Lipid Segregation Explains Selective Toxicity of a Series of Fragments Derived from the Human Cathelicidin LL-37
Bibliographic record
Abstract
The only human cathelicidin, the 37-residue peptide LL-37, exhibits antimicrobial activity against both gram-positive and gram-negative bacteria. We studied the ability of several fragments of LL-37, exhibiting different antimicrobial activities, to interact with membranes whose compositions mimic the cytoplasmic membranes of gram-positive or of gram-negative bacteria. These fragments are as follows: KR-12, the smallest active segment of LL-37, with the sequence KRIVQRIKDFLR, which exhibits antimicrobial activity only against gram-negative bacteria; a slightly smaller peptide, RI-10, missing the two cationic residues at the N and C termini of KR-12, which has been shown not to have any antimicrobial activity; a longer peptide, GF-17, which shows antimicrobial activity against gram-positive as well as gram-negative bacteria; and GF-17D3, with 3 D-amino-acid residues, which is also selective only for gram-negative bacteria. Those fragments with the capacity to cluster anionic lipids away from zwitterionic lipids in a membrane exhibit selective toxicity toward bacteria containing zwitterionic as well as anionic lipids in their cytoplasmic membranes but not toward bacteria with only anionic lipids. This finding allows for the prediction of the bacterial-species selectivity of certain agents and paves the way for designing new antimicrobials targeted specifically toward gram-negative bacteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".