posted on 2019-01-28, 00:00authored byZhe Li, Yiyou Huang, Yinuo Wu, Jingyi Chen, Deyan Wu, Chang-Guo Zhan, Hai-Bin Luo
Accurate prediction of absolute protein–ligand
binding free
energy could considerably enhance the success rate of structure-based
drug design but is extremely challenging and time-consuming. Free
energy perturbation (FEP) has been proven reliable but is limited
to prediction of relative binding free energies of similar ligands
(with only minor structural differences) in binding with a same drug
target in practical drug design applications. Herein, a Gaussian algorithm-enhanced
FEP (GA-FEP) protocol has been developed to enhance the FEP simulation
performance, enabling to efficiently carry out the FEP simulations
on vanishing the whole ligand and, thus, predict the absolute binding
free energies (ABFEs). Using the GA-FEP protocol, the FEP simulations
for the ABFE calculation (denoted as GA-FEP/ABFE) can achieve a satisfactory
accuracy for both structurally similar and diverse ligands in a dataset
of more than 100 receptor–ligand systems. Further, our GA-FEP/ABFE-guided
lead optimization against phosphodiesterase-10 led to the discovery
of a subnanomolar inhibitor (IC50 = 0.87 nM, ∼2000-fold
improvement in potency) with cocrystal confirmation.