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Enhanced Sampling Algorithms

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Book cover Biomolecular Simulations

Part of the book series: Methods in Molecular Biology ((MIMB,volume 924))

Abstract

In biomolecular systems (especially all-atom models) with many degrees of freedom such as proteins and nucleic acids, there exist an astronomically large number of local-minimum-energy states. Conventional simulations in the canonical ensemble are of little use, because they tend to get trapped in states of these energy local minima. Enhanced conformational sampling techniques are thus in great demand. A simulation in generalized ensemble performs a random walk in potential energy space and can overcome this difficulty. From only one simulation run, one can obtain canonical-ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review uses of the generalized-ensemble algorithms in biomolecular systems. Three well-known methods, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described first. Both Monte Carlo and molecular dynamics versions of the algorithms are given. We then present various extensions of these three generalized-ensemble algorithms. The effectiveness of the methods is tested with short peptide and protein systems.

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Acknowledgments

Some of the results were obtained by the computations on the supercomputersat the Institute for Molecular Science, Okazaki, and the Institute for Solid State Physics, University of Tokyo, Japan. This work was supported, in part, by Grants-in-Aid for Scientific Research on Innovative Areas (“Fluctuations and Biological Functions”) and for the Next-Generation Super Computing Project, Nanoscience Program, and Computational Materials Science Initiative from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.

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Authors and Affiliations

  1. Department of Physics, Keio University, Yokohama, Kanagawa, Japan

    Ayori Mitsutake

  2. Department of Physics, Nagoya University, Nagoya, Aichi, Japan

    Yoshiharu Mori & Yuko Okamoto

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  1. Ayori Mitsutake
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  2. Yoshiharu Mori
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  3. Yuko Okamoto
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Correspondence to Yuko Okamoto .

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  1. DSIMB, UMR-S 665, INSERM, rue Alexandre Cabanel 6, Paris, 75015, France

    Luca Monticelli

  2. , Department of Applied Physics, Aalto University, Otakaari, AALTO, FI-00076, Finland

    Emppu Salonen

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Mitsutake, A., Mori, Y., Okamoto, Y. (2013). Enhanced Sampling Algorithms. In: Monticelli, L., Salonen, E. (eds) Biomolecular Simulations. Methods in Molecular Biology, vol 924. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-017-5_7

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  • DOI: https://doi.org/10.1007/978-1-62703-017-5_7

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