An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

  • Arvind Ramanathan
  • Pratul K. Agarwal
  • Maria Kurnikova
  • Christopher J. Langmead
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5541)


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.


Molecular Dynamic Simulation Reconstruction Error Collective Behavior Protein Dynamic Molecular Dynamic Trajectory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Arvind Ramanathan
    • 1
  • Pratul K. Agarwal
    • 2
  • Maria Kurnikova
    • 3
  • Christopher J. Langmead
    • 1
    • 4
  1. 1.Lane Center for Computational BiologyCarnegie Mellon UniversityUSA
  2. 2.Computational Biology Institute, and Computer Science and Mathematics DivisionOak Ridge National LaboratoryUSA
  3. 3.Chemistry DepartmentCarnegie Mellon UniversityUSA
  4. 4.Computer Science Department, School of Computer ScienceCarnegie Mellon UniversityUSA

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