title: Predicting protein functions from redundancies in large-scale protein interaction networks.
abstract: Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.
authors.affiliation: NASA Advanced Supercomputing Division, National Aeronautics and Space Administration, Ames Research Center, Moffet Field, CA 94035, USA.
authors.forename: Manoj Pratim
authors.initials: MP
authors.lastname: Samanta
authors.forename: Shoudan
authors.initials: S
authors.lastname: Liang
mesh_terms: D030562:Databases, Protein; D016571:Neural Networks, Computer; D011336:Probability; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D012269:Ribosomal Proteins / Q000737:chemistry / Q000145:classification; D012441:Saccharomyces cerevisiae; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins; D012269:Ribosomal Proteins; D029701:Saccharomyces cerevisiae Proteins