Electrostatic interaction optimization improves catalytic rates and thermotolerance on xylanases

Understanding the aspects that contribute to improving proteins' biochemical properties is of high relevance for protein engineering. Properties such as the catalytic rate, thermal stability, and thermal resistance are crucial for applying enzymes in the industry. Different interactions can influence those biochemical properties of an enzyme. Among them, the surface charge-charge interactions have been a target of particular attention. In this study, we employ the Tanford-Kirkwood solvent accessibility model using the Monte Carlo algorithm (TKSA-MC) to predict possible interactions that could improve stability and catalytic rate of a WT xylanase (XynA(WT)) and its M6 xylanase (XynA(M6)) mutant. The modeling prediction indicates that mutating from a lysine in position 99 to a glutamic acid (K99E) favors the native state stabilization in both xylanases. Our lab results showed that mutated xylanases had their thermotolerance and catalytic rate increased, which conferred higher processivity of delignified sugarcane bagasse. The TKSA-MC approach employed here is presented as an efficient computational-based design strategy that can be applied to improve the thermal resistance of enzymes with industrial and biotechnological applications.

Automated summary

Understanding the factors that improve proteins' biochemical properties is crucial for protein engineering, with surface charge-charge interactions being particularly important. The TKSA-MC model was used to predict beneficial mutations for enhancing stability and catalytic rate in xylanases, with experimental results demonstrating increased thermotolerance and catalytic efficiency in mutated enzymes. This computational-based design strategy shows promise for improving the thermal resistance of enzymes with industrial and biotechnological applications.