Mechanism of action of pseudopteroxazole and pseudopterosin G: Diterpenes from marine origin
Bibliographic record
Abstract
Abstract Pseudopteroxazole (Ptx) and the pseudopterosins are marine natural products with promising antibacterial potential. While Ptx has attracted interest for its antimycobacterial activity, pseudopterosins are active against several clinically relevant pathogens. Both compound classes exhibit low cytotoxicity and accessibility to targeted synthesis, yet their antibacterial mechanisms remain elusive. In this study, we investigated the modes of action of Ptx and pseudopterosin G (PsG) in Bacillus subtilis employing an unbiased approach that combines gel‐based proteomics with a mathematical similarity analysis of response profiles. Proteomic responses to sublethal concentrations of Ptx and PsG were compared to a library of antibiotic stress response profiles revealing that both induce a stress response characteristic for agents targeting the bacterial cell envelope by interfering with membrane‐bound steps of cell wall biosynthesis. Microscopy‐based assays confirmed that both compounds compromise the integrity of the bacterial cell wall without disrupting the membrane potential. Furthermore, LC‐MSE analysis showed that the greater potency of PsG against B. subtilis, reflected in a lower MIC and a more pronounced proteomic response, may be rooted in a more effective association with and penetration of B. subtilis cells. We conclude that Ptx and PsG target the integrity of the gram‐positive cell wall.
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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) | 0.000 | 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) | 0.001 | 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".