Ng Hui Wen, Zhang Wenqian, Shu Mao, Luo Heng, Ge Weigong, Perkins Roger, Tong Weida, Hong Huixiao
BMC Bioinformatics. 2014;15 Suppl 11(Suppl 11):S4. doi: 10.1186/1471-2105-15-S11-S4. Epub 2014 Oct 21.
Endocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERα, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders.
The approach combined separate molecular docking models for ER agonist and antagonist conformations. The ability of this approach to differentiate agonists and antagonists was first evaluated using true agonists and antagonists extracted from the crystal structures available in the protein data bank (PDB), and then further validated using a larger set of ligands from the literature. The usefulness of the approach was demonstrated with enrichment analysis in data sets with a large number of decoy ligands.
The performance of individual agonist and antagonist docking models was found comparable to similar models in the literature. When combined in a competitive docking approach, they provided the ability to discriminate agonists from antagonists with good accuracy, as well as the ability to efficiently select true agonists and antagonists from decoys during enrichment analysis.
This approach enables evaluation of potential ER biological function changes caused by chemicals bound to the receptor which, in turn, allows the assessment of a chemical's endocrine disrupting potential. The approach can be used not only by regulatory authorities to perform risk assessments on potential EDCs but also by the industry in drug discovery projects to screen for potential agonists and antagonists.
内分泌干扰化学物（EDCs）是一类外源性化合物，通常通过与核受体蛋白直接或间接相互作用来干扰脊椎动物的内分泌系统。雌激素受体（ERs）是特别重要的蛋白质靶点，许多EDCs是ER结合剂，能够改变正常的稳态转录和信号通路。一种具有雌激素活性的外源性物质可以作为激动剂或拮抗剂与ER结合，分别增加或抑制转录。与激动剂和拮抗剂结合的ER复合物中的受体构象不同，并且取决于与跨组织类型而异的共调节蛋白的相互作用。化学物质内分泌干扰潜力的评估不仅取决于与ERs的结合亲和力，还取决于可能改变受体构象及其随后结合DNA反应元件并启动转录能力的变化。利用ERα的激动剂和拮抗剂构象，我们开发了一种计算机模拟方法，可用于区分潜在结合剂的激动剂与拮抗剂状态。
该方法结合了针对ER激动剂和拮抗剂构象的单独分子对接模型。首先使用从蛋白质数据库（PDB）中可用的晶体结构中提取的真实激动剂和拮抗剂评估该方法区分激动剂和拮抗剂的能力，然后使用文献中的一大组配体进一步验证。通过在具有大量诱饵配体的数据集中进行富集分析证明了该方法的实用性。
发现单个激动剂和拮抗剂对接模型的性能与文献中类似模型相当。当以竞争性对接方法结合时，它们能够以良好的准确性区分激动剂和拮抗剂，并且能够在富集分析期间从诱饵中有效地选择真实的激动剂和拮抗剂。
这种方法能够评估与受体结合的化学物质引起的潜在ER生物学功能变化，进而允许评估化学物质的内分泌干扰潜力。该方法不仅可供监管机构用于对潜在EDCs进行风险评估，也可供制药行业在药物发现项目中筛选潜在的激动剂和拮抗剂。
