{"id":25167146,"url":"https://github.com/biocomputingup/alphafold-disorder","last_synced_at":"2025-10-07T02:23:12.104Z","repository":{"id":81784615,"uuid":"402731279","full_name":"BioComputingUP/AlphaFold-disorder","owner":"BioComputingUP","description":"Predict disorder and disorder binding from AlphaFold structures","archived":false,"fork":false,"pushed_at":"2024-11-25T13:59:33.000Z","size":39,"stargazers_count":14,"open_issues_count":0,"forks_count":4,"subscribers_count":6,"default_branch":"main","last_synced_at":"2024-11-25T14:49:25.213Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BioComputingUP.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":"2021-09-03T10:27:24.000Z","updated_at":"2024-11-25T13:59:36.000Z","dependencies_parsed_at":"2023-05-22T02:45:45.443Z","dependency_job_id":null,"html_url":"https://github.com/BioComputingUP/AlphaFold-disorder","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioComputingUP%2FAlphaFold-disorder","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioComputingUP%2FAlphaFold-disorder/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioComputingUP%2FAlphaFold-disorder/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioComputingUP%2FAlphaFold-disorder/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BioComputingUP","download_url":"https://codeload.github.com/BioComputingUP/AlphaFold-disorder/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":237911251,"owners_count":19386039,"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":"2025-02-09T06:19:43.308Z","updated_at":"2025-10-07T02:23:07.052Z","avatar_url":"https://github.com/BioComputingUP.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AlphaFold-disorder\n#### Disorder and binding region detection from AlphaFold predicted structures\n\nThe script parses and processes PDB files generated by AlphaFold. It expects the pLDDT score in the B-factor column. As intermediate (mandatory) step it calculates the Relative Solvent Accessibility (RSA) as provided by DSSP and BioPython.\n\n\n#### Dependencies\n- Python3\n- NumPy\n- Pandas\n- BioPython\n- DSSP 3.x (\"mkdssp\" executable)\n\n#### Usage\n\nThe script takes in input a folder with PDB files and writes two TSV files.\n\n    python3 alphafold_disorder.py -i pdbs/ -o out.tsv\n\n\n##### Additional parameters\n\n- ***rsa_window*** (default 25) - RSA values are smoothed over a window centered on the residue to predict\n- ***rsa_threshold*** (default 0.581) - Binding predictions are overweighted when disorder prediction is above this threshold\n\nBoth parameters take a space separated list of values (floats). The program generates an output for each possible combination of the provided lists.\n\n##### Output format\n\n###### TSV\nBy default, the program uses \nthe TSV format and generates two files ***out_data.tsv*** and ***out_pred.tsv***, representing intermediate calculation \n(DSSP output) and the final prediction, respectively. \nThe last two columns (**disorder-*****\u003crsa_window\u003e***, **binding-*****\u003crsa_window\u003e-\u003crsa_threshold\u003e***) \nare the relevant ones representing the disorder and binding propensities.\n```\nname    pos     aa      lddt    disorder        rsa     disorder-25     binding-25-0.581\nP47710  1       M       0.688   0.312   1.000   0.680   0.869\nP47710  2       R       0.832   0.168   0.879   0.691   0.929\nP47710  3       L       0.850   0.150   0.854   0.696   0.937\nP47710  4       L       0.863   0.137   0.756   0.705   0.943\n...\nQ5RJL0  67      V       0.502   0.498   0.951   0.896   0.791\nQ5RJL0  68      L       0.511   0.489   1.000   0.881   0.795\nQ5RJL0  69      P       0.449   0.551   0.787   0.866   0.769\nQ5RJL0  70      R       0.514   0.486   1.000   0.864   0.796\n...\n```\n\n\n###### CAID\nThe CAID format can be generated with the command below. \n\n    python3 alphafold_disorder.py -i pdbs/ -o out.tsv -f caid\n\nThe program will generate different files for different types of prediction and different combination of parameters: \n- out_disorder.dat, disorder based on pLDDT\n- out_disorder-\u003crsa_window\u003e.dat, disorder based on RSA and smoothed over a window\n- out_binding-\u003crsa_window\u003e-\u003crsa_threshold\u003e.dat, binding prediction wighted based on a threshold on the smoothed RSA\n\n```\n\u003eP47710\n1       M       0.68\n2       R       0.691\n3       L       0.696\n4       L       0.705\n...\n67      V       0.896\n68      L       0.881\n69      P       0.866\n70      R       0.864\n...\n```\n\n#### How to cite\n\nPiovesan D, Monzon AM, Tosatto SCE.\u003cbr /\u003e\nIntrinsic protein disorder and conditional folding in AlphaFoldDB. \nProtein Sci. 2022 Nov;31(11):e4466.\u003cbr /\u003e\nPMID: [36210722](https://pubmed.ncbi.nlm.nih.gov/36210722/)\nPMCID: [PMC9601767](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601767/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiocomputingup%2Falphafold-disorder","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiocomputingup%2Falphafold-disorder","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiocomputingup%2Falphafold-disorder/lists"}