Simulating Met-Enkephalin With Population Annealing Molecular Dynamics

Abstract

Met-enkephalin, one of the smallest opiate peptides and an important neuro-transmitter, is a widely used benchmarking problem in the field of molecular simulation. Through its range of possible low-temperature conformations separated by free-energy barriers it was previously found to be hard to thermalize using straight canonical molecular dynamics simulations. Here, we demonstrate how one can use the recently proposed population annealing molecular dynamics scheme to overcome these difficulties. We show how the use of multi-histogram reweighting allows one to accurately estimate the density of states of the system and hence derive estimates such as the potential energy as quasi continuous functions of temperature. We further investigate the free-energy surface as a function of end-to-end distance and radius-of-gyration and observe two distinct basins of attraction.

Monte Carlo Simulation of Long Hard‐Sphere Polymer Chains in Two to Five Dimensions

“…[14] Methodologically, we generalized the relatively new population annealing method for MC [15][16][17][18] to population annealing molecular dynamics (PAMD) simulations. [19][20][21] For protein studies this new method may turn out to be superior to the currently usually employed parallel tempering simulations, in particular when implemented on massively parallel computer architectures (such as graphics processing units (GPUs) [22,23] ). A benchmark comparison for the B1 domain of protein G is in preparation.…”

Monte Carlo Simulation of Long Hard-Sphere Polymer Chains in Two to Five Dimensions

“…Methodologically, we generalized the relatively new population annealing method for MC [15][16][17][18] to population annealing molecular dynamics (PAMD) simulations [19][20][21]. For protein studies this new method may turn out to be superior to the currently usually employed parallel tempering simulations, in particular when implemented on massively parallel computer architectures (such as graphics processing units (GPUs) [22,23]).…”