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|Title:||Macrostate identification from biomolecular simulations through time series analysis|
|Citation:||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|
|Appears in Collections:||Staff Publications|
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