Literature DB >> 26606620

Block Covariance Overlap Method and Convergence in Molecular Dynamics Simulation.

Tod D Romo1, Alan Grossfield1.   

Abstract

Molecular dynamics (MD) is a powerful tool for understanding the fluctuations of biomolecular systems. It is, however, subject to statistical errors in its sampling of the underlying distribution of states. One must understand these errors in order to draw meaningful conclusions from the simulation. This is becoming ever more critical as MD simulations of even larger systems are attempted. We present here a new method for determining the extent of convergence that relies on measures of the fluctuation space sampled by the simulation without any a priori knowledge of states or partitioning of the configuration space. This method reveals long correlation times, even for simple systems, and suggests caution when interpreting macromolecular simulations. We also compare this method with previous efforts to characterize the sampling of MD simulation.

Year:  2011        PMID: 26606620     DOI: 10.1021/ct2002754

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  14 in total

1.  Unknown unknowns: the challenge of systematic and statistical error in molecular dynamics simulations.

Authors:  Tod D Romo; Alan Grossfield
Journal:  Biophys J       Date:  2014-04-15       Impact factor: 4.033

2.  Lightweight object oriented structure analysis: tools for building tools to analyze molecular dynamics simulations.

Authors:  Tod D Romo; Nicholas Leioatts; Alan Grossfield
Journal:  J Comput Chem       Date:  2014-10-18       Impact factor: 3.376

3.  Accelerating Membrane Simulations with Hydrogen Mass Repartitioning.

Authors:  Curtis Balusek; Hyea Hwang; Chun Hon Lau; Karl Lundquist; Anthony Hazel; Anna Pavlova; Diane L Lynch; Patricia H Reggio; Yi Wang; James C Gumbart
Journal:  J Chem Theory Comput       Date:  2019-07-02       Impact factor: 6.006

4.  Characterization of Amyloidogenic Peptide Aggregability in Helical Subspace.

Authors:  Shayon Bhattacharya; Liang Xu; Damien Thompson
Journal:  Methods Mol Biol       Date:  2022

5.  Simulation Best Practices for Lipid Membranes [Article v1.0].

Authors:  David J Smith; Jeffery B Klauda; Alexander J Sodt
Journal:  Living J Comput Mol Sci       Date:  2019-01-09

6.  Elastic Network Models are Robust to Variations in Formalism.

Authors:  Nicholas Leioatts; Tod D Romo; Alan Grossfield
Journal:  J Chem Theory Comput       Date:  2012-06-05       Impact factor: 6.006

7.  Structure-based simulations reveal concerted dynamics of GPCR activation.

Authors:  Nicholas Leioatts; Pooja Suresh; Tod D Romo; Alan Grossfield
Journal:  Proteins       Date:  2014-06-09

8.  Differences in the intrinsic spatial dynamics of the chromatin contribute to cell differentiation.

Authors:  She Zhang; Fangyuan Chen; Ivet Bahar
Journal:  Nucleic Acids Res       Date:  2020-02-20       Impact factor: 16.971

9.  Influence of Chirality of Crizotinib on Its MTH1 Protein Inhibitory Activity: Insight from Molecular Dynamics Simulations and Binding Free Energy Calculations.

Authors:  Yuzhen Niu; Dabo Pan; Danfeng Shi; Qifeng Bai; Huanxiang Liu; Xiaojun Yao
Journal:  PLoS One       Date:  2015-12-17       Impact factor: 3.240

10.  Molecular dynamics simulations on the Tre1 G protein-coupled receptor: exploring the role of the arginine of the NRY motif in Tre1 structure.

Authors:  Margaret M Pruitt; Monica H Lamm; Clark R Coffman
Journal:  BMC Struct Biol       Date:  2013-09-18
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