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Updated: Sep 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores
Johannes Karges1, Ryjul W Stokes1, Seth M Cohen1
1Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, United States.
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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