Headgroup structure and fatty acid chain length of the acidic phospholipids modulate the interaction of membrane mimetic vesicles with the antimicrobial peptide protegrin‐1
Bibliographic record
Abstract
The interaction of protegrin-1 (PG-1), a small beta-sheet antimicrobial peptide with acidic phospholipid model membranes was investigated by differential scanning calorimetry. We found that PG-1 can distinguish between liposomes of the anionic phospholipids DPPG, DPPS and DPPA, eventhough the headgroups of these phospholipids all have the same net charge and they carry the same hydrocarbon chains. Specifically, PG-1 had only a minor effect on the thermotropic phase behavior of DPPA liposomes, while it interacted preferentially with the fluid phase of DPPS. Furthermore, PG-1 could induce a phase separation in DPPG liposomes resulting in the formation of peptide-rich domains even at low concentrations of the peptide. However, this peptide-rich domain was not evident when the fatty acyl chains were longer or shorter by two carbon atoms. In addition, PG-1 can also form peptide-rich domains in DPPS vesicles but only at high concentrations of the peptide. These results suggest that in addition to an overall negative charge, the structural features of the phospholipid headgroups, lipid packing and thus membrane fluidity will influence the interaction with PG-1, thereby modulating its biological activity.
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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) | 0.001 | 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".