揭示四苗永安汤治疗腹主动脉瘤的潜力:网络药理学、机器学习、分子对接和动力学模拟相结合的综合研究

IF 3.1 4区 生物学 Q2 BIOLOGY
Ming Xie , Yufeng Zhang , Ming Zhao , Xiandeng Li , Yong Xue , Guobao Chen , Jia Liu , Haibing Hua
{"title":"揭示四苗永安汤治疗腹主动脉瘤的潜力:网络药理学、机器学习、分子对接和动力学模拟相结合的综合研究","authors":"Ming Xie ,&nbsp;Yufeng Zhang ,&nbsp;Ming Zhao ,&nbsp;Xiandeng Li ,&nbsp;Yong Xue ,&nbsp;Guobao Chen ,&nbsp;Jia Liu ,&nbsp;Haibing Hua","doi":"10.1016/j.compbiolchem.2025.108701","DOIUrl":null,"url":null,"abstract":"<div><div>Abdominal aortic aneurysm (AAA) is a progressive and life-threatening vascular disorder characterized by abnormal dilation of the abdominal aorta and a high risk of rupture. Current pharmacological interventions remain limited in efficacy, highlighting the need for alternative therapeutic strategies. Si-Miao-Yong-An Decoction (SMYAD), a classical formula in traditional Chinese medicine, has demonstrated anti-inflammatory and vascular-protective effects, yet its underlying mechanisms in AAA treatment remain unclear. This study employed an integrative approach combining network pharmacology, machine learning, and molecular modeling to elucidate the pharmacological basis of SMYAD against AAA. A total of 106 bioactive compounds and 235 putative targets were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. These were cross-referenced with disease-associated and differentially expressed genes from GEO datasets, identifying 15 targets potentially involved in AAA pathogenesis. Functional enrichment analyses revealed their involvement in the interleukin-17 and tumor necrosis factor signaling pathways. Integrated PPI network analysis and 3 machine learning algorithms jointly identified 6 hub genes (IL6, PTGS2, IL1B, FOS, MAOA, and COL1A1) as central to AAA pathology. Gene expression profiling and ROC curve analysis further supported the diagnostic relevance of these targets. Five key compounds—quercetin, luteolin, kaempferol, isorhamnetin, and stigmasterol—exhibited strong binding affinities with the identified hub targets. Molecular docking and dynamics simulations confirmed stable interactions between the selected compounds and their targets. Overall, this study provides mechanistic insights into the multi-target actions of SMYAD in AAA and offers theoretical support for its potential clinical application.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"120 ","pages":"Article 108701"},"PeriodicalIF":3.1000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Uncovering the therapeutic potential of Si-Miao-Yong-An decoction in abdominal aortic aneurysm: An integrative study combining network pharmacology, machine learning, molecular docking and dynamics simulation\",\"authors\":\"Ming Xie ,&nbsp;Yufeng Zhang ,&nbsp;Ming Zhao ,&nbsp;Xiandeng Li ,&nbsp;Yong Xue ,&nbsp;Guobao Chen ,&nbsp;Jia Liu ,&nbsp;Haibing Hua\",\"doi\":\"10.1016/j.compbiolchem.2025.108701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Abdominal aortic aneurysm (AAA) is a progressive and life-threatening vascular disorder characterized by abnormal dilation of the abdominal aorta and a high risk of rupture. Current pharmacological interventions remain limited in efficacy, highlighting the need for alternative therapeutic strategies. Si-Miao-Yong-An Decoction (SMYAD), a classical formula in traditional Chinese medicine, has demonstrated anti-inflammatory and vascular-protective effects, yet its underlying mechanisms in AAA treatment remain unclear. This study employed an integrative approach combining network pharmacology, machine learning, and molecular modeling to elucidate the pharmacological basis of SMYAD against AAA. A total of 106 bioactive compounds and 235 putative targets were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. These were cross-referenced with disease-associated and differentially expressed genes from GEO datasets, identifying 15 targets potentially involved in AAA pathogenesis. Functional enrichment analyses revealed their involvement in the interleukin-17 and tumor necrosis factor signaling pathways. Integrated PPI network analysis and 3 machine learning algorithms jointly identified 6 hub genes (IL6, PTGS2, IL1B, FOS, MAOA, and COL1A1) as central to AAA pathology. Gene expression profiling and ROC curve analysis further supported the diagnostic relevance of these targets. Five key compounds—quercetin, luteolin, kaempferol, isorhamnetin, and stigmasterol—exhibited strong binding affinities with the identified hub targets. Molecular docking and dynamics simulations confirmed stable interactions between the selected compounds and their targets. Overall, this study provides mechanistic insights into the multi-target actions of SMYAD in AAA and offers theoretical support for its potential clinical application.</div></div>\",\"PeriodicalId\":10616,\"journal\":{\"name\":\"Computational Biology and Chemistry\",\"volume\":\"120 \",\"pages\":\"Article 108701\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Biology and Chemistry\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1476927125003627\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Biology and Chemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1476927125003627","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

腹主动脉瘤(AAA)是一种进行性和危及生命的血管疾病,其特征是腹主动脉异常扩张和破裂的高风险。目前的药物干预在疗效上仍然有限,这突出了对替代治疗策略的需求。四妙永安汤(SMYAD)是一种经典的中药方剂,具有抗炎和血管保护作用,但其治疗AAA的机制尚不清楚。本研究采用网络药理学、机器学习和分子建模相结合的方法,从中药系统药理学数据库和分析平台数据库中共鉴定出106种生物活性化合物和235种推定靶点,阐明了SMYAD抗AAA的药理基础。这些基因与GEO数据集中的疾病相关基因和差异表达基因交叉对照,确定了15个潜在参与AAA发病机制的靶点。功能富集分析显示它们参与白细胞介素-17和肿瘤坏死因子信号通路。综合PPI网络分析和3种机器学习算法共同确定了6个中心基因(IL6、PTGS2、IL1B、FOS、MAOA和COL1A1)是AAA病理的核心。基因表达谱和ROC曲线分析进一步支持这些靶点的诊断相关性。5个关键化合物——槲皮素、木犀草素、山奈酚、异鼠李素和豆甾醇——与鉴定的枢纽靶点表现出很强的结合亲和力。分子对接和动力学模拟证实了所选化合物与其靶标之间稳定的相互作用。总的来说,本研究为SMYAD在AAA中的多靶点作用提供了机制见解,并为其潜在的临床应用提供了理论支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncovering the therapeutic potential of Si-Miao-Yong-An decoction in abdominal aortic aneurysm: An integrative study combining network pharmacology, machine learning, molecular docking and dynamics simulation
Abdominal aortic aneurysm (AAA) is a progressive and life-threatening vascular disorder characterized by abnormal dilation of the abdominal aorta and a high risk of rupture. Current pharmacological interventions remain limited in efficacy, highlighting the need for alternative therapeutic strategies. Si-Miao-Yong-An Decoction (SMYAD), a classical formula in traditional Chinese medicine, has demonstrated anti-inflammatory and vascular-protective effects, yet its underlying mechanisms in AAA treatment remain unclear. This study employed an integrative approach combining network pharmacology, machine learning, and molecular modeling to elucidate the pharmacological basis of SMYAD against AAA. A total of 106 bioactive compounds and 235 putative targets were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. These were cross-referenced with disease-associated and differentially expressed genes from GEO datasets, identifying 15 targets potentially involved in AAA pathogenesis. Functional enrichment analyses revealed their involvement in the interleukin-17 and tumor necrosis factor signaling pathways. Integrated PPI network analysis and 3 machine learning algorithms jointly identified 6 hub genes (IL6, PTGS2, IL1B, FOS, MAOA, and COL1A1) as central to AAA pathology. Gene expression profiling and ROC curve analysis further supported the diagnostic relevance of these targets. Five key compounds—quercetin, luteolin, kaempferol, isorhamnetin, and stigmasterol—exhibited strong binding affinities with the identified hub targets. Molecular docking and dynamics simulations confirmed stable interactions between the selected compounds and their targets. Overall, this study provides mechanistic insights into the multi-target actions of SMYAD in AAA and offers theoretical support for its potential clinical application.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信