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 , Yufeng Zhang , Ming Zhao , Xiandeng Li , Yong Xue , Guobao Chen , Jia Liu , 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 , Yufeng Zhang , Ming Zhao , Xiandeng Li , Yong Xue , Guobao Chen , Jia Liu , 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}
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.
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.