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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]
+ Show Author Affiliations
  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. doi:10.1016/j.sbi.2018.09.005.
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Kasson, Peter M., & Jha, Shantenu. Adaptive ensemble simulations of biomolecules. United States. https://doi.org/10.1016/j.sbi.2018.09.005
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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.
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@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 = {Tue Sep 25 00:00:00 EDT 2018},
month = {Tue Sep 25 00:00:00 EDT 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.sbi.2018.09.005
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Cited by: 16 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 referenced in this record:

Convergence and reproducibility in molecular dynamics simulations of the DNA duplex d(GCACGAACGAACGAACGC)
journal, May 2015

  • Galindo-Murillo, Rodrigo; Roe, Daniel R.; Cheatham, Thomas E.
  • Biochimica et Biophysica Acta (BBA) - General Subjects, Vol. 1850, Issue 5
  • DOI: 10.1016/j.bbagen.2014.09.007

Ensemble molecular dynamics yields submillisecond kinetics and intermediates of membrane fusion
journal, July 2006

  • Kasson, P. M.; Kelley, N. W.; Singhal, N.
  • Proceedings of the National Academy of Sciences, Vol. 103, Issue 32
  • DOI: 10.1073/pnas.0601597103

Atomic-Resolution Simulations Predict a Transition State for Vesicle Fusion Defined by Contact of a Few Lipid Tails
journal, June 2010

Conformational Intermediate That Controls KPC-2 Catalysis and Beta-Lactam Drug Resistance
journal, February 2018

Water Ordering at Membrane Interfaces Controls Fusion Dynamics
journal, March 2011

  • Kasson, Peter M.; Lindahl, Erik; Pande, Vijay S.
  • Journal of the American Chemical Society, Vol. 133, Issue 11
  • DOI: 10.1021/ja200310d

Finding Transition Pathways Using the String Method with Swarms of Trajectories
journal, March 2008

  • Pan, Albert C.; Sezer, Deniz; Roux, Benoît
  • The Journal of Physical Chemistry B, Vol. 112, Issue 11
  • DOI: 10.1021/jp0777059

Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations
journal, November 2009

  • Noé, Frank; Schütte, Christof; Vanden-Eijnden, Eric
  • Proceedings of the National Academy of Sciences, Vol. 106, Issue 45
  • DOI: 10.1073/pnas.0905466106

Accurate Ensemble Molecular Dynamics Binding Free Energy Ranking of Multidrug-Resistant HIV-1 Proteases
journal, April 2010

  • Sadiq, S. Kashif; Wright, David W.; Kenway, Owain A.
  • Journal of Chemical Information and Modeling, Vol. 50, Issue 5
  • DOI: 10.1021/ci100007w

Mapping Long-Range Interactions in α-Synuclein using Spin-Label NMR and Ensemble Molecular Dynamics Simulations
journal, January 2005

  • Dedmon, Matthew M.; Lindorff-Larsen, Kresten; Christodoulou, John
  • Journal of the American Chemical Society, Vol. 127, Issue 2
  • DOI: 10.1021/ja044834j

Calculation of the distribution of eigenvalues and eigenvectors in Markovian state models for molecular dynamics
journal, June 2007

  • Hinrichs, Nina Singhal; Pande, Vijay S.
  • The Journal of Chemical Physics, Vol. 126, Issue 24
  • DOI: 10.1063/1.2740261

Enhanced Modeling via Network Theory: Adaptive Sampling of Markov State Models
journal, February 2010

  • Bowman, Gregory R.; Ensign, Daniel L.; Pande, Vijay S.
  • Journal of Chemical Theory and Computation, Vol. 6, Issue 3
  • DOI: 10.1021/ct900620b

WESTPA: An Interoperable, Highly Scalable Software Package for Weighted Ensemble Simulation and Analysis
journal, January 2015

  • Zwier, Matthew C.; Adelman, Joshua L.; Kaus, Joseph W.
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 2
  • DOI: 10.1021/ct5010615

