3. Results
3.1. Active compounds screening
A total of 51 effective
ingredients of TwHF that satisfied
OB \(\geq\) 30% and DL \(\geq\) 0.18 were selected from TCMSP. Among
them, only 41 candidate compounds have the two dimensional (2D)
structure, canonical SMILES, and PubChem ID (Table 1).
3.2. Targets Identification of
TwHF and CVD.
In total, 827 candidate targets for TwHF were identified using
SwissTargetPrediction
(Supplementary
Table). 76 known CVD targets were obtained from the DrugBank database,
358 known CVD targets were collected from the GeneCards database, and
474 known CVD-related targets were found from the OMIM database
(Supplementary Table). Then, 802 known CVD targets were identified by
eliminating the repeated CVD-related targets. Finally, we compared the
targets of CVD and TwHF, and the
178 same targets were collected for subsequent analysis
(Supplementary
Table).
3.3. PPI Network Construction and Analysis
A total of 178 identified targets
were uploaded to the STRING database to identify the functional
partnerships and interactions between them (Figure 2). A total of 178
gene entries with prioritized interactive scores and were screened,
which served as the key putative targets involved in the effects of TwHF
on CVD. 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 according
to the highest interactive scores and the most interaction.
3.4. GO and KEGG Pathway Enrichment Analyses
The major targets could be categorized into various functional modules
by Gene Ontology enrichment analysis. An introduction of the GO analysis
was discovered with the enriched conditions in the biological process
(BP), cellular component (CC), and molecular function categories (MF)
(Figure 3). Depending on the outcomes 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 178 targets were 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).
3.5 Construction of network
The network visualization of TwHF-targets-CVD-GO-KEGG were generated by
using Cytoscape software (Figure 5).
3.6. 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 5 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).