Iwaloye Opeyemi, Elekofehinti Olusola Olalekan, Oluwarotimi Emmanuel Ayo, Kikiowo Babatom Iwa, Fadipe Toyin Mary
Bioinformatics and Molecular Biology Unit, Department of Biochemistry, Federal University of Technology, Akure, Ondo State Nigeria.
In Silico Pharmacol. 2020 Sep 12;8(1):2. doi: 10.1007/s40203-020-00054-x. eCollection 2020.
Over activity of Glycogen synthase kinase-3β (GSK-3β), a serine/threonine-protein kinase has been implicated in a number of diseases including stroke, type II diabetes and Alzheimer disease (AD). This study aimed to find novel inhibitors of GSK-3β from phyto-constituents of with the aid of computational analysis. Molecular docking, induced-fit docking (IFD), calculation of binding free energy via the MM-GBSA approach and Lipinski's rule of five (RO5) were employed to filter the compounds and determine their druggability. Most importantly, the compounds pIC were predicted by machine learning-based model generated by AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best model obtained was Model kpls_desc_38 (R = 0.8467 and Q = 0.8069), and this external validated model was utilized to predict the bioactivities of the lead compounds. While a number of characterized compounds from showed better docking score, binding free energy alongside adherence to RO5 than co-cystallized ligand, only three compounds (salvianolic acid C, ellagic acid and naringenin) showed more satisfactory pIC. The results obtained in this study can be useful to design potent inhibitors of GSK-3β.
糖原合酶激酶-3β（GSK-3β）是一种丝氨酸/苏氨酸蛋白激酶，其活性过高与包括中风、II型糖尿病和阿尔茨海默病（AD）在内的多种疾病有关。本研究旨在借助计算分析从植物成分中寻找GSK-3β的新型抑制剂。采用分子对接、诱导契合对接（IFD）、通过MM-GBSA方法计算结合自由能以及Lipinski五规则（RO5）来筛选化合物并确定其成药可能性。最重要的是，通过AutoQSAR算法生成的基于机器学习的模型预测化合物的pIC。对生成的模型进行验证以确认其预测模型。获得的最佳模型是Model kpls_desc_38（R = 0.8467，Q = 0.8069），并利用该外部验证模型预测先导化合物的生物活性。虽然从[植物名称未给出]中得到的许多已表征化合物显示出比共结晶配体更好的对接分数、结合自由能以及对RO5的符合度，但只有三种化合物（丹酚酸C、鞣花酸和柚皮素）显示出更令人满意的pIC。本研究获得的结果可用于设计有效的GSK-3β抑制剂。
