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Computational Biotransformation Profile of Emerging Phenolic Pollutants by Cytochromes P450: Phenol-Coupling Mechanism.

Fangjie GuoLihong ChaiShubin ZhangHaiying YuWeiping LiuKasper P KeppLi Ji
Published in: Environmental science & technology (2020)
Phenols are ubiquitous environmental pollutants, whose biotransformation involving phenol coupling catalyzed by cytochromes P450 may produce more lipophilic and toxic metabolites. Density functional theory (DFT) computations were performed to explore the debated phenol-coupling mechanisms, taking triclosan as a model substrate. We find that a diradical pathway facilitated by compound I and protonated compound II of P450 is favored vs alternative radical addition or electron-transfer mechanisms. The identified diradical coupling resembles a "two-state reactivity" from compound I characterized by significantly high rebound barriers of the phenoxy radicals, which can be formulated into three equations for calculating the ratio [coupling]/[hydroxylation]. A higher barrier for rebound than for H-abstraction in high-spin triclosan can facilitate the phenoxy radical dissociation and thus enable phenol coupling, while H-abstraction/radical rebound causing phenol hydroxylation via minor rebound barriers mostly occurs via the low-spin state. Therefore, oxidation of triclosan by P450 fits the first equation with a ratio [coupling]/[hydroxylation] of 1:4, consistent with experimental data indicating different extents of triclosan coupling (6-40%). The high rebound barrier of phenoxy radicals, as a key for the mechanistic identification of phenol coupling vs hydroxylation, originates from their weak electron donor ability due to spin aromatic delocalization. We envision that the revealed mechanism can be extended to the cross-coupling reactions between different phenolic pollutants, and the coupling reactions of several other aromatic pollutants, to infer unknown metabolites.
Keyphrases
  • room temperature
  • electron transfer
  • density functional theory
  • ionic liquid
  • ms ms
  • heavy metals
  • single molecule
  • molecular dynamics
  • hydrogen peroxide
  • risk assessment
  • deep learning
  • machine learning
  • crystal structure