Computational biophysics and drug discovery need much faster, better, and in some cases completely reformulated physical modeling of protein solvation and of protein-protein interactions: for designing macrocyclic compounds that can sandwich into large protein-protein interfaces; for modeling biochemical pathways; for computing multi-antibody motions, binding and recognition; for formulating therapeutic protein solutions against folding and aggregation instabilities; and to mitigate against diseases of protein aggregation. Achieving fast, accurate and scalable modeling of proteins that are large or in complexes or aggregates, and that are in water, requires a team that can innovate from four largely non- overlapping research communities: atomistic protein MD, protein-protein docking, protein-colloid liquid-state theory, and water statistical mechanics. Combining these approaches is needed for big advances toward fast and accurate computer modeling on biologically relevant time and space scales, with proper statistical mechanics. Here, our team is 6 PIs that have already been pairwise highly collaborative (42 joint papers), and that each bring forefront capabilities (Simmerling, a key developer or AMBER and GBNECK; Kozakov, developer of CLUSPRO, top protein-protein interaction webserver in CAPRI; Coutsias, mathematical geometer whose BRIKARD gives proven acceleration of constrained search by 100x; Hribar-Lee, whose Wertheim Theory successfully predicts simple protein aggregation; Fennell, developer of SEA, a fast accurate water model; and Dill, developer of statistical mechanical models of water and of MELD, an MD accelerator that has proven successful in CASP). Our 5-year Aims include: (A) Going beyond rigid protein-protein docking, to include conformational flexibility, atomic detail, scalability to large systems, and affinities. (B) Predicting protein and antibody aggregation hot-spots and dependencies on salts and excipients. (C) Developing AmberSB force fields with next generation implicit solvent, and faster, more accurate surface-area calculations, with blind testing in CASP, SAMPL and CAPRI events. (D) Developing ?super-fast? analytical water models for solution equilibria, and for water dynamics, such as diffusion, viscosities and transport at surfaces and through pores. A Team Management Plan is proposed to optimize collaborative research with concerted leadership, and to provide for ongoing communication, engagement and the development of collective intelligence.

Public Health Relevance

To understand biological processes in health and disease, and to develop drugs and other therapies, we need computational models for protein molecules and how they interact with each other in the wet environments inside and around our cells. Here we develop models based on physical principles and geometrical descriptions of the interactions that determine how proteins behave. These models will aid, for example, in: developing new classes of drugs, called macrocycles, that sandwich between proteins; developing antibody and other protein therapeutics; and understanding diseases such as Alzheimer?s and Huntington?s, which involve unhealthy clumping of proteins.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project with Complex Structure (RM1)
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Special Emphasis Panel (ZRG1)
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Lyster, Peter
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State University New York Stony Brook
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Stony Brook
United States
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