Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, P.R. China.
School of Chemical Engineering and Light Industry, Guangdong University of Technology, Guangzhou, 510006, P.R. China.
Sci Rep. 2017 Sep 14;7(1):11525. doi: 10.1038/s41598-017-10618-1.
Drug resistance caused by excessive and indiscriminate antibiotic usage has become a serious public health problem. The need of finding new antibacterial drugs is more urgent than ever before. Tyrosyl-tRNA synthase was proved to be a potent target in combating drug-resistant bacteria. In silico methodologies including molecular docking and 3D-QSAR were employed to investigate a series of newly reported tyrosyl-tRNA synthase inhibitors of furanone derivatives. Both internal and external cross-validation were conducted to obtain high predictive and satisfactory CoMFA model (q = 0.611, r = 0.933, r = 0.954) and CoMSIA model (q = 0.546, r = 0.959, r = 0.923). Docking results, which correspond with CoMFA/CoMSIA contour maps, gave the information for interactive mode exploration. Ten new molecules designed on the basis of QSAR and docking models have been predicted more potent than the most active compound 3-(4-hydroxyphenyl)-4-(2-morpholinoethoxy)furan-2(5H)-one (15) in the literatures. The results expand our understanding of furanones as inhibitors of tyrosyl-tRNA synthase and could be helpful in rationally designing of new analogs with more potent inhibitory activities.
抗生素的过度和无差别使用导致的耐药性已成为严重的公共卫生问题。寻找新的抗菌药物的需求比以往任何时候都更加迫切。酪氨酸-tRNA 合成酶已被证明是对抗耐药菌的有效靶点。本研究采用分子对接和 3D-QSAR 等计算方法研究了一系列新报道的呋喃酮衍生物酪氨酸-tRNA 合成酶抑制剂。采用内部和外部交叉验证方法，获得了具有高预测能力和满意结果的 CoMFA 模型（q = 0.611，r = 0.933，r = 0.954）和 CoMSIA 模型（q = 0.546，r = 0.959，r = 0.923）。与 CoMFA/CoMSIA 轮廓图相对应的对接结果提供了交互模式探索的信息。基于 QSAR 和对接模型设计的 10 个新分子被预测比文献中最有效的化合物 3-(4-羟基苯基)-4-(2-吗啉乙氧基)呋喃-2(5H)-酮（15）具有更强的活性。研究结果扩展了我们对呋喃酮作为酪氨酸-tRNA 合成酶抑制剂的认识，并有助于合理设计具有更强抑制活性的新型类似物。
