Analysis of the Mechanism of Action of Fangji Fuling Decoction in the Treatment of Sepsis-Induced AKI Based on Network Pharmacology and Molecular Docking

雨荣 骆
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引用次数: 0

Abstract

目的:采用网络药理学和分子对接技术,对防己茯苓汤治疗脓毒症AKI的作用机制进行预测性研究。方法:先通过TCMSP数据库收集防己茯苓汤中药物组成的活性成分,SwissTargetPrediction搜索活性成分所作用的靶点蛋白。再通过GeneCards、PharmGKb、数据库、TTD数据库收集脓毒症AKI靶点蛋白,选择药物成分以及对应的靶点蛋白,利用Cytoscape构建药物–成分–靶点–疾病网络,将药物–疾病交集靶点录入STRING分析后导入Cytoscape软件,进一步构建PPI网络图。在DAVID平台获取交集靶点的作图数据,在微生信平台进行GO富集分析和KEGG通路分析。最后使用MOE和Pymol进行分子对接和渲染。结果:筛选出防己茯苓汤活性成分126种及药物靶点993个,疾病靶点3301个,防己茯苓汤治疗脓毒症AKI作用靶点448个,其中联苯双酯、(E)-1-(2,4-二羟基苯基)-3-(2,2-二甲基色烯-6-基)丙-2-烯-1-酮、四脉银胶菊素A、β-谷甾醇、茯苓酸A为治疗疾病的核心成分,AKT1、TNF、TP53、CASP3、MAPK3为治疗疾病的核心靶点。GO富集分析得到1742个结果,主要富集于蛋白质磷酸化、磷酸化作用、炎症反应、ATP结合、蛋白丝氨酸激酶活性等过程,KEGG通路分析得到189条通路,主要通路为癌症通路、脂质和动脉粥样硬化、乙型肝炎等。分子对接验证了药物成分和疾病靶点蛋白有良好的结合能力。其中核心靶点TP53与β-谷甾醇的结合能最小,为−7.95 kg/mol。结论:本研究采用网络药理学和分子对接技术,预测性地研究了防己茯苓汤通过多成分–多靶点–多通路治疗脓毒症AKI的机制,为临床治疗脓毒症AKI提供了证明,并为进一步提高该类病症的临床治疗效果提供了参考。Objective: To predict the mechanism of action of Fangji Fuling Decoction in treating sepsis-induced AKI by using network pharmacology and molecular docking technology. Methods: The active ingredients of Fangji Fuling Decoction were first collected through the TCMSP database, and the target proteins acted by the active ingredients were searched by SwissTargetPrediction. Then, the target proteins of sepsis-induced AKI were collected through GeneCards, PharmGKb, database, and TTD database, and the drug ingredients and corresponding target proteins were selected. The drug-ingredient-target-disease network was constructed using Cytoscape. The drug-disease intersection targets were entered into STRING analysis and then imported into Cytoscape software to further construct the PPI network diagram. The mapping data of the intersection targets were obtained on the DAVID platform, and GO enrichment analysis and KEGG pathway analysis were performed on the Microbiological Information Platform. Finally, MOE and Pymol were used for molecular docking and rendering. Results: A total of 126 active ingredients and 993 drug targets, 3301 disease targets of Fangji Fuling Decoction were screened, and 448 targets of Fangji Fuling Decoction in the treatment of sepsis AKI were found, among which bifendate, (E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one,guaiacin A, β-sitosterol, and pachymic acid A were the core components for the treatment of diseases, and AKT1, TNF, TP53, CASP3, and MAPK3 were the core targets for the treatment of diseases. GO enrichment analysis obtained 1742 results, which were mainly enriched in protein phosphorylation, phosphorylation, inflammatory response, ATP binding, protein serine kinase activity, etc. KEGG pathway analysis obtained 189 pathways, and the main pathways were cancer pathways, lipids and atherosclerosis, hepatitis B, etc. Molecular docking verified that the drug components and disease target proteins had good binding ability. Among them, the binding energy of the core target TP53 with β-sitosterol was the smallest, which was −7.95 kg/mol. Conclusion: This study used network pharmacology and molecular docking technology to predictively study the mechanism of Fangji Fuling Decoction in treating sepsis AKI through multiple components, multiple targets, and multiple pathways, which provided evidence for the clinical treatment of sepsis AKI and provided a reference for further improving the clinical treatment effect of this type of disease.
