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通过二维和三维定量构效关系、分子动力学模拟结合药代动力学预测,对新型喹唑啉类化合物进行计算机辅助设计,作为潜在的结核分枝杆菌 PknB 抑制剂。

In silico design of novel quinazoline-based compounds as potential Mycobacterium tuberculosis PknB inhibitors through 2D and 3D-QSAR, molecular dynamics simulations combined with pharmacokinetic predictions.

机构信息

Department of Chemistry, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand.

Division of Chemistry, Faculty of Science, Nakhon Phanom University, Nakhon Phanom, 48000, Thailand.

出版信息

J Mol Graph Model. 2022 Sep;115:108231. doi: 10.1016/j.jmgm.2022.108231. Epub 2022 May 28.

Abstract

Serine/threonine protein kinase B (PknB) is essential to Mycobacterium tuberculosis (M. tuberculosis) cell division and metabolism and a potential anti-tuberculosis drug target. Here we apply Hologram Quantitative Structure Activity Relationship (HQSAR) and three-dimensional QSAR (Comparative Molecular Similarity Indices Analysis (CoMSIA)) methods to investigate structural requirements for PknB inhibition by a series of previously described quinazoline derivatives. PknB binding of quinazolines was evaluated by molecular dynamics (MD) simulations of the catalytic domain and binding energies calculated by Molecular Mechanics/Poisson Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) methods. Evaluation of a training set against experimental data showed both HQSAR and CoMSIA models to reliably predict quinazoline binding to PknB, and identified the quinazoline core and overall hydrophobicity as the major contributors to affinity. Calculated binding energies also agreed with experiment, and MD simulations identified hydrogen bonds to Glu93 and Val95, and hydrophobic interactions with Gly18, Phe19, Gly20, Val25, Thr99 and Met155, as crucial to PknB binding. Based on these results, additional quinazolines were designed and evaluated in silico, with HQSAR and CoMSIA models identifying sixteen compounds, with predicted PknB binding superior to the template, whose activity spectra and physicochemical, pharmacokinetic, and anti-M. tuberculosis properties were assessed. Compound, D060, bearing additional ortho- and meta-methyl groups on its R substituent, was superior to template regarding PknB inhibition and % caseum fraction unbound, and equivalent in other aspects, although predictions identified hepatotoxicity as a likely issue with the quinazoline series. These data provide a structural basis for rational design of quinazoline derivatives with more potent PknB inhibitory activity as candidate anti-tuberculosis agents.

摘要

丝氨酸/苏氨酸蛋白激酶 B(PknB)是结核分枝杆菌(M. tuberculosis)细胞分裂和代谢所必需的,也是一种潜在的抗结核药物靶点。在这里,我们应用全息定量构效关系(HQSAR)和三维定量构效关系(比较分子相似性指数分析(CoMSIA))方法研究了一系列先前描述的喹唑啉衍生物对 PknB 抑制的结构要求。通过对催化结构域进行分子动力学(MD)模拟来评估喹唑啉与 PknB 的结合,并用分子力学/泊松-玻尔兹曼表面面积(MM-PBSA)和分子力学/广义 Born 表面面积(MM-GBSA)方法计算结合能。通过将训练集与实验数据进行评估,发现 HQSAR 和 CoMSIA 模型都能可靠地预测喹唑啉与 PknB 的结合,并且确定喹唑啉核心和整体疏水性是亲和力的主要贡献者。计算出的结合能也与实验结果一致,MD 模拟确定了与 Glu93 和 Val95 的氢键,以及与 Gly18、Phe19、Gly20、Val25、Thr99 和 Met155 的疏水相互作用,这些都是 PknB 结合的关键。基于这些结果,设计并在计算机上评估了额外的喹唑啉,HQSAR 和 CoMSIA 模型确定了 16 种化合物,它们的预测 PknB 结合优于模板,其活性谱、理化性质、药代动力学和抗结核性质都得到了评估。带有额外的邻位和间位甲基取代基的化合物 D060 在 PknB 抑制和未结合乳清分数方面优于模板,而在其他方面则相当,尽管预测表明该喹唑啉系列可能具有肝毒性问题。这些数据为合理设计具有更强 PknB 抑制活性的喹唑啉衍生物提供了结构基础,可作为候选抗结核药物。

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