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 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.
['Garzón|José Ignacio|JI|', 'Deng|Lei|L|', 'Murray|Diana|D|', 'Shapira|Sagi|S|', 'Petrey|Donald|D|', 'Honig|Barry|B|0000-0002-2480-6696']
D019295:Computational Biology D030562:Databases, Protein D006801:Humans D058977:Molecular Sequence Annotation D060066:Protein Interaction Maps D020543:Proteome
function annotation human computational biology machine learning protein interactions systems biology