*J. Chodera, N. Singhal, V. S. Pande, K. Dill, and W. Swope. Journal of Chemical Physics, (2007)*

SUMMARY: In order to break up calculations to run on Folding@home and then repiece them together in order to act like a single, very, very, very fast computer, we need special algorithms. We are constantly trying to improve our methods in these directions and this paper represents our latest state of the art in this direction.

ABSTRACT: To meet the challenge of modeling the conformational dynamics of biological macromolecules over long timescales, much recent effort has been devoted to constructing stochastic kinetic models, often in the form of discrete-state Markov models, from short molecular dynamics simulations. To construct useful models that faithfully represent dynamics at the timescales of interest, it is necessary to decompose configuration space into a set of kinetically metastable states. Previous attempts to define these states have relied upon either prior knowledge of the slow degrees of freedom or on the application of conformational clustering techniques which assume that conformationally distinct clusters are also kinetically distinct. Here, we present a first version of an automatic algorithm for the discovery of kinetically metastable states that is generally applicable to solvated macromolecules. Given molecular dynamics trajectories initiated from a well-defined starting distribution, the algorithm discovers long-lived, kinetically metastable states through successive iterations of partitioning and aggregating conformation space into kinetically related regions. We apply this method to three peptides in explicit solvent terminally blocked alanine, the engineered 12-residue beta-hairpin trpzip2, and the 21-residue helical Fs peptide to assess its ability to generate physically meaningful states and faithful kinetic models.