Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates.

Abstract

Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme-substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.