Electrostatic interaction optimization improves catalytic rates and thermotolerance on xylanases.

Area of Science:

• Biochemistry
• Protein Engineering
• Computational Biology

Background:

• Improving protein biochemical properties like catalytic rate and thermal stability is crucial for industrial enzyme applications.
• Surface charge-charge interactions are key factors influencing enzyme stability and function.
• Xylanases are important industrial enzymes, and enhancing their properties is of significant interest.

Purpose of the Study:

• To predict and validate mutations that improve the stability and catalytic rate of xylanase enzymes.
• To explore the use of the Tanford-Kirkwood solvent accessibility model with Monte Carlo algorithm (TKSA-MC) for protein engineering.

Main Methods:

• Utilized the Tanford-Kirkwood solvent accessibility model with the Monte Carlo algorithm (TKSA-MC) for computational prediction.
• Modeled wild-type xylanase (XynAWT) and its M6 mutant (XynAM6).
• Performed laboratory experiments to validate computational predictions.

Main Results:

• Computational modeling predicted that a lysine to glutamic acid mutation at position 99 (K99E) stabilizes the native state of both xylanases.
• Experimental results demonstrated increased thermotolerance and catalytic rates in the mutated xylanases.
• The mutated enzymes showed enhanced processivity on delignified sugarcane bagasse.

Conclusions:

• The K99E mutation effectively enhances xylanase thermotolerance and catalytic efficiency.
• The TKSA-MC approach is a viable computational strategy for designing enzymes with improved thermal resistance.
• This method holds potential for advancing industrial and biotechnological enzyme applications.