Fang Jiansong, Yang Ranyao, Gao Li, Yang Shengqian, Pang Xiaocong, Li Chao, He Yangyang, Liu Ai-Lin, Du Guan-Hua
Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Xian Nong Tan Street, Beijing, 100050, People's Republic of China.
Mol Divers. 2015 Feb;19(1):149-62. doi: 10.1007/s11030-014-9561-3. Epub 2014 Dec 16.
Cyclin-dependent kinase 5 (CDK5) has emerged as a principal therapeutic target for Alzheimer's disease. It is highly desirable to develop computational models that can predict the inhibitory effects of a compound towards CDK5 activity. In this study, two machine learning tools (naive Bayesian and recursive partitioning) were used to generate four single classifiers from a large dataset containing 462 CDK5 inhibitors and 1,500 non-inhibitors. Then, two types of consensus models [combined classifier-artificial neural networks (CC-ANNs) and consensus prediction] were applied to combine four single classifiers to obtain superior performance. The results showed that both consensus models outperformed four single classifiers, and (MCC = 0.806) was superior to consensus prediction (MCC = 0.711) for an external test set. To illustrate the practical applications of the CC-ANN model in virtual screening, an in-house dataset containing 29,170 compounds was screened, and 40 compounds were selected for further bioactivity assays. The assay results showed that 13 out of 40 compounds exerted CDK5/p35 inhibitory activities with IC50 values ranging from 9.23 to 229.76 μM. Interestingly, three new scaffolds that had not been previously reported as CDK5 inhibitors were found in this study. These studies prove that our protocol is an effective approach to predict small-molecule CDK5 affinity and identify novel lead compounds.
细胞周期蛋白依赖性激酶5（CDK5）已成为阿尔茨海默病的主要治疗靶点。开发能够预测化合物对CDK5活性抑制作用的计算模型非常必要。在本研究中，使用了两种机器学习工具（朴素贝叶斯和递归划分）从一个包含462种CDK5抑制剂和1500种非抑制剂的大型数据集中生成四个单分类器。然后，应用两种类型的共识模型[组合分类器-人工神经网络（CC-ANNs）和共识预测]来组合四个单分类器以获得更好的性能。结果表明，两种共识模型均优于四个单分类器，并且对于外部测试集，CC-ANNs（马修斯相关系数MCC = 0.806）优于共识预测（MCC = 0.711）。为了说明CC-ANN模型在虚拟筛选中的实际应用，对一个包含29170种化合物的内部数据集进行了筛选，并选择了40种化合物进行进一步的生物活性测定。测定结果表明，40种化合物中有13种具有CDK5/p35抑制活性，IC50值范围为9.23至229.76μM。有趣的是，在本研究中发现了三种以前未报道为CDK5抑制剂的新骨架。这些研究证明，我们的方案是预测小分子对CDK5亲和力并鉴定新型先导化合物的有效方法。
