Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge

Abstract

Interest in ligand binding kinetics has been growing rapidly, as it is being discovered in more and more systems that ligand residence time is the crucial factor governing drug efficacy. Many enhanced sampling methods have been developed with the goal of predicting ligand binding rates ($k_{\text {on}}$) and/or ligand unbinding rates ($k_{\text {off}}$) through explicit simulation of ligand binding pathways, and these methods work by very different mechanisms. Although there is not yet a blind challenge for ligand binding kinetics, here we take advantage of experimental measurements and rigorously computed benchmarks to compare estimates of $K_D$ calculated as the ratio of two rates: $k_{\text {off}}/k_{\text {on}}$. These rates were determined using a new enhanced sampling method based on the weighted ensemble framework that we call “REVO”: Reweighting of Ensembles by Variance Optimization. This is a further development of the WExplore enhanced sampling method, in which trajectory cloning and merging steps are guided not by the definition of sampling regions, but by maximizing trajectory variance. Here we obtain estimates of $k_{\text {on}}$ and $k_{\text {off}}$ that are consistent across multiple simulations, with an average log10-scale standard deviation of 0.28 for on-rates and 0.56 for off-rates, which is well within an order of magnitude and far better than previously observed for previous applications of the WExplore algorithm. Our rank ordering of the three host–guest pairs agrees with the reference calculations, however our predicted $\Delta G$ values were systematically lower than the reference by an average of 4.2 kcal/mol. Using tree network visualizations of the trajectories in the REVO algorithm, and conformation space networks for each system, we analyze the results of our sampling, and hypothesize sources of discrepancy between our $K_D$ values and the reference. We also motivate the direct inclusion of $k_{\text {on}}$ and $k_{\text {off}}$ challenges in future iterations of SAMPL, to further develop the field of ligand binding kinetics prediction and modeling.