Increasing the Efficiency of Ensemble Molecular Dynamics Simulations with Termination of Unproductive Trajectories Identified at Runtime

The application of molecular dynamics (MD) simulations to study increasingly larger and more complex systems is challenged by the required amounts of trajectory data needed to sample their conformational space appropriately. The analysis and interpretation phase of such massive data sets that have to be stored and fed to the various algorithms to reveal the dynamic behaviors of the systems and the underlying energetics in structural terms related to functional mechanisms are also a significant challenge. To develop computational means that can address these challenges, we are developing a software framework that can increase the efficiency of this process. We present one component of this framework that can reduce the size of the accumulating data set while maintaining the structural attributes, distribution, and relative probability ranking of the minima in the free energy map for the system. This framework component utilizes early termination of individual trajectories identified as unproductive in the sampling of conformational space. The criteria for termination are derived quantities such as collective variables (CVs) and secondary quantities calculated from the time series of CVs. They are computed and applied during the trajectory generation. The approach is illustrated with simulations of the FS peptide and evaluated from comparisons between the free energy surfaces calculated from ensembles of complete, unabridged simulations with those obtained from ensembles in which ∼5-50% of trajectories were terminated early. Our early termination approach can optimize computational efficiency while achieving a robust representation of conformational space.