Simulation of peptide conformatinoal equilibria has played a major role in optimization of implicit solvent force fields. Yet, generating well-converged conformational ensembles for non-trivial peptides have been difficult even with the popular replica exchange method. We describe a multi-scale enhanced sampling (MSES) method where efficient topology-based coarse-grained models are coupled with all-atom ones to enhance the sampling of atomistic protein energy landscape. The bias from the coupling is removed by Hamiltonian replica exchange, thus allowing one to benefit simultaneously from faster transitions of coarse-grained modeling and accuracy of atomistic force fields. The method is demonstrated by calculating the conformational equilibria of several small but nontrivial �-hairpins with varied stabilities. We anticipate MSES to be highly useful in futhur optimization of implicit solvent.

Accelerate sampling in atomistic energy landscapes using coarse-grained models for implicit solvent optimization

248th National Meeting (2014)

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