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Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An

Kazuhiro Maeda1, Masashi Hirano2, Taka Hayashi3

  • 1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan.

Environmental Science & Technology
|December 22, 2023
PubMed
Summary

This study developed an explainable machine learning model to screen per- and polyfluoroalkyl substances (PFAS) that bind to PPARα. Molecular size and electrostatic properties are key factors influencing PFAS-PPARα binding, with implications for alternative PFAS safety.

Keywords:
PFASPPARαQSARSHAPmachine learningmolecular descriptor

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Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Computational Chemistry

Background:

  • Per- and polyfluoroalkyl substances (PFAS) disrupt hepatic lipid metabolism by binding to human peroxisome proliferator-activated receptor alpha (PPARα).
  • Rapid screening of PFAS for PPARα binding is crucial for assessing potential health risks.
  • Traditional machine learning models for PFAS screening lack interpretability, hindering understanding of structure-activity relationships.

Purpose of the Study:

  • To develop a novel, explainable machine learning approach for rapid screening of PFAS that bind to PPARα.
  • To identify key molecular descriptors that govern the interaction between PFAS and PPARα.
  • To assess the potential risks associated with alternative PFAS compounds.

Main Methods:

  • Calculated PPARα-PFAS binding scores and 206 molecular descriptors for various PFAS.
  • Employed systematic and objective selection of molecular descriptors to build a predictive model.
  • Utilized an explainable machine learning approach to interpret the binding mechanisms.

Main Results:

  • Developed a highly predictive machine learning model using only three molecular descriptors: molecular size (b_single) and electrostatic properties (BCUT_PEOE_3, PEOE_VSA_PPOS).
  • Identified molecular size and electrostatic properties as critical factors for PPARα-PFAS binding.
  • Observed that alternative PFAS with higher carbon content and ether groups show increased PPARα affinity.

Conclusions:

  • An explainable machine learning model can effectively screen PFAS for PPARα binding.
  • Molecular size and electrostatic features are significant determinants of PFAS-PPARα interactions.
  • Further biological validation is necessary to confirm the toxicity of alternative PFAS with high PPARα affinity.