An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

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...

• Type: Conference
• Year: 2009
• Where: RECOMB
• Authors: Arvind Ramanathan, Pratul K. Agarwal, Maria Kurnikova, Christopher James Langmead
• Keywords: Collective Behavior, Computational Biology, Dynamics Simulations Progress, Molecular Dynamics Simulations, RECOMB 2009