Bhargava Kiran, Nath Rajendra, Seth Prahlad Kumar, Pant Kamlesh Kumar, Dixit Rakesh Kumar
Department of Pharmacology and Therapeutics, King George's Medical University Erstwhile CSMMU, Lucknow 226003, UP,India.
Biotech Park, Lucknow-226021, UP, India.
Bioinformation. 2014 Jan 29;10(1):8-12. doi: 10.6026/97320630010008. eCollection 2014.
In this work, 3D model of D2 dopamine receptor was determined by comparative homology modeling program MODELLER. The computed model's energy was minimized and validated using PROCHECK and Errat tool to obtain a stable model structure and was submitted in Protein Model Database (PMDB-ID: PM0079251). Stable model was used for molecular docking against Risperidone and their 15 derivatives using AutoDock 4.2, which resulted in energy-based descriptors such as Binding Energy, Ligand Efficiency, Inhib Constant, Intermol energy, vdW + Hbond + desolv Energy, Electrostatic Energy, Total Internal Energy and Torsional Energy. After that, we have built quantitative structure activity relationship (QSAR) model, which was trained and tested on Risperidone and their 15 derivatives having activity value pKi in µM. For QSAR modeling, Multiple Linear Regression model was engendered using energy-based descriptors yielding correlation coefficient r2 of 0.513. To assess the predictive performance of QSAR models, different cross-validation procedures were adopted. Our results suggests that ligand-receptor binding interactions for D2 employing QSAR modeling seems to be a promising approach for prediction of pKi value of novel antagonists against D2 receptor.
在本研究中，通过比较同源性建模程序MODELLER确定了D2多巴胺受体的三维模型。使用PROCHECK和Errat工具对计算得到的模型能量进行最小化和验证，以获得稳定的模型结构，并将其提交至蛋白质模型数据库（PMDB-ID：PM0079251）。使用AutoDock 4.2将稳定的模型用于与利培酮及其15种衍生物进行分子对接，从而得到基于能量的描述符，如结合能、配体效率、抑制常数、分子间能量、范德华力+氢键+去溶剂化能量、静电能、总内能和扭转能。之后，我们构建了定量构效关系（QSAR）模型，该模型在利培酮及其15种活性值以微摩尔为单位的pKi衍生物上进行训练和测试。对于QSAR建模，使用基于能量的描述符生成了多元线性回归模型，相关系数r2为0.513。为了评估QSAR模型的预测性能，采用了不同的交叉验证程序。我们的结果表明，采用QSAR建模研究D2的配体-受体结合相互作用似乎是预测新型D2受体拮抗剂pKi值的一种有前景的方法。
