Weighted Ensemble Simulation: Review of Methodology, Applications, and Software

被引:215
|
作者
Zuckerman, Daniel M. [1 ]
Chong, Lillian T. [2 ]
机构
[1] Oregon Hlth & Sci Univ, Dept Biomed Engn, Portland, OR 97239 USA
[2] Univ Pittsburgh, Dept Chem, Pittsburgh, PA 15260 USA
来源
基金
美国国家科学基金会;
关键词
cell modeling; kinetics; molecular dynamics; path sampling; rare events; weighted ensemble; ACCELERATED MOLECULAR-DYNAMICS; BROWNIAN DYNAMICS; PROTEIN ASSOCIATION; HIGHLY EFFICIENT; COMPUTATION; ALGORITHMS; KINETICS; SYSTEMS; EXPLORATION; MECHANISM;
D O I
10.1146/annurev-biophys-070816-033834
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. TheWEstrategy can achieve superlinear scaling-the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes-protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation.
引用
收藏
页码:43 / 57
页数:15
相关论文
共 50 条
  • [31] WURS: a simulation software for university rankings—software review
    Enis Siniksaran
    M. Hakan Satman
    Scientometrics, 2020, 122 : 701 - 717
  • [32] Machine learning guided weighted ensemble for rare event simulation in biophysics
    Prabhakar, Praveen Ranganath
    Ray, Dhiman
    Andricioaei, Ioan
    BIOPHYSICAL JOURNAL, 2023, 122 (03) : 423A - 423A
  • [33] Simulation of graded video impairment by weighted summation: Validation of the methodology
    Libert, JM
    Fenimore, CP
    Roitman, P
    MULTIMEDIA SYSTEMS AND APPLICATIONS II, 1999, 3845 : 254 - 265
  • [34] Weighted-Ensemble Brownian dynamics simulation of biomolecular reaction rates
    Rojnuckarin, A
    Livesay, DR
    Subramaniam, S
    BIOPHYSICAL JOURNAL, 1999, 76 (01) : A111 - A111
  • [36] Diffusion-weighted MR of the brain: methodology and clinical applications
    Mascalchi, M
    Filippi, M
    Floris, R
    Fonda, C
    Gasparotti, R
    Villari, N
    RADIOLOGIA MEDICA, 2005, 109 (03): : 155 - 197
  • [37] Parallel simulation by time segmentation: Methodology and applications
    HoseyniNasab, M
    Andradottir, S
    1996 WINTER SIMULATION CONFERENCE PROCEEDINGS, 1996, : 376 - 381
  • [38] Simulation Applications in the Operation Phase - Methodology for Classification
    Kain, Sebastian
    Frank, Timo
    Merz, Martin
    Schiler, Frank
    Heuschmann, Christian
    ATP EDITION, 2010, (06): : 50 - 59
  • [39] Review on Network Virtualization Simulation Software
    Wang, Yingshu
    Wang, Xu
    Zuo, Yu
    Liu, Qing
    Zhang, Juanjuan
    Yuan, Shu
    Yu, Fucai
    Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 2020, 55 (01): : 34 - 40
  • [40] Ship simulation software finds broader applications
    VanDoren, V
    CONTROL ENGINEERING, 1996, 43 (04) : 110 - 110