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

ABSTRACT: 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(sp³)-H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild-type enzyme. After three rounds of dir

SUBMITTER: King BR 

PROVIDER: S-EPMC11723803 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature