Study on Anti-Inflammatory Mechanism of Angelica pubescens Based on Network Pharmacology and Molecular Docking

Abstract

References and data show that AP has a certain effect on alleviating inflammation. Based on the methods of network pharmacology and molecular docking, this paper predicts the potential mechanism of anti-inflammatory effect of the effective components. Methods: Active components and target genes of AP were screened out by SymMap, an associated database of TCM syndromes. First, screen out the active components according to the setting conditions, and its molecular structure file was obtained from the PubChem database. The target genes of anti-inflammatory effect were obtained from GeneCards database with “anti-inflammation effect” as the keyword, and then the common gene targets between AP and anti-inflammatory effect were screened. The PPI network diagram was constructed with Cytoscape 3.80 software to screen the core genes. The GO function and KEGG pathway of the core genes were enriched and analyzed by David database; 3D view of proteins encoded by the core gene from the PDB database, conduct molecular docking between the active components and the core proteins in Auto Dock Vina software, and made a heat map with binding free energy. Results: The main anti-inflammatory components were O-Acetylcolumbianetin, isoindigo, Nodakenetin, Marmesin, Diphencyprone; The core targets are TNF, VEGFA, IL6, TP53, IL1B, ESR1, MMP9, PPARG, Jun, CASP3, PTGS2. AP participated in cytokine-mediated signaling pathway, response to drug, positive regulation of gene expression, and other processes by regulating the combination of extracellular space, cell surface with protein and enzyme, and then exert anti-inflammatory activity. The signal pathways mainly involved IL-17 signaling pathway, hepatitis B, TNF signaling pathway, inflammatory bowel disease, rheumatoid arthritis, etc.; Through molecular docking, it was found that the key targets were MMP9, TNF, PTGS2, ESR1, JUN, and PPARG, while the active components which ha,d a strong effect on these genes were O-Acetylcolumbianetin, isoindigo, Nodakenetin, Marmesin, Diphencyprone. Conclusion: This study used network pharmacology and molecular docking methods to predict the potential active components, target genes, and signal pathways of the anti-inflammatory effect of AP, so as to provide a theoretical reference for the follow-up experimental research and clinical treatment of AP.