An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

Authors:
Arvind Ramanathan;Pratul K. Agarwal;Maria Kurnikova;Christopher J. Langmead
Affiliations:
Lane Center for Computational Biology, Carnegie Mellon University,;Computational Biology Institute, and Computer Science and Mathematics Division, Oak Ridge National Laboratory,;Chemistry Department, Carnegie Mellon University,;Lane Center for Computational Biology, Carnegie Mellon University, and Computer Science Department, School of Computer Science, Carnegie Mellon University,
Venue:
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Year:
2009

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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.