Identification of genes important for the physical interaction between protein pairs through reverse PCA (rPCA)

Ifat Lev, Marina Volpe, Shay Ben-Aroya

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Cells contain many important protein complexes involved in performing and regulating structural, metabolic, and signaling functions. Understanding physical and functional interactions between proteins in living systems is of vital importance in biology. The importance of protein-protein interactions (PPIs) has led to the development of several powerful methodologies and techniques to detect them. All of this information has enabled the creation of large protein-interaction networks. One important challenge in biology is to understand how protein complexes respond to genetic perturbations. Here we describe a systematic genetic assay termed "reverse PCA," which allows the identification of genes whose products are required for modulating the physical interaction between two given proteins. Our assay starts with a yeast strain in which the PPI of interest can be detected by resistance to the drug methotrexate, in the context of the protein-fragment complementation assay (PCA). By combining the synthetic genetic array (SGA) technology, we can systematically screen mutant libraries of the yeast Saccharomyces cerevisiae to identify trans-acting mutations that disrupt the physical interaction of interest. The identification of such mutants is valuable for unraveling important regulatory mechanisms, and for defining the response of the protein interactome to specific perturbations. Curr. Protoc. Cell Biol. 64:17.15.1-17.15.11.

Original languageEnglish
Pages (from-to)17.15.1-17.15.11
JournalCurrent Protocols in Cell Biology
StatePublished - 2 Sep 2014

Bibliographical note

Publisher Copyright:
© 2014 by John Wiley & Sons, Inc.


  • Protein-protein interaction (PPI)
  • Saccharomyces cerevisiae
  • Synthetic genetic array (SGA)
  • high-throughput technologies
  • protein complementation assay (PCA)


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