Unknown

Dataset Information

0

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.

SUBMITTER: Karges J 

PROVIDER: S-EPMC8919381 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

altmetric image

Publications

Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores.

Karges Johannes J   Stokes Ryjul W RW   Cohen Seth M SM  

ACS medicinal chemistry letters 20220224 3


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 consi  ...[more]

Similar Datasets

| S-EPMC6249039 | biostudies-literature
| S-EPMC6489498 | biostudies-literature
| S-EPMC4148168 | biostudies-literature
| S-EPMC8137660 | biostudies-literature
| S-EPMC10703974 | biostudies-literature
| S-EPMC9454149 | biostudies-literature
| S-EPMC6030886 | biostudies-literature
| S-EPMC6933848 | biostudies-literature
| S-EPMC5046193 | biostudies-literature
| S-EPMC10724026 | biostudies-literature