A Mixed-Bait-Strategy for Protein Interactome Mapping

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DESCRIPTION (provided by applicant): Understanding protein function on a genome-wide scale is a main goal of biology. An important step towards achieving this goal is through unraveling protein interactome networks. Despite advances in high- throughput technologies, library-against-library screening with proteome-wide coverage remains a daunting task. This proposal presents a novel pooling and deconvolution strategy (MBS) that is generally applicable to maximize screening efficiency in a wide variety of situations. MBS has three key components: imaginary tagging (binary coding) of baits, combinatorial mix-bait screening, and built-in prey-bait tracking and cross- validation. MBS reaches significantly higher coverage than conventional single-bait strategy under a wide range of experimental errors. MBS has the potential to greatly accelerate interactome mapping from yeast to human and is useful for many other library-against-library screens (including drug screening). The key advantages of MBS include better accuracy, coverage, and efficiency. Its versatility lies in imaginary tagging, which is universally applicable regardless of the nature of the query (molecules, cells, organisms, etc). Protein-protein interactions (PPIs) underlie a wide range of physiological and disease processes. Despite advances in high-throughput technologies, large-scale PPI mapping remains a daunting task due to the huge demand of time and resources. We introduce a novel strategy (called MBS) that will greatly reduces the number of screens needed while simultaneously increasing accuracy and coverage. MBS-guided community efforts should greatly accelerate interactome mapping from yeast to human, and find many other biomedical applications.