Hong H, Fang H, Xie Q, Perkins R, Sheehan D M, Tong W
Northrop Grumman Information Technology, Jefferson, AR 72079, USA.
SAR QSAR Environ Res. 2003 Oct-Dec;14(5-6):373-88. doi: 10.1080/10629360310001623962.
A large number of natural, synthetic and environmental chemicals are capable of disrupting the endocrine systems of experimental animals, wildlife and humans. These so-called endocrine disrupting chemicals (EDCs), some mimic the functions of the endogenous androgens, have become a concern to the public health. Androgens play an important role in many physiological processes, including the development and maintenance of male sexual characteristics. A common mechanism for androgen to produce both normal and adverse effects is binding to the androgen receptor (AR). In this study, we used Comparative Molecular Field Analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) technique, to examine AR-ligand binding affinities. A CoMFA model with r2 = 0.902 and q2 = 0.571 was developed using a large training data set containing 146 structurally diverse natural, synthetic, and environmental chemicals with a 10(6)-fold range of relative binding affinity (RBA). By comparing the binding characteristics derived from the CoMFA contour map with these observed in a human AR crystal structure, we found that the steric and electrostatic properties encoded in this training data set are necessary and sufficient to describe the RBA of AR ligands. Finally, the CoMFA model was challenged with an external test data set; the predicted results were close to the actual values with average difference of 0.637 logRBA. This study demonstrates the utility of this CoMFA model for real-world use in predicting the AR binding affinities of structurally diverse chemicals over a wide RBA range.
大量天然、合成及环境化学物质能够干扰实验动物、野生动物及人类的内分泌系统。这些所谓的内分泌干扰化学物质(EDCs),其中一些可模拟内源性雄激素的功能,已成为公共卫生领域的一个关注点。雄激素在许多生理过程中发挥重要作用,包括男性性征的发育和维持。雄激素产生正常和不良影响的一个共同机制是与雄激素受体(AR)结合。在本研究中,我们使用比较分子场分析(CoMFA)这一三维定量构效关系(3D-QSAR)技术来研究AR-配体结合亲和力。使用一个包含146种结构多样的天然、合成及环境化学物质的大型训练数据集开发了一个r2 = 0.902且q2 = 0.571的CoMFA模型,这些化学物质的相对结合亲和力(RBA)范围为10^6倍。通过将从CoMFA等高线图得出的结合特征与在人类AR晶体结构中观察到的特征进行比较,我们发现该训练数据集中编码的空间和静电性质对于描述AR配体的RBA是必要且充分的。最后,用一个外部测试数据集对CoMFA模型进行验证;预测结果与实际值接近,平均差异为0.637 logRBA。本研究证明了该CoMFA模型在实际应用中用于预测结构多样的化学物质在宽RBA范围内的AR结合亲和力的实用性。