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首页> 外文期刊>Journal of Biomolecular Structure and Dynamics >Molecular docking and CoMFA studies of thiazoloquin(az)olin(on)es as CD38 inhibitors: determination of inhibitory mechanism, pharmacophore interactions, and design of new inhibitors
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Molecular docking and CoMFA studies of thiazoloquin(az)olin(on)es as CD38 inhibitors: determination of inhibitory mechanism, pharmacophore interactions, and design of new inhibitors

机译：作为CD38抑制剂的噻唑喹（AZ）Olin（AZ）Olin（AN）的分子对接和COMFA研究：抑制机制，药物相互作用和新抑制剂设计的测定

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In this research, molecular docking and 3D-QSAR studies were carried out on a series of 79 thiazoloquin(az)olin(on)es as CD38 inhibitors. Based on docking results, four interactions including hydrogen bonding with main chain of GLU-226 (H-M-GLU-226), Van der Waals interactions with side chain of TRP-125 (V-S-TRP-125), TRP-189 (V-S-TRP-189), and THR-221 (V-S-THR-221) were considered as pharmacological interactions. Active conformation of each ligand was extracted from docking studies and was used for carrying out 3D-QSAR modeling. Comparative molecular field analysis (CoMFA) was performed on CD38 inhibitory activities of these compounds on human and mouse. We developed CoMFA models with five components as optimum models for both data-sets. For human data-set, a model with high predictive power was developed. R-2, RMSE, and F-test values for training set of this model were .94, .24, and 179.58, respectively, and R-2 and RMSE for its test set were .92 and .32, respectively. The q(2) and RMSE values for leave-one-out cross validation test on training set were .78 and .46, respectively, that demonstrate created model is robust. Based on extracted steric and electrostatic contour maps for this model, three inhibitors with pIC(50) larger than 8.85 were designed.
机译：在该研究中，在AS CD38抑制剂的一系列79硫代喹蛋白（AZ）Olin（ON）ES上进行分子对接和3D QSAR研究。基于对接结果，四个相互作用，包括Glu-226（HM-Glu-226）主链的氢键，与TRP-125（VS-TRP-125），TRP-189（VS-）相互作用TRP-189）和THR-221（VS-THR-221）被认为是药理相互作用。从对接研究中提取每个配体的主动构象，并用于进行3D QSAR建模。对比较分子田间分析（COMFA）对人和小鼠的这些化合物的CD38抑制活性进行。我们开发了具有五个组件的COMFA型号，作为数据集的最佳模型。对于人类数据集，开发了一种具有高预测力的模型。该模型训练集的R-2，RMSE和F检验值分别为.94，.24和179.58，以及其测试集的R-2和RMSE分别为.92和.32。训练集的休假交叉验证测试的Q（2）和RMSE值分别为.78和.46，分别显示所创建的模型是强大的。基于该模型的提取的空间和静电轮廓图，设计了三种具有大于8.85的PIC（50）的抑制剂。

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