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Efficient Energy Computation for Monte Carlo Simulation of Proteins

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Algorithms in Bioinformatics (WABI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 2812))

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Abstract

Monte Carlo simulation (MCS) is a common methodology to compute pathways and thermodynamic properties of proteins. A simulation run is a series of random steps in conformation space, each perturbing some degrees of freedom of the molecule. A step is accepted with a probability that depends on the change in value of an energy function. Typical energy functions sum many terms. The most costly ones to compute are contributed by atom pairs closer than some cutoff distance. This paper introduces a new method that speeds up MCS by efficiently computing the energy at each step. The method exploits the facts that proteins are long kinematic chains and that few degrees of freedom are changed at each step. A novel data structure, called the ChainTree, captures both the kinematics and the shape of a protein at successive levels of detail. It is used to find all atom pairs contributing to the energy. It also makes it possible to identify partial energy sums left unchanged by a perturbation, thus allowing the energy value to be incrementally updated. Computational tests on four proteins of sizes ranging from 68 to 755 amino acids show that MCS with the ChainTree method is significantly faster (as much as 12 times faster for the largest protein) than with the widely used grid method. They also indicate that speed-up increases with larger proteins.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Stanford University, Stanford, CA, 94305, USA

    Itay Lotan, Fabian Schwarzer & Jean-Claude Latombe

Authors
  1. Itay Lotan
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  2. Fabian Schwarzer
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  3. Jean-Claude Latombe
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Editor information

Editors and Affiliations

  1. Department of Biomathematical Sciences, The Mount Sinai School of Medicine, 10029-6574, New York, NY

    Gary Benson

  2. Institute of Biomedical and Life Sciences, Division of Environmental and Evolutionary Biology, University of Glasgow, G12 8QQ, Glasgow, Scotland

    Roderic D. M. Page

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© 2003 Springer-Verlag Berlin Heidelberg

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Lotan, I., Schwarzer, F., Latombe, JC. (2003). Efficient Energy Computation for Monte Carlo Simulation of Proteins. In: Benson, G., Page, R.D.M. (eds) Algorithms in Bioinformatics. WABI 2003. Lecture Notes in Computer Science(), vol 2812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39763-2_26

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  • DOI: https://doi.org/10.1007/978-3-540-39763-2_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20076-5

  • Online ISBN: 978-3-540-39763-2

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