In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods

Molecules

Rugang Zhong¹  Lijiao Zhao¹  Xiaodong Sun¹  Tengjiao Fan²  Yue Zhou²  Xin Cui²  Yuxing Hao³  Guohui Sun³  Yongzhen Peng³  Ting Ren³ 

[1] Bioengineering, Beijing University of Technology, Beijing 100124, China;;Viral Oncology, College of Life Science &;Beijing Key Laboratory of Environmental &

关键词: MGMT;    anticancer alkylating agents;    resistance;    inhibitors;    QSAR;    classification;   

DOI  :  10.3390/molecules23112892

来源: DOAJ

【 摘 要 】

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 ED₅₀ 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_Loo = 0.83, R² = 0.87, Q²_ext = 0.67, and R²_ext = 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.

【 授权许可】

Unknown