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利用天然荧光化合物香豆雌酚进行雌激素受体的高通量筛选测定。

High-throughput screening assays for estrogen receptor by using coumestrol, a natural fluorescence compound.

作者信息

Wang Caihua, Li Changhao, Zhou Haibing, Huang Jian

机构信息

1College of Life Sciences, Wuhan University, Wuhan, China.

出版信息

J Biomol Screen. 2014 Feb;19(2):253-8. doi: 10.1177/1087057113502673. Epub 2013 Sep 9.

Abstract

Estrogen receptor (ER) is a ligand-inducible transcriptional factor involving in cell growth, differentiation, and diseases, so detection and identification of compounds having estrogenic effects are of great importance in the drug discovery industry. We have developed and validated a rapid, simple, and homogeneous method that can detect estrogenic compounds. This human ERα/β binding assay uses fluorescence polarization (FP) by applying an autofluorescent phytoestrogen, coumestrol (CS). A nonspecific adsorption assay shows that no obvious nonspecific adsorption is detected between CS and ERs. In the Scatchard plot analysis, the convex curve exhibits a positive cooperative binding, indicating that the binding of CS induces a conformational change of the ER to form a dimer and increases the affinity for the additional CS. In the Hill plot analysis, CS shows moderate binding affinity with both ERα and ERβ, and the measured Kd of CS is 32.66 µM and 36.14 µM, respectively, indicating that CS is applicable to the ER binding assay for determination of potent ligands of moderate binding affinity. Four typical ligands are selected to verify the ER binding assays, and the results are consistent with the reported data. All of above make the FP method based on CS suitable for high-throughput screening.

摘要

雌激素受体(ER)是一种配体诱导型转录因子,参与细胞生长、分化及疾病过程,因此在药物研发行业中,检测和鉴定具有雌激素效应的化合物至关重要。我们开发并验证了一种能够检测雌激素化合物的快速、简便且均相的方法。这种人ERα/β结合测定法通过应用一种自发荧光的植物雌激素香豆雌酚(CS)来利用荧光偏振(FP)技术。非特异性吸附测定表明,在CS与ER之间未检测到明显的非特异性吸附。在Scatchard图分析中,凸曲线呈现正协同结合,表明CS的结合诱导了ER的构象变化以形成二聚体,并增加了对额外CS的亲和力。在Hill图分析中,CS与ERα和ERβ均表现出中等结合亲和力,测得的CS的Kd分别为32.66 μM和36.14 μM,表明CS适用于ER结合测定以确定具有中等结合亲和力的强效配体。选择了四种典型配体来验证ER结合测定,结果与报道数据一致。以上所有使得基于CS的FP方法适用于高通量筛选。

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