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Gene & Protein in Disease In silico insights on fisetin’s antidepressant effects

MalaCard (accessible at: https://www.malacards.org/), cellular component (CC), and biological process (BP).
and the Human Phenotype Ontology (HPO) database For the KEGG cascade analysis, we focused on the 10 key
(accessible at: https://hpo.jax.org/). After the initial search, genes, applying a false discovery rate (FDR) and a P-value
we utilized the UniProt database (http://www.uniprot.org/ cutoff of <0.05. This FDR cutoff ensures that a maximum of
uniprot/) to verify and standardize the protein names, 5% of the results considered significant are false positives,
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ensuring they reflect the official nomenclature. In addition, enhancing the reliability of our analysis. The enrichment
we specified the source species for each identified protein, results for KEGG pathways were illustrated using dot plots,
providing clarity and context to our findings. which were generated through the ShinyGO 0.80 platform.
This comprehensive approach allowed us to effectively
2.4. Identification of common targets and disease- visualize and interpret the biological significance of the
target network construction identified pathways and gene functions.
To elucidate the interactions between MDD-associated 2.7. Molecular docking
targets and FT’s potential targets, we conducted an
intersection analysis. This was visualized using Venn We selected five top targets obtained from PPI to conduct
diagrams created with an online platform (accessible molecular docking. The 3D structures of these proteins
at http://bioinformatics.psb.ugent.be/webtools/Venn/). were retrieved from the Protein Data Bank (PDB;
Subsequently, we utilized the Panther Classification System accessible at: https://www.rcsb.org/) using their respective
to categorize the proteins linked to the anti-MDD effects “PDB” file suffixes. For FT, its 3D structure was sourced
of FT (accessible at http://www.pantherdb.org/). Moreover, from the PubChem database in the “SDF” format. To
we employed Cytoscape software (Ver. 3.9.0, accessible at visualize interactions between core targets and the FT, we
https://cytoscape.org/) for the development of a network employed CB-Dock2 (accessible at: https://cadd.labshare.
illustrating the relationship between diseases, ingredients, cn/cb-dock2/php/index.php/). This tool allowed us to
targets, and pathways. After constructing this network, we analyze the docking results, offering important insights
carried out a network topology analysis using the “Analyze into the interactions between the key targets and the active
Network” feature. This comprehensive approach allowed compound. Notably, CB-Dock2 surpasses other leading
for a detailed understanding of the connections between methods due to its precision in identifying binding sites
the various components involved. 25 and predicting binding poses, thanks to its advanced
knowledge-based docking engine. In our visualizations,
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2.5. PPI network the ligand and receptor were represented using the
The PPI network was constructed by submitting the “spacefill” and “cartoon” styles, respectively. 29
identified potential therapeutic targets of MDD to the 3. Results
STRING database (accessible at https://string-db.org), with
the species filter was set to “Homo sapiens.” All interactions 3.1. Pharmacology and toxicity of FT
within the resulting network were retained if they had The pharmacological and toxicological profile of FT
confidence scores of 0.4 or higher, indicating medium (PubChem ID: 5281614, Molecular ID: MOL013179)
to high confidence levels. Subsequently, this interaction offers important insights into its potential therapeutic
network was imported into Cytoscape (accessible at applications and safety considerations. FT (C H O ;
10
15
6
http://apps.cytoscape.org/apps/networkanalyzer, Version molecular weight, MW: 286.24 g/mol; Figure 1A),
3.9.0) for further visualization. The CytoNCA plug-in exhibits an oral bioavailability of 52.6%, indicating good
was employed to compute key network metrics, including absorption following oral administration. However, it
“Degree,” “Betweenness,” and “Closeness.” From these has a blood-brain barrier (BBB) permeability score of
analyses, the top ten targets were identified based on their −0.69, suggesting limited ability to cross the BBB. The
degree, marking them as the core targets of interest. DL score is reported at 0.24. The compound has a high
gastrointestinal absorption rating and a topological polar
2.6. GO and KEGG
surface area of 111.13 Å. Moreover, FT has 6 hydrogen
The enrichment analysis for GO and the KEGG pathways bond acceptors, 4 hydrogen bond donors, and 1 rotatable
was performed using the ShinyGO 0.80 platform bond, contributing to its overall molecular flexibility.
(accessible at: https://bioinformatics.sdstate.edu/go80/). Toxicological predictions indicate that FT is active in
26
A significance threshold of <0.05 was established to select hepatotoxicity, neurotoxicity, respiratory toxicity, and
the ten most significant GO terms for further analysis. immunotoxicity, while being inactive for nephrotoxicity,
The results were visually represented using bar plots, cardiotoxicity, carcinogenicity, mutagenicity, cytotoxicity,
highlighting the categories of molecular function (MF), and clinical toxicity. The predicted median lethal dose

Volume 4 Issue 1 (2025) 3 doi: 10.36922/gpd.4846