Back to Search Start Over

Free Energy Reconstruction from Logarithmic Mean-Force Dynamics Using Multiple Nonequilibrium Trajectories.

Authors :
Morishita T
Yonezawa Y
Ito AM
Source :
Journal of chemical theory and computation [J Chem Theory Comput] 2017 Jul 11; Vol. 13 (7), pp. 3106-3119. Date of Electronic Publication: 2017 Jun 26.
Publication Year :
2017

Abstract

Efficient and reliable estimation of the mean force (MF), the derivatives of the free energy with respect to a set of collective variables (CVs), has been a challenging problem because free energy differences are often computed by integrating the MF. Among various methods for computing free energy differences, logarithmic mean-force dynamics (LogMFD) [ Morishita et al., Phys. Rev. E 2012 , 85 , 066702 ] invokes the conservation law in classical mechanics to integrate the MF, which allows us to estimate the free energy profile along the CVs on-the-fly. Here, we present a method called parallel dynamics, which improves the estimation of the MF by employing multiple replicas of the system and is straightforwardly incorporated in LogMFD or a related method. In the parallel dynamics, the MF is evaluated by a nonequilibrium path-ensemble using the multiple replicas based on the Crooks-Jarzynski nonequilibrium work relation. Thanks to the Crooks relation, realizing full-equilibrium states is no longer mandatory for estimating the MF. Additionally, sampling in the hidden subspace orthogonal to the CV space is highly improved with appropriate weights for each metastable state (if any), which is hardly achievable by typical free energy computational methods. We illustrate how to implement parallel dynamics by combining it with LogMFD, which we call logarithmic parallel dynamics (LogPD). Biosystems of alanine dipeptide and adenylate kinase in explicit water are employed as benchmark systems to which LogPD is applied to demonstrate the effect of multiple replicas on the accuracy and efficiency in estimating the free energy profiles using parallel dynamics.

Details

Language :
English
ISSN :
1549-9626
Volume :
13
Issue :
7
Database :
MEDLINE
Journal :
Journal of chemical theory and computation
Publication Type :
Academic Journal
Accession number :
28602083
Full Text :
https://doi.org/10.1021/acs.jctc.7b00252