Abstract：
BACKGROUND: At present, drug development for treating Alzheimer\'s disease (AD) is still highly challenging. Eriodictyol (ERD) has shown great potential in treating AD, but its molecular mechanism is unknown.
OBJECTIVE: We aimed to explore the potential targets and mechanisms of ERD in the treatment of AD through network pharmacology, molecular docking, and molecular dynamics simulations.
METHODS: ERD-related targets were predicted based on the CTD, SEA, PharmMapper, Swiss TargetPrediction, and ETCM databases, and AD-related targets were predicted through the TTD, OMIM, DrugBank, GeneCards, Disgenet, and PharmGKB databases. Protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomics analyses (KEGG) were used to analyse the potential targets and key pathways of the anti-AD effect of ERD. Subsequently, potential DEGs affected by AD were analysed using the AlzData database, and their relationships with ERD were evaluated through molecular docking and molecular dynamics simulations.
RESULTS: A total of 198 ERD-related targets, 3716 AD-related targets, and 122 intersecting targets were identified. GO annotation analysis revealed 1497 biological processes, 78 cellular components, and 132 molecular functions of 15 core targets. KEGG enrichment analysis identified 168 signalling pathways. We ultimately identified 9 DEGs associated with AD through analysis of the AlzData data. Molecular docking results showed good affinity between the selected targets and ERD, with PTGS2, HSP90AA1, and BCL2. The interactions were confirmed by molecular dynamics simulations.
CONCLUSIONS: ERD exerts anti-AD effects through multiple targets, pathways, and levels, providing a theoretical foundation and valuable reference for the development of ERD as a natural anti-AD drug.
OBJECTIVE: We aimed to explore the potential targets and mechanisms of ERD in the treatment of AD through network pharmacology, molecular docking, and molecular dynamics simulations.
METHODS: ERD-related targets were predicted based on the CTD, SEA, PharmMapper, Swiss TargetPrediction, and ETCM databases, and AD-related targets were predicted through the TTD, OMIM, DrugBank, GeneCards, Disgenet, and PharmGKB databases. Protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomics analyses (KEGG) were used to analyse the potential targets and key pathways of the anti-AD effect of ERD. Subsequently, potential DEGs affected by AD were analysed using the AlzData database, and their relationships with ERD were evaluated through molecular docking and molecular dynamics simulations.
RESULTS: A total of 198 ERD-related targets, 3716 AD-related targets, and 122 intersecting targets were identified. GO annotation analysis revealed 1497 biological processes, 78 cellular components, and 132 molecular functions of 15 core targets. KEGG enrichment analysis identified 168 signalling pathways. We ultimately identified 9 DEGs associated with AD through analysis of the AlzData data. Molecular docking results showed good affinity between the selected targets and ERD, with PTGS2, HSP90AA1, and BCL2. The interactions were confirmed by molecular dynamics simulations.
CONCLUSIONS: ERD exerts anti-AD effects through multiple targets, pathways, and levels, providing a theoretical foundation and valuable reference for the development of ERD as a natural anti-AD drug.
摘要：
背景：目前，治疗阿尔茨海默病（AD）的药物开发仍然具有很大的挑战性。Eriodictyol（ERD）在治疗AD方面显示出巨大的潜力，但其分子机制尚不清楚。
目的：我们旨在通过网络药理学探索ERD治疗AD的潜在靶点和机制，分子对接,和分子动力学模拟。
方法：基于CTD预测ERD相关目标，SEA,PharmMapper,瑞士目标预测，和ETCM数据库，并通过TTD预测AD相关靶标，OMIM,DrugBank,GeneCards,Disgenet,和PharmGKB数据库。蛋白质-蛋白质相互作用，基因本体论（GO），和京都基因百科全书和基因组学分析（KEGG）用于分析ERD抗AD作用的潜在靶标和关键途径。随后，使用AlzData数据库分析了受AD影响的潜在DEG，并通过分子对接和分子动力学模拟评估了它们与ERD的关系。
结果：总共198个与ERD相关的目标，3716个AD相关目标，并确定了122个相交目标。GO注释分析揭示了1497个生物过程，78个细胞组件，和15个核心靶标的132个分子功能。KEGG富集分析确定了168个信号通路。通过分析AlzData数据，我们最终确定了9个与AD相关的DEGs。分子对接结果表明，所选靶标与ERD之间具有良好的亲和力，与PTGS2，HSP90AA1和BCL2。通过分子动力学模拟证实了相互作用。
结论：ERD通过多个靶点发挥抗AD作用，通路,和水平，为开发ERD作为天然抗AD药物提供理论基础和有价值的参考。
目的：我们旨在通过网络药理学探索ERD治疗AD的潜在靶点和机制，分子对接,和分子动力学模拟。
方法：基于CTD预测ERD相关目标，SEA,PharmMapper,瑞士目标预测，和ETCM数据库，并通过TTD预测AD相关靶标，OMIM,DrugBank,GeneCards,Disgenet,和PharmGKB数据库。蛋白质-蛋白质相互作用，基因本体论（GO），和京都基因百科全书和基因组学分析（KEGG）用于分析ERD抗AD作用的潜在靶标和关键途径。随后，使用AlzData数据库分析了受AD影响的潜在DEG，并通过分子对接和分子动力学模拟评估了它们与ERD的关系。
结果：总共198个与ERD相关的目标，3716个AD相关目标，并确定了122个相交目标。GO注释分析揭示了1497个生物过程，78个细胞组件，和15个核心靶标的132个分子功能。KEGG富集分析确定了168个信号通路。通过分析AlzData数据，我们最终确定了9个与AD相关的DEGs。分子对接结果表明，所选靶标与ERD之间具有良好的亲和力，与PTGS2，HSP90AA1和BCL2。通过分子动力学模拟证实了相互作用。
结论：ERD通过多个靶点发挥抗AD作用，通路,和水平，为开发ERD作为天然抗AD药物提供理论基础和有价值的参考。
