Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores

Abstract

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.

Figure 1

Workflow for modeling metalloenzyme–inhibitor interactions. Protein surface (PA_N) shown in gray, active site metals shown in cyan, and MBPs and inhibitors shown as sticks colored by atom type.

Figure 2

Comparison of MBP binding poses from crystallographic determined structures (gray carbons, PDB entry codes shown) to computational docking (green carbons) using GOLD.

Figure 3

Comparison of binding modes of crystallographically determined structures (gray carbons, PDB entry codes shown) to computationally derived inhibitor poses (green carbons).

Figure 4

Distribution of RMSD values of computational and experimental binding poses for metalloenzyme inhibitors. Color-coded by metalloenzyme: hCAII (blue), KDM (orange), and PA_N (green).