Using path sampling to build better Markovian state models: Predicting the folding rate and mechanism of a tryptophan zipper beta hairpin.

Nina Singhal, Christopher D. Snow, and Vijay S. Pande. Journal of Chemical Physics (2004)

SUMMARY: How can Folding@home use thousands to millions of CPUs to efficiently simulate long timescale biomolecular dynamics? This paper outlines the “Markovian State Model” method which is the foundation of how most new Folding@home calculations are performed. The MSM method allows for a very efficient use of uncoupled simulations, as one would easily get from distributed computing.

TECHNICAL ABSTRACT: We propose an efficient method for the prediction of protein folding rate constants and mechanisms. We use molecular dynamics simulation data to build Markovian state models (MSMs), discrete representations of the pathways sampled. Using these MSMs, we can quickly calculate the folding probability (Pfold) and mean first passage time of all the sampled points. In addition, we provide techniques for evaluating these values under perturbed conditions without expensive recomputations. To demonstrate this method on a challenging system, we apply these techniques to a two-dimensional model energy landscape and the folding of a tryptophan zipper beta hairpin.