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A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality

DOI: 10.1186/1471-2105-8-236

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Using an unsupervised probabilistic scoring scheme, we assigned a confidence score to each interaction in the matrix-model interpretation of the large-scale yeast mass-spectrometry data sets. The scoring metric proved more accurate than the filtering schemes used in the original data sets. We then took a high-confidence subset of these interactions and derived a set of complexes using MCL. The complexes show high correlation with existing annotations. Hierarchical organization of some protein complexes is evident from inter-complex interactions.We demonstrate that our scoring method can generate an integrated high-confidence subset of observed matrix-model interactions, which we subsequently used to derive an accurate map of yeast complexes. Our results indicate that essentiality is a product of the protein complex rather than the individual protein, and that we have achieved near saturation of the yeast high-abundance, rich-media-expressed "complex-ome."The molecular machines that carry out basic cellular processes are typically not individual proteins but protein complexes. Even in the relatively simple model organism Saccharomyces cerevisiae, most machines that process and store biological information are in fact large protein complexes comprised of many subunits.The path from measuring protein interactions to defining complexes has been well studied. Experimental and computational methods have provided over 50,000 putative yeast protein-protein interactions to date, although a substantial fraction of these may be spurious[1,2]. An array of analytical methods aimed at generating high-quality complexes from these data have been applied, including both unsupervised [3-5] and trained [6,7] techniques. Other genomic and proteomic data sets, such as gene expression, knockout phenotype, subcellular localization, and genetic interaction profiles, and phylogenetic profiles [5,6,8-10], have also been integrated with the raw interaction data in an effort to broaden and dee


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