基于网络药理学、分子对接和分子动力学模拟,探索二甲双胍在阿尔茨海默病和2型糖尿病中的作用机制。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2023-09-27 eCollection Date: 2023-01-01 DOI:10.1177/20420188231187493
Xin Shi, Lingling Li, Zhiyao Liu, Fangqi Wang, Hailiang Huang
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引用次数: 0

摘要

背景:二甲双胍已被证明对治疗2型糖尿病(T2D)非常有效,也被认为对阿尔茨海默病(AD)有价值。计算机模拟技术已经成为探索机制的一种创新方法。目的:探讨二甲双胍治疗AD和T2D的潜在机制。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。从GeneCards、OMIM、TTD、Drugbank、PharmGKB和DisGeNET中检索到AD和T2D的致病基因。二甲双胍与AD和T2D靶点的交叉点用维恩图表示。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百科全书(KEGG)途径的富集信息制成条形图和气泡图。AutoDockTools、Pymol和Chem3D用于分子对接。Gromacs软件用于对最佳结合靶蛋白进行分子动力学(MD)模拟。结果:共获得115个二甲双胍治疗AD和T2D的关键靶点。GO分析表明,该生物过程主要涉及对激素的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、神经退行性变和内分泌抵抗通路。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的结合最好。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。结论:二甲双胍具有调节氧化应激、糖异生和炎症的作用,这可能是二甲双胍改善T2D和AD常见病理特征的作用机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Exploring the mechanism of metformin action in Alzheimer's disease and type 2 diabetes based on network pharmacology, molecular docking, and molecular dynamic simulation.

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.

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