Network pharmacology-based analysis of Zukamu granules for the treatment of COVID-19

Abstract

Introduction: Zukamu granules may play a potential role in the fight against the Coronavirus, COVID-19. The purpose of this study was to explore the mechanisms of Zukamu granules using network pharmacology combined with molecular docking.

Methods: The Traditional Chinese Medicine systems pharmacology (TCMSP) database was used to filter the active compounds and the targets of each drug in the prescription. The Genecards and OMIM databases were used for identifying the targets related to COVID-19. The STRING database was used to analyze the intersection targets. Compound - target interaction and protein-protein interaction networks were constructed using Cytoscape to decipher the anti-COVID-19 mechanisms of action of the prescription. The Kyoto Encyclopedia of Genes and Genome (KEGG) pathway and Gene Ontology (GO) enrichment analysis was performed to investigate the molecular mechanisms of action. Finally, the interaction between the targets and the active compounds was verified by molecular docking technology.

Results: A total of 66 targets were identified. Further analysis identified 10 most important targets and 12 key compounds. Besides, 1340 biological processes, 43 cell compositions, and 87 molecular function items were obtained (P < 0.05). One hundred and thirty pathways were obtained (P < 0.05). The results of molecular docking showed that there was a stable binding between the active compounds and the targets.

Conclusion: Analysis of the constructed pharmacological network results allowed for the prediction and interpretation of the multi-constituent, multi-targeted, and multi-pathway mechanisms of Zukamu granules as a potential source for supportive treatment of COVID-19.

Keywords: ALB, Serum Albumin; BP, Biological Process; CASP3, Caspase-3; CC, Cell Composition; CCND1, Cyclin D1; COVID-19, Corona Virus Disease 2019; Covid-19; EGFR, Epidermal Growth Factor Receptor; FOS, C-FOS; GO, Gene Ontology; IL-6, Interleukin- 6; INS, Insulin; KEGG, Kyoto Encyclopedia of Genes and Genome; MAPK8, Mitogen Activated Protein Kinase 8; MF, Molecular Function; MYC, Muscarinic Acetylcholine Receptor; Molecular docking; Network pharmacology; PPI, Protein-Protein Interaction; Pulmonary fibrosis; TCMSP, Traditional Chinese Medicine systems pharmacology; VEGFA, Vascular Endothelial Growth Factor-A; Zukamu granule.

Fig. 1

The workflow.

Fig. 2

The compound - target interaction network. Note: All the regular hexagons in the network represented compounds, circles represented drugs, and diamonds represented targets. All the edges represented the interaction between drugs and compounds or compounds and targets.

Fig. 3

Venn diagram of the intersection targets. Note: The intersection part represented the common targets.

Fig. 4

PPI network of the 65 intersection targets. Note: The larger the degree value of the node was, the larger the node size was, and the brighter the node color was. The larger the combined score was, the larger the edge size was, and the darker the color was.

Fig. 5

Core targets. Note: A total of 30 core targets were identified. The horizontal axis represented the number of connected nodes.

Fig. 6

The results of GO-BP enrichment analysis (showing the top 20). Note: The color of terms turned from blue to red. The smaller the adjusted P value was, the redder the bubble was.

Fig. 7

The results of GO—CC enrichment analysis (showing the top 20). Note: The color of terms turned from blue to red. The smaller the adjusted P value was, the redder the bubble.

Fig. 8

The results of GO-MF enrichment analysis (showing the top 20). Note: The color of terms turned from blue to red. The smaller the adjusted P value was, the redder the bubble.

Fig. 9

The results of KEGG pathway enrichment analysis (showing the top 20). Note: The color of terms turned from blue to red. The smaller the adjusted P value was, the redder the bubble.

Fig. 10

3D map of molecular docking. Note: 1. Luteolin-CASP3; 2. Luteolin-EGFR; 3. Luteolin-VEGFA; 4. Luteolin-IL6; 5. Quercetin-CASP3; 6. Quercetin-EGFR; 7. Quercetin-VEGFA; 8. Quercetin-IL6.

Fig. 11

Intermolecular hydrogen bonds between the active compounds and the targets. Note: 1. Luteolin-CASP3; 2. Luteolin-EGFR; 3. Luteolin-VEGFA; 4. Luteolin-IL6; 5. Quercetin-CASP3; 6. Quercetin-EGFR; 7. Quercetin-VEGFA; 8. Quercetin-IL6.