Generalized Ensemble Molecular Dynamics Methods

Part of the Scientific Computation book series (SCIENTCOMP)


Generalized ensemble molecular dynamics simulation methods can be used to improve the sampling of lower energy configurations.


  1. Andricioaei, I., Straub, J.E.: Generalized simulated annealing algorithms using Tsallis statistics: application to conformational optimization of a tetrapeptide. Phys. Rev. E 53, R3055 (1996a)CrossRefADSGoogle Scholar
  2. Andricioaei, I., Straub, J.E.: On Monte Carlo and molecular dynamics methods inspired by Tsallis statistics: methodology, optimization, and application to atomic clusters. J. Chem. Phys. 107, 9117–9124 (1997)CrossRefADSGoogle Scholar
  3. Bartels, C., Karplus, M.: Probability distributions for complex systems: adaptive umbrella sampling of the potential energy. J. Phys. Chem. B 102, 865–880 (1998)CrossRefGoogle Scholar
  4. Barth, E.J., Laird, B.B., Leimkuhler, B.J.: Generating generalised distributions from dynamical simulation. J. Chem. Phys. 118, 5759–5768 (2003)CrossRefADSGoogle Scholar
  5. Bashford, D., Case, D.A.: Generalized Born model of macromolecular salvation effects. Annu. Rev. Phys. Chem. 51, 129–152 (2000)CrossRefADSGoogle Scholar
  6. Berg, B.A.: Markov Chain Monte Carlo Simulations and their Statistical Analysis. World Scientific, Singapore (2004)CrossRefzbMATHGoogle Scholar
  7. Berg, B.A., Celik, T.: New approach to spin-glass simulations. Phys. Rev. Lett. 69, 2292–2295 (1992)CrossRefADSGoogle Scholar
  8. Berg, B.A., Neuhaus, T.: Multicanonical algorithms for first order phase transitions. Phys. Lett. B267, 249–253 (1991)CrossRefADSGoogle Scholar
  9. Berg, B.A., Neuhaus, T.: Multicanonical ensemble: A new approach to simulate first-order phase transitions. Phys. Rev. Lett. 68, 9–12 (1992)CrossRefADSGoogle Scholar
  10. Cheng, X., Cui, G., Hornak, V., Simmerling, C.: Modified replica exchange simulation methods for local structure refinement. J. Phys. Chem. B 109, 8220–8230 (2005)CrossRefGoogle Scholar
  11. Chodera, J.D., Swope, W.C., Pitera, J.W., Seok, C., Dill, K.A.: Use of the weighted histogram analysis method for the analysis of simulated and parallel tempering simulations. J. Chem. Theory Comput. 3, 26–41 (2007)CrossRefGoogle Scholar
  12. de Oliveira, P.M.C.: Broad histogram simulation: microcanonical ising dynamics. Int. J. Mod. Phys. C 9, 497–503 (1998)CrossRefADSGoogle Scholar
  13. de Oliveira, P.M.C., Penna, T.J.P., Herrmann, H.J.: Broad histogram method. Braz. J. Phys. 26, 677 (1996)ADSGoogle Scholar
  14. Earl, D.J., Deem, M.W.: Parallel tempering: theory, applications, and new perspectives. Phys. Chem. Chem. Phys. 7, 3910 (2005)CrossRefGoogle Scholar
  15. Escobedo, F.A., Martinez-Veracoechea, F.J.: Optimized expanded ensembles for simulations involving molecular insertions and deletions. I. Closed systems. J. Chem. Phys. 127, 174103 (2007)ADSGoogle Scholar
  16. Falcioni, M., Deem, M.W.: A biased Monte Carlo scheme for zeolite structure solution. J. Chem. Phys. 110(3), 1754 (1999)CrossRefADSGoogle Scholar
  17. Fukuda, I., Nakamura, H.: Tsallis dynamics using the Nosé-Hoover approach. Phys. Rev. E 65, 026105 (2002)CrossRefADSMathSciNetGoogle Scholar
  18. Fukunishi, H., Watanabe, O., Takada, S.: On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: aplacation to protein structure prediction. J. Chem. Phys. 116, 9058–9067 (2002)CrossRefADSGoogle Scholar
  19. Gallicchio, E., Andrec, M., Felts, A.K., Levy, R.M.: Temperature weighted histogram analysis method, replica exchange, and transition paths. J. Phys. Chem. B 109, 6722–6731 (2005)CrossRefGoogle Scholar
  20. Garcia, A.E., Onuchic, J.N.: Folding a protein in a computer: an atomic description of the folding/unfolding of protein A. Proc. Natl. Acad. Sci. USA 100, 13898–13903 (2003)CrossRefADSGoogle Scholar
  21. Geyer, G.J.: Practical Markov chain Monte Carlo. Stat. Sci. 7, 473–483 (1992)CrossRefGoogle Scholar
  22. Gront, D., Kolinski, A.: Efficient scheme for optimization of parallel tempering Monte Carlo method. J. Phys. Condens. Matter 19, 036225 (2007)CrossRefADSGoogle Scholar
