Background Metformin, which has been shown to be highly effective in treating type 2 diabetes (T2D), is also believed to be valuable for Alzheimer's disease (AD). Computer simulation techniques have emerged as an innovative approach to explore mechanisms. Objective To study the potential mechanism of metformin action in AD and T2D. Methods The chemical structure of metformin was obtained from PubChem. The targets of metformin were obtained from PubChem, Pharm Mapper, Batman, SwissTargetPrediction, DrugBank, and PubMed. The pathogenic genes of AD and T2D were retrieved from the GeneCards, OMIM, TTD, Drugbank, PharmGKB, and DisGeNET. The intersection of metformin with the targets of AD and T2D is represented by a Venn diagram. The protein-protein interaction (PPI) and core targets networks of intersected targets were constructed by Cytoscape 3.7.1. The enrichment information of GO and Kyoto Encyclopedia of Gene and Genomics (KEGG) pathways obtained by the Metascape was made into a bar chart and a bubble diagram. AutoDockTools, Pymol, and Chem3D were used for the molecular docking. Gromacs software was used to perform molecular dynamics (MD) simulation of the best binding target protein. Results A total of 115 key targets of metformin for AD and T2D were obtained. GO analysis showed that biological process mainly involved response to hormones and the regulation of ion transport. Cellular component was enriched in the cell body and axon. Molecular function mainly involved kinase binding and signal receptor regulator activity. The KEGG pathway was mainly enriched in pathways of cancer, neurodegeneration, and endocrine resistance. Core targets mainly included TP53, TNF, VEGFA, HIF1A, IL1B, IGF1, ESR1, SIRT1, CAT, and CXCL8. The molecular docking results showed best binding of metformin to CAT. MD simulation further indicated that the CAT-metformin complex could bind well and converge relatively stable at 30 ns. Conclusion Metformin exerts its effects on regulating oxidative stress, gluconeogenesis and inflammation, which may be the mechanism of action of metformin to improve the common pathological features of T2D and AD.

中文翻译：

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基于网络药理学、分子对接、分子动力学模拟探索二甲双胍治疗阿尔茨海默病和2型糖尿病的作用机制。

背景 二甲双胍已被证明对治疗 2 型糖尿病 (T2D) 非常有效，也被认为对治疗阿尔茨海默病 (AD) 也有价值。计算机模拟技术已成为探索机制的创新方法。目的探讨二甲双胍治疗AD和T2D的潜在作用机制。方法从PubChem获得二甲双胍的化学结构。二甲双胍的靶标来自 PubChem、Pharm Mapper、Batman、SwissTargetPrediction、DrugBank 和 PubMed。AD和T2D的致病基因从GeneCards、OMIM、TTD、Drugbank、PharmGKB和DisGeNET中检索。二甲双胍与 AD 和 T2D 目标的交集由维恩图表示。蛋白质-蛋白质相互作用（PPI）和交叉靶标的核心靶标网络由Cytoscape 3.7.1构建。将Metascape获得的GO和京都基因与基因组学百科全书（KEGG）通路的富集信息制成条形图和气泡图。AutoDockTools、Pymol 和 Chem3D 用于分子对接。使用Gromacs软件对最佳结合靶蛋白进行分子动力学（MD）模拟。结果共获得二甲双胍治疗AD和T2D的115个关键靶点。GO分析表明，生物过程主要涉及激素反应和离子转运的调节。细胞成分在细胞体和轴突中富集。分子功能主要涉及激酶结合和信号受体调节活性。KEGG通路主要富集于癌症、神经退行性疾病、内分泌抵抗通路。核心靶点主要包括TP53、TNF、VEGFA、HIF1A、IL1B、IGF1、ESR1、SIRT1、CAT和CXCL8。分子对接结果显示二甲双胍与CAT的结合最佳。MD模拟进一步表明CAT-二甲双胍复合物可以很好地结合并且在30 ns时相对稳定地收敛。结论 二甲双胍发挥调节氧化应激、糖异生和炎症的作用，这可能是二甲双胍改善T2D和AD共同病理特征的作用机制。