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

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.