Efficient sampling in fragment-based protein structure prediction using an estimation of distribution algorithm.

ABSTRACT: Fragment assembly is a powerful method of protein structure prediction that builds protein models from a pool of candidate fragments taken from known structures. Stochastic sampling is subsequently used to refine the models. The structures are first represented as coarse-grained models and then as all-atom models for computational efficiency. Many models have to be generated independently due to the stochastic nature of the sampling methods used to search for the global minimum in a complex energy landscape. In this paper we present EdaFold(AA), a fragment-based approach which shares information between the generated models and steers the search towards native-like regions. A distribution over fragments is estimated from a pool of low energy all-atom models. This iteratively-refined distri

SUBMITTER: Simoncini D 

PROVIDER: S-EPMC3723781 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature