Mechanistic in silico modeling of bisphenols to predict estrogen and glucocorticoid disrupting potentials

辅活化剂 竞争行为 生物信息学 化学 雌激素受体 糖皮质激素受体 雌激素受体α 糖皮质激素 内分泌干扰物 兴奋剂 受体 药理学 内分泌系统 生物 生物化学 激素 内分泌学 遗传学 转录因子 医学 基因 癌症 精神科 乳腺癌 侵略
作者
Qinchang Chen,Chengzhuo Zhou,Wei Shi,Xiaoxiang Wang,Pu Xia,Maoyong Song,Jing Liu,Hao Zhu,Xiaowei Zhang,Si Wei,Yu H
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:728: 138854-138854 被引量:11
标识
DOI:10.1016/j.scitotenv.2020.138854
摘要

Endocrine disrupting chemicals (EDCs) can act as agonists, antagonists or mixed agonists/antagonists toward estrogen receptor α (ERα) and glucocorticoid receptor (GR) in a tissue- and cell-specific manner. However, the activation/inhibition mechanism by which structurally different chemicals induce various types of disruption remain ambiguous. This unrevealed theory limited the in silico modeling of EDCs and the prioritization of potential EDCs for experimental testing. As a kind of chemical widely used in manufacture, bisphenols (BPs) have attracted great attentions on their potential endocrine disrupting effects. BPs used in this study exhibited pure agonistic, pure antagonistic or mixed agonistic/antagonistic activities toward ERα and/or GR. According to the mechanistic modeling, the pure agonistic and pure antagonistic activities were attributed to a single type of protein conformation induced by BPs-ERα and/or BPs-GR interactions, whereas the mixed agonistic/antagonistic activities were attributed to multiple conformations that concomitantly exist. After interacting with BPs, the active conformation recruits coactivator to induce agonistic activity and the blocked conformation inhibits coactivator to induce antagonistic activity, whereas the concomitantly-existing multiple conformations (active, blocked and competing conformations) recruit coactivator, recruit corepressor and/or inhibit coactivator to dually induce the agonistic and antagonistic activities. Therefore, the in silico modeling in this study can not only predict ERα and GR disrupting activities but also, especially, identify the potential mechanisms. This mechanistic study breaks the current bottleneck of computational toxicology and can be widely used to prioritize potential estrogen/glucocorticoid disruptor for experimental testing in both pre-clinic and clinic studies.

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