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

By Daniel M. Zuckerman, Lillian T. Chong. Annual Review of Biophysics, 2017

305 citations recorded by the source corpus.

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. The WE strategy 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.

Subfields: Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2456c2bab5c3591518db6c4d8f81719240413bf8

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Citations in the corpus

Dynamics of proteins in solution (2019, background): “…these aspects, we refer the reader to Okumura et al. (2017); Riest et al. (2018); Das et al. (2018); Riest and Nägele (2015); Liu et al. (2018); Mitsutake and Takano (2018); Zuckerman and Chong (2017); Feig et al. (2017); Perilla et al. (2015); Schöneberg et al. (2014); Karplus and McCammon (2002).”
Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0] (2022, methodology): “The approach relies on running relatively short MD simulation and terminating simulations that are not making progress towards the target state while replicating simulations that are instead progressing towards the end state [327].”
Protein storytelling through physics (2020, background): “Theweighted ensemble method ( 78 ) also fences off conformational space into zones but then spawns new daughter trajectories whenever a trajectory hits a fence that leads to an “ interesting ” area of the phase space. reweighting scheme to learn the unbiased time evolution of the trajectory ( 79 ).”