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基于药效团比对的组蛋白去乙酰化酶抑制剂的3D-QSAR研究

3D-QSAR studies of HDACs inhibitors using pharmacophore-based alignment.

作者信息

Chen Yadong, Li Huifang, Tang Wanquan, Zhu Chengchao, Jiang Yongjun, Zou Jianwei, Yu Qingsen, You Qidong

机构信息

Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, PR China.

出版信息

Eur J Med Chem. 2009 Jul;44(7):2868-76. doi: 10.1016/j.ejmech.2008.12.008. Epub 2008 Dec 16.

DOI:10.1016/j.ejmech.2008.12.008
PMID:19136179
Abstract

Histone deacetylases (HDACs) enzyme is a promising target for the development of anticancer drugs. The enzyme-bound conformation of Trichostatin A (TSA) (PDB ID:1C3R) as an inhibitor of HDACs was used to manually construct a pharmacophore model. This model was then successfully used to identify the bioactive conformation and align flexible and structurally diverse molecules. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on hydroxamate-based HDACs inhibitors based on phamacophore alignment. The best predictions were obtained with CoMFA standard model (q(2) = 0.726, r(2) = 0.998) and CoMSIA model combined with steric, electrostatic, hydrophobic, hydrogen bond donor and acceptor fields (q(2) = 0.610, r(2) = 0.995). Both of the models were validated by an external test set, which gave a satisfactory predictive r(2) value of 0.800 and 0.732, respectively. Graphical interpretation of the results revealed important structural features of the inhibitors related to the active site of HDACs. The results may be exploited for further design and virtual screening for some novel HDACs inhibitors.

摘要

组蛋白去乙酰化酶(HDACs)是开发抗癌药物的一个有前景的靶点。作为HDACs抑制剂的曲古抑菌素A(TSA)(蛋白质数据银行ID:1C3R)的酶结合构象被用于手动构建一个药效团模型。然后该模型成功用于识别生物活性构象并对灵活且结构多样的分子进行比对。基于药效团比对,对基于异羟肟酸的HDACs抑制剂进行了比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)。使用CoMFA标准模型(交叉验证系数q(2)=0.726,相关系数r(2)=0.998)和结合了立体、静电、静电的、疏水的、氢键供体和受体场的CoMSIA模型(q(2)=0.610,r(2)=0.995)获得了最佳预测结果。两个模型均通过外部测试集进行了验证,外部测试集分别给出了令人满意的预测r(2)值0.800和0.732。结果的图形解释揭示了与HDACs活性位点相关的抑制剂的重要结构特征。这些结果可用于进一步设计和虚拟筛选一些新型HDACs抑制剂。

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