Long-Range Electrostaticsin Serine Proteases: Machine Learning-Driven Reaction Sampling YieldsInsights for Enzyme Design

Computational enzyme design is a promising technique for producing novel enzymes for industrial and clinical needs. A key challenge that this technique faces is to consistently achieve the desired activity. Fundamental studies of natural enzymes revealed critical contributions from second-shell – and even more distant – residues to their remarkable efficiency. In particular, such residues organize the internal electrostatic field to promote the reaction. Engineering such fields computationally proved to be a promising strategy, which, however, has some limitations. Charged residues necessarily form specific patterns of local interactions that may be exploited for structural integrity. As a result, it is impossible to probe the electrostatic field alone by substituting amino acids. We hypothesize that an approach that isolates the influences of residues’ charges from other influences could yield deeper insights. We use molecular modeling with AI-enhanced QM/MM reaction sampling to implement such an approach and apply it to a model serine protease subtilisin. We find that the negative charge 8 Å away from the catalytic site is crucial to achieving the enzyme’s catalytic efficiency, contributing more than 2 kcal/mol to lowering the barrier. In contrast, a positive charge from the second-closest charged residue opposes the efficiency of the reaction by raising the barrier by 0.8 kcal/mol. This result invites discussion into the role of this residue and trade-offs that might have taken place in the evolution of such enzymes. Our approach is transferable and can help investigate the evolution of electrostatic preorganization in other enzymes. We believe that the study and engineering of electrostatic fields in enzymes is a promising direction to advance both fundamental and applied enzymology and lead to the design of new powerful biocatalysts.