MENU
  • Scitation Home
SUBMIT YOUR ARTICLE

Thank you

For your interest in The Journal of Chemical Physics

Check for updates on crossmark
Prev Next
No Access Submitted: 12 December 2001 Accepted: 04 March 2002 Published Online: 06 May 2002

  • On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction

J. Chem. Phys. 116, 9058 (2002); https://doi.org/10.1063/1.1472510
Hiroaki Fukunishi
  • Graduate School of Science and Technology, Kobe University, Nada, Kobe 657-8501, Japan
    • Osamu Watanabe
  • Department of Chemistry, Faculty of Science, Kobe University, Nada, Kobe 657-8501, Japan
    • Shoji Takada
  • Department of Chemistry, Faculty of Science, Kobe University,
  • PRESTO, Japan Science and Technology, Rokkodai, Nada, Kobe 657-8501, Japan
    • more...View Contributors
    • Hiroaki Fukunishi
    • Osamu Watanabe
    • Shoji Takada
    ABSTRACT
    Motivated by the protein structure prediction problem, we develop two variants of the Hamiltonian replica exchange methods (REMs) for efficient configuration sampling, (1) the scaled hydrophobicity REM and (2) the phantom chain REM, and compare their performance with the ordinary REM. We first point out that the ordinary REM has a shortage for the application to large systems such as biomolecules and that the Hamiltonian REM, an alternative formulation of the REM, can give a remedy for it. We then propose two examples of the Hamiltonian REM that are suitable for a coarse-grained protein model. (1) The scaled hydrophobicity REM prepares replicas that are characterized by various strengths of hydrophobic interaction. The strongest interaction that mimics aqueous solution environment makes proteins folding, while weakened hydrophobicity unfolds proteins as in organic solvent. Exchange between these environments enables proteins to escape from misfolded traps and accelerate conformational search. This resembles the roles of molecular chaperone that assist proteins to fold in vivo. (2) The phantom chain REM uses replicas that allow various degrees of atomic overlaps. By allowing atomic overlap in some of replicas, the peptide chain can cross over itself, which can accelerate conformation sampling. Using a coarse-gained model we developed, we compute equilibrium probability distributions for poly-alanine 16-mer and for a small protein by these REMs and compare the accuracy of the results. We see that the scaled hydrophobicity REM is the most efficient method among the three REMs studied.

