首页> 外文期刊>Journal of Chemical Sciences >Assessing ligand efficiencies using template-based molecular docking and Tabu-clustering on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepin-2(1H)-one and-thione (TIBO) derivatives as HIV-1RT inhibitors
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Assessing ligand efficiencies using template-based molecular docking and Tabu-clustering on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepin-2(1H)-one and-thione (TIBO) derivatives as HIV-1RT inhibitors

机译:使用基于模板的分子对接和Tabu聚类对四氢咪唑基-[4,5,1-jk] [1,4]-苯并二氮杂-2-2(1H)-一和硫酮(TIBO)衍生物作为HIV-1RT评估配体效率抑制剂

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摘要

A template-based flexible docking simulation followed by ‘Tabu-clustering’ was performed on a series of 38 TIBO derivatives as HIV-1 reverse transcriptase (HIV-1 RT) inhibitors. Four different templates of the Cl-TIBO (1-REV) were created and used as reference templates for docking and aligning. On the basis of the optimal conformation of the ligands, when fitting to the template, the respective scoring functions were obtained; different ligand efficiencies were evaluated and analysed. Statistical modelling using artificial neural network (ANN: r 2 = 0.922) and multiple linear regression method (MLR: r 2 = 0.851) showed good correlation between the biological activity, binding affinity, and different ligand efficiencies of the compounds, which suggest the robustness of the template-based binding conformations of these inhibitors. Our studies suggest that, template-based docking followed by ‘Tabuclustering’ will give a better alignment of inhibitors with respect to the crystal coordinates and enhance the docking efficiency. Also, our study indicates that scoring functions based on 3D symmetry analysis along with heavy atoms count serve as a valuable tool for estimating the efficiency of the ligands. Thus, this is a novel method based on heavy atoms count predicting the binding affinity of the TIBO group of inhibitors, so that their therapeutic utility can be enhanced.
机译:对一系列38种TIBO衍生物作为HIV-1逆转录酶(HIV-1 RT)抑制剂进行了基于模板的灵活对接模拟,然后进行“ Tabu聚类”。创建了Cl-TIBO(1-REV)的四个不同模板,并将其用作对接和对齐的参考模板。根据配体的最佳构象,在拟合模板时,获得了相应的评分功能;评估和分析了不同的配体效率。使用人工神经网络(ANN:r 2 = 0.922)和多元线性回归方法(MLR:r 2 = 0.851)进行的统计建模显示了生物学活性,结合亲和力和不同配体效率之间的良好相关性化合物,表明这些抑制剂的基于模板的结合构象的稳健性。我们的研究表明,基于模板的对接后加上“ Tabuclustering”将使抑制剂相对于晶体坐标更好地对齐,并提高了对接效率。此外,我们的研究表明,基于3D对称性分析的评分功能以及重原子计数可作为评估配体效率的宝贵工具。因此,这是一种基于重原子数预测TIBO抑制剂结合亲和力的新颖方法,因此可以提高其治疗效用。

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