靶向TNF的植物抗炎先导化合物的计算机模拟筛选

Exploring the phytoconstituents targeting TNF-α as potential lead compounds to treat inflammatory diseases: an in-silico approach

  • 摘要:
    目的通过计算机模拟进行分子对接,研究多种植物中的抗炎成分对肿瘤坏死因子α(TNF-α,一种参与炎症疾病的介质)的抑制作用。
    方法基于先前的研究,我们使用AutoDock Vina软件对不同药用植物的抗炎植物成分进行计算机模拟评估,以了解它们与TNF-α的结合模式(PDB ID:6OP0)。分子对接模拟分析中,网格箱(25 × 25 × 25)Å中心设为[–12.817×(–1.618)× 19.009]Å,网格间距为0.375 Å。此外,使用 Discovery Studio Client 2020 程序评估与靶标和配体氨基酸相关的二维和三维(2D和3D)氢键间的相互作用,并根据里宾斯基类药五规则和 SwissADME 数据库预测其理化性质,以支撑分子对接模拟结果。
    结果我们从选取的药用植物中筛选出200多种植物化合物与抗TNF-α蛋白结合,结合评分在–12.3至–2.5 kcal/mol范围内。其中,大黄素、芦荟大黄素、水黄皮籽素、紫花青素、附根含希米拉因、鞣花酸、欧前胡素、α-生育酚和八降葫芦素A表现出良好的结合亲和力,分别为–10.6、–10.0、–10.5、–10.1、–11.2、–10.3、–10.1、–10.1和–10.0 kcal/mol。此外,这些成分的吸收、分布、代谢、排泄及毒性(ADMET)特征参数均在正常范围内。
    结论根据初步研究结果,我们认为本研究选取的植物成分可以通过抑制 TNF-α靶标成为良好的抗炎候选物。可以将这些化合物作为治疗炎症的新成分进行进一步优化和验证,以开发更加有效、安全的抗炎药物。

     

    Abstract:
    ObjectiveTo explore the anti-inflammatory phytoconstituents from various plant sources as tumour necrosis factor-α (TNF-α)-inhibitor, a mediator involved in the inflammatory disorder, by in silico molecular docking.
    MethodsBased on previous findings, we performed the in silico assessment of anti-inflammatory phytoconstituents from different medicinal plants to understand their binding patterns against TNF-α (PDB ID: 6OP0) using AutoDock Vina. Molecular docking was performed by setting a grid box (25 × 25 × 25) Å centered at – 12.817 × (– 1.618) × 19.009 Å with 0.375 Å of grid spacing. Furthermore, Discovery Studio Client 2020 program was utilized to assess two- and three-dimensional (2D and 3D) hydrogen-bond interactions concerning an amino acid of target and ligand. Physicochemical properties were reported using the Lipinski’s rule and SwissADME database to support the in silico findings.
    ResultsFrom the selected medicinal plants, more than 200 phytocompounds were screened against TNF-α protein with binding scores in the range of – 12.3 to – 2.5 kcal/mol. Amongst them, emodin, aloe-emodin, pongamol, purpuritenin, semiglabrin, ellagic acid, imperatorin, α-tocopherol, and octanorcucurbitacin A showed good binding affinity as – 10.6, – 10.0, – 10.5, – 10.1, – 11.2, – 10.3, – 10.1, – 10.1, and – 10.0 kcal/mol, respectively. Also, the absorption, distribution, metabolism, excretion, and toxicology (ADMET) profiles were well within acceptable limits.
    ConclusionBased on our preliminary findings, we conclude that the selected phytoconstituents have the potential to be good anti-inflammatory candidates by inhibiting the TNF-α target. These compounds can be further optimized and validated as new therapeutic components to develop more effective and safe anti-inflammatory drugs.

     

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