Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data

Abstract

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function. Biologists are currently producing large amounts of data focused on physical and genetic protein interactions. Physical interactions dictate the architecture of the cell in terms of how direct associations between molecules constitute protein complexes, while genetic interactions define functional relationships through cause-and-effect relationships between genes. Both of these types of interactions can indicate shared protein functions; however, these two types of interactions are commonly non-overlapping, making their interpretation difficult. Along these lines, it has been noted that genetic interactions commonly occur between members of the same protein complex as well as between functionally related complexes. Here, we present an integrated framework that incorporates both types of interactions to generate large maps of protein complexes as well as highlight connections between related complexes. The ability to rapidly integrate these two types of data in an automated fashion can accelerate the discovery of new members of protein complexes as well as identify functionally related cellular components.