Probing Coordination Number of Single-Atom Catalysts by d-Band Center-Regulated Luminescence.

It is essential to probe the coordination number (CN) because it is a crucial factor to ensure the catalytic capability of single-atom catalysts (SACs). Currently, synchrotron X-ray absorption spectroscopy (XAS) is widely used to measure the CN. However, the scarcity of synchrotron X-ray source and complicated data analysis restrict its wide applications in determining the CN of SACs. In this contribution, we have developed a d-band center-regulated acetone cataluminescence (CTL) probe for a rapid screening of the CN of Pt-SACs. It is disclosed that the CN-triggered CTL is attributed to the fact that the increased CN could induce the downward shift of d-band center position, which assists the acetone adsorption and promotes the subsequent catalytic reaction. In addition, the universality of the proposed acetone-CTL probe is verified by determining the CN of Fe-SACs. This work has opened a new avenue for exploring an alternative to synchrotron XAS for the determination of CN of SACs and even conventional metal catalysts through d-band center-regulated CTL.

Structure- and Data-Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity.

Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. Machine learning provides a promising approach for protein engineering, but activity prediction models for ATAs remain elusive due to the difficulty of obtaining high-quality training data. Thus, we first created variants of the ATA from Ruegeria sp. (3FCR) with improved catalytic activity (up to 2000-fold) as well as reversed stereoselectivity by a structure-dependent rational design and collected a high-quality dataset in this process. Subsequently, we designed a modified one-hot code to describe steric and electronic effects of substrates and residues within ATAs. Finally, we built a gradient boosting regression tree predictor for catalytic activity and stereoselectivity, and applied this for the data-driven design of optimized variants which then showed improved activity (up to 3-fold compared to the best variants previously identified). We also demonstrated that the model can predict the catalytic activity for ATA variants of another origin by retraining with a small set of additional data.