Potential candidates from a functional food Zanthoxyli Pericarpium (Sichuan pepper) for the management of hyperuricemia: high-through virtual screening, network pharmacology and dynamics simulations

Abstract

Introduction: Hyperuricemia (HUA) is a metabolic syndrome caused by purine metabolism disorders. Zanthoxyli Pericarpium (ZP) is a medicinal and food homologous plant, and its ripe peel is used to treat diseases and as a spice for cooking. Some studies have shown that ZP can inhibit the formation of xanthine oxidase and reduce the production of uric acid.

Methods: Through network pharmacology, ZP's potential targets and mechanisms for HUA treatment were identified. Databases like TCMSP, UniProt, and Swiss Target Prediction were utilized for ZP's active ingredients and targets. HUA-related targets were filtered using GeneCards, Drugbank, and Open Targets. Core targets for ZP's HUA treatment were mapped in a PPI network and analyzed with Cytoscape. GO and KEGG pathway enrichments were conducted on intersected targets via DAVID. Molecular docking and virtual screening were performed to find optimal binding pockets, and ADMET screening assessed compound safety. Molecular dynamics simulations confirmed compound stability in binding sites.

Results: We identified 81 ZP active ingredient targets, 140 HUA-related targets, and 6 drug targets, with xanthine dehydrogenase (XDH) as the top core target. Molecular docking revealed ZP's active ingredients had strong binding to XDH. Virtual screening via Protein plus identified 48 compounds near the optimal binding pocket, with 2'-methylacetophenone, ledol, beta-sitosterol, and ethyl geranate as the most promising. Molecular dynamics simulations confirmed binding stability, suggesting ZP's potential in HUA prevention and the need for further experimental validation.

Conclusion: Our study provides foundations for exploring the mechanism of the lowering of uric acid by ZP and developing new products of ZP. The role of ZP in the diet may provide a new dietary strategy for the prevention of HUA, and more experimental studies are needed to confirm our results in the future.

Keywords: Zanthoxylum bungeanum; complementary and alternative medicine; hyperuricemia; medicine and food homologous plant; molecular docking; molecular dynamics simulation.

Figure 1

Detailed flow chart based on network pharmacology. ADMET, absorption, distribution, metabolism, excretion and toxicity; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 2

Identification and enrichment analysis of candidate targets for Zanthoxyli Pericarpium treating hyperuricemia. (A) Venn diagram showing the intersection of components of Zanthoxyli Pericarpium in the treatment of hyperuricemia. TCMSP, Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; Swiss, the SwissTarget Prediction database. CTD, the CTD database; Open Targets, the Open Targets database; Gene Cards, the GeneCards database. (B) Venn diagram of drug intersection of targets for the treatment of hyperuricemia. Drug, the drug targets including XDH, SLC22A12, SLC22A11, SLC22A8, SLC22A6, PANX1, TAS2R16, ABCC1, ABCC2 and NR1I2. HUA, hyperuricemia. (C) Zanthoxyli Pericarpium-Disease-Drug PPI network. The nodes represent proteins, and the connecting lines between the nodes indicate interactions between two proteins, with different colors corresponding to different interaction types. Multiple connecting lines indicate multiple interactions between two proteins. (D) Protein-protein interaction network according to CytoNCA classification. Nodes represent proteins and node-to-node links represent associations. The color of the circular node depends on the degree of the node connected. The key genes with the highest values are marked by red nodes in the network. Higher level nodes are considered to be important hubs of the network. (E) KEGG enrichment analysis of the candidate targets of ZP treating hyperuricemia. The Y axis of the Sankey bubble map represents the pathway name, the X axis represents the gene ratio, the color of the points is sorted according to the P-value, the gene name is located on the left side of the pathway, the line represents the membership relationship, and the size of the points represents the number of genes. (F) The GO enrichment analysis of the candidate targets of ZP treating hyperuricemia. BP, biological process. CC, cellular component. MF, molecular function.

Figure 3

Histogram and data table of molecular docking results in the treatment of hyperuricemia with Zanthoxyli Pericarpium. The color of the histogram represents ZP compounds, and the data table shows the number of molecular docking score intervals.

Figure 4

Structural diagram of molecular docking of candidate compounds. (A) HJ010-XDH, (B) HJ028-XDH, (C) HJ059-XDH and (D) HJ069-XDH.

Figure 5

Diagram of the structure of a druggable pocket and DoGSiteScorer Score content. The score contents are sorted by the highest simple score and the yellow area in the box shows the structure of the druggable pocket 0, which has the highest simple score.

Figure 6

Structural analyses of the apo-, HJ010-, HJ028-, HJ059- and HJ069-bound XDH based on molecular dynamics simulation. The four inhibitors’ values of selected parameters are illustrated as follows, including protein (A) RMSD, (B) ligand RMSD, (C) Rg, (D) SASA, (E) RMSF, (F) change in RMSF, (G) the number of hydrogen bonds and (H) the number of pairs within 0.35 mm.

Figure 7

Molecular dynamics simulation of 200 ns trajectories for apo- and HJ010- XDH complex. (A, B) RMSD (C) Radius of gyration for apo- and HJ010-XDH complex. (D) Solvent accessible surface area. (E) RMSF diagrams obtained from molecular dynamics simulation of apo- and HJ010- XDH complex. (F, G) Hydrogen bonds between ligands and proteins during molecular dynamics simulations.

Figure 8

CESTA for interaction between 2’-methylacetophenone and XDH. (A) A representative graph of CETSA and (B) the density of the bands were measured and shown as a line chart. Values are expressed as mean ±SEM (n = 3). (C) 2’-methylacetophenone and XDH molecular docking of the most fit predictive pocket schematic, via CB-Dcok2.