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<p><i>Yersinia enterocolitica</i> 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 <i>Yersinia enterocolitica</i> proteome using a subtractive proteomics approach. Subsequently, we repurposed FDA approved &amp; 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&#xa0;kcal/mol) and TCM hits (Vaccarin, Narirutin, Hinokiflavone; ≤ -9.5&#xa0;kcal/mol). Machine learning-based validation identified Hinokiflavone and Acetyldigitoxin as most potent binders. Molecular dynamics simulations (100 ns) revealed RMSD values &lt; 1&#xa0;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 <i>Yersinia enterocolitica</i>.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

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  • Zarrin Basharat,
  • Youssef Saeed Alghamdi,
  • Mutaib M. Mashraqi,
  • Hanan A. Ogaly,
  • Fatimah A.M. Al-Zahrani,
  • Calvin R. Wei,
  • Ibrar Ahmed,
  • Seil Kim

摘要

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.