基于网络药理学和分子对接分析防己复灵汤治疗败血症性AKI的作用机制
目的:采用网络药理学和分子对接技术,对防己茯苓汤治疗脓毒症AKI的作用机制进行预测性研究。方法:先通过TCMSP数据库收集防己茯苓汤中药物组成的活性成分,SwissTargetPrediction搜索活性成分所作用的靶点蛋白。再通过GeneCards、PharmGKb、数据库、TTD数据库收集脓毒症AKI靶点蛋白,选择药物成分以及对应的靶点蛋白,利用Cytoscape构建药物–成分–靶点–疾病网络,将药物–疾病交集靶点录入STRING分析后导入Cytoscape软件,进一步构建PPI网络图。在DAVID平台获取交集靶点的作图数据,在微生信平台进行GO富集分析和KEGG通路分析。最后使用MOE和Pymol进行分子对接和渲染。结果:筛选出防己茯苓汤活性成分126种及药物靶点993个,疾病靶点3301个,防己茯苓汤治疗脓毒症AKI作用靶点448个,其中联苯双酯、(E)-1-(2,4-二羟基苯基)-3-(2,2-二甲基色烯-6-基)丙-2-烯-1-酮、四脉银胶菊素A、β-谷甾醇、茯苓酸A为治疗疾病的核心成分,AKT1、TNF、TP53、CASP3、MAPK3为治疗疾病的核心靶点。GO富集分析得到1742个结果,主要富集于蛋白质磷酸化、磷酸化作用、炎症反应、ATP结合、蛋白丝氨酸激酶活性等过程,KEGG通路分析得到189条通路,主要通路为癌症通路、脂质和动脉粥样硬化、乙型肝炎等。分子对接验证了药物成分和疾病靶点蛋白有良好的结合能力。其中核心靶点TP53与β-谷甾醇的结合能最小,为−7.95 kg/mol。结论:本研究采用网络药理学和分子对接技术,预测性地研究了防己茯苓汤通过多成分–多靶点–多通路治疗脓毒症AKI的机制,为临床治疗脓毒症AKI提供了证明,并为进一步提高该类病症的临床治疗效果提供了参考。Objective: To predict the mechanism of action of Fangji Fuling Decoction in treating sepsis-induced AKI by using network pharmacology and molecular docking technology. Methods: The active ingredients of Fangji Fuling Decoction were first collected through the TCMSP database, and the target proteins acted by the active ingredients were searched by SwissTargetPrediction. Then, the target proteins of sepsis-induced AKI were collected through GeneCards, PharmGKb, database, and TTD database, and the drug ingredients and corresponding target proteins were selected. The drug-ingredient-target-disease network was constructed using Cytoscape. The drug-disease intersection targets were entered into STRING analysis and then imported into Cytoscape software to further construct the PPI network diagram. The mapping data of the intersection targets were obtained on the DAVID platform, and GO enrichment analysis and KEGG pathway analysis were performed on the Microbiological Information Platform. Finally, MOE and Pymol were used for molecular docking and rendering. Results: A total of 126 active ingredients and 993 drug targets, 3301 disease targets of Fangji Fuling Decoction were screened, and 448 targets of Fangji Fuling Decoction in the treatment of sepsis AKI were found, among which bifendate, (E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one,guaiacin A, β-sitosterol, and pachymic acid A were the core components for the treatment of diseases, and AKT1, TNF, TP53, CASP3, and MAPK3 were the core targets for the treatment of diseases. GO enrichment analysis obtained 1742 results, which were mainly enriched in protein phosphorylation, phosphorylation, inflammatory response, ATP binding, protein serine kinase activity, etc. KEGG pathway analysis obtained 189 pathways, and the main pathways were cancer pathways, lipids and atherosclerosis, hepatitis B, etc. Molecular docking verified that the drug components and disease target proteins had good binding ability. Among them, the binding energy of the core target TP53 with β-sitosterol was the smallest, which was −7.95 kg/mol. Conclusion: This study used network pharmacology and molecular docking technology to predictively study the mechanism of Fangji Fuling Decoction in treating sepsis AKI through multiple components, multiple targets, and multiple pathways, which provided evidence for the clinical treatment of sepsis AKI and provided a reference for further improving the clinical treatment effect of this type of disease.
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