posted on 2022-02-24, 16:10authored byJohannes Karges, Ryjul W. Stokes, Seth M. Cohen
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.