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Updated: Aug 16, 2025
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
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Analysis of Laboratory-Evolved Flavin-Dependent Halogenases Affords a Computational Model for Predicting Halogenase
Mary C Andorfer1,2,3, Declan Evans4,3, Song Yang4,5
1Department of Chemistry, University of Chicago, Chicago, IL 60637, USA.
Chem Catalysis
|December 26, 2022
Summary
Flavin-dependent halogenases (FDHs) offer selective aromatic halogenation. This study reveals key residues and catalytic mechanisms, enabling predictive models for biocatalysis applications.
Area of Science:
- Biochemistry
- Organic Chemistry
- Biocatalysis
Background:
- Flavin-dependent halogenases (FDHs) perform selective halogenation of aromatic compounds.
- Conventional methods often require harsh oxidants.
- Predictive models for FDH selectivity can enhance their synthetic utility.
Purpose of the Study:
- To analyze the structures and selectivity of FDH variants.
- To identify key residues influencing halogenase selectivity.
- To develop predictive models for FDH-catalyzed halogenation.
Main Methods:
- Protein engineering of FDH variants.
- X-ray crystallography to determine structures.
- Site-directed mutagenesis and reversion analysis.
- Computational chemistry: Density Functional Theory (DFT) and molecular dynamics (MD) simulations.
Main Results:
- Three FDH variants evolved for orthogonal tryptamine halogenation were analyzed.
- Key residues responsible for altered selectivity were identified through structural and mutational analysis.
- DFT and MD simulations supported hypohalous acid as the active halogenating species.
- A predictive model accurately forecasted the site selectivity of FDH variants.
Conclusions:
- Understanding FDH structure-selectivity relationships is crucial.
- Hypohalous acid is the likely active species in FDH catalysis.
- Developed predictive models can guide the application of FDHs in biocatalysis.
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