Worachartcheewan Apilak, Songtawee Napat, Siriwong Suphakit, Prachayasittikul Supaluk, Nantasenamat Chanin, Prachayasittikul Virapong
Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Department of Clinical Chemistry, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Med Chem. 2019;15(4):328-340. doi: 10.2174/1573406414666180924163756.
Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required.
This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity.
A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking.
The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO-CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed.
These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.
人类免疫缺陷病毒（HIV）是一种导致获得性免疫缺陷综合征（AIDS）的感染因子。因此，需要合理设计抑制剂以阻止该疾病的进展。
本研究旨在构建秋水仙碱及其具有抗HIV活性的衍生物的定量构效关系（QSAR）模型、进行分子对接并开展新的合理设计。
采用一组包含24种具有抗HIV活性的秋水仙碱及其衍生物的数据，运用机器学习方法（如多元线性回归（MLR）、人工神经网络（ANN）和支持向量机（SVM））来开发QSAR模型，并进行分子对接研究。
与抗HIV活性相关的重要描述符包括JGI2、Mor24u、Gm和R8p+描述符。通过留一法交叉验证（LOO-CV）和外部数据集的相关系数（Q2）和均方根误差（RMSE）观察到，模型的预测性能具有可接受的统计质量。特别是，ANN方法优于MLR和SVM方法，对于LOOCV集，其LOO-CV 2Q和RMSELOO-CV分别为0.7548和0.5735；对于外部验证，其Ext 2Q为0.8553，RMSEExt为0.6999。此外，病毒进入分子（gp120包膜糖蛋白）的分子对接揭示了该蛋白的关键相互作用残基（细胞受体CD4）以及秋水仙碱衍生物作为HIV进入抑制剂与HIV结构结合的位点部分偏好。此外，还提出了利用信息丰富的QSAR和分子对接对秋水仙碱衍生物进行新的合理设计。
这些发现为合理药物设计以及新型抗HIV药物的潜在开发提供了指导。
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