High-Precision Function Prediction using Conserved Interactions

Abstract

The recent availability of large data sets of protein- protein-interactions (PPIs) from various species offers new opportunities for functional genomics and proteomics. We describe a method for exploiting conserved and connected subgraphs (CCSs) in the PPI networks of multiple species for the prediction of protein function. Structural conservation is combined with functional conservation using a GeneOntology-based scoring scheme. We applied our method to the PPI networks of five species, i.e., E. coli, D. melanogaster, M. musculus, H. sapiens and S. cerevisiae. We detected surprisingly large CCSs for groups of thee species but not beyond. A manual analysis of the biological coherence of exemplary subgraphs strongly supports a close relationship between structural and functional conservation. Based on this observation, we devised an algorithm for function prediction based on CCS. Using our method, for instance, we predict new functional annotations for human based on mouse proteins with a precision of 70%.