The rugged energy landscape of proteins often leads conventional molecular dynamics simulations to get stuck in local minima leading to inefficient sampling and slow convergence. There are two main methods to improve sampling. First, by smoothing the underlying energy surface thereby encouraging barrier crossing, and second, by modifying the simulation velocities. There are many examples in the literature of the former, but velocity modification is a comparatively less well exploited approach.<br/><br/>In a method previously developed in our group called Reversible Digitally Filtered Molecular Dynamics (RDFMD), we have been able to induce conformational changes in proteins by amplifying low-frequency molecular vibrations via application of a Digital Filter to the velocity set. However, by applying the digital filter to the molecules, the system is no longer in equilibrium, leading to incorrect ensemble averages. To sample a statistical ensemble and still benefit from the application of the digital filter a novel method called Digitally Filtered Hybrid Monte Carlo (DFHMC) is proposed. This method builds on the work of Momentum Enhanced Hybrid Monte Carlo (MEHMC). Low frequency motions are selectively enhanced via application of a specifically designed Digital Filter (as in RDFMD), but equilibrium is maintained using a Hybrid Monte Carlo approach.<br/><br/>In this presentation, the theory behind the DFHMC method is explained. The capability of sampling from the canonical ensemble and the enhancement in energy barrier crossing, and hence convergence, is demonstrated on a simple 2D system. Furthermore as a model for the dihedral motions in proteins, the application of the DFHMC method to a single alanine dipeptide molecule in both the Generalized Born implicit solvent and explicit solvent water models, is discussed. Finally, the DFG loop flip in kinases are investigated via DFHMC. We show that DFHMC is able to yield enhanced conformational sampling and convergence of thermodynamic properties over conventional simulation approaches.

Enhancing conformational sampling by modifying the underlying velocity distribution: Digitally filtered hybrid Monte Carlo

253rd National Meeting (2017)

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