Hit Identification of New Potent PqsR Antagonists as Inhibitors of Quorum Sensing in Planktonic and Biofilm Grown Pseudomonas aeruginosa
Bibliographic record
Abstract
Current treatments for P. aeruginosa infections are becoming less effective because of the increasing rates of multi-antibiotic resistance. Pharmacological targeting of virulence through inhibition of quorum sensing (QS) dependent virulence gene regulation has considerable therapeutic potential. In P. aeruginosa, the pqs QS system regulates the production of multiple virulence factors as well as biofilm maturation and is a promising approach for developing antimicrobial adjuvants for combatting drug resistance. In this work, we report the hit optimisation for a series of potent novel inhibitors of PqsR, a key regulator of the pqs system, bearing a 2-((5-methyl-5H-[1,2,4]triazino[5,6-b]indol-3-yl)thio)acetamide scaffold. The initial hit compound 7 (PAO1-L IC50 0.98 ± 0.02 μM, PA14 inactive at 10M) was obtained through a virtual screening campaign performed on the PqsR ligand binding domain using the University of Nottingham Managed Chemical Compound Collection. Hit optimisation gave compounds with enhanced potency against strains PAO1-L and PA14, evaluated using P. aeruginosa pqs-based QS bioreporter assays. Compound 40 (PAO1-L IC50 0.25 0.12 μM, PA14 IC50 0.34 0.03 μM) is one of the most potent PqsR antagonists reported showing significant inhibition of P. aeruginosa pyocyanin production and pqs system signaling in both planktonic cultures and biofilms. The co-crystal structure of 40 with the PqsR ligand binding domain revealed the specific binding interactions occurring between inhibitor and this key regulatory protein.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".