背景:中药小建中汤治疗慢性萎缩性胃炎疗效良好。然而,其药理机制尚未完全解释。
目的:本研究的目的是利用药物信息学方法寻找XJZ治疗CAG的潜在机制。
方法:网络药理学用于筛选关键化合物和关键靶标,MODELLER和GNNRefine用于修复和提炼蛋白质,Autodockvina被用来进行分子对接,使用ΔLin_F9XGB对对接结果进行评分,和Gromacs用于进行分子动力学模拟(MD)。
结果:山奈酚,licochalconeA,还有柚皮素,作为关键化合物获得,而AKT1,MAPK1,MAPK14,RELA,获得STAT1和STAT3作为关键靶标。在对接结果中,12个复合物得分大于5。它们运行50nsMD。AKT1-甘草查尔酮A和MAPK1-甘草查尔酮A的自由结合能小于-15kcal/mol,AKT1-柚皮素和STAT3-甘草查尔酮A小于-9kcal/mol。这些复合物在XJZ治疗CAG中是至关重要的。
结论:我们的研究结果表明,甘草查尔酮A可以作用于AKT1、MAPK1和STAT3,柚皮素可以作用于AKT1,对CAG发挥潜在的治疗作用。这项工作还提供了一种强大的方法,通过网络药理学的融合来解释中医的复杂机制,基于深度学习的蛋白质细化,分子对接,基于机器学习的绑定亲和力估计,MD模拟,和基于MM-PBSA的结合自由能估计。
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: Network pharmacology was used to screen out the key compounds and key targets, MODELLER and GNNRefine were used to repair and refine proteins, Autodock vina was employed to perform molecular docking, Δ Lin_F9XGB 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.
CONCLUSIONS: 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.