# Computational redesign and directed evolution of a lanthanide-dependent photoredox enzyme for enantioselective diol cleavage

**Authors:** Florian Leiss-Maier, Joshua Behringer, Ghulam Mustafa, Anna Heider, Rahel Mühlhofer, Andreas S. Klein, Michael Groll, Cathleen Zeymer

PMC · DOI: 10.1039/d5sc08010j · 2026-03-17

## TL;DR

Scientists improved a light-activated enzyme to selectively break carbon-carbon bonds in a specific way using AI and lab experiments.

## Contribution

Combining AI-guided redesign and directed evolution to create enantioselective lanthanide-dependent photoredox enzymes.

## Key findings

- Reducing cavity size enhanced substrate interactions and initial enantiocontrol.
- AI-guided redesign improved lanthanide binding kinetics.
- Directed evolution increased enantioselectivity of photocatalytic turnover.

## Abstract

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.

A combined approach of AI-guided protein redesign and directed evolution yields enantioselective PhotoLanZymes for cerium-dependent photocatalytic C–C bond cleavage.

## Full-text entities

- **Chemicals:** diol (MESH:D011276), Ce(iii/iv) (-), carbon (MESH:D002244), lanthanide (MESH:D028581)

## Figures

5 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13010360/full.md

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Source: https://tomesphere.com/paper/PMC13010360