Proteome mining of Yersinia Enterocolitica for drug targets and computational inhibitor identification with ADMET, anti-inflammation potential and formulation characteristics.

Yersinia enterocolitica infection can manifest as self-limiting gastroenteritis and may lead to more severe conditions, such as mesenteric lymphadenitis, reactive arthritis, or rare systemic infections. Fluoroquinolones and third-generation cephalosporins are the most effective treatment options but tetracyclines and co-trimoxazole effectiveness may vary based on resistance patterns. To explore new therapeutic options in case of antibiotic resistance, we initially mined drug targets from the Yersinia enterocolitica proteome using a subtractive proteomics approach. Subsequently, we repurposed FDA approved & Traditional Chinese Medicinal (TCM) compounds against its cell wall synthesis mechanism by targeting DD-transpeptidase. DrugRep screening prioritized FDA-approved hits (Digitoxin, Irinotecan, Acetyldigitoxin; ≤ -9.4 kcal/mol) and TCM hits (Vaccarin, Narirutin, Hinokiflavone; ≤ -9.5 kcal/mol). Machine learning-based validation identified Hinokiflavone and Acetyldigitoxin as most potent binders. Molecular dynamics simulations (100 ns) revealed RMSD values < 1 nm for all complexes, indicating stable binding. ADMET profiling predicted all compounds as non-allergenic and TCM compounds having poor absorption. SBE-β-cyclodextrin coupling with FormulationAI showed improved compound solubility and oral bioavailability. InflamNat predicted strong anti-inflammatory potential for Hinokiflavone, highlighting its dual role in antibacterial and host-directed immunomodulatory activity. These computational insights mark an initial step in drug discovery, prompting comprehensive testing of prioritized compounds against Yersinia enterocolitica.