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. 2020 Dec;53(12):e12949.
doi: 10.1111/cpr.12949. Epub 2020 Nov 3.

Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID-19

Qi-Dong Xia  1 , Yang Xun  1 , Jun-Lin Lu  1 , Yu-Chao Lu  1 , Yuan-Yuan Yang  1 , Peng Zhou  1 , Jia Hu  1 , Cong Li  1 , Shao-Gang Wang  1
Affiliations

Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID-19

Qi-Dong Xia et al. Cell Prolif. 2020 Dec.

Abstract

Objectives: Coronavirus disease 2019 (COVID-19) is rapidly spreading worldwide. Lianhua Qingwen capsule (LQC) has shown therapeutic effects in patients with COVID-19. This study is aimed to discover its molecular mechanism and provide potential drug targets.

Materials and methods: An LQC target and COVID-19-related gene set was established using the Traditional Chinese Medicine Systems Pharmacology database and seven disease-gene databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network were performed to discover the potential mechanism. Molecular docking was performed to visualize the patterns of interactions between the effective molecule and targeted protein.

Results: A gene set of 65 genes was generated. We then constructed a compound-target network that contained 234 nodes of active compounds and 916 edges of compound-target pairs. The GO and KEGG indicated that LQC can act by regulating immune response, apoptosis and virus infection. PPI network and subnetworks identified nine hub genes. The molecular docking was conducted on the most significant gene Akt1, which is involved in lung injury, lung fibrogenesis and virus infection. Six active compounds of LQC can enter the active pocket of Akt1, namely beta-carotene, kaempferol, luteolin, naringenin, quercetin and wogonin, thereby exerting potential therapeutic effects in COVID-19.

Conclusions: The network pharmacological strategy integrates molecular docking to unravel the molecular mechanism of LQC. Akt1 is a promising drug target to reduce tissue damage and help eliminate virus infection.

Keywords: Akt1; COVID-19; Chinese patent medicine; Lianhua Qingwen capsule; molecular docking; network pharmacology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Identification of the drug‐target interaction. A, Identification of the COVID‐19–related genes by taking a union of all the results from 7 database. B, Identification of the drug‐target disease‐related genes by taking an intersection of drug target genes and COVID‐19–related genes
Figure 2
Figure 2
Construction of the drug‐target pharmacology network and GO enrichment analysis. A, The drug‐targets interaction pharmacology network. Circles represent the small molecule active compounds in LQC. Each colour represents a traditional Chinese medicine ingredient. Hexagon represents the COVID‐19–related target genes, and edges represent the interaction between the small molecule compounds and the target genes. B, GO enrichment analysis of the target genes. Gene ratio refers to the ratio of enriched genes to all target genes, and counts refer to the number of the enriched genes
Figure 3
Figure 3
KEGG enrichment analysis and pathway map. A, KEGG enrichment analysis of the target enes. Gene ratio refers to the ratio of enriched genes to all target genes. Counts refer to the number of the enriched genes. B, Pathway map of apoptosis as the most significant enriched pathway
Figure 4
Figure 4
Protein‐Protein interaction (PPI) network. A, PPI network exported from STRING database. B, Annotations for the nodes and edges in the PPI network
Figure 5
Figure 5
Identification of key subnetwork using Cytoscape. A, PPI network and the first filtration by CytoNca, the yellow nodes were screened with each score higher than median. B, Subnetwork constructed by a second filtration via CytoNca. The yellow nodes were screened with a score higher than the median. C, Final key subnetwork screened after two filtrations using CytoNca. D, Key subnetwork of top 12 nodes analysed by CytoHubba
Figure 6
Figure 6
Screening of the key genes in the subnetwork and further molecular docking. A, Screening of the key genes by taking an intersection of the two key subnetworks. B, Molecular docking between the six small molecule ligands and protein 1UNP (encoded by AKT1), on the top shows the 3D structure of ligands and receptors, at the bottom shows the surface of the receptor and 3D structure of the ligands
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