Automated Event Detection and Activity Monitoring in Long Time-Scale Molecular Dynamics

Abstract

Molecular dynamics trajectories of biological systems contain many events of great scientific interest, including conformational transitions, folding processes, and translocations of ligands and reaction products. In proteins these events often correspond to high-level tertiary or quaternary structure rearrangements, which alter the contacts between amino acid residues. Due to advances in computer architecture and software, molecular dynamics trajectories representing such structure-changing events have become easy to generate, but their length complicates scientific interpretation. The goal of this work is to simplify an important part of the analysis workflow (and to complement traditional visual inspection) by automating the mining of long trajectories. We present several new algorithms and implementation techniques that enable the detection of significant structure-changing events in a molecular dynamics trajectory. These algorithms include a coarse graining of side chain contacts, a contact metric based on higher-order generalizations of the Delaunay tetrahedralization, and median filters for detecting significant shifts in the ensemble mean of the resulting time series. We have also developed numerical techniques for suppressing trivial re-crossing events and a new kernel-based estimator of the contact alteration activity. These methods will be disseminated in a newly developed package, “TimeScapes,” which is compatible with molecular dynamics trajectories generated from any of a variety of popular simulation programs. Tests on microsecond time scale simulations suggest that the implementation is efficient and requires very little parameterization. The analysis provides a detailed listing of broken and formed contacts, and reliably detects allosteric and folding transitions, as well as stable intermediates, in the protein dynamics.