Reverse PCA, a Systematic Approach for Identifying Genes Important for the Physical Interaction between Protein Pairs

Ifat Lev, Marina Volpe, Liron Goor, Nelly Levinton, Liach Emuna, Shay Ben-Aroya

Research output: Contribution to journalArticlepeer-review

Abstract

Protein-protein interactions (PPIs) are of central importance for many areas of biological research. Several complementary high-throughput technologies have been developed to study PPIs. The wealth of information that emerged from these technologies led to the first maps of the protein interactomes of several model organisms. Many changes can occur in protein complexes as a result of genetic and biochemical perturbations. In the absence of a suitable assay, such changes are difficult to identify, and thus have been poorly characterized. In this study, we present a novel genetic approach (termed "reverse PCA") that allows the identification of genes whose products are required for the physical interaction between two given proteins. Our assay starts with a yeast strain in which the interaction between two proteins of interest can be detected by resistance to the drug, methotrexate, in the context of the protein-fragment complementation assay (PCA). Using synthetic genetic array (SGA) technology, we can systematically screen mutant libraries of the yeast Saccharomyces cerevisiae to identify those mutations that disrupt the physical interaction of interest. We were able to successfully validate this novel approach by identifying mutants that dissociate the conserved interaction between Cia2 and Mms19, two proteins involved in Iron-Sulfur protein biogenesis and genome stability. This method will facilitate the study of protein structure-function relationships, and may help in elucidating the mechanisms that regulate PPIs.

Original languageEnglish
Article numbere1003838
JournalPLoS Genetics
Volume9
Issue number10
DOIs
StatePublished - Oct 2013

All Science Journal Classification (ASJC) codes

  • Genetics(clinical)
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Cancer Research

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