Literature DB >> 26583389

Using Multistate Reweighting to Rapidly and Efficiently Explore Molecular Simulation Parameters Space for Nonbonded Interactions.

Himanshu Paliwal1, Michael R Shirts1.   

Abstract

Multistate reweighting methods such as the multistate Bennett acceptance ratio (MBAR) can predict free energies and expectation values of thermodynamic observables at poorly sampled or unsampled thermodynamic states using simulations performed at only a few sampled states combined with single point energy reevaluations of these samples at the unsampled states. In this study, we demonstrate the power of this general reweighting formalism by exploring the effect of simulation parameters controlling Coulomb and Lennard-Jones cutoffs on free energy calculations and other observables. Using multistate reweighting, we can quickly identify, with very high sensitivity, the computationally least expensive nonbonded parameters required to obtain a specified accuracy in observables compared to the answer obtained using an expensive "gold standard" set of parameters. We specifically examine free energy estimates of three molecular transformations in a benchmark molecular set as well as the enthalpy of vaporization of TIP3P. The results demonstrates the power of this multistate reweighting approach for measuring changes in free energy differences or other estimators with respect to simulation or model parameters with very high precision and/or very low computational effort. The results also help to identify which simulation parameters affect free energy calculations and provide guidance to determine which simulation parameters are both appropriate and computationally efficient in general.

Year:  2013        PMID: 26583389     DOI: 10.1021/ct4005068

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


  5 in total

1.  Eigenvector method for umbrella sampling enables error analysis.

Authors:  Erik H Thiede; Brian Van Koten; Jonathan Weare; Aaron R Dinner
Journal:  J Chem Phys       Date:  2016-08-28       Impact factor: 3.488

2.  Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset.

Authors:  Michael R Shirts; Christoph Klein; Jason M Swails; Jian Yin; Michael K Gilson; David L Mobley; David A Case; Ellen D Zhong
Journal:  J Comput Aided Mol Des       Date:  2016-10-27       Impact factor: 3.686

3.  Best Practices for Foundations in Molecular Simulations [Article v1.0].

Authors:  Efrem Braun; Justin Gilmer; Heather B Mayes; David L Mobley; Jacob I Monroe; Samarjeet Prasad; Daniel M Zuckerman
Journal:  Living J Comput Mol Sci       Date:  2018-11-29

4.  Molecular simulation of water and hydration effects in different environments: challenges and developments for DFTB based models.

Authors:  Puja Goyal; Hu-Jun Qian; Stephan Irle; Xiya Lu; Daniel Roston; Toshifumi Mori; Marcus Elstner; Qiang Cui
Journal:  J Phys Chem B       Date:  2014-09-16       Impact factor: 2.991

Review 5.  "Dividing and Conquering" and "Caching" in Molecular Modeling.

Authors:  Xiaoyong Cao; Pu Tian
Journal:  Int J Mol Sci       Date:  2021-05-10       Impact factor: 5.923

  5 in total

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