Method for calculating combination of perfluoroalkyl and polyfluoroalkyl compounds and PPAR alpha based on pharmacophore model

By using pharmacophore models and molecular dynamics simulations, we have addressed the issues of narrow coverage, unclear mechanisms, and insufficient validation in PFAS-PPARα binding assessments, achieving efficient PFAS risk assessment and regulatory compatibility, and providing high-confidence binding candidates.

CN121687233APending Publication Date: 2026-03-17MATERNAL & CHILD HEALTH CARE HOSPITAL OF SHANDONG PROVINCE SHANDONG UNIV
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Patent Information

Application Number
CN202511619825.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-06
Publication Date
2026-03-17

AI Technical Summary

Technical Problem

Existing technologies cannot effectively cover the assessment of the binding of perfluorinated and polyfluoroalkyl compounds (PFAS) to peroxisome proliferator-activated receptor α (PPARα), resulting in problems such as narrow coverage, unclear mechanisms, insufficient validation, and poor regulatory suitability.

Method used

Using a pharmacophore-based approach, compound data were obtained from the EPA CompTox database, and molecular docking was performed using the CDOCKER algorithm in Discovery Studio to generate pharmacophore models. Combined with molecular dynamics simulations and cellular thermal displacement analysis, the binding potential of PFAS and PPARα was verified.

Benefits of technology

It enables large-scale screening of 10,946 PFAS, provides high-confidence binding candidates, ensures the stability and regulatory suitability of prediction results under physiological conditions, and supports grouped risk assessment of PFAS.

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Abstract

The invention relates to the technical field of computational toxicology, and discloses a method for calculating combination of perfluorinated and polyfluorinated alkyl compounds and PPARalpha based on a pharmacophore model, which comprises the following steps: acquiring a PFAS initial data set, acquiring a PPARalpha ligand binding domain crystal structure, and optimizing the structure; selecting representative PFAS to carry out molecular docking, and carrying out simulated annealing optimization to obtain an optimal binding posture; then generating a pharmacophore model; the models are screened; performing rigid fitting screening by using a pharmacophore model; carrying out molecular dynamics simulation on the representative hit object, and verifying the stability; protein is extracted by culturing cells in vitro, a thermal displacement experiment is carried out, and the thermal stability effect of PFAS on PPARalpha is verified. According to the method, comprehensive virtual screening of PFAS-PPAR alpha interaction based on pharmacophores is provided, a calculation framework based on protein and compound structures is established, and the method is suitable for environmental pollutant screening and safer chemical design.
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