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).