Computational Stabilization of a Non-Heme Iron Enzyme Enables Efficient Evolution of New Function.

Autor: King BR; Department of Chemistry, University of Washington, Seattle, Washington, 98195, USA., Sumida KH; Department of Chemistry, University of Washington, Seattle, Washington, 98195, USA.; Institute of Protein Design, University of Washington, Seattle, Washington, 98195, USA., Caruso JL; Department of Chemistry, University of Washington, Seattle, Washington, 98195, USA., Baker D; Institute of Protein Design, University of Washington, Seattle, Washington, 98195, USA.; Howard Hughes Medical Institute, University of Washington, Seattle, Washington, 98195, USA., Zalatan JG; Department of Chemistry, University of Washington, Seattle, Washington, 98195, USA.
Jazyk: angličtina
Zdroj: Angewandte Chemie (International ed. in English) [Angew Chem Int Ed Engl] 2024 Oct 12, pp. e202414705. Date of Electronic Publication: 2024 Oct 12.
DOI: 10.1002/anie.202414705
Abstrakt: Deep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. 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. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non-native, remote C(sp 3 )-H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild-type enzyme. After three rounds of directed evolution, we obtained a 6-fold activity increase from the wild-type parent and an 80-fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user-friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning-based protein stabilization tools in directed evolution workflows for novel biocatalysts.
(© 2024 Wiley-VCH GmbH.)
Databáze: MEDLINE
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