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

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.


| INTRODUC TI ON
Coronavirus disease 2019 (COVID- 19) is an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). 1 It can lead to fever, fatigue, dry cough, multiple organ dysfunction and death. 2 A total of 216 countries or regions have reported confirmed cases. By 4 September 2020, the number of patients has reached 26 171 112, including at least 1 million deaths. 3 As a public health emergency with international concern, COVID-19 has brought a disastrous impact on the global health system and economic system. 4 However, the drug that can cure SARS-CoV-2 infection is still elusive.
China has successfully controlled the domestic epidemic in a short period due to the strict epidemic policy. In this process, traditional Chinese medicine (TCM) has also made a great contribution. 5 A meta-analysis that incorporated 11 studies compared TCM plus western medicine with western medicine alone. 6 The pooled results showed that integrated TCM and western medicine generated a higher overall response rate, higher cure rate, lower severity illness rate and shorter hospital stay. 6 in clinical observation and randomized controlled trials (RCT).. [9][10][11] LC is a Chinese patent medicine composed of 13 ingredients. 12 LQC is widely used in preventing and treating viral influenza (eg, H1N1) in China. 12 In the present SARS-CoV-2 pandemic, an RCT with 259 participants found that LQC plus abidor was associated with a higher overall response rate and comparable adverse events than abidor alone in mild cases. 11 Another study developed a quadruple combination therapy including LQC and evaluated its efficacy. 10 After treatment, coagulation disorder in severe COVID-19 infection cases was significantly improved and patients in the combined therapy group had a better prognosis. 10 The cumulative evidence proved the capability of LQC to control SARS-CoV-2 infection. Therefore, the study aims to identify the active components of LQC related to SARS-CoV-2 defence and investigate the key targets of eliminating the infection.
Akt is a serine/threonine protein kinase that includes Akt1, Akt2 and Akt3. Recent studies showed that during SARS-CoV-2 infection, Akt is activated in a dose-dependent manner. 13 The PI3K/Akt/ mTOR pathway is also involved in lung injury, 14 lung fibrogenesis 15 and immune cell development. 16 The results indicate that Akt may be a therapeutic target for COVID-19. Network pharmacology is a novel method that integrates computer science and medicine, constructing and visualizing 'multi-gene, multi-target, multi-pathway' interaction network to evaluate the molecular mechanism of drugs. 17 This approach is perfectly suitable for the research of multi-component drug such as TCM due to their complex matrices nature. 18 Molecular docking refers to the process that a small molecular is spatially docked into a macromolecular and can score the complementary value at the binding sites, which is used for structure-based drug design. 19 In this study, we explored the molecular mechanism of the action of LQC in COVID-19 using network pharmacology and molecular docking. We found that Akt1 was a hub gene that LQC primarily regulated, suggesting a novel target for COVID-19 treatment.

| Obtaining the LQC target and COVID-19related gene set
First, we searched the main ingredients of Lianhua Qingwen capsule in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (https://tcmspw.com/) to obtain the active compounds and their target genes. 20 Specifically, we selected the "Herb name" by each ingredient of LQC, respectively. The search results showed a series of compounds in traditional Chinese medicine and their corresponding pharmacokinetic indicators. We filtered active compounds by setting the pharmacokinetic index that the oral bioavailability (OB) was greater than 30% and the drug-like (DL) index was > 0.18.
An LQC target and COVID-19-related gene set was obtained by intersecting the LQC target gene set and the COVID-19-related gene set.

| Compound-target pharmacology network and enrichment analysis
Based on the LQC target gene set and the COVID-19-related gene set, a compound-target network is constructed by means of Cytoscape version 3.8.0. 29 Enrichment analysis, including gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed to reveal the underlying mechanism through biological processes, cellular components, molecular function and key signalling pathways. The "clusterprofile" package in R software version 3.4.0 was used to performed enrichment analysis.

| Protein-protein interaction (PPI) network and critical subnetwork
The LQC target and COVID-19-related gene set was used to construct PPI network by using STRING database. 30 We set the parameter as moderate confidence (0.400). The PPI network from STRING was then imported into Cytoscape to investigate the critical subnetwork. We applied two methods to screen the core subnetwork.
Firstly, we used CytoNca plugin in Cytoscape to analyse the PPI network. 31 In detail, we filtered genes according to the primary score file calculated by CytoNca that each score of Betweenness, Closeness, Degree, Eigenvector, LAC, network scores was higher than the median value. We constructed a primary subnetwork using the filtered genes. The filter process was conducted again to acquire the final critical subnetwork. Another method we used to screen critical subnetwork was CytoHubba plugin in Cytoscape. This approach was to analyse the top 12 genes in the PPI network and to construct the critical subnetwork without checking the first-stage nodes.

