Mapping the first half of the REFERENCE human binary protein interactome

Dana-Farber Cancer Inst, Boston MA

Investigators

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Abstract

DESCRIPTION (provided by applicant): Complex biological systems and cellular networks underlie most genotype to phenotype relationships. In the last decade, basic concepts of network biology have been described, emphasizing why cellular networks are important to consider in biology. Importantly, it is becoming increasingly clear that more high quality empirically derived datasets are needed to better describe biological networks and genotype to phenotype relationships. The interactome of an organism is the network formed by the complete set of interactions that can occur in a physiologically relevant dynamic range between all its macromolecules, including protein-protein, DNA-protein, RNA-protein, and RNA-RNA interactions. In this proposal, we focus on high-throughput (HT), proteome-scale mapping of what we refer to as the REFERENCE human binary protein-protein interactome network map. Major innovations in this application enable to define a clear roadmap for completion of this REFERENCE map by the end of this decade. During this coming cycle, we will expand the human HT binary interactome map from ~15% coverage, which is the milestone of the current cycle, to ~50%. We will also briefly discuss how we foresee further expansion to near completeness thereafter. The accumulation of DNA sequencing data exploded for the Human Genome Sequencing project in the 1990s when four crucial elements were assembled: i) cosmids, BAC, and YAC clone resources covering most of the genome; ii) automated laser-fluorescence sequencing, iii) the PHRED score used to systematically assess sequencing data quality, and iv) the development of hands-off automated experimental steps. We describe below how the human binary interactome mapping project is reaching a similarly exploding phase: i) having significantly contributed to the ORFeome Collaboration (OC) we now have a nearly complete protein-coding ORF clone resource, ii) we developed a new strategy to apply the power of next-generation sequencing to interactome mapping, iii) we have published a new empirical framework that systematically assess interactome mapping data quality, and iv) we will describe new hands-off automated strategies that greatly increase throughput and decrease cost. Our specific aims are: i) to expand human binary interactome mapping to a full complement of protein-coding genes cloned by OC, ii) to reach ~50% coverage of the REFERENCE human binary interactome network map, and iii) to expand global network analyses of our newly mapped human binary interactome network.