title: Potential-based dynamical reweighting for Markov state models of protein dynamics.
abstract: As simulators attempt to replicate the dynamics of large cellular components in silico, problems related to sampling slow, glassy degrees of freedom in molecular systems will be amplified manyfold. It is tempting to augment simulation techniques with external biases to overcome such barriers with ease; biased simulations, however, offer little utility unless equilibrium properties of interest (both kinetic and thermodynamic) can be recovered from the data generated. In this Article, we present a general scheme that harnesses the power of Markov state models (MSMs) to extract equilibrium kinetic properties from molecular dynamics trajectories collected on biased potential energy surfaces. We first validate our reweighting protocol on a simple two-well potential, and we proceed to test our method on potential-biased simulations of the Trp-cage miniprotein. In both cases, we find that equilibrium populations, time scales, and dynamical processes are reliably reproduced as compared to gold standard, unbiased data sets. We go on to discuss the limitations of our dynamical reweighting approach, and we suggest auspicious target systems for further application.
authors.affiliation: Department of Chemistry, Stanford University , Stanford, California 94305, United States.
authors.forename: Jeffrey K
authors.initials: JK
authors.lastname: Weber
authors.affiliation: Department of Chemistry, Stanford University , Stanford, California 94305, United States.
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D007700:Kinetics; D008390:Markov Chains; D056004:Molecular Dynamics Simulation; D008970:Molecular Weight; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins