Literature DB >> 26588273

Efficient and Minimal Method to Bias Molecular Simulations with Experimental Data.

Andrew D White1, Gregory A Voth1.   

Abstract

A primary goal in molecular simulations is to modify the potential energy of a system so that properties of the simulation match experimental data. This is traditionally done through iterative cycles of simulation and reparameterization. An alternative approach is to bias the potential energy so that the system matches experimental data. This can be done while minimally changing the underlying free energy of the molecular simulation. Current minimal biasing methods require replicas, which can lead to unphysical dynamics and introduces new complexity: the choice of replica number and their properties. Here, we describe a new method, called experiment directed simulation that does not require replicas, converges rapidly, can match many data simultaneously, and minimally modifies the potential. The experiment directed simulation method is demonstrated on model systems and a three-component electrolyte simulation. The theory used to derive the method also provides insight into how changing a molecular force-field impacts the expected value of observables in simulation.

Year:  2014        PMID: 26588273     DOI: 10.1021/ct500320c

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


  16 in total

1.  Time-averaged order parameter restraints in molecular dynamics simulations.

Authors:  Niels Hansen; Fabian Heller; Nathan Schmid; Wilfred F van Gunsteren
Journal:  J Biomol NMR       Date:  2014-10-14       Impact factor: 2.835

2.  Ensemble-Biased Metadynamics: A Molecular Simulation Method to Sample Experimental Distributions.

Authors:  Fabrizio Marinelli; José D Faraldo-Gómez
Journal:  Biophys J       Date:  2015-06-16       Impact factor: 4.033

3.  Coarse-Grained Directed Simulation.

Authors:  Glen M Hocky; Thomas Dannenhoffer-Lafage; Gregory A Voth
Journal:  J Chem Theory Comput       Date:  2017-08-31       Impact factor: 6.006

4.  Scalable molecular dynamics on CPU and GPU architectures with NAMD.

Authors:  James C Phillips; David J Hardy; Julio D C Maia; John E Stone; João V Ribeiro; Rafael C Bernardi; Ronak Buch; Giacomo Fiorin; Jérôme Hénin; Wei Jiang; Ryan McGreevy; Marcelo C R Melo; Brian K Radak; Robert D Skeel; Abhishek Singharoy; Yi Wang; Benoît Roux; Aleksei Aksimentiev; Zaida Luthey-Schulten; Laxmikant V Kalé; Klaus Schulten; Christophe Chipot; Emad Tajkhorshid
Journal:  J Chem Phys       Date:  2020-07-28       Impact factor: 3.488

5.  Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data.

Authors:  Jennifer M Hays; David S Cafiso; Peter M Kasson
Journal:  J Phys Chem Lett       Date:  2019-06-07       Impact factor: 6.475

6.  Overview of the SAMPL6 host-guest binding affinity prediction challenge.

Authors:  Andrea Rizzi; Steven Murkli; John N McNeill; Wei Yao; Matthew Sullivan; Michael K Gilson; Michael W Chiu; Lyle Isaacs; Bruce C Gibb; David L Mobley; John D Chodera
Journal:  J Comput Aided Mol Des       Date:  2018-11-10       Impact factor: 3.686

7.  Development of reactive force fields using ab initio molecular dynamics simulation minimally biased to experimental data.

Authors:  Chen Chen; Christopher Arntsen; Gregory A Voth
Journal:  J Chem Phys       Date:  2017-10-28       Impact factor: 3.488

8.  Understanding and Tracking the Excess Proton in Ab Initio Simulations; Insights from IR Spectra.

Authors:  Chenghan Li; Jessica M J Swanson
Journal:  J Phys Chem B       Date:  2020-06-24       Impact factor: 2.991

9.  A simple and fast approach for predicting (1)H and (13)C chemical shifts: toward chemical shift-guided simulations of RNA.

Authors:  Aaron T Frank; Sean M Law; Charles L Brooks
Journal:  J Phys Chem B       Date:  2014-10-15       Impact factor: 2.991

10.  Bayesian refinement of protein structures and ensembles against SAXS data using molecular dynamics.

Authors:  Roman Shevchuk; Jochen S Hub
Journal:  PLoS Comput Biol       Date:  2017-10-18       Impact factor: 4.475

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