Molecular simulation-based investigation of thiazole derivatives as potential LasR inhibitors of Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa (P. aeruginosa), a very resilient pathogen, demonstrates a diverse array of virulence factors, the expression of which is closely linked to the quorum sensing(QS) mechanism, which facilitates cell-cell interaction. Quorum sensing (QS) inhibition is a promising strategy for combating bacterial infections. LasR, a transcriptional factor that controls the mechanism of QS in P. aeruginosa, is a promising target for therapeutic development, because a lot of research has been done on its structure. It has already been established that thiazoles and their compounds have anti-QS potential against P aeruginosa. The study aims to identify new LasR quorum sensing inhibitors (QSIs) derived from novel thiazoles utilizing a structure-based virtual screening technique using the ZINC database. A complete set of 800 molecules (a novel thiazole derivative library) were docked inside the active region of the LasR receptor before being screened using pharmacokinetic and toxicology studies. Among the derivatives that were examined, compounds D_152, D_153, and L_331 were selected as potential inhibitors of LasR in P. aeruginosa and further studied to obtain a crucial understanding of the binding interactions that take place between inhibitor ligands and LasR. The findings indicated that the pharmacophoric characteristics of the derivative D_152 were comparable to those of the reference thiazole molecule (TC). Moreover, the molecular docking investigations showed that derivative D_152 and reference compound TC both fit the LasR protein's active area well. Furthermore, TC and D_152's amino acid interaction graphs with LasR and CviR are nearly identical. Furthermore, compound D_152's ability to engage with the LasR binding site through the dissolution of the protein's dimer was demonstrated by molecular dynamics modeling tests conducted over a 50 ns time span, demonstrating its function as a LasR antagonist. Additionally, Density Functional Theory (DFT) study was conducted on compound D_152 in order to determine the electron density of a molecule. According to the research findings, the recently produced thiazole derivative (D_152) has the potential to be used as a QSI against the LasR receptor, which would speed up the fight against the pathogenicity of P. aeruginosa that is resistant to multiple drugs.
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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 | 0.001 | 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) | 0.003 | 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 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".