通过网络药理学、分子对接和分子动力学模拟探索舒筋健腰胶囊治疗抑郁症的潜在分子机制

IF 1.5 4区 医学 Q4 CHEMISTRY, MEDICINAL Current computer-aided drug design Pub Date : 2024-01-01 DOI:10.2174/1573409919666230619105254
Zhiyao Liu, Hailiang Huang, Ying Yu, Yuqi Jia, Lingling Li, Xin Shi, Fangqi Wang
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引用次数: 0

摘要

背景:舒筋解郁胶囊(SJC)是一种由金丝桃和刺五加配制而成的纯中药复方制剂。舒筋健腰胶囊已被批准用于抑郁症的临床治疗,但其作用机制尚不清楚:本研究采用网络药理学、分子对接和分子动力学模拟(MDS)等方法探讨澳捷治疗抑郁症的潜在机制:方法:利用 TCMSP、BATMAN-TCM 和 HERB 数据库,并查阅相关文献,筛选出连翘和刺五加的有效活性成分。利用 TCMSP、BATMAN-TCM、HERB 和 STITCH 数据库预测有效活性成分的潜在靶点。利用 GeneCards 数据库、DisGeNET 数据库和 GEO 数据集获得抑郁症靶点,并明确澳门博彩的网站与抑郁症的交叉靶点。利用STRING数据库和Cytoscape软件构建交叉靶点的蛋白-蛋白相互作用(PPI)网络,筛选核心靶点。对交叉靶点进行富集分析。然后构建接收操作者特征曲线(ROC)来验证核心靶点。利用 SwissADME 和 pkCSM 预测了核心活性成分的药代动力学特征。进行分子对接以验证核心活性成分与核心靶点的对接活性,并进行分子动力学模拟以评估对接复合物的准确性:结果:我们得到了以槲皮素、山柰醇、叶黄素和金丝桃素为核心活性成分的 15 种活性成分和 308 个潜在药物靶点。我们获得了 3598 个抑郁症靶点和 193 个澳门博彩的网站和抑郁症交叉靶点。利用 Cytoscape 3.8.2 软件共筛选出 9 个核心靶标(AKT1、TNF、IL6、IL1B、VEGFA、JUN、CASP3、MAPK3、PTGS2)。共有 442 个 GO 条目和 165 个 KEGG 通路(p 结论:这些研究结果表明,SJC 可用于治疗抑郁症:澳苷可通过槲皮素、山奈酚、叶黄素、金丝桃素等有效成分调节 PTGS2、CASP3 等靶点和 IL-17、TNF、MAPK 等信号通路,参与免疫炎症、氧化应激、细胞凋亡、神经发生等,从而治疗抑郁症。
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Exploring the Potential Molecular Mechanism of the Shugan Jieyu Capsule in the Treatment of Depression through Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Background: Shugan Jieyu Capsule (SJC) is a pure Chinese medicine compound prepared with Hypericum perforatum and Acanthopanacis senticosi. SJC has been approved for the clinical treatment of depression, but the mechanism of action is still unclear.

Objectives: Network pharmacology, molecular docking, and molecular dynamics simulation (MDS) were applied in the present study to explore the potential mechanism of SJC in the treatment of depression.

Methods: TCMSP, BATMAN-TCM, and HERB databases were used, and related literature was reviewed to screen the effective active ingredients of Hypericum perforatum and Acanthopanacis senticosi. TCMSP, BATMAN-TCM, HERB, and STITCH databases were used to predict the potential targets of effective active ingredients. GeneCards database, DisGeNET database, and GEO data set were used to obtain depression targets and clarify the intersection targets of SJC and depression. STRING database and Cytoscape software were used to build a protein-protein interaction (PPI) network of intersection targets and screen the core targets. The enrichment analysis on the intersection targets was conducted. Then the receiver operator characteristic (ROC) curve was constructed to verify the core targets. The pharmacokinetic characteristics of core active ingredients were predicted by SwissADME and pkCSM. Molecular docking was performed to verify the docking activity of the core active ingredients and core targets, and molecular dynamics simulations were performed to evaluate the accuracy of the docking complex.

Results: We obtained 15 active ingredients and 308 potential drug targets with quercetin, kaempferol, luteolin, and hyperforin as the core active ingredients. We obtained 3598 targets of depression and 193 intersection targets of SJC and depression. A total of 9 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2) were screened with Cytoscape 3.8.2 software. A total of 442 GO entries and 165 KEGG pathways (p <0.01) were obtained from the enrichment analysis of the intersection targets, mainly enriched in IL-17, TNF, and MAPK signaling pathways. The pharmacokinetic characteristics of the 4 core active ingredients indicated that they could play a role in SJC antidepressants with fewer side effects. Molecular docking showed that the 4 core active components could effectively bind to the 8 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2), which were related to depression by the ROC curve. MDS showed that the docking complex was stable.

Conclusion: SJC may treat depression by using active ingredients such as quercetin, kaempferol, luteolin, and hyperforin to regulate targets such as PTGS2 and CASP3 and signaling pathways such as IL-17, TNF, and MAPK, and participate in immune inflammation, oxidative stress, apoptosis, neurogenesis, etc.

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来源期刊
Current computer-aided drug design
Current computer-aided drug design 医学-计算机:跨学科应用
CiteScore
3.70
自引率
5.90%
发文量
46
审稿时长
>12 weeks
期刊介绍: Aims & Scope Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.
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