Abstact

De novo designed metalloenzymes and photoenzymes are a valuable addition to the biocatalytic toolbox. We previously introduced PhotoLanZymes (PLZ), a family of lanthanide-dependent photoredox enzymes that enable radical carbon-carbon bond cleavages of diol substrates upon Ce(iii/iv) binding and visible-light irradiation. While rational optimization increased their catalytic activity and photostability, the first generation of PLZ variants was limited by slow lanthanide binding and a lack of enantioselectivity. Here, we demonstrate that coupling computational redesign with directed evolution provides an effective strategy to overcome these limitations. First, we reduced the cavity size to enhance substrate interactions with the protein's active site, which facilitated initial enantiocontrol. Simultaneously, the AI-guided redesign approach improved the lanthanide binding kinetics. We then performed directed evolution to selectively accelerate the photocatalytic turnover for one of the substrate enantiomers, yielding a PLZ variant with markedly improved enantioselectivity. These results underscore the value of integrating AI-guided protein design with laboratory evolution to obtain stereoselective de novo metalloenzymes and photoenzymes.