Loop optimization of Trichoderma reesei endoglucanases for balancing the activity-stability trade-off through cross-strategy between machine learning and the B-factor analysis

Trichoderma reesei endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from T. reesei without reducing its activity counteracting the activity-stability trade-off. A cross-strategy combination of machine learning and B-factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single-site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%-18.06% and 49.85%-62.90%, respectively, compared with wild-type EGs. Furthermore, the mechanism explained mutant variants had lower root mean square deviation values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity-stability trade-off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross-strategy between machine learning and B-factor analysis improved superior enzyme activity-stability performance, which integrated structure-dependent and sequence-dependent information.

Automated summary

In this study, a cross-strategy combining machine learning and B-factor analysis was used to predict and optimize the thermostability and activity of Trichoderma reesei endoglucanases. The results showed that the optimized enzymes not only improved enzymatic activity but also had better thermal properties compared to the wild type. The study suggests that loop optimization is crucial for balancing the activity-stability trade-off and provides new insights into the interaction between loop region function and enzymatic characteristics.