Bioinformatic modelling of the action of active components of Morus alba

Abstract

To explore the mechanism of mulberry leaf in the treatment of hyperuricemia based on network pharmacology, and to verify the experimental results by molecular docking technology. How: The common target genes of mulberry leaf active components and hyperuricemia were found using TCMSP, Uniprot and Draw Venn Diagram database, and the PPI network diagram of key target was drawn. GO and KEGG concentration were carried out using David database, and molecular docking was carried out by Cytoscape software. Predict the binding degree of major compounds to key targets. Result: In this study, a total of 29 active components and 657 potential targets in mulberry leaves were screened, 908 disease targets related to hyperuricemia were identified, and 78 common targets of mulberry leaves and hyperuricemia were identified, and several key targets GAPDH, AlB, JUN, TNF, PPARG and ESR1 were screened for molecular docking. The results of docking showed that JUN, TNF, PPARG and ESR1 had good binding activity with anisodamine, arachidonic acid, quercetin and sanocin. It is predicted that JUN, TNF, PPARG and ESR1 may be the key targets of mulberry leaf in the treatment of hyperuricemia. The results of KEGG pathway enrichment analysis showed that mulberry leaves may treat hyperuricemia through several signaling pathways, such as TNF signaling pathway, cancer signaling pathway, PI3K-Akt signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Conclusion: Multiple active components in mulberry leaves have a network mechanism of synergistic action on hyperuricemia through multi-component, multi-target and multi-pathway, possibly by acting on core target genes such as JUN, TNF, PPARG and ESR1. Then, it regulates the cancer signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, NF-κB signaling pathway and other pathways to play a therapeutic role in hyperuricemia.