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Title: Adaptive ensemble simulations of biomolecules

Abstract

Recent advances in both theory and computational power have created opportunities to simulate biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble simulations are now widely used to compute a number of individual simulation trajectories and analyze statistics across them. Adaptive ensemble simulations offer a further level of sophistication and flexibility by enabling highlevel algorithms to control simulations-based on intermediate results. We review some of the adaptive ensemble algorithms and software infrastructure currently in use and outline where the complexities of implementing adaptive simulation have limited algorithmic innovation to date. In conclusion, we describe an adaptive ensemble API to overcome some of these barriers and more flexibly and simply express adaptive simulation algorithms to help realize the power of this type of simulation.

Authors:
 [1];  [2]
  1. Univ. of Virginia, Charlottesville, VA (United States); Uppsala Univ., Uppsala (Sweden)
  2. Rutgers Univ., Piscataway, NJ (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
OSTI Identifier:
1491694
Report Number(s):
BNL-210893-2019-JAAM
Journal ID: ISSN 0959-440X
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Current Opinion in Structural Biology
Additional Journal Information:
Journal Volume: 52; Journal Issue: C; Journal ID: ISSN 0959-440X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; biomolecules

Citation Formats

Kasson, Peter M., and Jha, Shantenu. Adaptive ensemble simulations of biomolecules. United States: N. p., 2018. Web. https://doi.org/10.1016/j.sbi.2018.09.005.
Kasson, Peter M., & Jha, Shantenu. Adaptive ensemble simulations of biomolecules. United States. https://doi.org/10.1016/j.sbi.2018.09.005
Kasson, Peter M., and Jha, Shantenu. Tue . "Adaptive ensemble simulations of biomolecules". United States. https://doi.org/10.1016/j.sbi.2018.09.005. https://www.osti.gov/servlets/purl/1491694.
@article{osti_1491694,
title = {Adaptive ensemble simulations of biomolecules},
author = {Kasson, Peter M. and Jha, Shantenu},
abstractNote = {Recent advances in both theory and computational power have created opportunities to simulate biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble simulations are now widely used to compute a number of individual simulation trajectories and analyze statistics across them. Adaptive ensemble simulations offer a further level of sophistication and flexibility by enabling highlevel algorithms to control simulations-based on intermediate results. We review some of the adaptive ensemble algorithms and software infrastructure currently in use and outline where the complexities of implementing adaptive simulation have limited algorithmic innovation to date. In conclusion, we describe an adaptive ensemble API to overcome some of these barriers and more flexibly and simply express adaptive simulation algorithms to help realize the power of this type of simulation.},
doi = {10.1016/j.sbi.2018.09.005},
journal = {Current Opinion in Structural Biology},
number = C,
volume = 52,
place = {United States},
year = {2018},
month = {9}
}

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Cited by: 3 works
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Figures / Tables:

Figure 1 Figure 1: Adaptive ensemble work diagrams. Panel (a) schematizes an asynchronous replica exchange loop. Ensemble members are run asynchronously, so there is no global barrier before exchange or analysis. This is not per se an adaptive concern but is required for many efficient adaptive algorithms. An ensemble analysis then testsmore » for convergence and either re-triggers the loop (perhaps with altered parameters) or writes a final output. Panel (b) schematizes more complex adaptive logic, where an initial simulation ensemble of protein-ligand interaction asynchronously triggers an analysis calculation (which could be clustering and Markov State Model construction). This analysis calculation either adaptively reseeds the ensemble simulation run or, if the run is converged, starts an ensemble free-energy-perturbation (FEP) calculation on a new ligand (lower branch). Depending on the result of this FEP calculation, it is either ‘accepted’ and a new Markov State Model calculation started with the new ligand, or it is ‘rejected’ and a new ligand tested. In all schemas, dark gray rectangles indicate ensemble simulations and light gray rectangles indicate analyses.« less

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Works referencing / citing this record:

Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer
book, December 2019

  • Ossyra, John; Sedova, Ada; Tharrington, Arnold
  • High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers, p. 397-417
  • DOI: 10.1007/978-3-030-34356-9_30

Highly Interactive, Steered Scientific Workflows on HPC Systems: Optimizing Design Solutions
book, December 2019

  • Ossyra, John R.; Sedova, Ada; Baker, Matthew B.
  • High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers, p. 514-527
  • DOI: 10.1007/978-3-030-34356-9_39

Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles
journal, January 2020

  • Kadupitiya, Jcs; Fox, Geoffrey C.; Jadhao, Vikram
  • The International Journal of High Performance Computing Applications, Vol. 34, Issue 3
  • DOI: 10.1177/1094342019899457

    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.