An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

作者：Ramanathan Arvind; Agarwal Pratul K; Kurnikova Maria; Langmead Christopher J^*

来源：Journal of Computational Biology, 2010, 17(3): 309-324.

DOI：10.1089/cmb.2009.0167

• biochemical networks
• computational molecular biology
• machine learning
• protein folding
• proteins

Collective behavior involving distally separate regions in a protein is known to widely affect its function. In this article, we present an online approach to study and characterize collective behavior in proteins as molecular dynamics (MD) 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 tensor analysis we identify (a) collective motions (i.e., dynamic couplings) and (b) time-points during the simulation where the collective motions suddenly change. We demonstrate the applicability of this method on two different protein simulations for barnase and cyclophilin A. We characterize the collective motions in these proteins using our method and analyze sudden changes in these motions. Taken together, our results indicate that tensor analysis is well suited to extracting information from MD trajectories in an online fashion.

• 出版日期2010-3