Replica Monte Carlo Simulation of Spin-Glasses
journal, November 1986

New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles
journal, February 1992

  • Lyubartsev, A. P.; Martsinovski, A. A.; Shevkunov, S. V.
  • The Journal of Chemical Physics, Vol. 96, Issue 3
  • DOI: 10.1063/1.462133

Extended Ensemble Monte Carlo
journal, June 2001

Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations
journal, May 2004

Determination of solvation free energies by adaptive expanded ensemble molecular dynamics
journal, February 2004

  • Åberg, K. Magnus; Lyubartsev, Alexander P.; Jacobsson, Sven P.
  • The Journal of Chemical Physics, Vol. 120, Issue 8
  • DOI: 10.1063/1.1642601

Adaptively biased molecular dynamics for free energy calculations
journal, April 2008

  • Babin, Volodymyr; Roland, Christopher; Sagui, Celeste
  • The Journal of Chemical Physics, Vol. 128, Issue 13
  • DOI: 10.1063/1.2844595

Improving the efficiency and reliability of free energy perturbation calculations using overlap sampling methods
journal, January 2003

  • Lu, Nandou; Kofke, David A.; Woolf, Thomas B.
  • Journal of Computational Chemistry, Vol. 25, Issue 1
  • DOI: 10.1002/jcc.10369

Rapid Computation of Thermodynamic Properties over Multidimensional Nonbonded Parameter Spaces Using Adaptive Multistate Reweighting
journal, February 2016

  • Naden, Levi N.; Shirts, Michael R.
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 4
  • DOI: 10.1021/acs.jctc.5b00869

Molecular Simulation Workflows as Parallel Algorithms: The Execution Engine of Copernicus, a Distributed High-Performance Computing Platform
journal, May 2015

  • Pronk, Sander; Pouya, Iman; Lundborg, Magnus
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 6
  • DOI: 10.1021/acs.jctc.5b00234

PLUMED: A portable plugin for free-energy calculations with molecular dynamics
journal, October 2009

  • Bonomi, Massimiliano; Branduardi, Davide; Bussi, Giovanni
  • Computer Physics Communications, Vol. 180, Issue 10
  • DOI: 10.1016/j.cpc.2009.05.011

PLUMED 2: New feathers for an old bird
journal, February 2014

  • Tribello, Gareth A.; Bonomi, Massimiliano; Branduardi, Davide
  • Computer Physics Communications, Vol. 185, Issue 2
  • DOI: 10.1016/j.cpc.2013.09.018

Efficient Reconstruction of Complex Free Energy Landscapes by Multiple Walkers Metadynamics †
journal, March 2006

  • Raiteri, Paolo; Laio, Alessandro; Gervasio, Francesco Luigi
  • The Journal of Physical Chemistry B, Vol. 110, Issue 8
  • DOI: 10.1021/jp054359r

Computing time scales from reaction coordinates by milestoning
journal, June 2004

  • Faradjian, Anton K.; Elber, Ron
  • The Journal of Chemical Physics, Vol. 120, Issue 23
  • DOI: 10.1063/1.1738640

Markovian milestoning with Voronoi tessellations
journal, May 2009

  • Vanden-Eijnden, Eric; Venturoli, Maddalena
  • The Journal of Chemical Physics, Vol. 130, Issue 19
  • DOI: 10.1063/1.3129843

The parallel replica dynamics method – Coming of age
journal, April 2015

Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm
journal, August 2017

  • Ahn, Surl-Hee; Grate, Jay W.; Darve, Eric F.
  • The Journal of Chemical Physics, Vol. 147, Issue 7
  • DOI: 10.1063/1.4999097

Extracting macroscopic stochastic dynamics: Model problems: Extracting Macroscopic Dynamics
journal, November 2002

