Antibacterial Properties of an Oligo-Acyl-Lysyl Hexamer Targeting Gram-Negative Species
Bibliographic record
Abstract
Toward developing new tools for fighting resistance to antibiotics, we investigated the antibacterial properties of a new decanoyl-based oligo-acyl-lysyl (OAK) hexamer, aminododecanoyl-lysyl-[aminodecanoyl-lysyl](5) (α(12)-5α(10)). The OAK exhibited preferential activity against Gram-negative bacteria (GNB), as determined using 36 strains, including diverse species, with an MIC(90) of 6.2 μM. The OAK's bactericidal mode of action was associated with rapid membrane depolarization and cell permeabilization, suggesting that the inner membrane was the primary target, whereas the observed binding affinity to lipoteichoic acid suggested that inefficacy against Gram-positive species resulted from a cell wall interaction preventing α(12)-5α(10) from reaching internal targets. Interestingly, perturbation of the inner membrane structure and function was preserved at sub-MIC values. This prompted us to assess the OAK's effect on the proton motive force-dependent efflux pump AcrAB-TolC, implicated in the low sensitivity of GNB to various antibiotics, including erythromycin. We found that under sub-MIC conditions, wild-type Escherichia coli was significantly more sensitive to erythromycin (the MIC dropped by >10-fold), unlike its acr-deletion mutant. Collectively, the data suggest a useful approach for treating GNB infections through overcoming antibiotic efflux.
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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".