| Molecular docking technology
The most significant gene from two critical subnetworks was se-

| Compound-target network
After discovering compound-target disease-related genes, we visualized the compound-target interaction network with 234 nodes and 916 edges by using Cytoscape 3.8.0 ( Figure 2A). Generally, one gene is targeted by multiple active compounds while one compound can target more than one gene. Among 65 genes, PTGS 2 is the most targeted gene by LQC ingredients.

| GO enrichment analysis
GO enrichment analysis was used to discover the underlying biological processes (BP), cellular components (CC) and molecular functions (MF) of the 65 target genes. By setting the filter as adjusted P-value <0.05 and q-value < 0.05, we obtained 1711 significant enriched GO terms. The top 10 terms were illustrated in Figure 2B. The GO terms suggested that these target genes played an essential role in host defence and response to stress. Additionally, we exhibited 7 GO terms related to virus invasion from the enrichment analysis results as Table 1, which suggested that these target genes play a significant role in virus infection.

| KEGG enrichment analysis
KEGG enrichment analysis was performed to discover those pathways enriched by the 65 target genes. The filter was also set as an adjusted P-value <0.05 and q-value < 0.05. A total of 151 KEGG pathways were significantly enriched, which showed that these target genes affected the pathways of bacterial and viral infection, the differentiation of immune cells and signal transduction pathways, as well as a series of important pathological processes such as apoptosis. The bubble plot of the most significant 30 KEGG pathways was shown in Figure 3A and the pathway map of the apoptosis was illustrated in Figure 3B. In addition, we extracted and exported the virus-related pathways as Table 2, and the pathway map can be acquired in the supplementary file.

| PPI network and core subnetwork
Protein-protein interaction network derived from STRING database showed that the proteins encoded by these target genes had complex interactions ( Figure 4A, B). We imported PPI network into Cytoscape for further analysis. Finally, two key subnetworks com-

| Molecular docking of active compounds and Akt1 encoding protein
We took an intersection of the two key subnetworks ( Figure 6A) and nine genes with their rank of significance. The most significant gene, Akt1, was selected to conduct molecular docking. We then obtained six active compounds targeting Akt1 protein from the compound-target interaction network. The compounds were betacarotene, kaempferol, luteolin, naringenin, quercetin and wogonin.
Subsequently, molecular docking indicated that all these six active compounds could easily enter and bind the active pocket of the Akt1 protein as shown in Figure 6B. The docking scores were recorded in Table 3.

| D ISCUSS I ON
Over the past ten months, COVID-19 has rapidly spread around the world. SARS-CoV-2 pandemic is still raging in most countries due to the lack of target drugs. Notably, China, as a country with a population of more than 1.3 billion, has successfully con- We mainly focus on the Akt1 gene as one of the critical nodes in the subnetworks and performed molecular docking between micromolecules and the coded protein. Akt1 is one of the serine/ threonine protein kinases call Akt kinase (Akt1, Akt2 and Akt3). 46 A previous study has shown that overexpressed constitutively active Akt1 can promote viral protein synthesis. 47 Also, activation of the PI3K/Akt pathway is indispensable for coxsackievirus B3 infection. 48 Dominant negative mutant of Akt1 can significantly dampen viral RNA expression and further reduce viral capsid protein expression and viral release. 48 The replication of another coronavirus, Middle East respiratory syndrome coronavirus, can be remarkably inhibited by administrating kinase inhibitors targeting the PI3K/Akt. 49 Collectively, Akt1 could be an ideal target with a broad-spectrum antiviral effect. After molecular docking, six molecules were found to directly interact with Akt1: beta-carotene, kaempferol, luteolin, naringenin, quercetin and wogonin. Among them, kaempferol has proven its protective effect against H9N2 swine influenza virus infection. 50 Quercetin is also a potent antiviral agent   F I G U R E 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

| CON CLUS ION
We uncovered the potential mechanisms of LQC by employing pharmacology network and molecular docking computational analyses. We believe these findings may aid the global fight against the COVID-19 pandemic.

ACK N OWLED G EM ENTS
We thank the authors for the development of the drug database and software.

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
Source data of this study is derived from the public repositories, as indicated in the section of 'Materials and Methods' of the manuscript. All data that support the findings of this study is available from the corresponding author upon reasonable request.