Carvacrol, a monoterpenoid, binds quorum sensing proteins (LasI and LasR) and swarming motility protein BswR of <i>Pseudomonas aeruginosa,</i> resulting in loss of pathogenicity: an in silico approach
Bibliographic record
Abstract
The pathogenic Pseudomonas aeruginosa utilizes a quorum-sensing pathway for biofilm formation. The quorum-sensing proteins LasI and LasR of the Las system, alongside the swarming motility protein BswR, play a crucial role in the biofilm-mediated antibiotic resistance phenomenon. In this in silico study, LasI, LasR, and BswR were the prime targets for binding studies by promising drug candidates like linalool, ferutinin, citronellal, and carvacrol. These monoterpenoid compounds are carefully considered for this study due to their reported anti-microbial activity. Among all, carvacrol exhibited the highest binding energies with LasI (−5.932 kcal/mol), LasR (−7.469 kcal/mol), and BswR (−4.42 kcal/mol). Furthermore, the MMGBSA scores between carvacrol and LasI, LasR, and BswR individually are −33.14, −54.22, and −41.86 kcal/mol, which further corroborated the strong binding. During 100 ns of simulation, the ligand binds to the active sites of these proteins through the H-bonds at Ile107 of LasI, Tyr47 of LasR, and Leu57 of BswR. In addition, the root-mean-square deviation values of the ligand-protein complex are within the appropriate range of less than 5 Å. Absorption, Distribution, Metabolism, Excretion, and Toxicity analysis confirmed that carvacrol has the most negligible toxicity to mammalian cells. Hence, this finding is the first report to show that carvacrol can inhibit the Pseudomonas aeruginosa biofilms.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".