The self-guided Langevin dynamics (SGLD) is a method to accelerate conformational searching. This method is unique in the way that it selectively enhances and suppresses molecular motions based on their frequency to accelerate conformational searching without modifying energy surfaces or raising temperatures. It has been applied to studies of many long time scale events, such as protein folding. Recent progress in the understanding of the conformational distribution in SGLD simulations makes SGLD also an accurate method for quantitative studies. The SGLD partition function provides a way to convert the SGLD conformational distribution to the canonical ensemble distribution and to calculate ensemble average properties through reweighting. Based on the SGLD partition function, this work presents a force-momentum-based self-guided Langevin dynamics (SGLDfp) simulation method to directly sample the canonical ensemble. This method includes interaction forces in its guiding force to compensate the perturbation caused by the momentum-based guiding force so that it can approximately sample the canonical ensemble. Using several example systems, we demonstrate that SGLDfp simulations can approximately maintain the canonical ensemble distribution and significantly accelerate conformational searching. With optimal parameters, SGLDfp and SGLD simulations can cross energy barriers of more than 15 kT and 20 kT, respectively, at similar rates for LD simulations to cross energy barriers of 10 kT. The SGLDfp method is size extensive and works well for large systems. For studies where preserving accessible conformational space is critical, such as free energy calculations and protein folding studies, SGLDfp is an efficient approach to search and sample the conformational space.
Skip Nav Destination
,
Article navigation
28 November 2011
Research Article|
November 23 2011
Force-momentum-based self-guided Langevin dynamics: A rapid sampling method that approaches the canonical ensemble
Xiongwu Wu;
Xiongwu Wu
a)
Laboratory of Computational Biology,
National Heart, Lung, and Blood Institute (NHLBI)
, National Institutes of Health (NIH), Bethesda, Maryland 20892, USA
Search for other works by this author on:
Bernard R. Brooks
Bernard R. Brooks
Laboratory of Computational Biology,
National Heart, Lung, and Blood Institute (NHLBI)
, National Institutes of Health (NIH), Bethesda, Maryland 20892, USA
Search for other works by this author on:
Xiongwu Wu
a)
Bernard R. Brooks
Laboratory of Computational Biology,
National Heart, Lung, and Blood Institute (NHLBI)
, National Institutes of Health (NIH), Bethesda, Maryland 20892, USA
a)
Author to whom correspondence should be addressed. Electronic mail: [email protected]. Telephone: 301-451-6251. Fax: 301-480-6496.
J. Chem. Phys. 135, 204101 (2011)
Article history
Received:
March 28 2011
Accepted:
November 01 2011
Citation
Xiongwu Wu, Bernard R. Brooks; Force-momentum-based self-guided Langevin dynamics: A rapid sampling method that approaches the canonical ensemble. J. Chem. Phys. 28 November 2011; 135 (20): 204101. https://doi.org/10.1063/1.3662489
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
The Amsterdam Modeling Suite
Evert Jan Baerends, Nestor F. Aguirre, et al.
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
Light–matter interaction at the nano- and molecular scale
Kaifeng Wu, Chufeng Zhang, et al.
Related Content
Reformulation of the self-guided molecular simulation method
J. Chem. Phys. (September 2020)
Replica exchanging self-guided Langevin dynamics for efficient and accurate conformational sampling
J. Chem. Phys. (July 2012)
Toward canonical ensemble distribution from self-guided Langevin dynamics simulation
J. Chem. Phys. (April 2011)
Self-guided enhanced sampling methods for thermodynamic averages
J. Chem. Phys. (January 2003)
Enhanced configurational sampling with hybrid non-equilibrium molecular dynamics–Monte Carlo propagator
J. Chem. Phys. (January 2018)