Journal of Molecular Modeling

, 21:243 | Cite as

Greedy replica exchange algorithm for heterogeneous computing grids

  • Christopher Lockhart
  • James O’Connor
  • Steven Armentrout
  • Dmitri K. Klimov
Original Paper


Replica exchange molecular dynamics (REMD) has become a valuable tool in studying complex biomolecular systems. However, its application on distributed computing grids is limited by the heterogeneity of this environment. In this study, we propose a REMD implementation referred to as greedy REMD (gREMD) suitable for computations on heterogeneous grids. To decentralize replica management, gREMD utilizes a precomputed schedule of exchange attempts between temperatures. Our comparison of gREMD against standard REMD suggests four main conclusions. First, gREMD accelerates grid REMD simulations by as much as 40 %. Second, gREMD increases CPU utilization rates in grid REMD by up to 60 %. Third, we argue that gREMD is expected to maintain approximately constant CPU utilization rates and simulation wall-clock times with the increase in the number of replicas. Finally, we show that gREMD correctly implements the REMD algorithm and reproduces the conformational ensemble of a short peptide sampled in our previous standard REMD simulations. We believe that gREMD can find its place in large-scale REMD simulations on heterogeneous computing grids.

Graphical Abstract

Standard replica exchange molecular dynamics (REMD) typically requires all replicas to complete prior to initiation of the replica exchange protocol. Greedy REMD decentralizes this process and therefore only requires a replica and its predetermined exchange partner to have finished simulations prior to initiating replica exchange. Because greedy REMD reduces the idle time associated with replica exchange tasks, it becomes particularly well suited for performing REMD on heterogeneous distributed computing environments.


Replica exchange molecular dynamics Conformational sampling Distributed grid-based simulations Decentralized replica management Aβ peptide 



This work was supported by the National Institute on Aging (National Institutes of Health) (grant R41 AG044022). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health.


