Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics

Mihaela E. Sardiu, Joshua M. Gilmore, Michael J. Carrozza, Bing Li, Jerry L. Workmann, Laurence Florens, Michael P. Washburn

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere7310
JournalPloS one
Volume4
Issue number10
DOIs
StatePublished - Oct 6 2009

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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    Sardiu, M. E., Gilmore, J. M., Carrozza, M. J., Li, B., Workmann, J. L., Florens, L., & Washburn, M. P. (2009). Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics. PloS one, 4(10), [e7310]. https://doi.org/10.1371/journal.pone.0007310