  • Huisinga, Wilhelm; Schütte, Christof; Stuart, Andrew M.
  • Communications on Pure and Applied Mathematics, Vol. 56, Issue 2
  • DOI: 10.1002/cpa.10057

Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory †
journal, May 2004

  • Swope, William C.; Pitera, Jed W.; Suits, Frank
  • The Journal of Physical Chemistry B, Vol. 108, Issue 21
  • DOI: 10.1021/jp037421y

Long‐Time Protein Folding Dynamics from Short‐Time Molecular Dynamics Simulations
journal, January 2006

  • Chodera, John D.; Swope, William C.; Pitera, Jed W.
  • Multiscale Modeling & Simulation, Vol. 5, Issue 4
  • DOI: 10.1137/06065146X

Markov models of molecular kinetics: Generation and validation
journal, May 2011

  • Prinz, Jan-Hendrik; Wu, Hao; Sarich, Marco
  • The Journal of Chemical Physics, Vol. 134, Issue 17
  • DOI: 10.1063/1.3565032

Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling
journal, June 2017

  • Plattner, Nuria; Doerr, Stefan; De Fabritiis, Gianni
  • Nature Chemistry, Vol. 9, Issue 10
  • DOI: 10.1038/nchem.2785

Leveraging the Information from Markov State Models To Improve the Convergence of Umbrella Sampling Simulations
journal, May 2016

  • Jo, Sunhwan; Suh, Donghyuk; He, Ziwei
  • The Journal of Physical Chemistry B, Vol. 120, Issue 33
  • DOI: 10.1021/acs.jpcb.6b05125

COMPUTING: Screen Savers of the World Unite!
journal, December 2000

Science in the Cloud: Accelerating Discovery in the 21st Century
journal, July 2012

  • Hellerstein, Joseph L.; Kohlhoff, Kai J.; Konerding, David E.
  • IEEE Internet Computing, Vol. 16, Issue 4
  • DOI: 10.1109/MIC.2012.87

MapReduce: simplified data processing on large clusters
journal, January 2008

  • Dean, Jeffrey; Ghemawat, Sanjay; Mehta, Brijesh
  • Communications of the ACM, Vol. 51, Issue 1
  • DOI: 10.1145/1327452.1327492

The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud
journal, May 2013

  • Wolstencroft, Katherine; Haines, Robert; Fellows, Donal
  • Nucleic Acids Research, Vol. 41, Issue W1
  • DOI: 10.1093/nar/gkt328

The Amber biomolecular simulation programs
journal, January 2005

  • Case, David A.; Cheatham, Thomas E.; Darden, Tom
  • Journal of Computational Chemistry, Vol. 26, Issue 16
  • DOI: 10.1002/jcc.20290

Scalable molecular dynamics with NAMD
journal, January 2005

  • Phillips, James C.; Braun, Rosemary; Wang, Wei
  • Journal of Computational Chemistry, Vol. 26, Issue 16, p. 1781-1802
  • DOI: 10.1002/jcc.20289

CHARMM: The biomolecular simulation program
journal, July 2009

  • Brooks, B. R.; Brooks, C. L.; Mackerell, A. D.
  • Journal of Computational Chemistry, Vol. 30, Issue 10
  • DOI: 10.1002/jcc.21287

GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
journal, February 2008

  • Hess, Berk; Kutzner, Carsten; van der Spoel, David
  • Journal of Chemical Theory and Computation, Vol. 4, Issue 3
  • DOI: 10.1021/ct700301q

GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit
journal, February 2013

Dynamic steering of HPC scientific workflows: A survey
journal, May 2015

Swift: A language for distributed parallel scripting
journal, September 2011

FireWorks: a dynamic workflow system designed for high-throughput applications: FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications
journal, May 2015

  • Jain, Anubhav; Ong, Shyue Ping; Chen, Wei
  • Concurrency and Computation: Practice and Experience, Vol. 27, Issue 17
  • DOI: 10.1002/cpe.3505

gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations
journal, June 2018

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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
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