Fully Automatable Relative Binding Free Energy Calculations with Enhanced Sampling Using FAST/MBAR

Abstract

Alchemical free energy (AFE) calculations are a useful tool in computational drug discovery. However, they typically involve relatively short (<10 ns) simulations, meaning that the initial coordinates and, more generally, the setup of the system have a significant effect on the obtained free energy values. To remedy this, we recently developed a fully adaptive version of the simulated tempering algorithm (FAST) and applied it in the context of sampling. In this work, we extend FAST to AFE calculations with and without enhanced sampling of a particular degree of freedom of interest (FAST/MBAR). We show that enhanced sampling significantly increases the mobility of the targeted degree of freedom at the cost of reduced sampling efficiency over λ space. On the other hand, the free energy calculations without explicit targeting of certain degrees of freedom retain initial-coordinate bias over longer time scales. Despite this, both protocols readily explore nanosecond-time-scale events, such as torsional rotation, due to the single-trajectory nature of FAST, making them less sensitive to the system preparation. It is shown that the robust automated nature of FAST/MBAR makes it a competitive alternative to conventional AFE methods.