title: Systematic identification of functional orthologs based on protein network comparison.
abstract: Annotating protein function across species is an important task that is often complicated by the presence of large paralogous gene families. Here, we report a novel strategy for identifying functionally related proteins that supplements sequence-based comparisons with information on conserved protein-protein interactions. First, the protein interaction networks of two species are aligned by assigning proteins to sequence homology clusters using the Inparanoid algorithm. Next, probabilistic inference is performed on the aligned networks to identify pairs of proteins, one from each species, that are likely to retain the same function based on conservation of their interacting partners. Applying this method to Drosophila melanogaster and Saccharomyces cerevisiae, we analyze 121 cases for which functional orthology assignment is ambiguous when sequence similarity is used alone. In 61 of these cases, the network supports a different protein pair than that favored by sequence comparisons. These results suggest that network analysis can be used to provide a key source of information for refining sequence-based homology searches.
authors.affiliation: Program in Bioinformatics, University of California at San Diego, La Jolla, California 92093, USA.
authors.forename: Sourav
authors.initials: S
authors.lastname: Bandyopadhyay
authors.forename: Roded
authors.initials: R
authors.lastname: Sharan
authors.forename: Trey
authors.initials: T
authors.lastname: Ideker
mesh_terms: D000818:Animals; D004331:Drosophila melanogaster / Q000235:genetics; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics