Shi Jingsheng, Zhao Guanglei, Wei Yibing
Division of Orthopaedic Surgery, Huashan Hospital, Fudan University, Shanghai, China.
Med Sci (Paris). 2018 Oct;34 Focus issue F1:52-58. doi: 10.1051/medsci/201834f110. Epub 2018 Nov 7.
The dynamic balance between acetylation and deacetylation of histones plays a crucial role in the epigenetic regulation of gene expression. It is equilibrated by two families of enzymes: histone acetyltransferases and histone deacetylases (HDACs). HDACs repress transcription by regulating the conformation of the higher-order chromatin structure. HDAC inhibitors have recently become a class of chemical agents for potential treatment of the abnormal chromatin remodeling process involved in certain cancers. In this study, we constructed a large dataset to predict the activity value of HDAC1 inhibitors. Each compound was represented with seven fingerprints, and computational models were subsequently developed to predict HDAC1 inhibitors via five machine learning methods. These methods include naïve Bayes, κ-nearest neighbor, C4.5 decision tree, random forest, and support vector machine (SVM) algorithms. The best predicting model was CDK fingerprint with SVM, which exhibited an accuracy of 0.89. This model also performed best in five-fold cross-validation. Some representative substructure alerts responsible for HDAC1 inhibitors were identified by using MoSS in KNIME, which could facilitate the identification of HDAC1 inhibitors.
组蛋白乙酰化与去乙酰化之间的动态平衡在基因表达的表观遗传调控中起着关键作用。它由两类酶来平衡：组蛋白乙酰转移酶和组蛋白去乙酰化酶（HDACs）。HDACs通过调节高阶染色质结构的构象来抑制转录。HDAC抑制剂最近已成为一类潜在的化学药物，用于治疗某些癌症中涉及的异常染色质重塑过程。在本研究中，我们构建了一个大型数据集来预测HDAC1抑制剂的活性值。每个化合物用七种指纹表示，随后通过五种机器学习方法开发计算模型来预测HDAC1抑制剂。这些方法包括朴素贝叶斯、κ最近邻、C4.5决策树、随机森林和支持向量机（SVM）算法。最佳预测模型是使用SVM的CDK指纹，其准确率为0.89。该模型在五折交叉验证中也表现最佳。通过在KNIME中使用MoSS识别出了一些负责HDAC1抑制剂的代表性亚结构警示，这有助于HDAC1抑制剂的识别。
