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Annual International Conference on Research in Computational Molecular Biology

RECOMB 2009: Research in Computational Molecular Biology pp 138–154Cite as

An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5541))

Abstract

Collective behavior involving distally separate regions in a protein is known to widely affect its function. In this paper, we present an online approach to study and characterize collective behavior in proteins as molecular dynamics simulations progress. Our representation of MD simulations as a stream of continuously evolving data allows us to succinctly capture spatial and temporal dependencies that may exist and analyze them efficiently using data mining techniques. By using multi-way analysis we identify (a) parts of the protein that are dynamically coupled, (b) constrained residues/ hinge sites that may potentially affect protein function and (c) time-points during the simulation where significant deviation in collective behavior occurred. We demonstrate the applicability of this method on two different protein simulations for barnase and cyclophilin A. For both these proteins we were able to identify constrained/ flexible regions, showing good agreement with experimental results and prior computational work. Similarly, for the two simulations, we were able to identify time windows where there were significant structural deviations. Of these time-windows, for both proteins, over 70% show collective displacements in two or more functionally relevant regions. Taken together, our results indicate that multi-way analysis techniques can be used to analyze protein dynamics and may be an attractive means to automatically track and monitor molecular dynamics simulations.

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Author information

Authors and Affiliations

  1. Lane Center for Computational Biology, Carnegie Mellon University, USA

    Arvind Ramanathan & Christopher J. Langmead

  2. Computational Biology Institute, and Computer Science and Mathematics Division, Oak Ridge National Laboratory, USA

    Pratul K. Agarwal

  3. Chemistry Department, Carnegie Mellon University, USA

    Maria Kurnikova

  4. Computer Science Department, School of Computer Science, Carnegie Mellon University, USA

    Christopher J. Langmead

Authors
  1. Arvind Ramanathan
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  2. Pratul K. Agarwal
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  3. Maria Kurnikova
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  4. Christopher J. Langmead
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Editor information

Editors and Affiliations

  1. Computer Science Department, James H. Clark Center, 318 Campus Drive, RM S266, CA 94305-5428,, Stanford, USA

    Serafim Batzoglou

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© 2009 Springer-Verlag Berlin Heidelberg

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Ramanathan, A., Agarwal, P.K., Kurnikova, M., Langmead, C.J. (2009). An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_10

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  • DOI: https://doi.org/10.1007/978-3-642-02008-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02007-0

  • Online ISBN: 978-3-642-02008-7

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