Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores

Computational modeling of inhibitors for metalloenzymes in virtual drug development campaigns has proven challenging. To overcome this limitation, a technique for predicting the binding pose of metal-binding pharmacophores (MBPs) is presented. Using a combination of density functional theory (DFT) calculations and docking using a genetic algorithm, inhibitor binding was evaluated in silico and compared with inhibitor-enzyme cocrystal structures. The predicted binding poses were found to be consistent with the cocrystal structures. The computational strategy presented represents a useful tool for predicting metalloenzyme-MBP interactions.