  23. Hansmann, U.H.E., Okamoto, Y.: Generalized-ensemble Monte Carlo method for systems with rough energy landscape. Phys. Rev. E 56(2), 2228–2233 (1997)CrossRefADSGoogle Scholar
  24. Hansmann, U.H.E., Okamoto, Y.: Annual Reviews in Computational Physics VI. World Scientific, Singapore (1999)Google Scholar
  25. Hansmann, U.H.E., Okamoto, Y., Eisenmenger, F.: Molecular dynamics, Langevin and hydrid Monte Carlo simulations in a multicanonical ensemble. Chem. Phys. Lett. 259, 321–330 (1996)Google Scholar
  26. Hoover, W.G.: Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A 31, 1695–1697 (1985a)CrossRefADSGoogle Scholar
  27. Hukushima, K., Nemoto, K.: Exchange Monte Carlo method and application to spin glass simulations. J. Phys. Soc. Jpn. 65, 1604–1608 (1996)CrossRefADSGoogle Scholar
  28. Jang, S., Shin, S., Pak, Y.: Replica-exchange method using the generalized effective potential. Phys. Rev. Lett. 91, 058305 (2003)CrossRefADSGoogle Scholar
  29. Jang, S., Kim, E., Pak, Y.: All-atom level direct folding simulation of ββα miniprotein. J. Chem. Phys. 128, 105102 (2008)CrossRefADSGoogle Scholar
  30. Kamberaj, H.: Conformational sampling enhancement of replica exchange molecular dynamics simulations using swarm particle intelligence. J. Chem. Phys. 143, 124105–8 (2015)CrossRefADSGoogle Scholar
  31. Kamberaj, H.: Faster protein folding using enhanced conformational sampling of molecular dynamics simulation. J. Mol. Graph. Model. 81, 32–49 (2018)CrossRefGoogle Scholar
  32. Kamberaj, H.: Advanced methods used in molecular dynamics simulation of macromolecules. In: Kale, S.A. (ed.) Mechanical Design, Materials and Manufacturing, pp. 57–134. Nova Science Publishers, Inc., New York (2019)Google Scholar
  33. Kamberaj, H., van der Vaart, A.: Multiple scaling replica exchange for the conformational sampling of biomolecules in explicit water. J. Chem. Phys. 127, 234102–234109 (2007)CrossRefADSGoogle Scholar
  34. Kamberaj, H., van der Vaart, A.: An optimised replica exchange method for molecular dynamics simulations. J. Chem. Phys. 130, 074904 (2009)CrossRefADSGoogle Scholar
  35. Karolak, A., van der Vaart, A.: Importance of local interactions for the stability of inhibitory helix 1 of Ets-1 in the apo state. Biophys. Chem. 165–166(3), 74–78 (2012)CrossRefGoogle Scholar
  36. Katzgraber, H.G., Trebst, S., Huse, D.A., Troyer, M.: Feedback-optimized parallel tempering Monte Carlo. J. Stat. Mech. 2006(3), P03018 (2006)CrossRefzbMATHGoogle Scholar
  37. Kim, J., Straub, J.E.: Optimal replica exchange method combined with Tsallis weight sampling. J. Chem. Phys. 130, 144114–11 (2009)CrossRefADSGoogle Scholar
  38. Kone, A., Kofke, D.A.: Selection of temperature intervals for parallel-tempering simulations. J. Chem. Phys. 122, 206101 (2005)CrossRefADSGoogle Scholar
  39. Li, X., O’Brien, C.P., Collier, G., Vellore, N.A., Wang, F., Latour, R.A.: An improved replica-exchange sampling method: temperature intervals with global energy reassignment. J. Chem. Phys. 127, 164116 (2007)CrossRefADSGoogle Scholar
  40. Liu, P., Kim, B., Friesner, R.A., Berne, B.J.: Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proc. Natl. Acad. Sci. USA 103(39), 13749–13754 (2005)CrossRefADSGoogle Scholar
  41. Nadler, W., Hansmann, U.H.E.: Generalized ensemble and tempering simulations: a unified view. Phys. Rev. E 75, 026109 (2007)CrossRefADSGoogle Scholar
  42. Nakajima, N., Nakamura, H., Kidera, A.: Multicanonical ensemble generated by molecular dynamics simulation for enhanced conformational sampling of peptides. J. Phys. Chem. B 101, 817–824 (1997)CrossRefGoogle Scholar
  43. Neal, R.M.: Sampling from multimodal distributions using tempered transitions. Stat. Comput. 6(4), 353–366 (1996)CrossRefMathSciNetGoogle Scholar
  44. Nosé, S.: A molecular dynamics method for simulation in the canonical ensemble. Mol. Phys. 52, 255 (1984c)CrossRefADSGoogle Scholar
  45. Okamoto, Y., Hansmann, U.H.E.: Thermodynamics of helix-coil transitions studied by multicanonical algorithms. J. Phys. Chem. 99, 11276–11287 (1995)CrossRefGoogle Scholar
