Achieving Long Timescale Sampling in Biomolecular Simulations

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Abstract:Scientific merit: Long timescale conformational fluctuations can be responsible for unusual protein electrostatic effects and remote modulation of enzymatic reactions. Due to the timescale limitation, accurate understanding of these events via computer simulation has been very challenging. The goal of this research is to develop and apply novel molecular dynamics simulation methods to quantitatively describe complex biomolecular phenomena that usually occur in much longer timescales than what conventional techniques can achieve. In this project, specialized computer simulation algorithms will be developed and employed to enhance the sampling power of biomolecular simulations. Specifically, the main objectives will be accomplished in two focused areas: (1) Quantitative prediction of protein electrostatic effects and understanding their coupling with protein conformational transitions; (2) Understanding how slow enzyme fluctuations prepare unique catalytic environments to activate chemical reactions. These studies will provide deeper understanding of the roles that protein conformational changes play in key biological processes.Broader impacts: The computer simulation methods under development will be distributed to general scientific community. Some specific algorithms will very possibly become major techniques in related research areas. The success of this research will motivate more problem-oriented sampling design efforts in the field of biomolecular simulation. Synergistic with the scientific project, a special graduate course "Time-scale and Length-scale in Biology" will be further developed. National and international meetings and workshops will continue to be organized by the author to motivate and catalyze the education of younger-generation scientists on the subject of quantitative computational biophysics. Efforts will continue in the involvement of undergraduate students and high school students in this research, using computer simulations to explore protein structures, dynamics, and functions.