Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling

Area of Science:

• Biochemistry
• Enzymology
• Computational Biology

Background:

• Hydrolases are vital enzymes in chemical, pharmaceutical, and environmental applications.
• Discovering mutations that improve hydrolase catalytic efficiency is crucial for enzyme engineering.
• Current methods for predicting rate-enhancing mutations are limited.

Purpose of the Study:

• To statistically profile single amino acid substitutions that perturb hydrolase activity.
• To identify features of mutations that lead to enhanced catalytic efficiency (rate acceleration).
• To develop a database (IntEnzyDB) for structure-kinetics data to facilitate machine learning.

Main Methods:

• Construction of IntEnzyDB, a relational database integrating hydrolase kinetics and structural data.
• Statistical analysis of mutation effects on turnover number (kcat) and efficiency (kcat/KM).
• Application of linear regression models to identify key residue and substrate geometric descriptors.

Main Results:

• Mutations to bulky nonpolar residues with hydrocarbon chains show a higher propensity for rate acceleration.
• Geometric descriptors of substrate and mutation residues influence rate perturbation for bulky nonpolar mutations.
• Rate enhancement propensity is independent of protein size; distal mutations (>10 Å) favor efficiency neutrality.

Conclusions:

• Statistical profiling of mutations provides insights into identifying rate-enhancing substitutions in hydrolases.
• The findings guide the rational design of novel hydrolases for biocatalysis.
• IntEnzyDB serves as a valuable resource for machine learning applications in enzyme engineering.