Role of lipopolysaccharide in antimicrobial and cell penetrating peptide membrane interactions probed by deuterium NMR of whole cells
Bibliographic record
Abstract
Understanding how non-lipid components of bacteria affect antimicrobial peptide (AMP)-induced membrane disruption is important for a comprehensive understanding of AMP mechanisms and informing AMP-based drug development. This study investigates how lipopolysaccharide (LPS) affects membrane disruption by the AMP MSI-78 and compares the results to the effect of TP2, a cell-penetrating peptide that crosses membrane bilayers without permeabilizing them. We destabilize the LPS layer of Escherichia coli (E. coli) cells via chelation of the stabilizing divalent cations. 2H NMR spectra of E. coli demonstrate that EDTA concentrations of 2.5 mM and 9.0 mM alone have very minor effects on lipid acyl chain order. Interestingly, we find that E. coli pre-treated with 9.0 mM EDTA before treatment with MSI-78 are more sensitive to AMP-induced acyl chain disruption, indicating that intact LPS reduces MSI-78-induced membrane disruption in E. coli. Surprisingly, we also found that at the level of 2H-NMR, the peptide-induced acyl chain disruption is similar for MSI-78 and TP2, although MSI-78 permeabilizes the bilayer and TP2 does not. Furthermore, LPS disruption appears to protect the bacteria from TP2, although it sensitizes them to MSI-78.
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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".