    REFERENCES
    Section:
    Previous sectionNext section

    1. 1. J. Moult, Curr. Opin. Biotechnol. 10, 583 (1999). Google ScholarCrossref
    2. 2. R. Bonneauand D. Baker, Annu. Rev. Biophys. Biomol. Struct. 30, 173 (2001). Google ScholarCrossref
    3. 3. D. Bakerand A. Sali, Science 294, 93 (2001). Google ScholarCrossref
    4. 4. K. T. Simons, C. Kooperberg, E. Huang, and D. Baker, J. Mol. Biol. 268, 209 (1997). Google ScholarCrossref
    5. 5. K. T. Simons, I. Ruczinski, C. Kooperberg, B. A. Fox, C. Bystroff, and D. Baker, Proteins 34, 82 (1999). Google ScholarCrossref
    6. 6. K. T. Simons, C. Strauss, and D. Baker, J. Mol. Biol. 306, 1191 (2001). Google ScholarCrossref, ISI
    7. 7. C. B. Anfinsen, Science 181, 223 (1973). Google ScholarCrossref, ISI
    8. 8. T. Lazaridisand M. Karplus, Proteins 35, 133 (1999). Google ScholarCrossref, ISI
    9. 9. A. R. Kinjo, A. Kidera, H. Nakamura, and K. Nishikawa, Eur. Biophys. J. 30, 1 (2001). Google ScholarCrossref
    10. 10. I. Ohmineand S. Saito, Acc. Chem. Res. 32, 741 (1999). Google ScholarCrossref
    11. 11. M. Mezard, G. Parisi, and M. A. Virasoro, Spin Glass Theory and Beyond (World Scientific, Singapore, 1987). Google Scholar
    12. 12. B. A. Bergand T. Neuhaus, Phys. Lett. B 267, 246 (1991). Google ScholarCrossref
    13. 13. A. Mitsutakeand Y. Sugita, and Y. Okamoto, Biopolymers 60, 96 (2001). Google ScholarCrossref, ISI
    14. 14. A. P. Lyubartsev, A. A. Martinovski, S. V. Shevkunov, and P. N. Vorontsov-Velyaminov, J. Chem. Phys. 96, 1776 (1992). Google ScholarScitation, ISI
    15. 15. E. Marinari, G. Parisi, Europhys. Lett. 19, 451 (1992). Google ScholarCrossref
    16. 16. K. Hukushimaand K. Nemoto, J. Phys. Soc. Jpn. 65, 1604 (1996). Google ScholarCrossref
    17. 17. Y. Sugitaand Y. Okamoto, Chem. Phys. Lett. 314, 141 (1999). Google ScholarCrossref, ISI
    18. 18. K. Hukushima, Phys. Rev. E 60, 3606 (1999). Google ScholarCrossref
    19. 19. D. Gront, A. Kolinski, and J. Skolnick, J. Chem. Phys. 113, 5065 (2000). Google ScholarScitation, ISI
    20. 20. Y. Sugita, A. Kitao, and Y. Okamoto, J. Chem. Phys. 113, 6042 (2000). Google ScholarScitation, ISI
    21. 21. Y. Sugitaand Y. Okamoto, Chem. Phys. Lett. 329, 261 (2000). Google ScholarCrossref
    22. 22. A. Mitutakeand Y. Okamoto, Chem. Phys. Lett. 332, 131 (2000). Google ScholarCrossref
    23. 23. R. Yamamotoand W. Kob, Phys. Rev. E 61, 5473 (2000). Google ScholarCrossref
    24. 24. Y. Zhangand J. Skolnick, J. Chem. Phys. 115, 5027 (2001). Google ScholarScitation
    25. 25. Y. Ishikawa, Y. Sugita, T. Nishikawa, and Y. Okamoto, Chem. Phys. Lett. 333, 199 (2001). Google ScholarCrossref
    26. 26. T. Okabe, M. Kawata, Y. Okamoto, and M. Mikami, Chem. Phys. Lett. 335, 435 (2001). Google ScholarCrossref
    27. 27. K. Y. Sanbonmatsuand A. E. Garcia, Proteins 46, 225 (2002). Google ScholarCrossref
    28. 28. M. C. Tesi, E. J. J. van Rensburg, E. Orlandini, and S. G. Whittington, J. Stat. Phys. 82, 155 (1996). Google ScholarCrossref, ISI
    29. 29. U. H. E. Hansmann, Chem. Phys. Lett. 281, 140 (1997). Google ScholarCrossref, ISI
    30. 30. Q. Yanand J. J. de Pablo, J. Chem. Phys. 111, 9509 (1999). Google ScholarScitation, ISI
    31. 31. A. M. Ferrenbergand R. H. Swendsen, Phys. Rev. Lett. 61, 2635 (1988). Google ScholarCrossref, ISI
    32. 32. A. M. Ferrenbergand R. H. Swendsen, Phys. Rev. Lett. 63, 1195 (1989). Google ScholarCrossref, ISI
    33. 33. S. Kumar, D. Bouzida, R. H. Swendsen, P. A. Kollman, and J. M. Rosenberg, J. Comput. Chem. 13, 1011 (1992). Google ScholarCrossref, ISI
    34. 34. S. Takada, Z. A. Luthey-Schulten, and P. G. Wolynes, J. Chem. Phys. 110, 11616 (1999). Google ScholarScitation
    35. 35. S. Takada, Proteins 42, 85 (2001). Google ScholarCrossref
    36. 36. Z. Xu, A. L. Horwich, and P. B. Sigler, Nature (London) 388, 741 (1997). Google ScholarCrossref
    37. 37. M. J. Todd, P. V. Viitanen, and G. H. Lorimer, Science 265, 659 (1994). Google ScholarCrossref
    38. 38. W. Braunand N. Go, J. Mol. Biol. 186, 611 (1985). Google ScholarCrossref
    39. 39. Y. Iba, G. Chikenji, and M. Kikuchi, J. Phys. Soc. Jpn. 67, 3327 (1998). Google ScholarCrossref
    40. 40. G. Chikenji, M. Kikuchi, and Y. Iba, Phys. Rev. Lett. 83, 1886 (1999). Google ScholarCrossref
    41. 41. P. G. de Gennes, Scaling Concepts in Polymer Physics (Cornell University Press, Ithaca, NY, 1979). Google Scholar
    42. 42. M. U. Johansson, M. D. Chathau, M. Wikstrom, S. Forsen, T. Drakenberg, and L. Bjorck, J. Mol. Biol. 266, 859 (1997). Google ScholarCrossref
    43. 43. A. Irback, F. Sjunnesson, and S. Wallin, Proc. Natl. Acad. Sci. U.S.A. 97, 13614 (2000). Google ScholarCrossref
    1. © 2002 American Institute of Physics.

    Article Metrics

    Views
    1,065
    Citations
    Crossref 580
    Web of Science
    ISI 547

    Altmetric

    Please Note: The number of views represents the full text views from December 2016 to date. Article views prior to December 2016 are not included.