基于深度学习和体内评估的全氟烷基和多氟烷基物质雌激素受体活性筛选

IF 7.3 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Xudi Pang, Miao Lu, Ying Yang, Huiming Cao, Yuzhen Sun, Zhen Zhou, Ling Wang, Yong Liang
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引用次数: 0

摘要

在过去的几十年里,暴露于全氟烷基和多氟烷基物质(PFAS)——一组因其环境持久性而臭名昭著的合成化学品——已被证明会造成更大的健康风险。尽管在之前的研究中报道了一些PFAS具有内分泌干扰毒性,但基于深度学习的准确预测模型以及与分子氟化效应相关的潜在结构特征仍然有限。为了解决这些问题,我们提出了一个叠加深度学习架构GXDNet,它集成了分子描述符和分子图来预测化合物的雌激素受体α (ERα)活性,与以前的模型相比,提高了泛化能力。随后,我们利用GXDNet模型筛选了10067个PFAS分子的ERα活性,并鉴定了潜在的ERα结合物。对接得分最高的代表性PFAS分子表明,氟化烷烃链的引入显著提高了亲本分子与ERα的结合亲和力,说明苯酚结构片段与氟化烷烃链的结合对提高配体对ERα的结合能力具有协同作用。结合模式、SHapley Additive Explanations分析和注意图强调了π-π堆叠和与苯酚基团的氢键相互作用的重要性,而氟化烷烃链增强了与活性口袋中疏水氨基酸的相互作用。斑马鱼模型的实验验证进一步证实了代表性PFAS分子的ERα活性。总的来说,目前的计算工作流程有利于新兴PFAS的毒理学筛选和加速环保型PFAS分子的开发,从而减轻与PFAS暴露相关的环境和健康风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment
Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have endocrine-disrupting toxicity in previous studies, accurate prediction models based on deep learning and the underlying structural characteristics related to the effect of molecular fluorination remain limited. To address these issues, we proposed a stacking deep learning architecture, GXDNet, that integrates molecular descriptors and molecular graphs to predict the estrogen receptor α (ERα) activities of compounds, enhancing the generalization ability compared to previous models. Subsequently, we screened the ERα activity of 10,067 PFAS molecules using the GXDNet model and identified potential ERα binders. The representative PFAS molecules with the top docking scores showed that the introduction of fluorinated alkane chains significantly increased the binding affinities of parent molecules with ERα, suggesting that the combination of phenol structural fragments and fluorinated alkane chains has a synergistic effect in improving the binding capacity of the ligands to ERα. The binding modes, SHapley Additive Explanations analysis, and attention map emphasized the importance of π-π stacking and hydrogen bonding interactions with the phenol group, while the fluorinated alkane chain enhanced the interaction with the hydrophobic amino acids of the active pocket. Experimental validation using zebrafish models further confirmed the ERα activity of the representative PFAS molecules. Overall, the current computational workflow is beneficial for the toxicological screening of emerging PFAS and accelerating the development of eco-friendly PFAS molecules, thereby mitigating the environmental and health risks associated with PFAS exposure.
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来源期刊
Environmental Pollution
Environmental Pollution 环境科学-环境科学
CiteScore
16.00
自引率
6.70%
发文量
2082
审稿时长
2.9 months
期刊介绍: Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.
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