In silico prediction of physical protein interactions and characterization of interactome orphans

This paper presents FpClass, a prediction method for physical protein-protein interactions. The method is benchmarked against experimental data and is used to predict, among others, partners of interactome 'orphans'. Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining–based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions ( http://ophid.utoronto.ca/fpclass/ ) and the prediction software ( http://www.cs.utoronto.ca/~juris/data/fpclass/ ).