对接(动物)
化学
自动停靠
AKT1型
山奈酚
柚皮素
药理学
计算生物学
医学
生物化学
类黄酮
蛋白激酶B
生物信息学
信号转导
生物
护理部
基因
抗氧化剂
作者
Lian Xu,Kaidi Fan,Xuemei Qin,Yuetao Liu
出处
期刊:Current Computer - Aided Drug Design
[Bentham Science Publishers]
日期:2023-07-21
卷期号:20 (5): 598-615
被引量:1
标识
DOI:10.2174/1573409919666230720141115
摘要
BACKGROUND: Traditional Chinese medicine (TCM) Xiao Jianzhong Tang (XJZ) has a favorable efficacy in the treatment of chronic atrophic gastritis (CAG). However, its pharmacological mechanism has not been fully explained. OBJECTIVE: The purpose of this study was to find the potential mechanism of XJZ in the treatment of CAG using pharmacocoinformatics approaches. METHODS: XGB was used to score the docking results, and Gromacs was used to perform molecular dynamics simulations (MD). RESULTS: Kaempferol, licochalcone A, and naringenin, were obtained as key compounds, while AKT1, MAPK1, MAPK14, RELA, STAT1, and STAT3 were acquired as key targets. Among docking results, 12 complexes scored greater than five. They were run for 50ns MD. The free binding energy of AKT1-licochalcone A and MAPK1-licochalcone A was less than -15 kcal/mol and AKT1-naringenin and STAT3-licochalcone A was less than -9 kcal/mol. These complexes were crucial in XJZ treating CAG. CONCLUSION: Our findings suggest that licochalcone A could act on AKT1, MAPK1, and STAT3, and naringenin could act on AKT1 to play the potential therapeutic effect on CAG. The work also provides a powerful approach to interpreting the complex mechanism of TCM through the amalgamation of network pharmacology, deep learning-based protein refinement, molecular docking, machine learning-based binding affinity estimation, MD simulations, and MM-PBSA-based estimation of binding free energy.
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