Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme...

Area of Science:

• Biochemistry and Molecular Biology
• Computational Chemistry
• Enzyme Engineering

Background:

• Computational enzyme design aims to create novel enzymes but struggles to consistently achieve desired activity.
• Natural enzymes utilize distant residues to optimize internal electrostatic fields for remarkable catalytic efficiency.
• Current computational methods face limitations in isolating the electrostatic effects of charged residues.

Purpose of the Study:

• To develop and apply a novel computational approach to isolate and quantify the influence of electrostatic fields on enzyme catalysis.
• To investigate the specific contributions of charged residues, including those in the second-shell, to the catalytic efficiency of subtilisin.
• To provide deeper insights into the role of electrostatic preorganization in enzyme evolution and function.

Main Methods:

• Employed molecular modeling combined with AI-enhanced Quantum Mechanics/Molecular Mechanics (QM/MM) reaction sampling.
• Applied the developed approach to a model serine protease, subtilisin.
• Quantified the impact of specific charged residues on the activation energy barrier of the enzymatic reaction.

Main Results:

• A negative charge located 8 Å from the catalytic site was found to be crucial for catalytic efficiency, reducing the activation barrier by over 2 kcal/mol.
• A positive charge from a nearby residue was shown to oppose catalytic efficiency by increasing the activation barrier by 0.8 kcal/mol.
• Demonstrated the significant, quantifiable impact of distant electrostatic interactions on enzyme performance.

Conclusions:

• The study highlights the critical role of electrostatic preorganization, mediated by distant charged residues, in achieving high enzyme catalytic efficiency.
• The developed computational approach effectively isolates electrostatic influences, offering a transferable method for studying enzyme evolution and engineering.
• Findings suggest that targeted engineering of electrostatic fields holds significant potential for designing novel, highly efficient biocatalysts for industrial and clinical applications.