Screening of active components and their targets
92 active ingredients of Glycyrrhiza Uralensis Fisch, 18 of Amygdalus Communis Vas, and 21 of EphedraHerba were screened, including 98 active ingredients such as quercetin, luteolin, kaempferol, see details in Table 1. The active ingredients of Glycyrrhiza Uralensis Fisch had 1725 targets, Amygdalus Communis Vas 201 targets, and EphedraHerba 494 targets. After the merger, delete duplicate values and the targets that have not been experimentally verified. Finally, we obtained 237 targets.
Table 1 Information on active compounds
MOL_ID
|
MOL_Name
|
MOL_Mass
|
OB (%)
|
DL
|
source
|
degree
|
MOL000098
|
quercetin
|
302.25
|
46.43
|
0.28
|
multidrug
|
75
|
MOL000006
|
luteolin
|
286.25
|
36.16
|
0.25
|
Ephedra Herba
|
39
|
MOL000422
|
kaempferol
|
286.25
|
41.88
|
0.24
|
multidrug
|
29
|
MOL004328
|
naringenin
|
272.27
|
59.29
|
0.21
|
multidrug
|
20
|
MOL003896
|
7-Methoxy-2-methyl isoflavone
|
266.31
|
42.56
|
0.2
|
Glycyrrhiza Uralensis Fisch
|
17
|
MOL000497
|
licochalcone a
|
338.43
|
40.79
|
0.29
|
Glycyrrhiza Uralensis Fisch
|
17
|
MOL002565
|
Medicarpin
|
270.3
|
49.22
|
0.34
|
Glycyrrhiza Uralensis Fisch
|
15
|
MOL000392
|
formononetin
|
268.28
|
69.67
|
0.21
|
Glycyrrhiza Uralensis Fisch
|
15
|
MOL000358
|
beta-sitosterol
|
414.79
|
36.91
|
0.75
|
Ephedra Herba
|
15
|
MOL004891
|
shinpterocarpin
|
322.38
|
80.3
|
0.73
|
Glycyrrhiza Uralensis Fisch
|
14
|
MOL010921
|
estrone
|
270.4
|
53.56
|
0.32
|
Amygdalus Communis Vas
|
10
|
MOL012922
|
l-SPD
|
327.41
|
87.35
|
0.54
|
Amygdalus Communis Vas
|
10
|
Abbreviation: MOL: molecule, OB: oral bioavailability, DL: drug-likeness
Screening of potential targets
3734 targets of chronic cough were obtained from the GeneCards. Depending on experience, targets with a score greater than the median were set as potential targets of chronic cough. For example, the maximum score of targets obtained from the GeneCards database was 87.54, the minimum was 0.33, the median was 5.12, so the targets with score > 5.12 were the potential targets for chronic cough. Combined with the DRUGBANK database to supplement applicable targets, delete duplicate values after merging, and finally get 821 targets. Finally, obtain 113 targets of SAD to treat chronic cough through the intersection of the targets of the active components and the related target of chronic cough, and draw the Venny diagram (Fig. 2).
Construction of component-target and PPI network
Cytoscape3.7.2 was used to construct the component-target network of SAD (Fig. 3). There were 211 nodes (98 compounds and 113 targets) and 938 edges. After analysis, quercetin, luteolin, kaempferol, naringenin, 7-Methoxy-2-methyl isoflavone were highly connected and they had more targets than other components.
The 113 targets that were related to the treatment of chronic cough by SAD were uploaded to the STRING1.0 platform to obtain the PPI network by setting the combining score > 0.4. The PPI network (Fig. 4a) is an undirected graph about the interaction of proteins, in which the module was used to describe some areas with high protein density, which was considered to be a biologically meaningful collection that can represent protein complexes or proteins in the same pathway [24]. Therefore, it was necessary to further identify its internal module. The MCODE plugin in Cytoscape3.7.2 can be used to analyze the interaction using the complex molecular detection algorithm where a module with a score >3 was significant. Finally, 3 clusters were obtained. In Figure 4b, Module 1 contains 53 nodes and 1064 edges with a score of 40.923. In Figure 4c, Module 2 contains 16 nodes and 31 edges with a score of 4.133. In Figure 4d, Module 3 contains 4 nodes and 5 edges with a score of 3.500.
GO and KEGG enrichment analysis
The DAVID platform was utilized to perform GO and KEGG enrichment analysis. It indicated that many targets and pathways were closely related to chronic cough. GO analysis showed that 2281 GO terms were enriched in chronic cough: 2062 in biological processes, 77 in cellular components, and 142 in molecular functions. As shown in Fig. 5a, the main biological processes involved cellular response to oxidative stress, response to nutrient levels, response to molecule of bacterial origin, response to lipopolysaccharide. As shown in Fig. 5b, the functions of related targets to regulate chronic cough were mainly enriched in phosphatase binding, protein phosphatase binding, cytokine receptor binding and kinase regulator activity. According to KEGG analysis, 148 pathways were related to the treatment of chronic cough, including PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Fluid shear stress and atherosclerosis and Kaposi sarcoma-associated herpesvirus infection, as shown in Fig. 5d.
Construction of the target-pathway network
Cytoscape3.7.2 was used to construct the target-pathway network of SAD (Fig. 6) and topological analysis of 20 pathways and 75 related targets was performed. It suggested that AKT1, MAPK1, MAPK3 and RELA were highly connected, and may be the principal targets of SAD in the treatment of chronic cough.
Verification of Molecular Docking
The quercetin and luteolin are respectively docked with the corresponding targets, setting the binding energy < -5 kcal/mol as the screening criterion, which is believed that the molecules meeting this condition may be connected to the target [25]. Generally, the lower the binding energy, the more stable the conformation bound. It can be seen from Table 2 and Fig. 7 that quercetin and luteolin may bind to the targets to a certain extent.
Table 2 Information on molecular docking
NO.
|
Targets
|
PDB_ID
|
Compounds
|
Affinity(kcal/mol)
|
1
|
RELA
|
3GUT
|
Quercetin
Luteolin
|
-6.7
-7.3
|
2
|
AKT1
|
1UNP
|
Quercetin
Luteolin
|
-5.8
-6.1
|
3
|
MAPK1
|
4IZ5
|
Quercetin
Luteolin
|
-8.5
-8.3
|
4
|
EGFR
|
4LL0
|
Luteolin
|
-9.2
|
5
|
BCL2
|
5AGW
|
Quercetin
|
-7.8
|
6
|
EGF
|
1IVO
|
Quercetin
|
-8.3
|
7
|
SOD1
|
4XCR
|
Quercetin
|
-7.4
|
8
|
CAV1
|
Q03135(SWISS-MODEL)
|
Quercetin
|
-5.7
|