%0 Journal Article
%T Determining Protein Complex Connectivity Using a Probabilistic Deletion Network Derived from Quantitative Proteomics
%A Mihaela E. Sardiu
%A Joshua M. Gilmore
%A Michael J. Carrozza
%A Bing Li
%A Jerry L. Workman
%A Laurence Florens
%A Michael P. Washburn
%J PLOS ONE
%D 2009
%I Public Library of Science (PLoS)
%R 10.1371/journal.pone.0007310
%X Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.
%U http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0007310