Title: Active Learning Identifies Sulfur‐Based Enhancers for Fe(III)‐Protoporphyrin Catalysis: Recapitulating Features of Natural Oxidase and Beyond

Authors: Su, Pengkun; Zhan, Yue; Su, Yuming; Wang, Zhiye; Chen, Suyang; Jiang, Yibin; Hu, Huihui; Wang, Cheng

Abstract: Sequence‐controlled polymers, such as polypeptides, offer a versatile platform for tuning the microenvironment of catalytic centers, drawing inspiration from enzymes while enabling a larger design space, structural flexibility, automated synthesis, and compatibility with closed‐loop optimization. Here, we designed an artificial oxidase system by immobilizing Fe(III)‐protoporphyrin IX onto a lysine residue in synthetic decapeptides via amide linkage. Using hydrogen peroxide as the oxidant and acetophenone as a model substrate, we used an active‐learning‐guided closed‐loop workflow to prioritize peptide sequences across 233 variants over 20 rounds. Statistical analysis revealed that sulfur‐containing residues—cysteine and methionine—consistently enhanced activity when positioned adjacent to the coordination site. Notably, although sequence optimization began from random inputs, the algorithm quickly converged on cysteine‐containing motifs, consistent with features found in natural oxidases. Thioether‐containing methionine was also found to promote catalysis, extending the relevance of sulfur‐based coordination beyond naturally occurring systems. These findings demonstrate the application of data‐driven sequence design for developing tunable, enzyme‐inspired catalysts with simplified architectures.

Full-Link: https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202518756