be able to compute. We recommend at least 2 GB of GPU RAM so you can calculate contacts for big multiple sequence alignments (e.g for N=5000):\n\n\tGPU RAM\t\tLmax\tLmax(pad)\n\t1 GB\t\t353\t291\n\t2 GB\t\t512\t420\n\t3 GB\t\t635\t519\n\t5 GB\t\t829\t676\n\t6 GB\t\t911\t743\n\t8 GB\t\t1057\t861\n\t12 GB\t\t1302\t1059\n\nYou can calculate the memory requirements in bytes for L columns and N rows using the following formula:\n\n\t4*(4*(L*L*21*21 + L*20) + 23*N*L + N + L*L) + 2*N*L + 1024\n\nFor the padded version:\n\n\t4*(4*(L*L*32*21 + L*20) + 23*N*L + N + L*L) + 2*N*L + 1024\n\n## Installation\nWe recommend compiling CCMpred on the machine that should run the computations so that it can be optimized for the appropriate CPU/GPU architecture.\n\n### Downloading\nIf you want to compile the most recent version, use the follwing to clone both CCMpred and its submodules:\n\n\tgit clone --recursive https://github.com/soedinglab/CCMpred.git\n\n### Compilation\nWith the sourcecode ready, simply run cmake with the default settings and libraries should be auto-detected:\n\n\tcmake .\n\tmake\n\nYou should find the compiled version of CCMpred at `bin/ccmpred`. To check if the CUDA libraries were detected, you can run `ldd bin/ccmpred` to see if CUDA was linked with the program, or simply run a prediction and check the program's output.\n\n## Useful scripts\n\nThe `scripts/` subdirectory contains some python scripts you might find useful - please make sure both NumPy and BioPython are installed to use them!\n\n  * `convert_alignment.py` - Use BioPython's `Bio.SeqIO` to convert a variety of alignment formats (FASTA, etc.) into the CCMpred alignment input format\n  * `top_couplings.py` - Extract the top couplings from an output contact maps in a list representation\n\n## License\nCCMpred is released under the GNU Affero General Public License v3 or later. See LICENSE for more details.\n\n## References\n\n\t[1] Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., \u0026 Aurell, E. (2013).\n\t    Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models.\n\t    Physical Review E, 87(1), 012707. doi:10.1103/PhysRevE.87.012707\n\n\t[2] Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S.-I., \u0026 Langmead, C. J. (2011).\n\t    Learning generative models for protein fold families.\n\t    Proteins, 79(4), 1061–78. doi:10.1002/prot.22934\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoedinglab%2FCCMpred","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsoedinglab%2FCCMpred","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoedinglab%2FCCMpred/lists"}