An <i>ab initio</i> approach to free-energy reconstruction using logarithmic mean force dynamics

Nakamura, Makoto; Obata, Masao; Morishita, Tetsuya; Oda, Tetsuji

doi:10.1063/1.4874654

Cited by 5 publications

(2 citation statements)

References 39 publications

(65 reference statements)

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“…LogMFD was introduced in Morishita et al ( 2012 ) and further discussed in Morishita et al ( 2013 ). In Nakamura et al ( 2014 ), it was combined with a Density Functional Theory approach to study the conformational dynamics of a glycine peptide. Similarly to what was suggested with equation ( 2 ), in LogMFD the collective variables are considered dynamical variables, and they are evolved using the following equation of motion

where σ and λ are real positive parameters discussed below, and

1

is again the inverse of an effective temperature, which is in general different from the physical temperature, although in LogMFD is often set as equal to it.…”

Section: Methods To Explore and Reconstruct The Free-energy Landscapementioning

confidence: 99%

Extended Phase-Space Methods for Enhanced Sampling in Molecular Simulations: A Review

Fujisaki

Moritsugu

Matsunaga

et al. 2015

Front. Bioeng. Biotechnol.

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Molecular Dynamics simulations are a powerful approach to study biomolecular conformational changes or protein–ligand, protein–protein, and protein–DNA/RNA interactions. Straightforward applications, however, are often hampered by incomplete sampling, since in a typical simulated trajectory the system will spend most of its time trapped by high energy barriers in restricted regions of the configuration space. Over the years, several techniques have been designed to overcome this problem and enhance space sampling. Here, we review a class of methods that rely on the idea of extending the set of dynamical variables of the system by adding extra ones associated to functions describing the process under study. In particular, we illustrate the Temperature Accelerated Molecular Dynamics (TAMD), Logarithmic Mean Force Dynamics (LogMFD), and Multiscale Enhanced Sampling (MSES) algorithms. We also discuss combinations with techniques for searching reaction paths. We show the advantages presented by this approach and how it allows to quickly sample important regions of the free-energy landscape via automatic exploration.

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where σ and λ are real positive parameters discussed below, and

1

is again the inverse of an effective temperature, which is in general different from the physical temperature, although in LogMFD is often set as equal to it.…”

Section: Methods To Explore and Reconstruct The Free-energy Landscapementioning

confidence: 99%

Extended Phase-Space Methods for Enhanced Sampling in Molecular Simulations: A Review

Fujisaki

Moritsugu

Matsunaga

et al. 2015

Front. Bioeng. Biotechnol.

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“…That is, a long X -trajectory that repeatedly visits the same point is not necessary, which may be of help to multidimensional free energy calculations. LogMFD has been applied to a biomolecule ,, [described by CHARM22 force field and density functional theory (DFT) with the GGA functional] and a two-dimensional silicon material, demonstrating its efficiency and applicability. In the latter case, an unexpected structural change was discovered in the course of the enhanced sampling of the atomic configuration by LogMFD.…”

Section: Introductionmentioning

confidence: 99%

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

Morishita

Yonezawa

Ito

2017

J. Chem. Theory Comput.

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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.

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Efficient Sampling of High-Dimensional Free Energy Landscapes: A Review of Parallel Bias Metadynamics

Alamdari

Sampath

Prakash

et al. 2021

Foundations of Molecular Modeling and Simulation

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An ab initio approach to free-energy reconstruction using logarithmic mean force dynamics

Cited by 5 publications

References 39 publications

Extended Phase-Space Methods for Enhanced Sampling in Molecular Simulations: A Review

Extended Phase-Space Methods for Enhanced Sampling in Molecular Simulations: A Review

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

Efficient Sampling of High-Dimensional Free Energy Landscapes: A Review of Parallel Bias Metadynamics

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