Ultrafast generation of atomistic biomolecular trajectories for methods development.

Abstract

Validation of new methods requires testing data of high (protein-like) complexity, that also can be exhaustively sampled to obtain a precise reference. In practice, however, these criteria are often mutually exclusive. The expense of integrating the equations of motion for molecular dynamics (MD) simulations, for example, typically makes high-quality sampling of complex systems infeasible. Simpler and faster models such as low dimensional potentials can be sampled extensively, but likely do not capture sufficient complexity to challenge cutting-edge new methods. Building on precedents in the literature, we demonstrate a general synthetic dynamics framework for efficiently propagating dynamics in a low dimensional latent space and projecting back to the full-dimensional coordinate space. This framework can efficiently produce extremely long trajectories, and enables rapid testing of enhanced sampling and analysis algorithms on highly non-trivial models. We demonstrate synthetic MD (synMD) trajectory generation based on a fine-grained Markov state model, stratified along slow coordinates to enforce realistic long-time dynamics. Each discrete state is mapped to an atomistic configuration, leading to reasonably realistic MD-mimicking trajectories. We show generation of atomistic synMD trajectories for the fast-folding miniprotein Trp-cage at a rate of over 8 seconds/day on a single core of a standard workstation CPU. We also demonstrate the use of synMD as a dynamics engine for the popular WESTPA implementation of the weighted ensemble enhanced sampling strategy. Although we currently use MSM generators, our implementation provides a generic interface for implementing different generators, such as have been proposed based on machine-learning approaches.