  46. Okur, A., Wickstrom, L., Layten, M., Geney, R., Song, K., Hornak, V., Simmerling, C.J.: Improved efficiency of replica exchange simulations through use of hybrid explicit/implicit salvation model. J. Chem. Theory Comput. 2, 420–433 (2006)CrossRefGoogle Scholar
  47. Pak, Y., Wang, S.: Folding of a 16-residue helical peptide using molecular dynamics simulation with Tsallis effective potential. J. Chem. Phys. 111, 4359 (1999)CrossRefADSGoogle Scholar
  48. Pak, Y., Wang, S.: Application of a molecular dynamics simulation method with a generalized effective potential to the exible molecular docking problems. J. Phys. Chem. B 104, 354–359 (2000)CrossRefGoogle Scholar
  49. Penna, T.J.P.: Traveling salesman problem and Tsallis statistics. Phys. Rev. E 51, R1 (1995)CrossRefADSGoogle Scholar
  50. Predescu, C., Predescu, M., Ciobanu, C.: The incomplete beta function law for parallel tempering sampling of classical canonical systems. J. Chem. Phys. 120(9), 4119–4128 (2004)CrossRefADSGoogle Scholar
  51. Predescu, C., Predescu, M., Ciobanu, C.V.: On the efficiency of exchange in parallel tempering Monte Carlo simulations. J. Phys. Chem. B 109, 4189–4196 (2005)CrossRefGoogle Scholar
  52. Rathore, N., Chopra, M., de Pablo, J.J.: Optimal allocation of replicas in parallel tempering simulations. J. Chem. Phys. 122, 024111 (2005)CrossRefADSGoogle Scholar
  53. Rogal, J., Bolhuis, P.G.: Multiple state transition path sampling. J. Chem. Phys. 129, 224107 (2008)CrossRefADSGoogle Scholar
  54. Sabo, D., Meuwly, M., Freeman, D.L., Doll, J.D.: A constant entropy increase model for the selection of parallel tempering ensembles. J. Chem. Phys. 128, 174109 (2008)CrossRefADSGoogle Scholar
  55. Schlick, T.: Molecular Modeling and Simulation. An Interdisciplinary Guide, 2nd edn. Springer, New York (2010)Google Scholar
  56. Sugita, Y., Okamoto, Y.: Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999)CrossRefADSGoogle Scholar
  57. Sugita, Y., Okamoto, Y.: Replica-exchange multicanonical algorithm and multicanonical replica-exchange method for simulating systems with rough energy landscape. Chem. Phys. Lett. 329, 261–270 (2000)CrossRefADSGoogle Scholar
  58. Trebst, S., Huse, D.A., Troyer, M.: Optimizing the ensemble for equilibrium in broad-histogram Monte Carlo. Phys. Rev. E 70, 046701 (2004)CrossRefADSGoogle Scholar
  59. Trebst, S., Troyer, M., Hansmann, U.H.E.: Optimized parallel tempering simulations of proteins. J. Chem. Phys. 124, 174903 (2006)CrossRefADSGoogle Scholar
  60. Tsallis, C.: Possible generalization of boltzmann-gibbs statistics. J. Stat. Phys. 52, 479–487 (1988)CrossRefADSMathSciNetzbMATHGoogle Scholar
  61. Tuckerman, M.E., Berne, B.J., Martyna, G.J.: Reversible multiple time step scale molecular dynamics. J. Chem. Phys. 97(3), 1990–2001 (1992)CrossRefADSGoogle Scholar
  62. Wang, F., Landau, D.P.: Efficient, multiple-range random walk algorithm to calculate the density of states. Phys. Rev. Lett. 86, 2050 (2001a)CrossRefADSGoogle Scholar
  63. Wang, F., Landau, D.P.: Efficient, multiple-range random walk algorithm to calculate the density of states. Phys. Rev. Lett. 86(10), 2050–2053 (2001b)CrossRefADSGoogle Scholar
  64. Wang, F., Landau, D.P.: Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram. Phys. Rev. E 64, 056101–16 (2001c)CrossRefADSGoogle Scholar
  65. Wang, J.S., Swendsen, R.H.: Replica Monte Carlo simulation of spin glasses. Phys. Rev. Lett. 57, 2607–2609 (1986)CrossRefADSMathSciNetGoogle Scholar
  66. Whitfield, T.W., Bu, L., Straub, J.E.: Generalized parallel sampling. Physica A: Statistical Mechanics and its Applications, 305:157–171, (2002)CrossRefADSzbMATHGoogle Scholar
  67. Zhou, R.: Free energy landscape of protein folding in water: explicit vs. implicit solvent. Proteins Struct. Funct. Bioinf. 53(2), 148–161 (2003)Google Scholar
  68. Zhou, R., Berne, B.J.: Can a continuum solvent model reproduce the free energy landscape of a β-hairpin folding in water? Proc. Natl. Acad. Sci. USA 99, 12777–12782 (2002)CrossRefADSGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer EngineeringInternational Balkan UniversitySkopjeNorth Macedonia
  2. 2.Advanced Computing Research CenterUniversity of New York TiranaTiranaAlbania

Personalised recommendations