AF: SMALL: Computational Framework for Characterizing Protein Conformational Landscapes

University Of Massachusetts Boston, Dorchester MA

Investigators

Abstract

Proteins are involved in virtually every process in life. The relationship between protein structure, dynamics and function has challenged experimental biologists and computer scientists for years, but many research questions remain open due to the complexity of events such as protein folding, binding and domain motion. Existing computational methods to analyze the structural and functional properties of protein conformational spaces are limited due to the high complexity and high dimensionality of events such as protein folding and binding. Educational and outreach activities will be implemented through the following: a) Interdisciplinary collaborations with researchers inside and outside UMass Boston. b) Training and mentoring the research of undergraduate and graduate students, including women and students from under-represented groups in science. Active work with women and students from under-represented groups will be pursued through the Bridges to the baccalaureate program, the UMB women in science club and the IMSD program at UMass Boston. Proposed algorithms exploit the geometric and biophysical properties of proteins to efficiently characterize their conformational space and detect interesting regions that may be functionally important but are hard to determine experimentally. The three following related research problems will be explored: 1. Characterize Protein Flexibility and Constraints: Methods will be developed for effective sampling of protein conformational landscape by combining concepts from computational geometry and biophysics. The emphasis of this part will be on rigidity analysis and probabilistic methods. 2. Effective Low-Dimensional Representation of the Conformational Space: Effective feature selection and dimensionality reduction techniques for reliable representation of the conformational space will be tested. These methods represent high dimensional, complex data using a smaller number of variables while preserving essential information. This will facilitate the analysis and characterization of protein dynamics. 3. Characterizing Intermediate Conformations: Powerful algorithmic methods that combine geometry, mathematical and topological tool will be developed to effectively analyze the protein conformational spaces and discover highly populated regions. The emphasis will be on clustering methods, detection of outliers and handling noise and multiple constrains. The goal is to identify important structural and functional properties of protein conformational spaces such as the shape and number of low energy minima, high energy barriers and intermediate states.