背景:中国贞洁树Vitexnegundo(VN)是南亚和东南亚的一种流行草药,具有多种健康益处,包括抑制肿瘤生长和诱导多种肿瘤细胞凋亡的能力。文献显示对乳腺癌的研究很少,很少关注疾病的分子机制和目标,生物网络,和活性成分。探索天然化合物作为可能的治疗选择是一种古老但仍然有希望的药物发现和开发方法。这项研究使用了全面的计算和统计方法来筛选潜在的候选药物。
方法:使用SwissADME鉴定VN的活性成分和靶标,SwissTargetPrediction,STITCH,IMPPAT数据库,KNapSAcK数据库,和文学。在OMIM和GeneCards数据库中搜索与乳腺癌相关的可能靶标。PASS在线服务器用于检查活性代谢物(Pa)对抗乳腺癌的概率。为了建立蛋白质-蛋白质相互作用(PPI)网络,将疾病和药物靶标的交集上传到STITCH数据库.利用Cytoscape软件对网络的拓扑参数进行分析,以识别集线器目标。使用Metascape和ShinyGO分析基因本体论(GO),使用David数据库和SR图进行京都基因和基因组百科全书(KEGG)富集分析,并使用FunRich研究了表达位点和蛋白质结构域。我们雇佣了AutoDockvina,DiscoveryStudio,和UCSFChimeraX软件和辅助工具进行分子对接和分析。Zincpharmer用于药效基团作图。ADMET分析是使用ADMETSAR进行的,瑞士ADME,ADMETLab服务器,和mypresto使用GROMACS进行分子动力学模拟(MDS)。
结果:共鉴定了65个靶标和21个活性成分。对通过PPI联网建设选择的20个枢纽目标进行了进一步调查。富集分析结果表明,关键因素是P,淀粉样β反应,细胞对淀粉样β的反应,Pos.reg.有丝分裂细胞周期的G2/M过渡,对有毒物质的反应。分子对接,药效基团作图,和MD模拟结果表明芹菜素,山奈酚,木犀草素与CDK1和CDK6蛋白呈正相互作用。
结论:这项研究是第一个使用网络药理学,分子对接,药效基团作图,和MD模拟来识别活性成分,分子靶标,和负责VN抗乳腺癌的关键生物学途径。该研究为该领域的进一步研究提供了理论依据。
The Chinese chaste tree Vitexnegundo (VN) is a popular herb in South and Southeast Asia that has several health benefits, including the ability to inhibit tumor growth and induce apoptosis in multiple tumors. Literature revealed scanty research on breast cancer, with little focus on the molecular mechanism of the disease and an emphasis on targets, biological networks, and active components. Exploring natural compounds as possible therapeutic options is an old but still promising approach for drug discovery and development. This study used a thorough computational and statistical method to screen potential drug candidates.
The active ingredients and targets of VN were identified using SwissADME, SwissTargetPrediction, STITCH, IMPPAT database, KNapSAcK database, and literature. The OMIM and GeneCards databases were searched for possible targets related to breast cancer. The PASS online server was used to check the probability of active metabolite (Pa) against breast cancer. To build protein-protein interactions (PPI) networking, the intersection of disease and drug targets was uploaded to the STITCH database. Cytoscape software was used to analyze the topology parameters of networking to identify hub targets. Gene Ontology (GO) was analyzed using Metascape and ShinyGO, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed using the David database and SR plot, and the site of expression and protein domain were studied using FunRich. We employed AutoDockvina, Discovery Studio, and UCSF ChimeraX software and auxiliary tools for molecular docking and analysis. Zincpharmer was used for pharmacophore mapping. ADMET analysis was conducted using ADMETsar, Swiss ADME, ADMETLab servers, and mypresto using GROMACS for molecular dynamics simulation (MDS).
A total of 65 targets and 21 active ingredients were identified. Further investigation was conducted on 20 hub targets selected through PPI networking construction. The enrichment analysis results indicated that the key factors were P, amyloid-beta response, cellular response to amyloid- beta, Pos. reg. of G2/M transition of the mitotic cell cycle, and response to a toxic substance. The molecular docking, pharmacophore mapping, and MD simulation results indicated that apigenin, kaempferol, and luteolin positively interacted with CDK1 and CDK6 proteins.
This study is the first to use network pharmacology, molecular docking, pharmacophore mapping, and MD simulation to identify the active ingredients, molecular targets, and critical biological pathways responsible for VN anti-breast cancer. The study provides a theoretical basis for further research in this area.