Active ingredients screening
Total of 51 effective ingredients of TwHF that satisfied DL ≥ 0.18 and OB ≥ 30% were screened from TCMSP. Among them, only 41 candidate compounds have the 2D structure, SMILES and PubChem ID (Table 1).
Table 1
A list of the final selected compounds from TwHF for network analysis.
Molecule ID
|
Molecule Name
|
Structure
|
OB(%)
|
DL
|
MOL003233
|
Triptofordin B2
|
|
107.71
|
0.76
|
MOL003209
|
Celallocinnine
|
|
83.47
|
0.59
|
MOL003188
|
Tripchlorolide
|
|
78.72
|
0.72
|
MOL003206
|
Canin
|
|
77.41
|
0.33
|
MOL003225
|
Hypodiolide A
|
|
76.13
|
0.49
|
MOL003279
|
99694-86-7
|
|
75.23
|
0.66
|
MOL003208
|
Celafurine
|
|
72.94
|
0.44
|
MOL003244
|
Triptonide
|
|
68.45
|
0.68
|
MOL005828
|
nobiletin
|
|
61.67
|
0.52
|
MOL002058
|
40957-99-1
|
|
57.2
|
0.62
|
MOL003217
|
Isoxanthohumol
|
|
56.81
|
0.39
|
MOL003224
|
Tripdiotolnide
|
|
56.4
|
0.67
|
MOL000211
|
Mairin
|
|
55.38
|
0.78
|
MOL003187
|
triptolide
|
|
51.29
|
0.68
|
MOL003280
|
TRIPTONOLIDE
|
|
49.51
|
0.49
|
MOL003185
|
(1R,4aR,10aS)-5-hydroxy-1-(hydroxymethyl)-7-isopropyl-8-methoxy-1,4a-dimethyl-4,9,10,10a-tetrahydro-3H-phenanthren-2-one
|
|
48.84
|
0.38
|
MOL003248
|
Triptonoterpene
|
|
48.57
|
0.28
|
MOL003196
|
Tryptophenolide
|
|
48.5
|
0.44
|
MOL003211
|
Celaxanthin
|
|
47.37
|
0.58
|
MOL003267
|
Wilformine
|
|
46.32
|
0.2
|
MOL003184
|
81827-74-9
|
|
45.42
|
0.53
|
MOL011169
|
Peroxyergosterol
|
|
44.39
|
0.82
|
MOL000449
|
Stigmasterol
|
|
43.83
|
0.76
|
MOL003245
|
Triptonoditerpenic acid
|
|
42.56
|
0.39
|
MOL000422
|
kaempferol
|
|
41.88
|
0.24
|
MOL003231
|
Triptoditerpenic acid B
|
|
40.02
|
0.36
|
MOL003232
|
Triptofordin B1
|
|
39.55
|
0.84
|
MOL000296
|
hederagenin
|
|
36.91
|
0.75
|
MOL000358
|
beta-sitosterol
|
|
36.91
|
0.75
|
MOL003222
|
Salazinic acid
|
|
36.34
|
0.76
|
MOL003189
|
WILFORLIDE A
|
|
35.66
|
0.72
|
MOL003229
|
Triptinin B
|
|
34.73
|
0.32
|
MOL003266
|
21-Hydroxy-30-norhopan-22-one
|
|
34.11
|
0.77
|
MOL003238
|
Triptofordin F1
|
|
33.91
|
0.6
|
MOL003239
|
Triptofordin F2
|
|
33.62
|
0.67
|
MOL003278
|
salaspermic acid
|
|
32.19
|
0.63
|
MOL003235
|
Triptofordin D1
|
|
32
|
0.75
|
MOL003241
|
Triptofordin F4
|
|
31.37
|
0.67
|
MOL003242
|
Triptofordinine A2
|
|
30.78
|
0.47
|
MOL003236
|
Triptofordin D2
|
|
30.38
|
0.69
|
MOL003210
|
Celapanine
|
|
30.18
|
0.82
|
Targets Identification of TwHF and CVD
In total, 827 candidate targets for TwHF were identified using SwissTargetPrediction (Supplementary Table). 76 known CVD targets were found from the DrugBank database, 358 known CVD targets were collected from the GeneCards database, and 474 known CVD-related targets were obtained from the OMIM database (Supplementary Table). Finally, 802 CVD-related targets were identified by removing the repeated targets. The 178 potential targets were collected for subsequent analysis after comparing the targets of CVD and TwHF (Supplementary Table).
PPI Network Construction and Analysis
The PPI network was generated by uploading these 178 identified targets to the STITCH database (Figure 2). AKT1, amyloid precursor protein (APP), Mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA) and cellular tumor antigen p53 (TP53) was identified based on the highest interactive scores and the most interaction. These five genes were considered to be the key putative targets involved in the effects of TwHF on CVD.
GO and KEGG Pathway Enrichment Analyses
The 178 candidate targets were selected for GO and KEGG pathway enrichment analyses. The top ten GO analyses of biological process (BP), cellular component (CC), and molecular function categories (MF) were screened(Figure 3). As the results of GO enrichment, the enriched biological process categories were dominated by ERBB signaling pathway, regulation of generation of precursor metabolites and energy, peptidyl-serine phosphorylation, aging, peptidyl-serine modification, regulation of developmental growth, neuron death, regulation of DNA metabolic process, cellular response to peptide, and response to oxidative stress. Cell component analysis showed that spindle mainly accounted for the largest proportion. The enriched molecular function categories were dominated by phosphatase binding and protein serine/threonine kinase activity.
The KEGG pathway analysis showed that these targets were mainly associated with cancer, melanoma, platinum drug resistance, glioma, chronic myeloid leukemia, endocrine resistance, sphingolipid signaling pathway, neurotrophin signaling pathway, thyroid hormone signaling pathway, apoptosis, cellular senescence, hepatitis C, and hepatitis B (Figure 4).
Construction of network
The network visualization of TwHF-targets-CVD-GO-KEGG were generated by using Cytoscape software (Figure 5).
Molecular Docking
The crystal structures of potential targets, including AKT1 (PDB: 6CCY), APP (PDB:5BUO), MAPK1 (PDB:6SIG), PIK3CA (PDB:4TTU) and TP53 (PDB: 6RZ3) were collected from the RCSB Protein Data Bank (Figure 6). Figure 6 showed celaxanthin binds to AKT1 with a binding pocket consisting of SER-240 (2.9 Å); hypodiolide A fails to bind to APP without a binding pocket; triptofordin B2 binds to MAPK1 with a binding pocket consisting of SER-153 (3.3 Å) and ARG-155 (3.3 Å); triptofordin B2 binds to PIK3CA with a binding pocket consisting of GLN-582 (3.1 Å); Celallocinnine binds to TP53 with a binding pocket consisting of LEU-111 (3.2 and 3.0 Å), ASN-131 (3.1 Å) and TYR-126 (2.9 Å).