Please use this identifier to cite or link to this item:
|Title:||Macrostate identification from biomolecular simulations through time series analysis||Authors:||Zhou, Weizhuang.
Verma, Chandra S.
|Keywords:||DRNTU::Science::Biological sciences||Issue Date:||2012||Source:||Zhou, W., Motakis, E., Fuentes, G., & Verma, C. S. (2012). Macrostate identification from biomolecular simulations through time series analysis. Journal of chemical information and modeling, 52(9), 2319-2324.||Series/Report no.:||Journal of chemical information and modeling||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.||URI:||https://hdl.handle.net/10356/105296
|DOI:||10.1021/ci300341v||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SBS Journal Articles|
Updated on Jan 19, 2023
Web of ScienceTM
Updated on Jan 22, 2023
Page view(s) 20567
Updated on Feb 5, 2023
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.