Assessing Fluoroacetate Defluorination Potential across Diverse Enzymes Using Quantum Chemistry

Fluorinated organic compounds are persistent environmental contaminants due to the exceptional strength of the carbon-fluorine bond, rendering biological defluorination both rare and mechanistically challenging. Fluoroacetate dehalogenase from Rhodopseudomonas palustris (strain ATCC BAA-98/CGA009; RPA1163) is one of the few experimentally characterized enzymes capable of C-F bond cleavage and provides a model system for understanding enzymatic defluorination. Here, we establish a mechanistically informed, multiscale computational framework to identify and characterize fluoroacetate dehalogenase-like enzymes across diverse bacterial lineages. Starting from sequence-based screening, 184 candidate proteins spanning nine bacterial classes were identified, from which 12 representative systems were selected for detailed analysis. High-confidence structural models generated with AlphaFold2 were subjected to microsecond-scale molecular dynamics simulations to assess conformational stability and active-site organization. To probe catalytic determinants at the electronic-structure level, QM/MM calculations combined with local vibrational mode analysis were employed to quantify hydrogen-bonding interactions within the binding pocket and relate them to catalytic competence. Across all systems, we observe a conserved network of active-site interactions that stabilizes substrate binding and is consistent with experimentally characterized defluorinases. Notably, specific homologues exhibit hydrogen-bonding patterns and active-site geometries closely matching the reference enzyme, suggesting a previously unrecognized distribution of defluorinase activity across multiple bacterial classes. These results identify key interaction motifs that differentiate likely active enzymes from inactive homologues and provide a mechanistic basis for C-F bond activation in this enzyme family. Overall, this work demonstrates how integrated multiscale simulations can be used to connect sequence diversity to catalytic function in challenging enzymatic reactions. The identified candidates and mechanistic descriptors provide a foundation for the discovery, engineering, and experimental characterization of defluorinase enzymes, opening opportunities for the bioremediation of fluorinated pollutants, including PFAS compounds.