Predicting for anti-(mutant) SARS-CoV-2 and anti-inflammation compounds of Lianhua Qingwen Capsules in treating COVID-19

Abstract

Background: Lianhua Qingwen Capsules (LHQW) is a traditional Chinese medicine prescription commonly used to treat viral influenza in China. There has been sufficient evidence that LHQW could effectively treat COVID-19. Nevertheless, the potential anti-(mutant) SARS-CoV-2 and anti-inflammation compounds in LHQW are still vague.

Methods: The compounds of LHQW and targets were collected from TCMSP, TCMID, Shanghai Institute of Organic Chemistry of CAS database, and relevant literature. Autodock Vina was used to carry out molecular docking. The pkCSM platform to predict the relevant parameters of compound absorption in vivo. The protein-protein interaction (PPI) network was constructed by the STRING database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was carried out by Database for Annotation, Visualization, and Integrated Discovery (DAVID). The anti-(mutant) SARS-CoV-2 and anti-inflammation networks were constructed on the Cytoscape platform.

Results: 280 compounds, 16 targets related to SARS-CoV-2, and 54 targets related to cytokine storm were obtained by screening. The key pathways Toll-like receptor signaling, NOD-like receptor signal pathway, and Jak-STAT signaling pathway, and the core targets IL6 were obtained by PPI network and KEGG pathway enrichment analysis. The network analysis predicted and discussed the 16 main anti-SARS-CoV-2 active compounds and 12 main anti-inflammation active compounds. Ochnaflavone and Hypericin are potential anti-mutant virus compounds in LHQW.

Conclusions: In summary, this study explored the potential anti-(mutant) SARS-CoV-2 and anti-inflammation compounds of LHQW against COVID-19, which can provide new ideas and valuable references for discovering active compounds in the treatment of COVID-19.

Keywords: COVID-19; Lianhua Qingwen Capsules (LHQW); Molecular docking; Network pharmacology.

Fig. 1

The flowchart of this study for predicting potential active compounds in LHQW against COVID-19

Fig. 2

A The Compounds screening process, methods include Remove duplicates, Molecular docking screening, Lipinski's Rule of Five, and ADMET parameters. B Distribution of potentially active compounds, mainly Flavonoids, Terpenoids, and Alkaloids

Fig. 3

A The protein–protein interaction (PPI) network. In the network, the relationship between protein and protein is closely connected. Node Fill Color Mapping refers to the color of each target point in the PPI network from yellow to orange, representing the degree value of the target point from small to large. B KEGG enrichment analysis of cytokine targets. The bubble color is green to red, representing that the P value of the pathway is from large to small. Gene number refers to the number of all annotated genes contained in the pathway. The larger the bubble, the greater the number of all annotated genes in the pathway. Gene Ratio refers to the ratio of the number of genes located in the pathway among all annotated genes to the number of all annotated genes

Fig. 4

A Anti-(mutant) SARS-CoV-2 and B anti-inflammation network. The black connection indicated that there is a connection between nodes. Orange rectangle = active compounds of LHQW; blue diamond = targets related to SARS-CoV-2; red diamond = targets related to cytokine storm; green square = KEGG pathway

Fig. 5

The predicted binding mode of Ochnaflavone and Hypericin with five mutant S glycoproteins. The light magenta color represents the active compounds. Translucent green is the mutant S glycoproteins; Slate is the amino acid residues

Fig. 6

The underlying mechanism of LHQW in the treatment of COVID-19. The active compounds in LHQW can treat COVID-19 in two ways: anti-(mutant) SARS-CoV-2 and anti-inflammation