当前位置: X-MOL 学术Environ. Sci. Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2024-02-29 , DOI: 10.1021/acs.est.3c05974
Ettayapuram Ramaprasad Azhagiya Singam 1 , Kathleen A Durkin 1 , Michele A La Merrill 2 , J David Furlow 3 , Jen-Chywan Wang 4 , Martyn T Smith 5
Affiliation  

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.

中文翻译:


大量全氟烷基和多氟烷基物质与十个核受体相互作用的预测



全氟烷基物质和多氟烷基物质 (PFAS) 是持久性有毒化学品,对人类健康和环境构成重大危害。由于体外和体内实验的时间和成本,筛选大量化学物质通过调节核受体 (NR) 的活性来充当内分泌干扰物的能力具有挑战性。因此,我们需要计算方法来筛选这些化学物质,并快速对它们进行优先排序以进行进一步的测试。在这里,我们利用分子建模和机器学习预测来识别 4545 种 PFAS 与 10 种不同 NR 之间的潜在相互作用。结果表明,一些 PFAS 可以与多种受体牢固结合。此外,与不同受体结合的 PFAS 可以具有分布在整个化学空间中的非常不同的结构。对这些计算机结果进行生物学验证应该是重中之重。
更新日期:2024-02-29
down
wechat
bug