Bioinformatics Study of Virulence Factors of Gastrointestinal Pathogens Clostridioides Difficile, Salmonella Enterica, and Enterococcus Faecalis Targeted by Apigenin
Apigenin is a naturally occurring flavonoid widely reported to possess antioxidant and antibacterial activities. Recent studies suggest that apigenin may act as an anti-virulence agent by targeting bacterial proteins involved in pathogenicity rather than directly killing bacterial cells. However, molecular interactions between apigenin and virulence-associated proteins of gastrointestinal pathogens have not been comprehensively explored using bioinformatics-based approaches. This study aimed to analyze and identify virulence-related proteins of Clostridioides difficile 630, Salmonella enterica CT18, and Enterococcus faecalis V583 that are potentially targeted by apigenin through a bioinformatics approach. Protein–compound interaction networks were constructed using STITCH v5.0, followed by functional classification and virulence prediction using VICMPred and VirulentPred. Immunogenic properties of the identified proteins were evaluated through B-cell epitope prediction and major histocompatibility complex class I and II binding analyses, while subcellular localization was predicted using PSORTb v3.0. The results demonstrated that apigenin interacted with ten proteins in each bacterial species, predominantly involving ATP-binding cassette transporters, membrane biosynthesis enzymes such as FabZ, catalase, and regulatory proteins associated with bacterial survival and adaptation. Epitope analysis revealed that all identified virulence-associated proteins contained high-scoring immunogenic regions, while subcellular localization analysis indicated that most proteins were localized in the cytoplasmic membrane, suggesting accessibility to small-molecule targeting. These findings suggest that apigenin has the potential to interact with key virulence-associated proteins of major gastrointestinal pathogens through molecular interaction–based mechanisms. Nevertheless, further experimental validation is required to confirm the biological relevance and mechanistic actions of apigenin.
Authors
- Rian Ka Praja (ORCID: https://orcid.org/0000-0003-2557-4206)
- Muhammad Alif
- Natalia Sri Martani
Institutions
- University of Palangka Raya (ID)
Publication Details
- Journal
- Optimum: Journal of Economics and Development (University of Ahmad Dahlan Yogyakarta)
- Published
- 2026-03-31
- DOI
- https://doi.org/10.12928/mf.v23i1.31811
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00