高等学校化学学报

高等学校化学学报 ›› 2010, Vol. 31 ›› Issue (5): 938.

• 研究论文 • 上一篇    下一篇

基于药效团模型的DHODH抑制剂构效关系研究

鲍红娟1,2, 唐亚林1, 徐筱杰2, 向俊锋1, 郑智慧3, 路新华3   

  1. 1. 中国科学院化学研究所, 分子动态与稳态国家重点实验室, 北京分子科学国家重点实验室, 北京 100190;
    2. 北京大学化学与分子工程学院, 北京 100871;
    3. 华北制药集团新药研究开发有限责任公司, 石家庄 050015
  • 收稿日期:2009-07-20 出版日期:2010-05-10 发布日期:2010-05-10
  • 通讯作者: 唐亚林, 男, 博士, 研究员, 博士生导师, 主要从事化学生物学和分析化学研究. E-mail: tangyl@iccas.ac.cn; 徐筱杰, 男, 教授, 博士生导师, 主要从事药物分子设计及新药研发等研究. E-mail: xiaojxu@pku.edu.cn

Structure-activity Relationship Studies on Inhibitors of Dihydroorotate Dehydrogenase Based on Pharmacophore Model

BAO Hong-Juan1,2, TANG Ya-Lin1*, XU Xiao-Jie2*, XIANG Jun-Feng1, ZHENG Zhi-Hui3, LU Xin-Hua3   

  1. 1. State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China;
    2. College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China;
    3. North China Pharmaceutical Group, New Drug Research and Development Co. Ltd., Shijiazhuang 050015, China

  • Received:2009-07-20 Online:2010-05-10 Published:2010-05-10
  • Contact: TANG Ya-Lin. E-mail: tangyl@iccas.ac.cn; XU Xiao-Jie. E-mail: xiaojxu@pku.edu.cn

PDF (PC)

被引次数

23

摘要：

利用药效团模型研究二氢乳清酸脱氢酶(Dihydroorotate dehydrogenase, DHODH)抑制剂的构效关系, 为DHODH抑制剂的虚拟筛选提供新的方法. 以31个具有DHODH抑制活性的化合物为训练集化合物, 半数抑制浓度(IC50)范围为7~63000 nmol/L, 利用Catalyst/HypoGen算法构建DHODH抑制剂药效团模型, 通过对训练集化合物多个构象进行叠合, 提取药效团特征及三维空间限制构建药效团模型. 利用基于CatScramble 的交叉验证方法及评价模型对已知活性化合物的活性预测能力, 确定较优药效团模型. 模型包含1个氢键受体、3个疏水中心, 表征了受体配体相互作用时可能发生的氢键相互作用、疏水相互作用和π-π相互作用, 4个药效特征在三维空间的排列概括了DHODH抑制剂产生活性的结构特点. 所得较优模型对训练集化合物及测试集化合物的计算活性值与实验活性值的相关系数分别为0.8405和0.8788. 利用药效团模型对来源于微生物的系列化合物进行虚拟筛选, 筛选出59个预测活性较好的化合物, 可作为进一步药物研发的候选化合物.

关键词: DHODH抑制剂; 构效关系; 药效团模型; 虚拟筛选

Abstract:

The pharmacophore model of dihydroorotate dehydrogenase inhibitors was established guide the investigation on virtual screening for new dihydroorotate dehydrogenase inhibitors. Based on the training set composed of 31 DHODH inhibitors, pharmacophore models were generated by HypoGen program of the Catalyst software. The IC50 of the inhibitors varied from 7 to 63000 nmol/L. The pharmacophore models were a set of 3D pharmacophore features, which were constructed by the generation of conformational models and alignments of conformations based on the training set. The best model was validated to be highly predictive by two methods, namely, test set prediction and CatScramble method. This model consisted of two hydrogen-bond acceptors, and two hydrophobic regions. These features in the three-dimensional arrangement in the pharmacophore model could be characterized as hydrogen bond interaction, hydrophobic interaction and π-π interaction between ligand and acceptor. These features play an important role in determining the activities of bioactive molecules. The model′s correlation coefficient between the estimated and true activities for compounds constituting the training set were 0.8405, and for compounds from microbial metabolites were 0.8788. Using the pharmacophore model of DHODH inhibitors, 59 compounds with good estimated activities were found from microbial metabolites. New DHODH inhibitors may be found from these candidate compounds.

Key words: Dihydroorotate dehydrogenase inhibitor; Structure-activity relationship; Pharmacophore model; Virtual screening

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