Abstract
In biomolecular systems with many degrees of freedom such as proteins and nucleic acids, there exists an astronomically large number of local-minimum free energy states. Conventional simulations in the canonical ensemble encounter with great difficulty, because they tend to get trapped in states of these local minima. Enhanced conformational sampling techniques are thus in great demand. A simulation in generalized ensemble performs a random walk in potential energy, volume, and other physical quantities or their corresponding conjugate parameters such as temperature, pressure, etc. and can overcome this difficulty. From only one simulation run, one can obtain canonical ensemble averages of physical quantities as functions of temperature, pressure, etc. by the reweighting techniques. In this chapter, we review uses of the generalized-ensemble algorithms in biomolecular systems. A well-known method, namely, replica-exchange method, is described first. We then present various extensions of the replica-exchange method. The effectiveness of the methods is tested with protein folding and ligand docking simulations.
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Hansmann UHE, Okamoto Y (1999) New Monte Carlo algorithms for protein folding. Curr Opin Struct Biol 9:177–183
Mitsutake A, Sugita Y, Okamoto Y (2001) Generalized-ensemble algorithms for molecular simulations of biopolymers. Biopolymers 60:96–123
Sugita Y, Okamoto Y (2002) Free-energy calculations in protein folding by generalized-ensemble algorithms. In: Schlick T, Gan HH (eds) Lecture notes in computational science and engineering. Springer, Berlin, pp 304–332, e-print: cond-mat/0102296
Okumura H, Itoh SG, Okamoto Y (2012) Generalized-ensemble algorithms for simulations of complex molecular systems. In: Leszezynski J, Shukla MK (eds) Practical aspects of computational chemistry II. Springer, Dordrecht, pp 69–101
Mitsutake A, Mori Y, Okamoto Y (2012) Enhanced sampling algorithms. In: Monticelli L, Salonen E (eds) Biomolecular simulations: methods and protocols. Humana Press, New York, pp 153–195
Okamoto Y, Kokubo H, Tanaka T (2013) Ligand docking simulations by generalized-ensemble algorithms. In: Karabencheva-Christova T (ed) Advances in protein chemistry and structural biology, vol 92. Academic, Burlington, pp 63–91
Yoda T, Sugita Y, Okamoto Y (2014) Protein folding simulations by generalized-ensemble algorithms. In: Han K-L, Zhang X, Yang M-J (eds) Protein conformational dynamics, advances in experimental medicine and biology, vol 805. Springer, Berlin, pp 1–27
Hukushima K, Nemoto K (1996) Exchange Monte Carlo method and application to spin glass simulations. J Phys Soc Jpn 65:1604–1608
Marinari E, Parisi G, Ruiz-Lorenzo JJ (1997) Numerical simulations of spin glass systems. In: Young AP (ed) Spin glasses and random fields. World Scientific, Singapore, pp 59–98
Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151
Sugita Y, Kitao A, Okamoto Y (2000) Multidimensional replica-exchange method for free-energy calculations. J Chem Phys 113:6042–6051
Fukunishi F, Watanabe O, Takada S (2002) On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J Chem Phys 116:9058–9067
Torrie GM, Valleau JP (1997) Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 23:187–199
Mitsutake A, Okamoto Y (2009) Multidimensional generalized-ensemble algorithms for complex systems. J Chem Phys 130:214105 (14 pages)
Mitsutake A (2009) Simulated-tempering replica-exchange method for the multidimensional version. J Chem Phys 131:094105 (15 pages)
Okabe T, Kawata M, Okamoto Y, Mikami M (2001) Replica-exchange Monte Carlo method for the isobaric-isothermal ensemble. Chem Phys Lett 335:435–439
Okumura H, Okamoto Y (2006) Multibaric-multithermal ensemble molecular dynamics simulations. J Comput Chem 27:379–395
Mori Y, Okamoto Y (2010) Generalized-ensemble algorithms for the isobaric-isothermal ensemble. J Phys Soc Jpn 79:074003 (5 pages)
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092
Mori Y, Okamoto Y (2010) Replica-exchange molecular dynamics simulations for various constant temperature algorithms. J Phys Soc Jpn 79:074001 (8 pages)
Ferrenberg AM, Swendsen RH (1989) Optimized Monte Carlo data analysis. Phys Rev Lett 63:1195–1198
Kumar S, Bouzida D, Swendsen RH, Kollman PA, Rosenberg JM (1992) The weighted histogram analysis method for free-energy calculations on biomolecules. 1. The method. J Comput Chem 13:1011–1021
Mitsutake A, Sugita Y, Okamoto Y (2003) Replica-exchange multicanonical and multicanonical replica-exchange Monte Carlo simulations of peptides I. Formulation and benchmark test. J Chem Phys 118:6664–6675
Kokubo H, Okamoto Y (2004) Prediction of transmembrane helix configurations by replica-exchange simulations. Chem Phys Lett 383:397–402
Kokubo H, Okamoto Y (2004) Prediction of membrane protein structures by replica-exchange Monte Carlo simulations: case of two helices. J Chem Phys 120:10837–10847
Kokubo H, Okamoto Y (2009) Analysis of helix-helix interactions of bacteriorhodopsin by replica-exchange simulations. Biophys J 96:765–776
Urano R, Kokubo H, Okamoto Y (2015) Predictions of tertiary structures of α-helical membrane proteins by replica-exchange method with consideration of helix deformations. J Phys Soc Jpn 84:084802 (12 pages)
Kokubo H, Tanaka T, Okamoto Y (2011) Ab initio prediction of protein-ligand binding structures by replica-exchange umbrella sampling simulations. J Comput Chem 32:2810–2821
Kokubo H, Tanaka T, Okamoto Y (2013) Prediction of protein-ligand binding structures by replica-exchange umbrella sampling simulations: application to kinase systems. J Chem Theory Comput 9:4660–4671
Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748
Kokubo H, Tanaka T, Okamoto Y (2013) Two-dimensional replica-exchange method for predicting protein-ligand binding structures. J Comput Chem 34:2601–2614
Okamoto Y, Kokubo H, Tanaka T (2014) Prediction of ligand binding affinity by the combination of replica-exchange method and double-decoupling method. J Chem Theory Comput 10:3563–3569
Acknowledgments
The author thanks his co-workers for useful discussions. In particular, he is grateful to Drs. A. Kitao, H. Kokubo, A. Mitsutake, Y. Sugita, T. Tanaka, and R. Urano for collaborations that led to the results presented in the present chapter. This work was supported, in part, by the Grant-in-Aid for Scientific Research on Innovative Areas (“Fluctuations and Biological Functions”) from MEXT, Japan.
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Okamoto, Y. (2016). Structural Fluctuations of Proteins in Folding and Ligand Docking Studied by Replica-Exchange Simulations. In: Terazima, M., Kataoka, M., Ueoka, R., Okamoto, Y. (eds) Molecular Science of Fluctuations Toward Biological Functions . Springer, Tokyo. https://doi.org/10.1007/978-4-431-55840-8_9
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DOI: https://doi.org/10.1007/978-4-431-55840-8_9
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