Accelerated Enzyme Evolution: CSFp Approach for Enhanced D-Lac Activity

This study presents the CSFp approach for accelerating enzyme evolution, demonstrating a remarkable 56-fold increase in D-Lac activity. Through evolutionary processes, the CSFp strategy yielded 45 mutants, with a notable 69% exhibiting D-Lac activity. Among these mutants, 16 displayed activity levels exceeding an order of magnitude, with the top-performing variant, N96S/A271E/F274Y/F308G, showcasing a significant 56-fold increase in activity (Figure 6). Our findings demonstrate the efficacy of integrating in silico and experimental methodologies in expediting the enhancement of D-Lac activity. This approach facilitates the evolution of enzymes with unknown crystal structures more rapidly and robustly. While employing a single in silico software package might yield marginal enhancements in enzyme activity, the synergistic impact of multiple in silico modules working in tandem is more likely to produce the optimal outcome we seek. Therefore, the CSFp strategy presented in this study offers a powerful tool for computational enzyme evolution. By significantly reducing screening efforts and rapidly evolving enzymes without experimental structural information or definitive enzyme transition state models, this approach significantly broadens the applicability of structure predictors within the field of enzyme design.