<sup>2</sup>H Solid-State Nuclear Magnetic Resonance Investigation of Whole <i>Escherichia coli</i> Interacting with Antimicrobial Peptide MSI-78
Bibliographic record
Abstract
A key aspect of the activity of antimicrobial peptides (AMPs) is their interaction with membranes. Efforts to elucidate their detailed mechanisms have focused on applying biophysical methods, including nuclear magnetic resonance (NMR), to AMPs in model lipid systems. However, these highly simplified systems fail to capture many of the features of the much more complex cell envelopes with which AMPs interact in vivo. To address this issue, we have designed a procedure to incorporate high levels of (2)H NMR labels specifically into the cell membrane of Escherichia coli and used this approach to study the interactions between the AMP MSI-78 and the membranes of intact bacteria. The (2)H NMR spectra of these membrane-deuterated bacteria can be reproduced in the absence and presence of MSI-78. Because the (2)H NMR data provide a quantitative measure of lipid disorder, they directly report on the lipid bilayer disruption central to the function of AMPs, in the context of intact bacteria. Addition of MSI-78 to the bacteria leads to decreases in the order of the lipid acyl chains. The molar peptide:lipid ratios required to observe the effects of MSI-78 on acyl chain order are approximately 30 times greater than the ratios needed to observe effects in model lipid systems and approximately 100 times less than the ratios required to observe inhibition of cell growth in biological assays. The observations thus suggest that MSI-78 disrupts the bilayer even at sublethal AMP levels and that a large fraction of the peptide does not actually reach the inner membrane.
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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".