All data support the published articel "Loop-optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade-off through cross-strategy between machine learning and the B-factor analysis"

<em>Trichoderma reesei</em> endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from<em> T.reesei</em> 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 RMSD 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.