Interaction of LL-37 human cathelicidin peptide with a model microbial-like lipid membrane
Bibliographic record
Abstract
The only representative of cathelicidin peptides in humans is LL-37, a multifunctional antimicrobial peptide (AMP) that is a part of the innate immune response. Details of the LL-37 direct activity against pathogens are not well understood at the molecular level. Here, we present research on the mechanism of interaction between LL-37 and a model multicomponent bilayer lipid membrane (BLM), mimicking microbial cell membrane. Electrochemical impedance spectroscopy (EIS), high-resolution atomic force microscopy (AFM) imaging, and polarization-modulation infrared reflection-absorption spectroscopy (PM-IRRAS) were applied to study the peptide influence on a model microbial-like membrane. We show that LL-37 causes changes in the phospholipid molecules conformation and orientation, leading to membrane disintegration, significantly affecting the membrane electrical parameters, such as capacitance and resistance. High-resolution AFM imaging shows topographical and mechanical effects of such disintegration, while PM-IRRAS data indicates that introduction of LL-37 causes changes in the phospholipid acyl chains from all-trans to gauche conformations. Moreover, the presence of LL-37 significantly alters the value of the phospholipid tilt angle. Altogether, our results suggest a "carpet" membrane dissolution followed by a detergent-like membrane disruption mechanism upon LL-37 activity. This research gives a novel insight into the understanding of LL-37 influence on multicomponent model membranes and a promising contribution to the development of LL-37-derived therapeutic agents against drug-resistant bacteria.
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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".