Accelerate sampling in atomistic energy landscapes using topology-based coarse-grained models

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dc.contributor.author Zhang, Weihong
dc.contributor.author Chen, Jianhan
dc.date.accessioned 2014-12-03T22:53:43Z
dc.date.available 2014-12-03T22:53:43Z
dc.date.issued 2014-12-03
dc.identifier.uri http://hdl.handle.net/2097/18788
dc.description.abstract We describe a multiscale 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 β-hairpins with varied stabilities. en_US
dc.language.iso en_US en_US
dc.relation.uri http://pubs.acs.org/doi/abs/10.1021/ct500031v en_US
dc.rights This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright (c) American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://pubs.acs.org/doi/abs/10.1021/ct500031v. Permission to archive granted by American Chemical Society, Sept. 30, 2014. en_US
dc.subject Multi-Scale en_US
dc.subject Enhanced Sampling en_US
dc.subject Implicit Solvent en_US
dc.subject Protein Folding en_US
dc.subject Replica Exchange en_US
dc.subject Conformational Ensemble en_US
dc.subject Hairpin en_US
dc.title Accelerate sampling in atomistic energy landscapes using topology-based coarse-grained models en_US
dc.type Article (author version) en_US
dc.date.published 2014 en_US
dc.citation.doi 10.1021/ct500031v en_US
dc.citation.epage 923 en_US
dc.citation.issue 3 en_US
dc.citation.jtitle Journal of Chemical Theory and Computation en_US
dc.citation.spage 918 en_US
dc.citation.volume 10 en_US
dc.contributor.authoreid jianhanc en_US


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