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 higher (up to 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 interactions that have not been previously reported. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating interactions in multi-protein complexes, correlating predictions with disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners of a given protein can be used to annotate the function of that protein. We provide annotations for most of the human proteome including many currently annotated as having unknown function.<br/><br/>We also report a new template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner’s performance is evaluated based on its ability to identify known targets of FDA-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases.<br/>

Structure-based prediction of protein-protein and protein-ligand interactions on a genomic scale

254th National Meeting (2017)

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