Role of Charge in Lipid Vesicle Binding and Vesicle Surface Saturation by Gaduscidin-1 and Gaduscidin-2
Bibliographic record
Abstract
The histidine-rich antimicrobial peptides Gad-1 and Gad-2, from paralogous genes in cod, provide an opportunity to examine the effect of charge and nonelectrostatic factors on peptide-vesicle interaction and on peptide antimicrobial activity. In this study, the dependence of vesicle ζ-potential on peptide concentration has been used to examine the binding of these peptides to model vesicle surfaces at pH = 5.0, for which the charges of Gad-1 and Gad-2 are +8 and +5, respectively, and at pH = 7.0, where their charges are +3 and +1, respectively. Interpreting the observed ζ-potential behaviors as examples of Langmuir adsorption isotherms, it is possible to infer the equilibrium constant for peptide-vesicle binding, the fraction of the peptide bound at low peptide concentration, and the maximum peptide-to-lipid ratio when the vesicle surface is saturated at high peptide concentration. For both peptides, higher peptide charge is found to be correlated with a lower fraction of the peptide being bound to vesicle surfaces at low peptide concentration and with a smaller maximum bound peptide-to-lipid ratio at high peptide concentration. The equilibrium binding constant, on the other hand, is more strongly correlated with the peptide sequence than with the charge. Gad-1, which has been shown to be more biologically active than Gad-2, displayed a significantly higher equilibrium binding constant. These observations suggest that while the maximum peptide density on the vesicle surface is limited by electrostatic interactions, the free energy of peptide binding, like the observed antimicrobial activities of the Gad peptides, is also sensitive to other peptide factors which might, for example, influence hydrophobic interactions.
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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 | 0.001 | 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".