CCF: EAGER: Dimension Reduction and Optimization Methods for Flexible Refinement of Protein Docking
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abstract
Proteins are the ''workhorse'' molecules in cells. Their interactions with each other, nucleic acids, ligands, and other molecules underlie many important cellular processes. Therefore, elucidating protein interactions, preferentially at the atomic level, is important to understanding these processes and treating them in diseased cells. Protein docking achieves the purpose computationally by finding the protein conformation of the lowest free energy values. Solving such a free energy minimization problem is challenging for two reasons. First, the search space is extremely high-dimensional because proteins are not rigid bodies but rather flexible when interacting. Secondly, the free energy function is very costly to evaluate and rugged to optimize.Intellectual MeritThe proposed research directly addresses these challenges to protein docking at the refinement stage. First, the dimension of the search space will be substantially lowered. Although proteins consist of hundreds to thousands of atoms, not all collective motions of those atoms are physically meaningful. The proposed methods will decompose the space of collective atomic motions into that of rigid-body motions and that of a few relevant flexibility motions by applying novel normal mode analysis (NMA) tools. Second, the search efficiency for the energy minimum will be substantially improved. Energy landscape in the reduced search space will be characterized and state-of-the-art search methods will be applied for energy minimization in the space. The proposed research will also produce insights on the reduced conformational space of protein interactions and the landscape of free energy functions in the reduced space.Broader ImpactsThe ability to predict how proteins interact with efficiency and accuracy is of tremendous value to understanding the structural and functional organization of life, developing diagnosis and therapeutics tools to cure diseases, and discovering approaches to improve bioenergy yields. The proposed research is interdisciplinary and thus expected to be useful to biologists who need protein docking tools to aid their study as well as computer scientists who need to apply or develop dimension reduction and optimization methods. Project will provide training opportunities to students in developing interdisciplinary skills.
