Literature DB >> 26077712

Accurately modeling nanosecond protein dynamics requires at least microseconds of simulation.

Gregory R Bowman1.   

Abstract

Advances in hardware and algorithms have greatly extended the timescales accessible to molecular simulation. This article assesses whether such long timescale simulations improve our ability to calculate order parameters that describe conformational heterogeneity on ps-ns timescales or if force fields are now a limiting factor. Order parameters from experiment are compared with order parameters calculated in three different ways from simulations ranging from 10 ns to 100 μs in length. Importantly, bootstrapping is employed to assess the variability in results for each simulation length. The results of 10-100 ns timescale simulations are highly variable, possibly explaining the variation in levels of agreement between simulation and experiment in published works examining different proteins. Fortunately, microsecond timescale simulations improve both the accuracy and precision of calculated order parameters, at least so long as the full exponential fit or truncated average approximation is used instead of the common long-time limit approximation. The improved precision of these long timescale simulations allows a statistically sound comparison of a number of modern force fields, such as Amber03, Amber99sb-ILDN, and Charmm27. While there is some variation between these force fields, they generally give very similar results for sufficiently long simulations. The fact that so much simulation is required to precisely capture ps-ns timescale processes suggests that extremely extensive simulations are required for slower processes. Advanced sampling techniques could aid greatly, however, such methods will need to maintain accurate kinetics if they are to be of value for calculating dynamical properties like order parameters.
© 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Keywords:  conformational heterogeneity; enhanced sampling; force fields; molecular dynamics; order parameters

Mesh:

Substances:

Year:  2015        PMID: 26077712     DOI: 10.1002/jcc.23973

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  18 in total

1.  Optimized parameter selection reveals trends in Markov state models for protein folding.

Authors:  Brooke E Husic; Robert T McGibbon; Mohammad M Sultan; Vijay S Pande
Journal:  J Chem Phys       Date:  2016-11-21       Impact factor: 3.488

2.  On the ability of molecular dynamics force fields to recapitulate NMR derived protein side chain order parameters.

Authors:  Evan S O'Brien; A Joshua Wand; Kim A Sharp
Journal:  Protein Sci       Date:  2016-04-04       Impact factor: 6.725

Review 3.  Advanced Methods for Accessing Protein Shape-Shifting Present New Therapeutic Opportunities.

Authors:  Catherine R Knoverek; Gaya K Amarasinghe; Gregory R Bowman
Journal:  Trends Biochem Sci       Date:  2018-12-14       Impact factor: 13.807

4.  Quantifying Allosteric Communication via Both Concerted Structural Changes and Conformational Disorder with CARDS.

Authors:  Sukrit Singh; Gregory R Bowman
Journal:  J Chem Theory Comput       Date:  2017-03-22       Impact factor: 6.006

Review 5.  NMR Methods for Characterizing the Basic Side Chains of Proteins: Electrostatic Interactions, Hydrogen Bonds, and Conformational Dynamics.

Authors:  Dan Nguyen; Chuanying Chen; B Montgomery Pettitt; Junji Iwahara
Journal:  Methods Enzymol       Date:  2018-09-27       Impact factor: 1.600

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

Authors:  Martín Carballo-Pacheco; Birgit Strodel
Journal:  Protein Sci       Date:  2016-10-26       Impact factor: 6.725

7.  Structure and Dynamics of PD-L1 and an Ultra-High-Affinity PD-1 Receptor Mutant.

Authors:  Roberta Pascolutti; Xianqiang Sun; Joseph Kao; Roy L Maute; Aaron M Ring; Gregory R Bowman; Andrew C Kruse
Journal:  Structure       Date:  2016-09-08       Impact factor: 5.006

8.  Changes in conformational dynamics of basic side chains upon protein-DNA association.

Authors:  Alexandre Esadze; Chuanying Chen; Levani Zandarashvili; Sourav Roy; B Montgometry Pettitt; Junji Iwahara
Journal:  Nucleic Acids Res       Date:  2016-06-10       Impact factor: 16.971

9.  Validating Molecular Dynamics Simulations against Experimental Observables in Light of Underlying Conformational Ensembles.

Authors:  Matthew Carter Childers; Valerie Daggett
Journal:  J Phys Chem B       Date:  2018-06-21       Impact factor: 2.991

Review 10.  Measuring Entropy in Molecular Recognition by Proteins.

Authors:  A Joshua Wand; Kim A Sharp
Journal:  Annu Rev Biophys       Date:  2018-01-18       Impact factor: 12.981

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.