Characterizing protein energy landscape by self-learning multiscale simulations: application to a designed β-hairpin.

ABSTRACT: Characterizing the energy landscape of proteins at atomic resolution is still a very challenging problem, since it simultaneously requires high accuracy in estimating specific interactions and high efficiency in conformational sampling. Here, for these two requirements to meet, we extended the self-learning multiscale simulation (SLMS) method developed recently and applied it to the designed β-hairpin CLN025. The SLMS integrates all-atom and coarse-grained (CG) models in an iterative way such that the conformational sampling is performed by the CG model, the AA energy is used to calibrate the energy landscape, and the CG model is improved by the calibrated energy landscape. We extended the SLMS in two aspects, use of the energy decomposition for self-learning of the CG potential and a two-

SUBMITTER: Li W 

PROVIDER: S-EPMC2965946 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature