Calculating potentials of mean force from steered molecular dynamics simulations
J. Chem. Phys. 120, 5946 (2004); doi:10.1063/1.1651473
Issue Date: 1 April 2004
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Steered molecular dynamics (SMD) permits efficient investigations of molecular processes by focusing on selected degrees of freedom. We explain how one can, in the framework of SMD, employ Jarzynski's equality (also known as the nonequilibrium work relation) to calculate potentials of mean force (PMF). We outline the theory that serves this purpose and connects nonequilibrium processes (such as SMD simulations) with equilibrium properties (such as the PMF). We review the derivation of Jarzynski's equality, generalize it to isobaricisothermal processes, and discuss its implications in relation to the second law of thermodynamics and computer simulations. In the relevant regime of steering by means of stiff springs, we demonstrate that the work on the system is Gaussian-distributed regardless of the speed of the process simulated. In this case, the cumulant expansion of Jarzynski's equality can be safely terminated at second order. We illustrate the PMF calculation method for an exemplary simulation and demonstrate the Gaussian nature of the resulting work distribution. ©2004 American Institute of Physics.
| History: | Received 3 December 2003; accepted 7 January 2004 |
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http://link.aip.org/link/?JCPSA6/120/5946/1 |
KEYWORDS and PACS
Gaussian distribution,
molecular dynamics method,
digital simulation,
molecular biophysics,
nonequilibrium thermodynamics
- 87.15.He
Biomolecular dynamics and conformational changes - 87.15.Aa
Theory and modeling in molecular biophysics; computer simulation - 05.70.Ln
Nonequilibrium and irreversible thermodynamics - 02.50.Ng
Distribution theory and Monte Carlo studies - 31.15.Qg
Molecular dynamics and other numerical methods (atoms and molecules) - YEAR: 2004
RELATED DATABASES
PUBLICATION DATA
0021-9606 (print)
1089-7690 (online)
REFERENCES (52)
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- A. R. Leach, Molecular Modelling, Principles and Applications (AddisonWesley, Essex, 1996).
- Computational Biochemistry and Biophysics, edited by O. M. Becker, A. D. MacKerell, Jr., B. Roux, and M. Watanabe (Marcel Dekker, New York, 2001).
- S. Izrailev, S. Stepaniants, B. Isralewitz, D. Kosztin, H. Lu, F. Molnar, W. Wriggers, and K. Schulten, in Computational Molecular Dynamics: Challenges, Methods, Ideas, Vol. 4 in Lecture Notes in Computational Science and Engineering, edited by P. Deuflhard, J. Hermans, B. Leimkuhler, A. E. Mark, S. Reich, and R. D. Skeel (Springer-Verlag, Berlin, 1998), pp. 3965.
- B. Isralewitz, M. Gao, and K. Schulten,
Curr. Opin. Struct. Biol. 11, 224 (2001) . - A. Krammer, H. Lu, B. Isralewitz, K. Schulten, and V. Vogel,
Proc. Natl. Acad. Sci. U.S.A. 96, 1351 (1999) . - M. Gao, M. Wilmanns, and K. Schulten,
Biophys. J. 83, 3435 (2002) . - M. Gao, D. Craig, V. Vogel, and K. Schulten,
J. Mol. Biol. 323, 939 (2002) . - S. Izrailev, S. Stepaniants, M. Balsera, Y. Oono, and K. Schulten,
Biophys. J. 72, 1568 (1997) . - M. V. Bayas, K. Schulten, and D. Leckband,
Biophys. J. 84, 2223 (2003) . - C. Jarzynski, Phys. Rev. Lett. 78, 2690 (1997).
- C. Jarzynski, Phys. Rev. E 56, 5018 (1997).
- G. E. Crooks, Phys. Rev. E 61, 2361 (2000).
- C. Jarzynski,
J. Stat. Phys. 98, 77 (2000) . - T. Hatano and S. Sasa, Phys. Rev. Lett. 86, 3463 (2001).
- Y. Oono and M. Paniconi,
Prog. Theor. Phys. Suppl. 130, 29 (1998) . - J. Liphardt, S. Dumont, S. B. Smith, I. Tinoco, Jr., and C. Bustamante,
Science 296, 1832 (2002) . - D. A. Hendrix and C. Jarzynski, J. Chem. Phys. 114, 5974 (2001).
- G. Hummer and A. Szabo,
Proc. Natl. Acad. Sci. U.S.A. 98, 3658 (2001) . - G. Hummer, J. Chem. Phys. 114, 7330 (2001).
- D. M. Zuckerman and T. B. Woolf,
Chem. Phys. Lett. 351, 445 (2002) . - D. M. Zuckerman and T. B. Woolf, Phys. Rev. Lett. 89, 180602 (2002).
- J. Gore, F. Ritort, and C. Bustamante,
Proc. Natl. Acad. Sci. U.S.A. 100, 12564 (2003) . - M. Ø. Jensen, S. Park, E. Tajkhorshid, and K. Schulten,
Proc. Natl. Acad. Sci. U.S.A. 99, 6731 (2002) . - R. Amaro, E. Tajkhorshid, and Z. Luthey-Schulten,
Proc. Natl. Acad. Sci. U.S.A. 100, 7599 (2003) . - S. Park, F. Khalili-Araghi, E. Tajkhorshid, and K. Schulten, J. Chem. Phys. 119, 3559 (2003).
- D. J. Evans and D. J. Searles, Phys. Rev. E 50, 1645 (1994).
- G. Gallavotti and E. G. D. Cohen, Phys. Rev. Lett. 74, 2694 (1995).
- S. E. Feller, Y. Zhang, R. W. Pastor, and B. R. Brooks, J. Chem. Phys. 103, 4613 (1995).
- In thermodynamics, the term isothermal usually means that the temperature of the system remains constant during the process. Here we use the term to mean that the system is in contact with a bath at constant temperature, though the temperature of the system is not necessarily constant during the process. In fact, the system's temperature is not even defined when we deal with nonequilibrium processes. The same applies to the term isobaric.
- Generalization of Jarzynski's equality to the isobaricisothermal ensemble was discussed in G. E. Crooks,
J. Stat. Phys. 90, 1481 (1998) . - E. Fermi, Thermodynamics (Dover, New York, 1937).
- C. Jarzynski,
J. Stat. Phys. 96, 415 (1999) . - M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids (Oxford University Press, New York, 1987).
- D. Frenkel and B. Smit, Understanding Molecular Simulation: From Algorithms to Applications, 2nd ed. (Academic, San Diego, 2002).
- C. W. Gardiner, Handbook of Stochastic Methods, 2nd ed. (Springer-Verlag, Berlin, 1985).
- There is a third type of method, such as the isokinetic Gaussian thermostat, in which equations of motion are modified by the addition of deterministic terms, but without introducing new degrees of freedom. The nonequilibrium work relation has been established for the isokinetic Gaussian thermostat. See, D. J. Evans,
Mol. Phys. 101, 1551 (2003) . - S. Nosé, J. Chem. Phys. 81, 511 (1984).
- W. G. Hoover, Phys. Rev. A 31, 1695 (1985).
- H. Goldstein, Classical Mechanics, 2nd ed. (AddisonWesley, Reading, 1980).
- G. Binnig, C. F. Quate, and C. Gerber, Phys. Rev. Lett. 56, 930 (1986).
- J. Marcinkiewicz,
Math. Z. 44, 612 (1939) . - R. H. Wood, W. C. F. Mühlbauer, and P. T. Thompson,
J. Phys. Chem. 95, 6670 (1991) . - J. Hermans,
J. Phys. Chem. 95, 9029 (1991) . - O. Mazonka and C. Jarzynski, cond-mat/9912121 (1999).
- N. G. van Kampen, Stochastic Processes in Physics and Chemistry (NorthHolland, Amsterdam, 1981).
- In fact, any of


, 

, 


c, and 
2
c can be used to estimate the diffusion coefficient [see Eq. (91)]. However, data of 

and 


c are generally noisier than those of 

and 
2
c. (Notice that 

and 
2
c can be expressed in terms of integrations of 

and 


c, respectively.) And the use of 

requires the knowledge of the exact PMF. It is the simplest to use 
2
c, or
W2
c. - W. Humphrey, A. Dalke, and K. Schulten,
J. Mol. Graphics 14, 33 (1996) . - L. Kalé, R. Skeel, M. Bhandarkar, R. Brunner, A. Gursoy, N. Krawetz, J. Phillips, A. Shinozaki, K. Varadarajan, and K. Schulten,
J. Comput. Phys. 151, 283 (1999) . - A. D. MacKerell, Jr., D. Bashford, M. Bellott et al.,
J. Phys. Chem. B 102, 3586 (1998) . - Recall that the Gaussian nature of the work distribution depends on the assumption of the overdamped Langevin equation (Sec. III D). It is desirable to test this assumption in simulations.
- M. Plischke and B. Bergersen, Equilibrium Statistical Mechanics, 2nd ed. (Word Scientific, Singapore, 1994).
- One way of doing this is the Langevin piston method, which is introduced in Sec. II G.








