Exploration of Annona muricata (Annonaceae) in the treatment of hyperlipidemia through network pharmacology and molecular docking

Abstract

Soursop (Annona muricata L.) is one of the plants that have antihyperlipidemic effects, but its underlying mechanism of action remains unknown. Previous investigations used TCMSP, KNApSAcK, ETCM, SwissTargetPrediction, SuperPred, CTD, and TTD to identify potential targets of soursop as antihyperlipidemic. Therefore, this study aims to explore soursop active compounds and demonstrate their mechanisms against hyperlipidemia through network pharmacology and molecular docking. OB and drug-likeness properties of the compounds from A. muricata were screened based on Lipinski’s Ro5 (Lipinski’s Rule of Five) parameters. Subsequently, the network of the compound–target–disease–pathways was constructed using Cytoscape. The target PPI (protein-protein interaction) network was built using STRING and the core targets were analyzed using GO with KEGG. The main active compounds against the targets were confirmed by molecular docking analysis. Based on the results, 158 compounds were identified in A. muricata, and the human body was found to absorb 56. It was discovered that 20 compounds were associated with cholesterol disease. The highest degree of the disease pathway of target compounds disease was annomuricin, XDH, myocardial ischemia, and metabolic pathways, respectively. The PPI showed GAPDH (glyceraldehyde-3-phosphate dehydrogenase) protein also has the highest degree. BP, CC, MF, and KEEG enrichments that play important roles are the response to drugs, plasma membranes, protein binding, and metabolic pathways. The molecular docking experiment confirmed the correlation between ligands and receptors (quercetin-XDH, coclaurine-ADRB3, fisetin, and robinetin-XDH) with binding energies of –9.3; –8.9; and –8.8 kcal mol–1, respectively. The interactions between ligands and receptors are hydrogen, alkyl, Pi-alkyl, Pi-sigma, and van der Waals bonds. It was discovered that A. muricata provided therapeutic effects, involving multi-compounds, multi-targets, multi-diseases, and multi-pathways as well as deep insight into the pathogenesis of hyperlipidemia. This can be used to design new drugs and develop novel therapies to treat hyperlipidemia.