Proteome mining of Yersinia Enterocolitica for drug targets and computational inhibitor identification with ADMET, anti-inflammation potential and formulation characteristics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".