Literature DB >> 10919998

Biased Brownian dynamics for rate constant calculation.

G Zou1, R D Skeel, S Subramaniam.   

Abstract

An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampling of trajectories is then biased, but the sampling is unbiased when the trajectory outcomes are multiplied by their weights. With a suitable choice of the biasing force, more reacted trajectories are sampled. As a consequence, the variance of the estimate is reduced. In our test case, biased Brownian dynamics gives a sevenfold improvement in central processing unit (CPU) time with the choice of a simple centripetal biasing force.

Mesh:

Year:  2000        PMID: 10919998      PMCID: PMC1300964          DOI: 10.1016/S0006-3495(00)76322-3

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  1 in total

1.  Weighted-ensemble Brownian dynamics simulations for protein association reactions.

Authors:  G A Huber; S Kim
Journal:  Biophys J       Date:  1996-01       Impact factor: 4.033

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2.  Robust biased Brownian dynamics for rate constant calculation.

Authors:  Gang Zou; Robert D Skeel
Journal:  Biophys J       Date:  2003-10       Impact factor: 4.033

3.  Finite element solution of the steady-state Smoluchowski equation for rate constant calculations.

Authors:  Yuhua Song; Yongjie Zhang; Tongye Shen; Chandrajit L Bajaj; J Andrew McCammon; Nathan A Baker
Journal:  Biophys J       Date:  2004-04       Impact factor: 4.033

Review 4.  Understanding ligand-receptor non-covalent binding kinetics using molecular modeling.

Authors:  Zhiye Tang; Christopher C Roberts; Chia-En A Chang
Journal:  Front Biosci (Landmark Ed)       Date:  2017-01-01

5.  Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

Authors:  Zhong-Ru Xie; Jiawen Chen; Yinghao Wu
Journal:  Sci Rep       Date:  2017-04-18       Impact factor: 4.379

6.  Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association.

Authors:  Kalyani Dhusia; Zhaoqian Su; Yinghao Wu
Journal:  Biomolecules       Date:  2020-07-15
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