Exploring the energy landscapes of protein folding simulations with Bayesian computation.

ABSTRACT: Nested sampling is a Bayesian sampling technique developed to explore probability distributions localized in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model. The nested sampling algorithm also provides an efficient way to calculate free energies and the expectation value of thermodynamic observables at any temperature, through a simple post processing of the output. Previous applications of the algorithm have yielded large efficiency gains over other sampling techniques, including parallel tempering. In this article, we describe a parallel implementation of the nested sampling algorithm and its application to the problem of protein folding in a Gō-like force field of empiric

SUBMITTER: Burkoff NS 

PROVIDER: S-EPMC3283771 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature