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 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.
Ramanathan
Arvind
A
Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Agarwal
Pratul K
PK
Kurnikova
Maria
M
Langmead
Christopher J
CJ
D000596:Amino Acids / Q000032:analysis; D001426:Bacterial Proteins; D019295:Computational Biology / Q000379:methods*; D021984:Cyclophilin A / Q000737:chemistry; D057225:Data Mining / Q000379:methods*; D006860:Hydrogen Bonding; D020407:Internet; D056004:Molecular Dynamics Simulation; D018583:Pliability; D017433:Protein Structure, Secondary; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D012260:Ribonucleases / Q000737:chemistry
D000596:Amino Acids; D001426:Bacterial Proteins; D011506:Proteins; D012260:Ribonucleases; C019760:Bacillus amyloliquefaciens ribonuclease; D021984:Cyclophilin A