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Originally published In Press as doi:10.1074/mcp.M600381-MCP200 on January 2, 2007.
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Molecular & Cellular Proteomics 6:439-450, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

Research

Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*,S

Sean R. Collinsa,b,c,d,k, Patrick Kemmerena,b,c,e,f, Xue-Chu Zhaog, Jack F. Greenblatth, Forrest Spencerg, Frank C. P. Holstegee, Jonathan S. Weissmana,b,i and Nevan J. Krogana,b,j,k

From the a Department of Cellular and Molecular Pharmacology and b The California Institute for Quantitative Biomedical Research, University of California, San Francisco, California 94158, e Department of Physiological Chemistry, Division of Biomedical Genetics, University Medical Center Utrecht, 3500 Utrecht, The Netherlands, g McKusick-Nathans Institute of Genetic Medicine, School of Medicine, The Johns Hopkins University, Baltimore, Maryland 21205, h Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada, and k Howard Hughes Medical Institute, San Francisco, California 94158

Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.

i Supported by the Howard Hughes Medical Institute. To whom correspondence may be addressed: University of California, 1700 4th St., San Francisco, CA 94143-2540. Tel.: 415-476-2980; Fax: 415-514-2073; E-mail: weissman{at}cmp.ucsf.edu

j Supported by a Sandler Family Fellowship. To whom correspondence may be addressed: University of California, 1700 4th St., San Francisco, CA 94143-2540. Tel.: 415-476-2980; Fax: 415-514-2073; E-mail: krogan{at}cmp.ucsf.edu


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