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Markov Methods for Hierarchical Coarse-Graining of Large Protein Dynamics

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Research in Computational Molecular Biology (RECOMB 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3909))

Abstract

Elastic network models (ENMs), and in particular the Gaussian Network Model (GNM), have been widely used in recent years to gain insights into the machinery of proteins. The extension of ENMs to supramolecular assemblies/complexes presents computational challenges, however, due to the difficulty of retaining atomic details in mode decomposition of large systems’ dynamics. Here, we present a novel approach to address this problem. Based on a Markovian description of communication/interaction stochastics, we map the full-atom GNM representation into a hierarchy of lower resolution networks, perform the analysis in the reduced space(s) and reconstruct the detailed models’ dynamics with minimal loss of data. The approach (hGNM) applied to chaperonin GroEL-GroES demonstrates that the shape and frequency dispersion of the dominant 25 modes of motion predicted by a full-residue (8015 nodes) GNM analysis are almost identically reproduced by reducing the complex into a network of 35 soft nodes.

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Author information

Authors and Affiliations

  1. Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Chakra Chennubhotla & Ivet Bahar

Authors
  1. Chakra Chennubhotla
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  2. Ivet Bahar
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology and Università di Padova,  

    Alberto Apostolico

  2. Topic Chairs, P.O. Box

    Concettina Guerra

  3. Center for Molecular Biology and Computer Sciecne Department, Brown University, 115 Waterman St., 02912, Providence, RI, USA

    Sorin Istrail

  4. University of California, San Diego, USA

    Pavel A. Pevzner

  5. Department of Molecular and Computational Biology, University of Southern California, 1050 Childs Way, 90089-2910, Los Angeles, CA, USA

    Michael Waterman

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© 2006 Springer-Verlag Berlin Heidelberg

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Chennubhotla, C., Bahar, I. (2006). Markov Methods for Hierarchical Coarse-Graining of Large Protein Dynamics. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_32

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  • DOI: https://doi.org/10.1007/11732990_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33295-4

  • Online ISBN: 978-3-540-33296-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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