The effect of membrane thickness on the membrane permeabilizing activity of the cyclic lipopeptide tolaasin II
Bibliographic record
Abstract
Tolaasin II is an amphiphilic, membrane-active, cyclic lipopeptide produced by Pseudomonas tolaasii and is responsible for brown blotch disease in mushroom. To better understand the mode of action and membrane selectivity of tolaasin II and related lipopeptides, its permeabilizing effect on liposomes of different membrane thickness was characterized. An equi-activity analysis served to distinguish between the effects of membrane partitioning and the intrinsic activity of the membrane-bound peptide. It was found that thicker membranes require higher local peptide concentrations to become leaky. More specifically, the mole ratio of membrane-bound peptide per lipid needed to induce 50% leakage of calcein within 1 h, Re50, increased monotonically with membrane thickness from 0.0016 for the 14:1 to 0.0070 for the 20:1 lipid-chains. Moreover, fast but limited leakage kinetics in the low-lipid regime were observed implying a mode of action based on membrane asymmetry stress in this time and concentration window. While the assembly of the peptide to oligomeric pores of defined length along the bilayer z-axis can in principle explain inhibition by increasing membrane thickness, it cannot account for the observed limited leakage. Therefore, reduced intrinsic membrane-permeabilizing activity with increasing membrane thickness is attributed here to the increased mechanical strength and order of thicker membranes.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".