New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

Atomic Engineering of Single-Atom Nanozymes for Biomedical Applications

Single-atom nanozymes (SAzymes) showcase not only uniformly dispersed active sites but also meticulously engineered coordination structures. These intricate architectures bestow upon them an exceptional catalytic prowess, thereby captivating numerous minds and heralding a new era of possibilities in the biomedical landscape. Tuning the microstructure of SAzymes on the atomic scale is a key factor in designing targeted SAzymes with desirable functions. This review first discusses and summarizes three strategies for designing SAzymes and their impact on reactivity in biocatalysis. The effects of choices of carrier, different synthesis methods, coordination modulation of first/second shell, and the type and number of metal active centers on the enzyme-like catalytic activity are unraveled. Next, a first attempt is made to summarize the biological applications of SAzymes in tumor therapy, biosensing, antimicrobial, anti-inflammatory, and other biological applications from different mechanisms. Finally, how SAzymes are designed and regulated for further realization of diverse biological applications is reviewed and prospected. It is envisaged that the comprehensive review presented within this exegesis will furnish novel perspectives and profound revelations regarding the biomedical applications of SAzymes.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.