Quantitative Structure-Activity Relationship Analysis and a Combined Ligand-Based/Structure-Based Virtual Screening Study for Glycogen Synthase Kinase-3

被引:5
作者
Fu, Gang [1 ,2 ]
Liu, Sheng [2 ]
Nan, Xiaofei [2 ]
Dale, Olivia R. [1 ]
Zhao, Zhendong [2 ]
Chen, Yixin [2 ]
Wilkins, Dawn E. [2 ]
Manly, Susan P. [3 ]
Cutler, Stephen J. [1 ,3 ]
Doerksen, Robert J. [1 ,3 ]
机构
[1] Univ Mississippi, Sch Pharm, Dept Med Chem, University, MS 38677 USA
[2] Univ Mississippi, Sch Engn, Dept Comp & Informat Sci, University, MS 38677 USA
[3] Univ Mississippi, Sch Pharm, Natl Ctr Nat Prod Res, University, MS 38677 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
Glycogen synthase kinase-3 inhibitors; High-throughput screening; Random forests; Structure-activity relationships; Support vector machines; POTENT INHIBITORS; SELECTIVE INHIBITORS; TAU PHOSPHORYLATION; GSK-3; INHIBITORS; DERIVATIVES; INDIRUBINS; DESIGN; IDENTIFICATION; CANCER;
D O I
10.1002/minf.201400045
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which regulates a wide range of cellular processes, involving various signalling pathways. GSK-3 beta has emerged as an important therapeutic target for diabetes and Alzheimer's disease. To identify structurally novel GSK-3 beta inhibitors, we performed virtual screening by implementing a combined ligand-based/structure-based approach, which included quantitative structure-activity relationship (QSAR) analysis and docking prediction. To integrate and analyze complex data sets from multiple experimental sources, we drafted and validated a hierarchical QSAR method, which adopts a two-level structure to take data heterogeneity into account. A collection of 728 GSK-3 inhibitors with diverse structural scaffolds was obtained from published papers that used different experimental assay protocols. Support vector machines and random forests were implemented with wrapper-based feature selection algorithms to construct predictive learning models. The best models for each single group of compounds were then used to build the final hierarchical QSAR model, with an overall R-2 of 0.752 for the 141 compounds in the test set. The compounds obtained from the virtual screening experiment were tested for GSK-3 beta inhibition. The bioassay results confirmed that 2 hit compounds are indeed GSK-3 beta inhibitors exhibiting sub-micromolar inhibitory activity, and therefore validated our combined ligandbased/structure-based approach as effective for virtual screening experiments.
引用
收藏
页码:627 / 640
页数:14
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