Laboratoire GBCM, EA 7528, Conservatoire National des Arts et Métiers, HESAM Université, CEDEX, F-75003 Paris, France.
Cells. 2019 Nov 13;8(11):1431. doi: 10.3390/cells8111431.
The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.
雄激素受体（AR）是一种转录因子，在性表型和神经肌肉发育中起着关键作用。AR 可以被外源性化合物（如药物或环境中的化学物质）调节，特别是通过模拟内源性激动剂配体作用的 AR 激动剂化合物，这些化合物可以恢复或改变 AR 内分泌系统的功能。AR 的激活必须得到正确的平衡，因此，寻找有效的 AR 激动剂化合物是非常有意义的，既可以用于治疗某些疾病，也可以预测与激动剂化学物质暴露相关的风险。开发方法和发表结构、亲和力和活性数据为开发合理的 AR 激动剂预测模型提供了良好的框架。本文提出了一种对接和药效团建模策略，以帮助识别 AR 激动剂化合物。所有模型均在 NR-DBIND 上进行训练，该数据库提供了 AR 的高质量结合数据，并在 Tox21 计划的 AR-激动剂活性测定中进行了测试。这两种方法在 NR-DBIND 数据集上都表现出了很高的性能，可以作为生物学家和毒理学家的起点。然而，药效团模型仍然需要数据支持，才能作为广泛的不良作用预测模型使用。
