Decoding the Functional Roles of Cationic Side Chains of the Major Antimicrobial Region of Human Cathelicidin LL-37
Bibliographic record
Abstract
Human cathelicidin LL-37 is a critical cationic antimicrobial peptide for host defense against infection, immune modulation, and wound healing. This article elucidates the functional roles of the cationic side chains of the major antimicrobial region of LL-37, corresponding to residues 17 to 32 (designated GF-17). Antimicrobial assays, killing kinetics studies, and vesicle leakage experiments all indicate that a conversion of lysines to arginines affected the ability of the peptide to kill the Gram-positive Staphylococcus aureus strain USA300. Alanine scanning experiments show that S. aureus is less sensitive than Escherichia coli to a single cationic residue mutation of GF-17. Among the five cationic residues, R23 appears to be somewhat important in killing S. aureus. However, R23 and K25 of GF-17 are of prime importance in killing the Gram-negative organism E. coli. In particular, R23 is essential for (i) rapid recognition, (ii) permeation of the E. coli outer membrane, (iii) clustering of anionic lipids in a membrane system mimicking the E. coli inner membrane, and (iv) membrane disruption. Bacterial aggregation (i.e., rapid recognition via charge neutralization) is the first step of the peptide action. Structurally, R23 is located in the interface (i.e., the first action layer), a situation ideal for the interactions listed above. In contrast, residues K18, R19, and R29 are on the hydrophilic surface of the amphipathic helix and play only a secondary role. Mapping of the functional spectrum of cationic residues of GF-17 provides a solid basis for engineering bacterium-specific antimicrobials using this highly potent template.
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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) | 0.000 | 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 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".