  Synthetic molecular dynamics (synMD) trajectories from learned generative
models have been proposed as a useful addition to the biomolecular simulation
toolbox. The computational expense of explicitly integrating the equations of
motion in molecular dynamics currently is a severe limit on the number and
length of trajectories which can be generated for complex systems. Approximate,
but more computationally efficient, generative models can be used in place of
explicit integration of the equations of motion, and can produce meaningful
trajectories at greatly reduced computational cost. Here, we demonstrate a very
simple synMD approach using a fine-grained Markov state model (MSM) with states
mapped to specific atomistic configurations, which provides an exactly solvable
reference. We anticipate this simple approach will enable rapid, effective
testing of enhanced sampling algorithms in highly non-trivial models for both
equilibrium and non-equilibrium problems. We demonstrate the use of a MSM to
generate atomistic synMD trajectories for the fast-folding miniprotein
Trp-cage, at a rate of over 200 milliseconds per day on a standard workstation.
We employ a non-standard clustering for MSM generation that appears to better
preserve kinetic properties at shorter lag times than a conventional MSM. We
also show a parallelizable workflow that backmaps discrete synMD trajectories
to full-coordinate representations at dynamic resolution for efficient
analysis.
