Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors.
| Autor: | Azhagiya Singam ER; Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, California 94720, United States., Durkin KA; Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, California 94720, United States., La Merrill MA; Department of Environmental Toxicology, University of California, Davis, California 95616, United States., Furlow JD; Department of Neurobiology, Physiology and Behavior, University of California, Davis California 95616, United States., Wang JC; Department of Nutritional Sciences and Toxicology, University of California, Berkeley, California 94720, United States., Smith MT; Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Environmental science & technology [Environ Sci Technol] 2024 Mar 12; Vol. 58 (10), pp. 4487-4499. Date of Electronic Publication: 2024 Feb 29. |
| DOI: | 10.1021/acs.est.3c05974 |
| Abstrakt: | Per- and poly-fluoroalkyl substances (PFASs) are persistent, toxic chemicals that pose significant hazards to human health and the environment. Screening large numbers of chemicals for their ability to act as endocrine disruptors by modulating the activity of nuclear receptors (NRs) is challenging because of the time and cost of in vitro and in vivo experiments. For this reason, we need computational approaches to screen these chemicals and quickly prioritize them for further testing. Here, we utilized molecular modeling and machine-learning predictions to identify potential interactions between 4545 PFASs with ten different NRs. The results show that some PFASs can bind strongly to several receptors. Further, PFASs that bind to different receptors can have very different structures spread throughout the chemical space. Biological validation of these in silico findings should be a high priority. |
| Databáze: | MEDLINE |
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