The interactome of an organism is the network formed by the complete set of physical interactions that can occur in a range of physiologically relevant concentrations, including protein-protein, DNA-protein, and RNA-protein interactions. The generation of proteome-wide interactome network maps with high specificity and high sensitivity is a necessary, although not sufficient, aspect in the quest of generating predictive macromolecular models at the scale of the whole cell. Moreover, these maps serve as a foundation for mechanistic and quantitative studies of poorly characterized predicted gene products and disease-associated proteins. There are two major approaches to experimentally map the protein-protein interactome network of any organism of interest: i) all pairwise combinations of pairs of predicted proteins can be experimentally tested to derive all possible binary physical interactions (binary interactome);and ii) all proteins can be tested to interrogate in which protein complex(es) they belong (co-complex interactome). It is well established that binary and co-complex approaches are complementary in providing increasingly accurate and complete interactome models. We propose to continue our efforts at systematically mapping the human binary interactome with the goal of generating and analyzing a new version of HT-Y2H interactions with high quality and high sensitivity for all pairwise combinations of predicted gene products for which we have at least one Gateway-cloned ORF available. This corresponds to a matrix of ~16,000 X 16,000 proteins, which represents ~50% of the complete matrix since we assume a total of ~22,000 protein-coding genes and limit the analysis to a single splice variant per gene. Our modified specific aims are to: i) Provide an expanded high-confidence/high-coverage version of the human binary interactome map ii) Validate human binary interaction data iii) Analyze the expanded human interactome model

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Gatlin, Christine L
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Dana-Farber Cancer Institute
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