Use of an Improved Matching Algorithm to Select Scaffolds for Enzyme Design Based on a Complex Active Site Model

Area of Science:

• Biochemistry and Molecular Biology
• Computational Chemistry
• Enzyme Engineering

Background:

• Native enzymes utilize active site preorganization to stabilize transition states, enhancing catalytic rates.
• Designing novel enzymes with high catalytic efficiency requires accurate modeling of active site preorganization.

Purpose of the Study:

• To develop and validate a complex active site model for de novo enzyme design.
• To improve the ProdaMatch algorithm for accurate prediction of transition states and active site preorganization.
• To assess the model's effectiveness in scaffold selection for specific enzymatic reactions.

Main Methods:

• Enhanced ProdaMatch algorithm with pruning strategies for active site modeling.
• Benchmark testing against native enzyme active sites, evaluating root-mean squared deviations and hydrogen bond recovery.
• Scaffold selection for p-nitrophenyl acetate and cephalexin hydrolysis using different catalytic motifs.

Main Results:

• The improved ProdaMatch algorithm accurately reproduced native active sites with < 1.0 Å RMSD for transition states and 91% hydrogen bond recovery.
• Scaffold selection identified 80 candidates for p-nitrophenyl acetate hydrolysis, including native esterases.
• A more flexible catalytic motif identified 40 scaffolds for cephalexin hydrolysis, including a relevant α-amino acid ester hydrolase.

Conclusions:

• Complex active site modeling with the improved ProdaMatch program is a viable strategy for de novo enzyme design.
• This approach facilitates the creation of preorganized active sites with high catalytic efficiencies for targeted reactions.
• The methodology shows promise for designing enzymes capable of catalyzing complex substrate transformations.