Analysis of mechanisms of Shenhuang Granule in treating severe COVID-19 based on network pharmacology and molecular docking

Abstract

Objective: Severe cases of coronavirus disease 2019 (COVID-19) are expected to have a worse prognosis than mild cases. Shenhuang Granule (SHG) has been shown to be a safe and effective treatment for severe COVID-19 in a previous randomized clinical trial, but the active chemical constituents and underlying mechanisms of action remain unknown. The goal of this study is to explore the chemical basis and mechanisms of SHG in the treatment of severe COVID-19, using network pharmacology.

Methods: Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was employed to screen chemical constituents of SHG. Putative therapeutic targets were predicted by searching traditional Chinese medicine system pharmacology database and analysis platform, SwissTargetPrediction, and Gene Expression Omnibus (GEO) databases. The target protein-protein interaction network and enrichment analysis were performed to investigate the hub genes and presumptive mechanisms. Molecular docking and molecular dynamics simulations were used to verify the stability and interaction between the key chemical constituents of SHG and COVID-19 protein targets.

Results: Forty-five chemical constituents of SHG were identified along with 131 corresponding therapeutic targets, including hub genes such as HSP90AA1, MMP9, CXCL8, PTGS2, IFNG, DNMT1, TYMS, MDM2, HDAC3 and ABCB1. Functional enrichment analysis indicated that SHG mainly acted on the neuroactive ligand-receptor interaction, calcium signaling pathway and cAMP signaling pathway. Molecular docking showed that the key constituents had a good affinity with the severe acute respiratory syndrome coronavirus 2 protein targets. Molecular dynamics simulations indicated that ginsenoside Rg4 formed a stable protein-ligand complex with helicase.

Conclusion: Multiple components of SHG regulated multiple targets to inhibit virus invasion and cytokine storm through several signaling pathways; this provides a scientific basis for clinical applications and further experiments.

Keywords: COVID-19; Mechanism; Molecular docking; Molecular dynamics simulation; Network pharmacology; SARS-CoV-2; Shenhuang granule.

Fig. 1

The base peak chromatograms of Shenhuang Granule. A: positive-ion mode; B: negative-ion mode. The details of the constituents represented by the peak numbers (clustered peaks marked with black arrows) are shownin Table 1.

Fig. 2

Volcano plot of differentially expressed genes related to severe coronavirus disease 2019. The red dots indicate upregulated genes and the blue dots indicate downregulated genes. |log₂(fold change)| > 2, adjusted P < 0.05.

Fig. 3

Venn diagram of the 131 therapeutic targets. SHG: Shenhuang Granule; COVID-19: coronavirus disease 2019.

Fig. 4

Herb-constituent-target network. DH: Rhei Radix Et Rhizoma; PGY: Herba Taraxaci; DXT: Caulis Sargentodoxae; RS: Radix Ginseng; SZ: Hirudo; FZ: Radix Aconiti Lateralis Preparata.

Fig. 5

The protein–protein interaction (PPI) network and the hub genes. A: the PPI network; B: the relationship between hub genes, chemical constituents and herbs; C: the top ten hub genes.

Fig. 6

Target-organ location network of tissue-specific expression gene in BioGPS.

Fig. 7

The GO and KEGG enrichment analysis with the 131 targets. A: GO biological process (BP) analysis; B: GO cellular composition (CC) analysis; C: GO molecular function (MF) analysis; D: KEGG pathway analysis. KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology.

Fig. 8

Molecular models (3D and 2D) of the selected chemical constituents binding to the COVID-19 protein targets. GTA: 7-methyl-GpppA; NCB site: nucleic acid binding site; Nsp16: nonstructural protein16; PLpro: papain-like protease; RdRp with RNA: RNA-dependent RNA polymerase; SAM: S-Adenosylmethionine. Green dotted lines: hydrogen bonds; Red combs: hydrophobic contacts.

Fig. 9

Molecular dynamics simulation of helicase-ginsenoside Rg4. A: root-mean-square deviation (RMSD) plot; B: root-mean-square fluctuation (RMSF) plot; C: Gibbs free energy landscape. Rg: radius of gyration.