Optimizing String Method's Reproducibility Using Generalized Solute Tempering Replica Exchange.

Obtaining accurate and reproducible free energies from molecular simulations is somewhat tricky due to incomplete knowledge of crucial slow degrees of freedom leading to hidden barriers that can stymie sampling. Employing a sufficiently large number of collective variables (CV) and ensuring ergodic sampling in orthogonal CV space, perhaps via tempering methods, can reduce these issues to some extent. For complex systems with high-dimensional free energy landscapes, both these approaches become computationally expensive. For high-dimensional landscapes, efficient exploration can be enabled by using temperature-accelerated MD (TAMD) and identification and characterization of minimum free energy pathways connecting minima can be found by using the string method (SM). Both TAMD and SM use mean-force estimates from finite MD simulations and are thus susceptible to sampling restrictions from hidden variables. A recent development in parallel tempering methods, "generalized replica exchange solute tempering" (gREST), can enhance sampling at a reasonable computational cost with its flexibility to target very specific "solutes" which can include arbitrary independent variables. Considering the advantages of both methods, we implement gREST-enabled TAMD and SM. By considering two different collective variable representations of the pentapeptide neurotransmitter met-enkephalin, we show that both gREST-enabled TAMD and SM yield more accurate and reproducible free energy predictions than TAMD and SM alone. Given the moderate computational cost of gREST compared with other replica-exchange methods, gREST-enabled SM represents a more attractive method for characterizing free energy minima and pathways among them for a large variety of systems.