title: Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper beta hairpin.
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 (P(fold)) 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.
authors.affiliation: Department of Computer Science, Stanford University, Stanford, California 94305, USA.
authors.forename: Nina
authors.initials: N
authors.lastname: Singhal
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Snow
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D019295:Computational Biology; D007700:Kinetics; D008390:Markov Chains; D015233:Models, Statistical; D017510:Protein Folding; D017433:Protein Structure, Secondary; D013816:Thermodynamics; D014364:Tryptophan / Q000737:chemistry*
chemical_list: D014364:Tryptophan