Design, Synthesis, and Antibacterial Evaluation of Novel Coumarin Derivatives Targeting DNA Gyrase in Proteus mirabilis
Bibliographic record
Abstract
Antibiotic resistance represents a serious global health threat, with Proteus mirabilis identified as a causative agent of multidrug resistance, particularly in catheter-associated urinary tract infections.DNA gyrase enzymes are vital targets in bacteria, making them a starting point for the development of new drugs.This study aimed to develop novel coumarin-derived compounds targeting DNA gyrase using a combined approach of in silico computational analysis and in vitro experiments.The amino acid sequence of the DNA gyrase subunit B of P. mirabilis was obtained from the UniProt database, and homology modeling was performed using the SWISS-MODEL tool.The threedimensional model demonstrated high quality (GMQE = 0.85, QMEAN Z-score = -1.05),and conserved catalytic sites (Tyr122, Ser87) were identified and confirmed using PyMOL.Coumarin derivatives were designed and optimized based on Lipinski's Rule of Five and pharmacokinetic criteria.Molecular docking was performed using Swiss Dock.The coumarin compounds showed promising results, with Coumarin-4 achieving the highest binding to the enzyme (G = -8.7 kcal/mol), superior to ciprofloxacin (G = -7.5 kcal/mol).In laboratory tests (minimum inhibitory concentration test) using Proteus mirabilis under standard conditions (Mueller-Hinton broth, 37, 24 hours), compounds Q3, Q4, and Q6 demonstrated antibacterial activity (MIC = 256 g/ml), approximately 128-fold less potent than ciprofloxacin (MIC = 2 g/ml), while Q5 and Q7 showed intermediate activity (MIC = 512 g/ml).Q2 showed no efficacy, while ciprofloxacin remained the best (MIC = 2 g/ml).The study demonstrates that rational drug design by combining molecular modeling and in vitro evaluation can produce promising new compounds for combating antibiotic-resistant pathogens such as P. mirabilis.Coumarin compounds, although they still require further structural optimization, are promising options as alternative antibiotics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".