Please use this identifier to cite or link to this item: https://doi.org/10.1021/ci300341v
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dc.titleMacrostate identification from biomolecular simulations through time series analysis
dc.contributor.authorZhou, W.
dc.contributor.authorMotakis, E.
dc.contributor.authorFuentes, G.
dc.contributor.authorVerma, C.S.
dc.date.accessioned2014-10-27T08:32:59Z
dc.date.available2014-10-27T08:32:59Z
dc.date.issued2012-09-24
dc.identifier.citationZhou, W., Motakis, E., Fuentes, G., Verma, C.S. (2012-09-24). Macrostate identification from biomolecular simulations through time series analysis. Journal of Chemical Information and Modeling 52 (9) : 2319-2324. ScholarBank@NUS Repository. https://doi.org/10.1021/ci300341v
dc.identifier.issn15499596
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/101050
dc.description.abstractThis paper builds upon the need for a more descriptive and accurate understanding of the landscape of intermolecular interactions, particularly those involving macromolecules such as proteins. For this, we need methods that move away from the single conformation description of binding events, toward a descriptive free energy landscape where different macrostates can coexist. Molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods provide an excellent approach for such a dynamic description of the binding events. An alternative to the standard method of the statistical reporting of such results is proposed. © 2012 American Chemical Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1021/ci300341v
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentBIOLOGICAL SCIENCES
dc.description.doi10.1021/ci300341v
dc.description.sourcetitleJournal of Chemical Information and Modeling
dc.description.volume52
dc.description.issue9
dc.description.page2319-2324
dc.description.codenJCISD
dc.identifier.isiut000308958400001
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