Interaction of Cationic Peptides with Bacterial Membranes
Bibliographic record
Abstract
A common feature of cationic peptides is that their site of action is at the membrane due to channel formation, and that they tend to possess strong selectivity towards then target membrane. For example, although moth cecropin and bee melittin are members of the same family of peptides that adopt amphipathic α-helical structures, the cecropins are strongly antibacterial and demonstrate minimal eukaryotic selectivity (i.e., toxicity), whereas melittin is a weak antibacterial compound but a potent toxin. Whereas the basis for selectivity is not completely understood, it has been shown to be due to the size of the transmembrane electrical potential gradient (up to −140 mV in bacterial cytoplasmic membranes compared with about −20 mV or less in eukaryotic membranes) and the lipid composition (bacterial membranes contain a large number of anionic lipids such as phosphatidyl glycerol and cardiolipin and lack cholesterol in their membranes). Gram-negative bacteria have an additional, outer membrane, and our data suggests that a further level of selectivity is expressed there in that there are Gram-positive bacteria-selective peptides that interact poorly with the outer membrane but (presumably) well with cytoplasmic membranes, whereas we have identified peptides that interact with the outer membrane, but are not bactericidal and thus do not interact with cytoplasmic membranes. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".