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

Abstract

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.

A Novel Strategy for Further Enhancing Superior Properties of Thermophilic Endoglucanase from Acidomyces richmondensis

“…The initial pH of the fermentation medium was adjusted to 5.0 using 10 M KOH. The spore suspension (10 6 spores/mL) was then used to inoculate 50 mL of the liquid medium in a 250 mL conical flask and was grown at 28 • C with agitation at 180 rpm for 5 days [23].…”

“…Endoglucanase activity was determined using 0.8% (w/v) carboxymethyl cellulose. Reactions were performed in 0.1 M sodium acetate buffer (pH 5.5) at 37 • C for 30 min [23]. The total amount of reduced sugars in the supernatants was determined using the dinitrosalicylic acid method.…”

“…The control used was the inactive sample taken at different time intervals. The sample was inactivated through boiling at 100 • C for 5 min [23].…”

“…To prepare the system for simulations, an energy minimization step was performed on the entire system using 5000 steps of steepest descent followed by 5000 steps of conjugate gradient minimization. After energy minimization, the system was gradually heated from 0 to 300 K over a period of 100 ps [23]. The temperature was ramped up slowly during the initial 90 ps, and then maintained at 300 K for the final 10 ps to allow for equilibration.…”

“…Although overcoming the challenge of improving stability without compromising activity remains a significant hurdle [22], studies suggest that selecting mutational target sites in regions distant from the catalytic center may be a viable approach for designing enzymes with enhanced thermostability and catalytic efficiency. For example, optimization of the loop regions of endoglucanases resulted in combined improvements of both enzymatic activity (17.2-18.0%) and thermostability (49.9-62.9%) compared to wild-type endoglucanases [23].…”