Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation.
Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation.
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DOI：
10.3390/biom11020329
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发表时间：
2021-02-22
期刊：
影响因子：
5.5
通讯作者：
Al-Harrasi A
中科院分区：
文献类型：
--
作者：
Halim SA;Sikandari AG;Khan A;Wadood A;Fatmi MQ;Csuk R;Al-Harrasi A
肿瘤坏死因子-α（TNF-α）是类风湿性关节炎和其他几种自身免疫性疾病的药物靶点。TNF-α与位于几种免疫细胞表面的TNF受体（TNFR）结合以发挥其作用。因此，使用能够阻碍TNF-α/TNFR复合物形成的抑制剂可能具有医学意义。本研究采用多种化学信息学方法，包括基于结构域的筛选、二维相似性搜索和药效团建模，筛选新的TNF-α抑制剂。随后，使用多重对接方案，并通过共识方法分析四重对接后结果。经过基于结构的虚拟筛选，17个化合物在所有对接程序中相互排在首位。这些鉴定的命中物靶向TNF-α二聚体并有效地阻断TNF-α/TNFR界面。所选命中物的预测药代动力学和生理学性质显示，在十七种化合物中，七种化合物（4、5、10、11、13-15）具有优异的ADMET特征。选择这7个化合物和另外3个分子（7、8和9）进行分子动力学模拟研究，以探讨配体诱导的TNF-α的结构和动力学行为，然后使用MM-PBSA计算配体-TNF-α结合自由能。MM-PBSA计算结果表明，化合物4、5、7和9与TNF-α的亲和力最强，化合物8、11、13-15与TNF-α的亲和力中等，化合物10与TNF-α的亲和力较弱。这项研究为设计更有效和选择性的TNF-α抑制剂提供了有价值的见解，这将有助于治疗炎症性疾病。
Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13–15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13–15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.
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影响因子：
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