Decoding Enzymatic Dechlorination with Multiscale Modeling: Mechanistic Insights into Native Haloalkane Dehalogenase from and Its Designed Variants.

Chlorinated hydrocarbons are widely used as solvents and synthetic intermediates, but their chemical persistence can cause hazardous environmental accumulation. Haloalkane dehalogenase from (DhlA) is a bacterial enzyme that naturally converts toxic chloroalkanes into less harmful alcohols. Using a multiscale approach based on the empirical valence bond method, we investigate the catalytic mechanism of 1,2-dichloroethane dehalogenation within DhlA and its mutants. The reaction proceeds through two chemical steps: a bimolecular nucleophilic substitution followed by hydrolysis to form the alcohol. Our simulations accurately reproduce experimentally observed activation barriers for both steps and reveal how specific amino acids influence catalytic efficiency. While the catalytic D124-H289-D260 triad is well established, our results show that secondary active-site residues affect the reaction rates by shaping an electrostatic network that controls a trade-off between the two chemical steps. This interplay means that improving one step may compromise the other, highlighting the complexity of enzyme optimization. Guided by extensive experimental data alongside generative AI predictions, we propose a multiple mutant with the potential for enhanced overall biocatalytic performance. These findings deepen the mechanistic understanding of DhlA and provide a predictive framework for the rational design of improved dehalogenases, with promising applications in biocatalytic degradation of environmental pollutants.