Halogenated organic pollutants (HOPs) represent a persistent class of environmental contaminants with high stability, bioaccumulation potential, and toxicity. Cytochrome P450 enzymes (CYP450s) play a pivotal role in their oxidative biotransformation, yet mechanistic details often remain unresolved by experimental methods alone. To bridge this gap, this review synthesizes recent advances in multiscale computational enzymology to establish an integrated workflow. This approach links molecular docking and molecular dynamics (MD) for conformational sampling with quantum mechanics and molecular mechanics (QM/MM) and density functional theory (DFT) for electronic structure analysis. We critically evaluate how this multiscale framework complements experimental data to resolve enzyme–substrate recognition, conformational gating, and bond-activation energetics. These are mechanistic details that single-scale approaches cannot provide. Furthermore, we discuss emerging data-driven tools while addressing critical limitations, including training-data bias in machine learning and the lack of standardization. By connecting atomistic mechanistic insights to broader environmental implications, we propose a roadmap for developing predictive models to support HOP risk assessment and bioremediation strategies.
Zhao et al. (Thu,) studied this question.