A Discard-and-Restart MD algorithm for the sampling of protein intermediate states

We introduce a Discard‑and‑Restart molecular dynamics (MD) algorithm
tailored for the sampling of realistic protein intermediate states. It
aids computational structure‑based drug discovery by reducing the
simulation times to compute a "quick sketch" of folding pathways
by up to 2000x. The algorithm iteratively performs short MD simulations
and measures their proximity to a target state via a collective variable
(CV) loss, which can be defined in a flexible fashion, locally or
globally.  Using the loss, if the trajectory proceeds toward the
target, the MD simulation continues. Otherwise, it is discarded, and a new
MD simulation is restarted, with new initial velocities randomly drawn
from a Maxwell‑Boltzmann distribution. The discard‑and‑restart algorithm
demonstrates efficacy and atomistic accuracy in capturing the folding
pathways in several contexts: (1) fast‑folding small protein domains; (2)
the folding intermediate of the prion protein PrP; and (3) the spontaneous
partial unfolding of α‑Tubulin, a crucial event for microtubule severing.
During each iteration of the algorithm, we can perform AI-based analysis
of the transitory conformations to find potential binding pockets, which
could represent druggable sites. Overall, our algorithm enables systematic
and computationally efficient exploration of conformational landscapes,
enhancing the design of ligands targeting dynamic protein states.