Springer Protocols: Abstract: 2. Monte Carlo Simulations

2. Monte Carlo Simulations

By: David J. Earl², Michael W. Deem³

Abstract

Full Text

Download PDF (285K)

A description of Monte Carlo methods for simulation of proteins is given. Advantages and disadvantages of the Monte Carlo approach are presented. The theoretical basis for calculating equilibrium properties of biological molecules by the Monte Carlo method is presented. Some of the standard and some of the more recent ways of performing Monte Carlo on proteins are presented. A discussion of the estimation of errors in properties calculated by Monte Carlo is given.

Affiliation(s): (2) Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
(3) Departments of Bioengineering and Physics and Astronomy, Rice University, Houston, TX, USA

Book Title: Molecular Modeling of Proteins

Series: Methods in Molecular Biology | Volume: 443 | Pub. Date: Apr-04-2008 | Page Range: 25-36 | DOI: 10.1007/978-1-59745-177-2_2

Subject: Bioinformatics

Key Words: Markov chain - Metropolis algorithm - Monte Carlo - Protein simulation - Stochastic methods

References

Download References

1.	1. Hu, J., Ma, A., and Dinner, A. R. (2006) Monte Carlo simulations of biomolecules: The MC module in CHARMM. J. Comput. Chem. 27, 203–216.

2.	2. Manousiouthakis, V. I. and Deem, M. W. (1999) Strict detailed balance is unnecessary in Monte Carlo simulation. J. Chem. Phys. 110, 2753–2756.

3.	3. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. N., and Teller, E. (1953) Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092.

4.	4. Metropolis, N. (1987) The beginning of the Monte Carlo method. Los Alamos Science, 12, 125–130.

5.	5. Frenkel, D. and Smit, B. (2002) Understanding Molecular Simulation: From Algorithms to Applications. Academic Press, San Diego, CA.

6.	6. Allen, M. P. and Tildesley, D. J. (1987) Computer Simulation of Liquids. Clarendon Press, Oxford.

7.	7. Siepmann, J. I. and Frenkel, D. (1992) Configurational-bias Monte Carlo: A new sampling scheme for flexible chains. Mol. Phys. 75, 59–70.

8.	8. Deem, M. W. and Bader, J. S. (1996) A configurational bias Monte Carlo method for linear and cyclic peptides. Mol. Phys. 87, 1245–1260.

9.	9. Dodd, L. R., Boone, T. D., and Theodorou, D. N. (1993) A concerted rotation algorithm for atomistic Monte Carlo simulation of polymer melts and glasses. Mol. Phys. 78, 961–996.

10.	10. Mavrantzas, V. G., Boone, T. D., Zevropoulou, E., and Theodorou, D. N. (1999) End-bridging Monte Carlo: A fast algorithm for atomistic simulation of condensed phases of long polymer chains. Macromolecules 32, 5072–5096.

11.	11. Wu, M. G. and Deem, M. W. (1999) Efficient Monte Carlo methods for cyclic peptides. Mol. Phys. 97, 559–580.

12.	12. Wu, M. G. and Deem, M. W. (1999) Analytical rebridging Monte Carlo: Application to cis/trans isomerization in proline-containing, cyclic peptides. J. Chem. Phys. 111, 6625–6632.

13.	13. Betancourt, M. R. (2005) Efficient Monte Carlo moves for polypeptide simulations. J. Chem. Phys. 123, 174905.

14.	14. Duane, S., Kennedy, A., Pendleton, B. J., and Roweth, D. (1987) Hybrid Monte Carlo. Phys. Rev. Lett. 195, 216–222.

15.	15. Mehlig, B., Heermann, D. W., and Forrest, B. M. (1992) Hybrid Monte Carlo method for condensed matter systems. Phys. Rev. B 45, 679–685.

16.	16. Tuckerman, M. E., Berne, B. J., and Martyna, G. J. (1992) Reversible multiple time scale molecular dynamics. J. Chem. Phys. 97, 1990–2001.

17.	17. Geyer, C. J. and Thompson, E. A. (1995) Annealing Markov-Chain Monte Carlo with applications to ancestral inference, J. Am. Stat. Assn. 90, 909–920.

18.	18. Kone, A. and Kofke, D. A. (2005) Selection of temperature intervals for parallel tempering simulations, J. Chem. Phys. 122, 206101.

19.	19. Rathore, N., Chopra, M., and de Pablo, J. J. (2005) Optimal allocation of replicas in parallel tempering simulations, J. Chem. Phys. 122, 024111.

20.	Katzgraber, H. G., Trebst, S., Huse, D. A., and Troyer, M. (2006) Feedback-optimized parallel tempering Monte Carlo, J. Stat. Mech.: Exp. & Theory P03018.

21.	21. Earl, D. J. and Deem, M. W. (2004) Optimal allocation of replicas to processors in parallel tempering simulations. J. Phys. Chem. B 108, 6844–6849.

22.	22. Earl, D. J. and Deem, M. W. (2005) Parallel tempering: theory, applications, and new perspectives, Phys. Chem. Chem. Phys. 7, 3910–3916.

23.	23. Wang, F. and Landau, D. P. (2001) Efficient, multiple-range random walk algorithm to calculate the density of states, Phys. Rev. Lett. 86, 2050–2053.

24.	24. Earl, D. J. and Deem, M. W. (2005) Markov chains of infinite order and asymptotic satisfaction of balance: Application to the adaptive integration method, J. Phys. Chem. B 109 , 6701–6704.

25.	25. Rathore, N., Knotts IV, T. A., and de Pablo, J. J. (2003) Configurational temperature density of states simulations of proteins, Biophys. J. 85, 3963–3968.

26.	26. Bradley, P., Misura, K. M. S., and Baker, D. (2005) Toward high-resolution de novo structure prediction for small proteins, Science 309, 1868–1871.

27.	27. Meiler, J. and Baker, D. (2003) Rapid protein fold determination using unassigned NMR data, Proc. Natl. Acad. Sci. USA 100, 15404–15409.

28.	28. Yang, X. and Saven, J. G. (2005) Computational methods for protein design sequence variability: biased Monte Carlo and replica exchange, Chem. Phys. Lett. 401, 205–210.

29.	http://www.ccl.net/cca/software/SOURCES/FORTRAN/allen-tildesley-book/index.shtml

30.	http://molsim.chem.uva.nl/frenkel smit/index.html

31.	http://www.mwdeem.rice.edu/rebridge/

32.	http://fulcrum.physbio.mssm.edu/~mezei/

Comments (Loading...)

Post comment/View all comments