Modeling protein interactions to interpret genetic variation

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A grand challenge for biology is to maximize the fundamental insights from high-throughput sequencing, which has become rapid and inexpensive. Structural information on proteins and their interactions is essential for understanding the effects of genetic variation. A vast amount of information on single amino acid variants (SAV) will be available from eukaryotic and prokaryotic organisms. We will develop an integrated approach for large-scale prediction of protein structures and their association. A database of predicted structures and complexes for model organisms will be established upon which genetic variants will be mapped and their phenotypic effect assessed. The Objectives of the proposed research are: (1) to develop high-throughput structure-based methods to predict interactions of experimentally determined and modeled proteins; (2) to develop advanced methodology for high-throughput modeling of individual proteins; (3) to generate genome-wide database of protein structures and protein-protein complexes for model organisms; and (4) to assess phenotypic effects of genetic variation. Approaches will be developed to discriminate non-interacting from interacting proteins, and to model the structures of protein-protein complexes, based on similarity to experimentally determined protein-protein complexes and on properties of the intermolecular energy landscape. A novel approach for fold detection will extend the number of proteins that can be modeled. A pipeline will be developed to integrate protein structure prediction with the prediction of protein-protein complexes, and use structure-based approaches to predict the effects of SAVs. This collaborative proposal combines highly complementary areas of expertise of the US team, on high-throughput modeling of protein-protein interactions, and the UK team, on protein structure prediction and SAV effects.