Interface-resolution domain-domain interactome map of the yeast complexome

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Project Summary/Abstract The physical interactome of an organism, which is the network formed by all physical interactions that can occur in a physiologically relevant dynamic range between all its macromolecules, including protein-protein, DNA- protein, and RNA-protein interactions, is a critical layer to allow us to understand cells from a global point-view. In this grant application, we will focus on a particular aspect of global interactome networks: the formation of all molecular machines or complexes needed to execute all major molecular reactions. While protein complexes are responsible for most biological processes, global models of the organization and architecture of complete sets of protein complexes, or “complexomes”, are still vastly incomplete for most species, including human. We propose to precisely map direct domain-domain interactions between subunits of the yeast complexome with a resolution approaching interface-level resolution. We will systematically fragment all open reading frames corresponding to all ~1,200 genes encoding the ~300 complexes of the yeast complexome and test for direct physical interactions between the corresponding encoded domains using four complementary protein-protein interaction (PPI) assays, one based on the reconstitution of the Gal4 protein in yeast (Gal4-Y2H) and the other three based on the reconstitution of a luciferase protein called NanoLuc in three different expression systems. We will then characterize the sequence requirements of the identified domain-domain interactions and first- generation models of complexes will be generated using interface prediction modeling. The long-term vision of this proposal is to generate binary protein interaction information inside each complex, which, eventually, will be extremely valuable to establish the complete architecture of the yeast complexome. This in turn will allow incorporating dynamic aspects of protein complexes in time and space and generating improving predictive models of genotype-phenotype relationships.