Qiu Tianyi, Wu Dingfeng, Qiu Jingxuan, Cao Zhiwei
School of Life Sciences and Technology, Shanghai 10th People's Hospital, Tongji University, No. 1239 SiPing Road, Shanghai, China.
The Institute of Biomedical Sciences, Fudan University, No. 138 Medical College Road, Shanghai, China.
J Cheminform. 2018 Apr 12;10(1):21. doi: 10.1186/s13321-018-0275-x.
Nuclear receptors (NR) are a class of proteins that are responsible for sensing steroid and thyroid hormones and certain other molecules. In that case, NR have the ability to regulate the expression of specific genes and associated with various diseases, which make it essential drug targets. Approaches which can predict the inhibition ability of compounds for different NR target should be particularly helpful for drug development. In this study, proteochemometric modelling was introduced to analysis the bioactivity between chemical compounds and NR targets. Results illustrated the ability of our PCM model for high-throughput NR-inhibitor screening after evaluated on both internal (AUC > 0.870) and external (AUC > 0.746) validation set. Moreover, in-silico predicted bioactive compounds were clustered according to structure similarity and a series of representative molecular scaffolds can be derived for five major NR targets. Through scaffolds analysis, those essential bioactive scaffolds of different NR target can be detected and compared. Generally, the methods and molecular scaffolds proposed in this article can not only help the screening of potential therapeutic NR-inhibitors but also able to guide the future NR-related drug discovery.
核受体（NR）是一类负责感知类固醇、甲状腺激素及某些其他分子的蛋白质。在这种情况下，核受体具有调节特定基因表达的能力，并与多种疾病相关，这使其成为重要的药物靶点。能够预测化合物对不同核受体靶点抑制能力的方法对药物开发尤为有用。在本研究中，引入了蛋白质化学计量学建模来分析化合物与核受体靶点之间的生物活性。结果表明，在内部（AUC > 0.870）和外部（AUC > 0.746）验证集上评估后，我们的PCM模型具有高通量核受体抑制剂筛选能力。此外，通过计算机模拟预测的生物活性化合物根据结构相似性进行聚类，可为五个主要核受体靶点衍生出一系列代表性分子骨架。通过骨架分析，可以检测和比较不同核受体靶点的那些关键生物活性骨架。总体而言，本文提出的方法和分子骨架不仅有助于筛选潜在的治疗性核受体抑制剂，还能够指导未来与核受体相关的药物发现。
