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. 2022 Feb 24;13(3):428-435.
doi: 10.1021/acsmedchemlett.1c00584. eCollection 2022 Mar 10.

Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores

Johannes Karges  1 , Ryjul W Stokes  1 , Seth M Cohen  1
Affiliations

Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores

Johannes Karges et al. ACS Med Chem Lett. .

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.

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Conflict of interest statement

The authors declare the following competing financial interest(s): S.M.C. is a cofounder of and has an equity interest in Cleave Therapeutics, Forge Therapeutics, and Blacksmith Medicines, companies that may potentially benefit from the research results. S.M.C. also serves on the Scientific Advisory Board for Blacksmith Medicines and serves on the Scientific Advisory Board and receives compensation from Forge Therapeutics. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

Figures

Figure 1
Figure 1
Workflow for modeling metalloenzyme–inhibitor interactions. Protein surface (PAN) shown in gray, active site metals shown in cyan, and MBPs and inhibitors shown as sticks colored by atom type.
Figure 2
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
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
Figure 4
Distribution of RMSD values of computational and experimental binding poses for metalloenzyme inhibitors. Color-coded by metalloenzyme: hCAII (blue), KDM (orange), and PAN (green).
See this image and copyright information in PMC

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