An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations

Abstract

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.

Evolutionarily Conserved Linkage between Enzyme Fold, Flexibility, and Catalysis

“…Note that Figures 1, 3, and 5 provide a list of λ N corresponding to the modes showing the largest coupling to the catalyzed reaction. Protein regions showing similar motions over the course of the reaction pathway were identified using a clustering methodology [82]. See Figure S11 and Text S1 for details of methodology for dynamical clustering and cross-correlations.…”

“…In particular, the large dynamical cross-correlation between different residue pairs followed by structural analysis was used to identify the chain of interactions in the networks. Additionally, as described in Text S1, a new methodology for dynamic clustering was used to identify enzyme regions that exhibit similar dynamical characteristics over the enzyme pathway [82]. Genomic analysis was performed using Clustal-W [84], and the structural analysis was aided by the PyMOL program [85].…”

Analysis of Somatic Mutations in Cancer: Molecular Mechanisms of Activation in the ErbB Family of Receptor Tyrosine Kinases

“…There are multiple measures of protein fluctuations (B-factor, RMSF, RMSD, etc. [171]), we choose to use RMSD here to track the specific protein deviation at each time point. Hydrophilic and hydrophobic interaction networks were characterized for each state as well [133-135,148].…”

Conformational Sub-states and Populations in Enzyme Catalysis

“…M. Burger, et al, 2012; Ramanathan, Agarwal, Kurnikova, & Langmead, 2010; Ramanathan, Savol, Agarwal, & Chennubhotla, 2012; Ramanathan, et al, 2011; Savol, Burger, Agarwal, Ramanathan, & Chennubhotla, 2011). QAA uses fourth-order statistics (for analytical convenience) to describe the atomic fluctuations and summarizes the internal motions using a small number of dominant anharmonic modes.…”