Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 2A Nanwei Road, Beijing 100050, China.
Molecules. 2018 Nov 6;23(11):2892. doi: 10.3390/molecules23112892.
O⁶-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O⁶-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q² = 0.83, R² = 0.87, Q² = 0.67, and R² = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors.
O⁶-甲基鸟嘌呤-DNA 甲基转移酶（MGMT）是一种独特的 DNA 修复酶，能够赋予细胞对烷化剂的抗性，而烷化剂会修饰鸟嘌呤的 O⁶ 位。因此，抑制肿瘤中的 MGMT 活性引起了癌症研究人员的极大兴趣，因为它可以显著提高这些烷化剂的抗癌疗效。在这项研究中，我们对总共 134 种与 ED 值（50%抑制浓度）相关的碱基类似物进行了定量构效关系（QSAR）和分类研究，这些碱基类似物与 MGMT 有关。所有化合物的分子信息均通过量子化学描述符和 Dragon 描述符进行描述。遗传算法（GA）和多元线性回归（MLR）分析相结合，建立了 QSAR 模型。基于六种分子指纹，七种机器学习方法生成了分类模型。通过内部和外部验证技术评估所有开发模型的性能。基于 84 种化合物，最佳 QSAR 模型的 Q²为 0.83，R²为 0.87，Q²为 0.67，R²为 0.69。QSAR 研究结果表明拓扑电荷指数、极化率、电离势（IP）和初级芳香胺的数量是碱基类似物抑制 MGMT 的主要贡献因素。对于分类研究，十个最佳模型的十折交叉验证准确率在 0.750 到 0.885 之间。外部测试集的准确率范围除 PubChem-Tree 模型外均在 0.800 到 0.880 之间，表明具有令人满意的预测能力。对于训练集和外部测试集，三个模型（Ext-SVM、Ext-Tree 和 Graph-RF）都显示出了较高且可靠的预测准确性。此外，通过信息增益和子结构频率分析方法，确定了几个用于表征 MGMT 抑制剂的代表性亚结构。我们的研究可能有助于进一步设计和快速识别潜在的 MGMT 抑制剂。
