Investigation of the Underlying Mechanism of Huangqi-Dangshen for Myasthenia Gravis Treatment via Molecular Docking and Network Pharmacology

Abstract

The herbal pairing of Huangqi and Dangshen (HD) is traditional Chinese herbal medicine and has been widely used in China, especially to treat myasthenia gravis (MG). However, the mechanism of HD on MG is unclear. Aim of the Study. This study aims to investigate HD's possible role in MG treatment. Materials and Methods. The TCMSP database was used to identify the active chemicals and their targets. The GeneCards, DisGeNET, and OMIM databases were used to search for MG-related targets. The STRING database was employed in order to identify the common PPI network targets. We next utilised Cytoscape 3.8.2 for target identification and the DAVID database for gene ontology (GO) function analysis as well as Encyclopaedia of Genomes (KEGG) pathway enrichment analysis on the selected targets. The AutoDock Vina software was used to test the affinity of essential components with the hub gene before concluding that the primary targets were corrected through molecular docking. Results. 41 active compounds were screened from HD, and the number of putative-identified target genes screened from HD was 112. There were 21 target genes that overlapped with the targets of MG, which were postulated to be potential treatment targets. Through further analysis, the results showed that the active compounds from HD (such as 7-methoxy-2-methylisoflavone, quercetin, luteolin, Kaempferol, and isorhamnetin) may achieve the purpose of treating MG by acting on some core targets and related pathways (such as EGFR, FOS, ESR2, MYC, ESR1, CASP3, and IL-6). Molecular docking findings demonstrated that these active molecules have a near-perfect ability to attach to the primary targets. Conclusion. Through network pharmacology, the findings in this study provide light on the coordinated action of several HD formula components, targets, and pathways. It provided a theoretical basis for further study of HD pharmacological action.

Figure 1

Flowchart of analysis performed in this research.

Figure 2

The interaction network diagram of TCM in treating diseases. (a) A Venn diagram of disease targets and drug targets; (b) a TCM component-target interaction network using myasthenia gravis and HQ-DS as the component and target, respectively.

Figure 3

Protein interaction network diagram. The top 30 targets of the five algorithms MNC, DEGREE, MCC, CLONESS, and EPC are shown in (a) all target protein interaction networks, (b) a Venn diagram, and (c) important target protein interaction network diagrams.

Figure 4

Subnetwork diagram. (a) Subnetwork diagram centered on EGFR, FOS, ESR2, and MYC. (b) Subnetwork diagram centered on ESR1, CASP3, and IL6.

Figure 5

GO-KEGG enrichment results (Top15). (a) BP analysis; (b) MF analysis; (c) CC analysis; (d) KEGG analysis.

Figure 6

Medicinal material-component-target-pathway diagram. (a) component-target-pathway diagram; (b) key target-component diagram (red is the component, blue is the target, and yellow is the pathway).

Figure 7

Molecular docking simulation diagram of target and compound. (a) quercetin-FOS; (b) luteolin-EGFR; (c) isorhamnetin-ESR2; (d) 7-methoxy-2-methylisoflavone-ESR2.