A computational interactome and functional annotation for the human proteome
We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current Pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2016-10-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/18715 |
| _version_ | 1811228089775030272 |
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| author | José Ignacio Garzón Lei Deng Diana Murray Sagi Shapira Donald Petrey Barry Honig |
| author_facet | José Ignacio Garzón Lei Deng Diana Murray Sagi Shapira Donald Petrey Barry Honig |
| author_sort | José Ignacio Garzón |
| collection | DOAJ |
| description | We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function. |
| first_indexed | 2024-04-12T09:52:38Z |
| format | Article |
| id | doaj.art-f89e7a154b88446d80f3afa2470caad7 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T09:52:38Z |
| publishDate | 2016-10-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-f89e7a154b88446d80f3afa2470caad72022-12-22T03:37:48ZengeLife Sciences Publications LtdeLife2050-084X2016-10-01510.7554/eLife.18715A computational interactome and functional annotation for the human proteomeJosé Ignacio Garzón0Lei Deng1Diana Murray2Sagi Shapira3Donald Petrey4Barry Honig5https://orcid.org/0000-0002-2480-6696Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United StatesCenter for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States; School of Software, Central South University, Changsha, ChinaCenter for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United StatesCenter for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States; Department of Microbiology and Immunology, Columbia University, New York, United StatesCenter for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States; Howard Hughes Medical Institute, Columbia University, New York, United StatesCenter for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States; Howard Hughes Medical Institute, Columbia University, New York, United States; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States; Department of Medicine, Columbia University, New York, United States; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United StatesWe present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.https://elifesciences.org/articles/18715protein interactionsfunction annotationmachine learning |
| spellingShingle | José Ignacio Garzón Lei Deng Diana Murray Sagi Shapira Donald Petrey Barry Honig A computational interactome and functional annotation for the human proteome eLife protein interactions function annotation machine learning |
| title | A computational interactome and functional annotation for the human proteome |
| title_full | A computational interactome and functional annotation for the human proteome |
| title_fullStr | A computational interactome and functional annotation for the human proteome |
| title_full_unstemmed | A computational interactome and functional annotation for the human proteome |
| title_short | A computational interactome and functional annotation for the human proteome |
| title_sort | computational interactome and functional annotation for the human proteome |
| topic | protein interactions function annotation machine learning |
| url | https://elifesciences.org/articles/18715 |
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