Immunomodulatory Functions of the Human Cathelicidin LL-37 (aa 13–31)-Derived Peptides are Associated with Predicted α-Helical Propensity and Hydrophobic Index
Bibliographic record
Abstract
The anti-endotoxin activity of the cationic peptide LL-37 and its derivative IG-19 is attributed to electrostatic interaction of the peptides' positive charge with negatively charged bacterial lipopolysaccharides (LPS), and in part to the alteration of intracellular mechanisms independent of peptide binding to LPS. We examined the immunomodulatory responses induced by IG-19 and four IG-19-derived scrambled peptides (IG-19a-d), in the presence and absence of LPS, in macrophages and peripheral blood-derived mononuclear cells. All peptides had identical net charge (+5) and amino acid composition, but different hydrophobicity and α-helical propensity. Peptide IG-19 suppressed LPS-induced cytokine/chemokine production by >90%, IG-19a and IG-19b suppressed it by 40-50%, and IG-19c and IG-19d did not suppress cytokine/chemokine production at all. In silico prediction algorithms and the peptide retention time (RT) on a C18 RP HPLC column indicated a linear association between α-helical propensity and hydrophobicity with the ability of the peptides to inhibit LPS-induced responses. Peptide RT exhibited a significant correlation (>70%) between the suppression of LPS-induced cytokine/chemokine production and peptide-induced production of the anti-inflammatory cytokine IL-1RA. These results indicate that RT on a C18 column can be used as a predictor for the immunomodulatory functions of cationic peptides. Overall, we demonstrated that the immunomodulatory functions of LL-37-derived peptides with identical positive charge and amino acid composition are directly associated with the predicted α-helical propensity and hydrophobicity of the peptides.
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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".