Please use this identifier to cite or link to this item: https://doi.org/10.1021/ci300341v
Title: Macrostate identification from biomolecular simulations through time series analysis
Authors: Zhou, W.
Motakis, E.
Fuentes, G.
Verma, C.S. 
Issue Date: 24-Sep-2012
Source: Zhou, 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
Abstract: This 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.
Source Title: Journal of Chemical Information and Modeling
URI: http://scholarbank.nus.edu.sg/handle/10635/101050
ISSN: 15499596
DOI: 10.1021/ci300341v
Appears in Collections:Staff Publications

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