TY - JOUR
T1 - Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae
AU - Collins, Sean R.
AU - Kemmeren, Patrick
AU - Zhao, Xue Chu
AU - Greenblatt, Jack F.
AU - Spencer, Forrest
AU - Hoolstege, Frank C.P.
AU - Weissman, Jonathan S.
AU - Krogan, Nevan J.
PY - 2007/3
Y1 - 2007/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=34147121646&partnerID=8YFLogxK
U2 - 10.1074/mcp.M600381-MCP200
DO - 10.1074/mcp.M600381-MCP200
M3 - Article
C2 - 17200106
AN - SCOPUS:34147121646
SN - 1535-9476
VL - 6
SP - 439
EP - 450
JO - Molecular and Cellular Proteomics
JF - Molecular and Cellular Proteomics
IS - 3
ER -