Literature DB >> 25725706

Dynamic properties of force fields.

F Vitalini1, A S J S Mey1, F Noé1, B G Keller2.   

Abstract

Molecular-dynamics simulations are increasingly used to study dynamic properties of biological systems. With this development, the ability of force fields to successfully predict relaxation timescales and the associated conformational exchange processes moves into focus. We assess to what extent the dynamic properties of model peptides (Ac-A-NHMe, Ac-V-NHMe, AVAVA, A10) differ when simulated with different force fields (AMBER ff99SB-ILDN, AMBER ff03, OPLS-AA/L, CHARMM27, and GROMOS43a1). The dynamic properties are extracted using Markov state models. For single-residue models (Ac-A-NHMe, Ac-V-NHMe), the slow conformational exchange processes are similar in all force fields, but the associated relaxation timescales differ by up to an order of magnitude. For the peptide systems, not only the relaxation timescales, but also the conformational exchange processes differ considerably across force fields. This finding calls the significance of dynamic interpretations of molecular-dynamics simulations into question.

Entities:  

Year:  2015        PMID: 25725706     DOI: 10.1063/1.4909549

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  15 in total

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Review 2.  Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics.

Authors:  Tatiana Maximova; Ryan Moffatt; Buyong Ma; Ruth Nussinov; Amarda Shehu
Journal:  PLoS Comput Biol       Date:  2016-04-28       Impact factor: 4.475

3.  Modeling the mechanism of CLN025 beta-hairpin formation.

Authors:  Keri A McKiernan; Brooke E Husic; Vijay S Pande
Journal:  J Chem Phys       Date:  2017-09-14       Impact factor: 3.488

4.  Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A.

Authors:  Jamie M Schiffer; Victoria A Feher; Robert D Malmstrom; Roxana Sida; Rommie E Amaro
Journal:  Biophys J       Date:  2016-10-18       Impact factor: 4.033

5.  Comparison of force fields for Alzheimer's A β42: A case study for intrinsically disordered proteins.

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Journal:  Protein Sci       Date:  2016-10-26       Impact factor: 6.725

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Journal:  Sci Data       Date:  2022-06-17       Impact factor: 8.501

Review 7.  Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective.

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Journal:  Front Mol Biosci       Date:  2022-06-08

8.  Simulation-Based Approaches for Determining Membrane Permeability of Small Compounds.

Authors:  Christopher T Lee; Jeffrey Comer; Conner Herndon; Nelson Leung; Anna Pavlova; Robert V Swift; Chris Tung; Christopher N Rowley; Rommie E Amaro; Christophe Chipot; Yi Wang; James C Gumbart
Journal:  J Chem Inf Model       Date:  2016-04-14       Impact factor: 4.956

9.  Machine Learning Force Fields.

Authors:  Oliver T Unke; Stefan Chmiela; Huziel E Sauceda; Michael Gastegger; Igor Poltavsky; Kristof T Schütt; Alexandre Tkatchenko; Klaus-Robert Müller
Journal:  Chem Rev       Date:  2021-03-11       Impact factor: 60.622

Review 10.  Bridging scales through multiscale modeling: a case study on protein kinase A.

Authors:  Britton W Boras; Sophia P Hirakis; Lane W Votapka; Robert D Malmstrom; Rommie E Amaro; Andrew D McCulloch
Journal:  Front Physiol       Date:  2015-09-09       Impact factor: 4.566

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