Assessing Fluoroacetate Defluorination Potential across Diverse Enzymes Using Quantum Chemistry

Area of Science:

• Biochemistry and Environmental Science
• Computational Biology and Cheminformatics

Background:

• Fluorinated organic compounds are persistent environmental pollutants due to the strong carbon-fluorine bond.
• Enzymatic defluorination is rare and mechanistically complex, with fluoroacetate dehalogenase serving as a key model system.

Purpose of the Study:

• To develop a multiscale computational framework for identifying and characterizing fluoroacetate dehalogenase-like enzymes.
• To understand the mechanistic basis of C-F bond cleavage in defluorinase enzymes.
• To discover novel defluorinase candidates across diverse bacterial lineages.

Main Methods:

• Sequence-based screening to identify candidate proteins.
• AlphaFold2 for high-confidence structural modeling.
• Microsecond-scale molecular dynamics simulations for stability and active-site analysis.
• QM/MM calculations and vibrational mode analysis for electronic structure and hydrogen-bonding interactions.

Main Results:

• Identified 184 candidate proteins across nine bacterial classes, with 12 selected for detailed analysis.
• Observed a conserved network of active-site interactions stabilizing substrate binding across systems.
• Found specific homologues with geometries and hydrogen-bonding patterns similar to the reference enzyme.
• Identified key interaction motifs differentiating active from inactive defluorinase homologues.

Conclusions:

• The study provides a mechanistic basis for C-F bond activation in defluorinase enzymes.
• Identified novel defluorinase candidates across multiple bacterial classes, suggesting a broader distribution of this activity.
• Demonstrated the utility of integrated multiscale simulations for linking sequence diversity to catalytic function.
• The findings support the discovery and engineering of defluorinases for bioremediation of fluorinated pollutants, including PFAS.