In order to rationally design molecular systems for dynamical properties, an important step is the ability to quantitatively predict dynamics of molecules at the atomic scale.  The traditional approach to molecular simulation, eg. running Molecular Dynamics simulations and analyzing the resulting trajectories, has numerous short comings.  In particular, such methods typically yield only anecdotal result and reach timescales thousands to millions of times too short to connect directly with experiment.  I will present a new approach to molecular simulation.  Through a combination of Molecular Dynamics simulation, Bayesian inference, and worldwide distributed computing, one can directly simulate in chemical detail, many complex, experimentally systems on length scales and timescales previously unimaginable.  I will give examples of how these new methods can lead to significant advances in our ability to predict the role of water in protein structure, dynamics, and function.  References V. S. Pande, K. Beauchamp, and G. R. Bowman.  Everything you wanted to know about Markov State Models but were afraid to ask.  Methods 52  99-105 (2010)K. Beauchamp, G. R. Bowman, T. J. Lane, L. Maibaum, I. S. Haque, and V. S. Pande.  MSMBuilder2: Modeling Conformational Dynamics at the Picosecond to Millisecond Scale. Journal of Computational and Theoretical Chemistry, 7  3412-3419 (2011).

New approach to molecular simulation: Predicting the role of water in protein structure, dynamics, and function

244th National Meeting (2012)

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