Current computer-aided drug design · 2024
Amalgamated Pharmacoinformatics Study to Investigate the Mechanism of Xiao Jianzhong Tang against Chronic Atrophic Gastritis.
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Lian Xu, Fan Kaidi, Qin Xuemei, Liu Yuetao
Аннотация
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.
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.
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.
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.