>>Published online before print October 17, 2003
Proc. Natl. Acad. Sci. USA, 10.1073/pnas.2132527100
Statistics
Predicting protein functions from redundancies in large-scale protein interaction networks
Manoj Pratim Samanta and Shoudan Liang *
National Aeronautics and Space Administration Advanced Supercomputing Division, National Aeronautics and Space Administration Ames Research Center, Moffet Field, CA 94035
Edited by Peter G. Wolynes, University of California at San Diego, La Jolla, CA, and approved August 14, 2003 (received for review April 28, 2003)
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 (89%) of the original associations.<<
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