Abstract
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 2000×. The algorithm iteratively performs short MD simulations and measures their proximity to a target state via a collective variable 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.