Department of Chemistry, Faculty of Science, University of Jordan, Amman, Jordan.
J Comput Chem. 2011 Feb;32(3):463-82. doi: 10.1002/jcc.21635. Epub 2010 Aug 20.
Glycosidases, including β-D-galactosidase, are involved in a variety of metabolic disorders, such as diabetes, viral or bacterial infections, and cancer. Accordingly, we were prompted to find new β-D-galactosidase inhibitors. Towards this end, we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were used to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Five pharmacophores emerged in the QSAR equations suggesting the existence of more than one binding mode accessible to ligands within β-D-galactosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were experimentally established by the identification of several β-D-galactosidase inhibitors retrieved via in silico search of two structural databases: the National Cancer Institute list of compounds and our in house built structural database of established drugs and agrochemicals.
糖苷酶，包括β-D-半乳糖苷酶，参与多种代谢紊乱，如糖尿病、病毒或细菌感染和癌症。因此，我们试图寻找新的β-D-半乳糖苷酶抑制剂。为此，我们使用一组 41 种已知抑制剂扫描了该酶的药效团空间。遗传算法和多元线性回归分析用于选择最佳的药效团模型和物理化学描述符组合，以产生一致和可预测的定量构效关系（QSAR）。在 QSAR 方程中出现了五个药效团，表明配体在β-D-半乳糖苷酶口袋中存在不止一种可及的结合模式。成功的药效团与严格的形状约束相结合，试图优化它们的接收器操作特性曲线轮廓。通过在两个结构数据库（国家癌症研究所化合物列表和我们内部构建的已建立药物和农用化学品结构数据库）中进行计算机搜索，识别出几种β-D-半乳糖苷酶抑制剂，从而实验验证了 QSAR 方程和相关药效团模型的有效性。
