This is a preprint.
Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function
- PMID: 39091854
- PMCID: PMC11290999
- DOI: 10.1101/2024.04.18.590141
Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function
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Computational Stabilization of a Non-Heme Iron Enzyme Enables Efficient Evolution of New Function.Angew Chem Int Ed Engl. 2025 Jan 10;64(2):e202414705. doi: 10.1002/anie.202414705. Epub 2024 Nov 11. Angew Chem Int Ed Engl. 2025. PMID: 39394803 Free PMC article.
Abstract
Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/αKG enzyme tP4H, we performed site-saturation mutagenesis with both the wild-type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. ProteinMPNN is user-friendly and widely-accessible, and straightforward structural criteria were sufficient to obtain stabilized, catalytically-functional variants of the Fe(II)/αKG enzymes tP4H and GriE. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts.
Conflict of interest statement
Competing Interests The authors declare that they have no competing interests.
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