State Transition Probability Analysis of Protein Folding based on Large Scale Molecular Dynamics Simulation

S. Tokunaga, K. Ikeda, S. Honda, Y. Sawada, Y. Muraoka, T. Noguchi, and M. Sekijima (Japan)


Molecular Dynamics Simulation, Protein Folding, Principal Component Analysis, Clustering, Hidden Markov Model.


Rapidly increasing computational power enables relatively long (100 nano to 1 micro seconds) molecular dynamics simulations in real time. A variety of methods are applied because of the large amount of trajectory data produced by these simulations. General analytical approaches are use to visualize transition states of a trajectory to show local minima of the energy landscape, because they are important for understanding the thermodynamics and kinetics of protein folding. We clustered trajectory data from molecular dynamics simulations. Several simulations and applied a hidden Markov model (HMM) to reveal the transition state pattern of protein folding. In addition, we developed a system that can be used to analyze huge amounts of simulation data.

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