ABSTRACT
This paper aimed to investigate the anti-influenza virus activity of the genus Paeonia, screen potential anti-influenza virus compounds and predict targets of anti-influenza virus to explore the mechanism of anti-influenza virus activity. First of all, a total of 301 compounds of the genus Paeonia were summarized from the literatures in recent ten years. The candidate active ingredients from the genus Paeonia were identified by database such as PubChem and Chemical Book. The ligands were constructed by ChemDraw, Avogadro and Discovery Studio Visualizer. Secondly, 23 potential anti-influenza virus targets were developed by combining the target database and the literatures. Uniprot database was used to find the anti-influenza virus targets, and RCSB was used to identify targets associated with anti-influenza virus activity as docked receptor proteins. QuickVina 2.0 software was used for molecular docking. Finally, the Cytoscape 3.5.1 software was used to map the potential activity compounds of the genus Paeonia against influenza virus and the anti-influenza virus target network. Uniprot online database was used to analyze the target GO enrichment and KEGG metabolic pathways. The results showed that 74 compounds of the genus Paeonia had anti-influenza virus effect and 18 potential anti-influenza virus targets were screened. GO analysis concluded that the mechanism of the genus Paeonia anti-influenza virus is consistent with the mechanism of NA anti-influenza virus in order to stop the sprouting, dispersion and diffusion of virus and reduce the ability of virus to infect, so that the infection can be restricted so as to achieve the anti-influenza virus effect.
ABSTRACT
This paper aimed to investigate the antibacterial activity of flowers and leaves from Paeonia rockii, screen antibacterial compounds and predict targets of antibacterial to explore its multi-component, multi-target antibacterial mechanism. In this study, minimal inhibitory concentration(MIC) of seven strains of Staphylococcus aureus, S. epidermidis， Bacillus subtilis, B. cereus, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa were determined by microdilution method. Uniprot databases was used to find the antibacterial targets, and RCSB was used to identify targets associated with antimicrobial activity as docked receptor proteins. The candidate active ingredients from flowers and leaves of P. rockii were identified by database such as PubChem. The ligands were constructed by ChemDraw, Avogadro and Discovery Studio Visualizer. QuickVina 2.0 software was used to molecular docking. Besides, the Cytoscape 3.5.1 software was used to construct activity compounds of flowers and leaves from P. rockii ingredients-targets network, and Uniprot software was used to analyze gene ontology and KEGG pathway. antibacterial experiments found antibacterial effect of the flowers and leaves from P. rockii, especially methanol extraction of flowers has the strongest antibacterial effect. The network pharmacology indicated that total 29 activity ingredients and their 18 targets were screened in flowers and leaves from P. rockii. Comparison of the active ingredients and the number of antimicrobial target networks, it is predicted that the antibacterial components are mainly flavonoids and phenolic acids and main mechanism of antibacterial is to inhibit the synthesis of bacterial proteins. In this study, potential antibacterial activity of flowers and leaves from P. rockii has be found by antibacterial experiments and network pharmacology screening. And this study provides new clues for further basic study on the antibacterial agents of flowers and leaves from P. rockii.