Journal of Chemical Physics
Feedback | Help | Exit
The Journal of Chemical Physics
   
 
 
 
Previous Article
Computer simulation of the phase behavior of a model membrane protein: Annexin V
The bulk thermodynamic properties of membrane proteins originate from a complex combination of molecular interactions. We propose a simple model based on the pair interactions between a model membrane...
Next Article
Thermodynamic control and dynamical regimes in protein folding
Monte Carlo simulations of a simple lattice model of protein folding show two distinct regimes depending on the chain length. The first regime well describes the folding of small protein sequences and...

Monte Carlo simulation of proteins through a random walk in energy space

J. Chem. Phys. 116, 7225 (2002); doi:10.1063/1.1463059

Issue Date: 22 April 2002

You are not logged in to this journal. Log in

Nitin Rathore and Juan J. de Pablo
Department of Chemical Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706
A Monte Carlo algorithm that performs a random walk in energy space has been used to study random coil–helix and random coil–beta sheet transitions in model proteins. This method permits estimation of the density of states of a protein via a random walk on the energy surface, thereby allowing the system to escape from local free-energy minima with relative ease. A cubic lattice model and a knowledge based force field are employed for these simulations. It is shown that, for a given amino acid sequence, the method is able to fold long polypeptides reproducibly. Its results compare favorably with those of annealing and parallel tempering simulations, which have been used before in the same context. This method is used to examine the effect of amino acid sequence and chain length on the folding of several designer polypeptides. ©2002 American Institute of Physics.
History: Received 5 November 2001; accepted 29 January 2002
Permalink: http://link.aip.org/link/?JCPSA6/116/7225/1
BUY THIS ARTICLE   (US$28)
Download HTML Download Sectioned HTML Download PDF (201 kB) View Cart

KEYWORDS and PACS

Keywords
PACS
  • 87.15.Cc
    Biological and medical physics Biomolecules: structure and physical properties Folding and sequence analysis
  • 87.15.Aa
    Biological and medical physics Biomolecules: structure and physical properties Theory and modeling; computer simulation
  • 87.15.By
    Biological and medical physics Biomolecules: structure and physical properties Structure and bonding
  • 87.14.Ee
    Biological and medical physics Biomolecules: types Proteins
  • 36.20.Fz
    Exotic atoms and molecules; macromolecules; clusters Macromolecules and polymer molecules Constitution (chains and sequences)
  • 36.20.Ey
    Exotic atoms and molecules; macromolecules; clusters Macromolecules and polymer molecules Conformation (statistics and dynamics)
  • 02.50.Ng
    Mathematical methods in physics Probability theory, stochastic processes, and statistics Distribution theory and Monte Carlo studies
  • 02.70.Uu
    Mathematical methods in physics Computational techniques Applications of Monte Carlo methods
  • 05.50.+q
    Statistical physics, thermodynamics, and nonlinear dynamical systems Lattice theory and statistics (Ising, Potts, etc.)
  • 33.15.Bh
    Molecular properties and interactions with photons Properties of molecules General molecular conformation and symmetry; stereochemistry
  • 05.40.Fb
    Statistical physics, thermodynamics, and nonlinear dynamical systems Fluctuation phenomena, random processes, noise, and Brownian motion Random walks and Levy flights
  • YEAR: 2002

RELATED DATABASES


To view database links for this article,
you need to log in.
To view database links for this article,
you need to log in.

PUBLICATION DATA

ISSN:
0021-9606 (print)   1089-7690 (online)
Publisher:
AIP is a member of CrossRef AIP

REFERENCES (19)
View in Separate Window/Tab
For access to fully linked references, you need to log in. For access to fully linked references, you need to Log in.
  1. J.-E. Shea and C. L. Brooks III, Ann. Rev. Phys. Chem. 52, 499 (2001).
  2. F. A. Escobedo and J. J. de Pablo, J. Chem. Phys. 105, 4391 (1996).
  3. Q. L. Yan and J. J. de Pablo, J. Chem. Phys. 113, 1276 (2000).
  4. U. H. E. Hansmann and Y. Okamoto, Phys. Rev. E 54, 5863 (1996).
  5. U. H. E. Hansmann and Y. Okamoto, Curr. Opin. Struct. Biol. 9, 177 (1999).
  6. Y. Okamoto, Int. J. Mod. Phys. C 10, 1571 (1999).
  7. Y. Okamoto and U. H. E. Hansmann, J. Phys. Chem. 99, 11276 (1995).
  8. D. Gront, A. Kolinski, and J. Skolnick, J. Chem. Phys. 113, 5065 (2000).
  9. Y. Sugita and Y. Okamoto, Chem. Phys. Lett. 329, 261 (2000).
  10. F. Yasar, T. Celik, B. A. Berg, and H. Meirovitch, J. Comput. Chem. 21, 1251 (2000).
  11. F. Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001).
  12. B. Ilkowski, J. Skolnick, and A. Kolinski, Macromol. Theory Simul. 9, 523 (2000).
  13. A. Kolinski, L. Jaroszewki, P. Rotkiewicz, and J. Skolnick, J. Phys. Chem. B 102, 4628 (1998).
  14. A. Kolinski, P. Rotkiewicz, B. Ilkowski, and J. Skolnick, Proteins 37, 592 (1999).
  15. A. Kolinski and J. Skolnick, Proteins 32, 475 (1998).
  16. D. P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics (Cambridge University Press, Cambridge, 2000).
  17. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, J. Chem. Phys. 21, 1087 (1953).
  18. J.-M. Shin and W. S. Oh, J. Phys. Chem. B 102, 6405 (1998).
  19. MMTSB (Multiscale Modeling Tools in Structural Biology) is a web based NIH research resource for the development and integration of modeling tools to explore multiresolution models in structural biology. The package is available at http://mmtsb.scripps.edu

CITING ARTICLES
For access to citing articles, you need to log in.
For access to citing articles, you need to Log in.


Copyright © American Institute of Physics