Combination of Markov state models and kinetic networks for the analysis of molecular dynamics simulations of peptide folding.

ABSTRACT: Atomistic molecular dynamics simulations of the TZ1 beta-hairpin peptide have been carried out using an implicit model for the solvent. The trajectories have been analyzed using a Markov state model defined on the projections along two significant observables and a kinetic network approach. The Markov state model allowed for an unbiased identification of the metastable states of the system, and provided the basis for commitment probability calculations performed on the kinetic network. The kinetic network analysis served to extract the main transition state for folding of the peptide and to validate the results from the Markov state analysis. The combination of the two techniques allowed for a consistent and concise characterization of the dynamics of the peptide. The slowest relaxation pr

SUBMITTER: Radford IH 

PROVIDER: S-EPMC3106446 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature