Lipopeptides from Bacillus Probiotics Can Target Transmembrane Receptors NOX4, EGFR, PDGFR, and OCTN2 Involved in Oxidative Stress and Oncogenesis
<i>Bacillus</i>-derived lipopeptides are known to possess diverse biological activities, including antimicrobial and anticancer properties, though the mechanisms of such effects at the molecular level remain incompletely understood. We investigated whether non-ribosomal peptide metabolites from <i>Bacillus</i> can directly interact with transmembrane receptors implicated in oxidative stress regulation and cancer progression (NOX4, EGFR, PDGFR, and OCTN2) using molecular docking and 200 ns molecular dynamics simulations of 11 lipopeptide metabolites. Molecular docking revealed several strong ligand-protein interactions, with plipastatin and fengycin emerging as lead compounds demonstrating the highest binding affinities to multiple receptors. For NOX4, iturin D showed the strongest docking score of -7.85 kcal/mol. Fengycin demonstrated a high docking score of -7.38 kcal/mol for PDGFR and -8.1 kcal/mol for EGFR. Plipastatin showed the strongest docking scores of -11.12 kcal/mol for EGFR and -8.7 kcal/mol for OCTN2. Molecular dynamics simulations confirmed complex stability for these lead compounds, with protein RMSD remaining stable at ~1.5 Å and ligand RMSD between 1.9 and 6 Å over 200 ns. Our findings suggest that plipastatin and fengycin may act as modulators of key receptors involved in oxidative stress and cancer-related signaling. However, those <i>in silico</i> predictions require experimental validation. This work provides the first computational evidence of potential lipopeptide-receptor interactions and establishes a foundation for future experimental investigation of probiotic-derived therapeutics.
Authors
- Evgeniya V. Prazdnova (ORCID: https://orcid.org/0000-0001-7823-9044)
- Radomir Viktorovich Skripnichenko (ORCID: https://orcid.org/0009-0000-8778-4554)
- Fadi F. Amirdzhanov (ORCID: https://orcid.org/0009-0008-1682-1344)
- Anuj Ranjan
Institutions
- Southern Federal University (RU)
Publication Details
- Journal
- BioTech
- Published
- 2026-01-06
- DOI
- https://doi.org/10.3390/biotech15010004
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and Higher Education of the Russian Federation