• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Toxicology and Applied Pharmacology >Predicting estrogen receptor binding of chemicals using a suite of in silico methods - Complementary approaches of (Q)SAR, molecular docking and molecular dynamics
【24h】

Predicting estrogen receptor binding of chemicals using a suite of in silico methods - Complementary approaches of (Q)SAR, molecular docking and molecular dynamics

机译：使用硅藻方法套件预测化学品的雌激素受体结合 - （Q）SAR，分子对接和分子动力学的互补方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the aim of obtaining reliable estimates of Estrogen Receptor (ER) binding for diverse classes of compounds, a weight of evidence approach using estimates from a suite of in Silico models was assessed. The predictivity of a simple Majority Consensus of (Q)SAR models was assessed using a test set of compounds with experimental Relative Binding Affinity (RBA) data. Molecular docking was also carried out and the binding energies of these compounds to the ERa receptor were determined. For a few selected compounds, including a known full agonist and antagonist, the intrinsic activity was determined using low-mode molecular dynamics methods. Individual (Q)SAR model predictivity varied, as expected, with some models showing high sensitivity, others higher specificity. However, the Majority Consensus (Q)SAR prediction showed a high accuracy and reasonably balanced sensitivity and specificity. Molecular docking provided quantitative information on strength of binding to the ERa receptor. For the 50 highest binding affinity compounds with positive RBA experimental values, just 5 of them were predicted to be non-binders by the Majority QSAR Consensus. Furthermore, agonist-specific assay experimental values for these 5 compounds were negative, which indicates that they may be ER antagonists. We also showed different scenarios of combining (Q)SAR results with Molecular docking classification of ER binding based on cut-off values of binding energies, providing a rational combined strategy to maximize terms of toxicological interest.
机译：目的是获得对不同类别化合物的雌激素受体（ER）结合的可靠估计，评估了使用来自硅模型套件的估计的证据方法。使用具有实验相对结合亲和力（RBA）数据的化合物的测试组评估（Q）SAR模型的简单大多数共识的预测性。还进行了分子对接，并测定这些化合物与ERA受体的结合能量。对于少数选定的化合物，包括已知的全激动剂和拮抗剂，使用低模式分子动力学方法测定内在活性。个人（Q）SAR模型预测性，正如预期的那样，一些模型显示出高灵敏度，其他模型具有更高的特异性。然而，大多数共识（Q）SAR预测显示出高精度和合理平衡的敏感性和特异性。分子对接提供了关于与时代受体的结合强度的定量信息。对于具有阳性RBA实验值的50个最高结合亲和力化合物，预计其中只有5个是非粘合剂的非粘合剂。此外，这5种化合物的激动剂特异性测定实验值为阴性，这表明它们可能是ER拮抗剂。我们还显示出不同的组合场景（Q）SAR结果，基于结合能的截止值，ER结合的分子对接分类，提供合理的组合策略，以最大限度地提高毒理学利益。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号