  1. 1.
    Swendsen RH, Wang J-S (1986) Replica Monte Carlo simulations of spin-glasses. Phys Rev Lett 57:2607–2609CrossRefGoogle Scholar
  2. 2.
    Hansmann U H E (1997) Parallel tempering algorithm for conformational studies of biological molecules. Chem Phys Lett 281:140–150CrossRefGoogle Scholar
  3. 3.
    Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151CrossRefGoogle Scholar
  4. 4.
    Fukunishi H, Watanabe O, Takada S (2002) On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction. J Chem Phys 116:9058–9067CrossRefGoogle Scholar
  5. 5.
    Yan Q, de Pablo J J (1999) Hyperparallel tempering Monte Carlo: Application to the Lennard–Jones fluid and the restricted primitive model. J Chem Phys 111:9509CrossRefGoogle Scholar
  6. 6.
    Okabe T, Kawata M, Okamoto Y, Mikami M (2001) Replica-exchange Monte Carlo method for the isobaric-isothermal ensemble. Chem Phys Lett 335:435–439CrossRefGoogle Scholar
  7. 7.
    Mori T, Jung J, Sugita Y (2013) Surface-tension replica-exchange molecular dynamics method for enhanced sampling of biological membrane systems. J Chem Theor Comp 9:5629–5640CrossRefGoogle Scholar
  8. 8.
    Kalé L, Skeel R, Bhandarkar M, Brunner R, Gursoy A, Krawetz N, Phillips J, Shinozaki A, Varadarajan K, Schulten K (1999) NAMD2: Greater scalability for parallel molecular dynamics. J. Comp. Phys. 151:283–312CrossRefGoogle Scholar
  9. 9.
    Gallicchio E, Levy RM, Parashar M (2008) Asynchronous replica exchange for molecular simulations. J Comp Chem 29:788–794CrossRefGoogle Scholar
  10. 10.
    Rhee YM, Pande VJ (2003) Multiplexed-replica exchange molecular dynamics method for protein folding simulation. Biophys J 84:775–786CrossRefGoogle Scholar
  11. 11.
    Hagen M, Kim B, Liu P, Friesner RA, Burne BJ (2007) Serial replica exchange. J Phys Chem B 111:1416–1423CrossRefGoogle Scholar
  12. 12.
    Huang X, Bowman GR, Pande VS (2008) Convergence of folding free energy landscapes via application of enhanced sampling methods in a distributed computing environment. J Chem Phys 128:205106CrossRefGoogle Scholar
  13. 13.
    Lockhart C, Klimov DK (2013) Revealing hidden helix propensity in Aβ peptides by molecular dynamics simulations. J Phys Chem B 117:12030–12038CrossRefGoogle Scholar
  14. 14.
    Nymeyer H, Ghanakaran S, Garćia AE (2004) Atomic simulations of protein folding using the replica exchange algorithm. Methods Enzymol 383:119–149CrossRefGoogle Scholar
  15. 15.
    Cecchini M, Rao F, Seeber M, Caflisch A (2004) Replica exchange molecular dynamics simulations of amyloid peptide aggregation. J Chem Phys 121:10748–10756CrossRefGoogle Scholar
  16. 16.
    Tsai H-H, Reches M, Tsai C-J, Gunasekaran K, Gazit E, Nussinov R (2005) Energy landscape of amyloidogenic peptide oligomerization by parallel-tempering molecular dynamics simulation: Significant role of Asn ladder. Proc. Natl. Acad. Sci. USA 102:8174–8179CrossRefGoogle Scholar
  17. 17.
    Baumketner A, Shea J-E (2007) The structure of the Alzheimer amyloid β 10-35 peptide probed through replica-exchange molecular dynamics simulations in explicit solvent. J Mol Biol 366:275–285CrossRefGoogle Scholar
  18. 18.
    Zheng W, Andrec M, Gallicchio E, Levy R M (2007) Simulating replica exchange simulations of protein folding with a kinetic network model. Proc Natl Acad Sci USA 104:15340–15345CrossRefGoogle Scholar
  19. 19.
    Lockhart C, Klimov D K (2012) Molecular interactions of Alzheimer’s biomarker FDDNP with Aβ peptide. Biophys J 103:2341–2351CrossRefGoogle Scholar
  20. 20.
    Lockhart C, Kim S, Klimov D K (2012) Explicit solvent molecular dynamics simulations of Aβ peptide interacting with ibuprofen ligands. J Phys Chem B 116:12922–12932CrossRefGoogle Scholar
  21. 21.
    Kim S, Klimov D K (2013) Binding to the lipid monolayer induces conformational transition in Aβ monomer. J Mol Model 19:737–750CrossRefGoogle Scholar
  22. 22.
    Lockhart C, Klimov DK (2014) Alzheimer’s Aβ10-40 peptide binds and penetrates DMPC bilayer: An isobaric-isothermal replica exchange molecular dynamics study. J Phys Chem B 118:2638–2648CrossRefGoogle Scholar
  23. 23.
    Lockhart C, Klimov DK (2014) Binding of Aβ peptide creates lipid density depression in DMPC bilayer. BBA Biomembranes 1838:2678–2688CrossRefGoogle Scholar
  24. 24.
    Buck M, Bouguet-Bonnet S, Pastor R W, MacKerell A D (2006) Importance of the CMAP correction to the CHARMM22 protein force field: Dynamics of hen lysozyme. Biophys J 90:L36–L38CrossRefGoogle Scholar
  25. 25.
    Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins Struct Funct Gen 23:566–579CrossRefGoogle Scholar
  26. 26.
    Ferrenberg A M, Swendsen R H (1989) Optimized Monte Carlo data analysis. Phys Rev Lett 63:1195–1198CrossRefGoogle Scholar
  27. 27.
    Han M, Hansmann UHE (2011) Replica exchange molecular dynamics of the thermodynamics of fibril growth of Alzheimer’s Aβ42 peptide. J Chem Phys 135:065101Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Christopher Lockhart
    • 1
  • James O’Connor
    • 2
  • Steven Armentrout
    • 2
  • Dmitri K. Klimov
    • 1
  1. 1.School of Systems BiologyGeorge Mason UniversityManassasUSA
  2. 2.Parabon Computation Inc.RestonUSA

Personalised recommendations