Insights into Molecular Interactions between a GAPDH-Related Fish Antimicrobial Peptide, Analogs Thereof, and Bacterial Membranes
Bibliographic record
Abstract
Interactions between SJGAP (skipjack tuna GAPDH-related antimicrobial peptide) and four analogs thereof with model bacterial membranes were studied using Fourier-transform infrared spectroscopy (FTIR) and molecular dynamics (MD) simulations. MD trajectory analyses showed that the N-terminal segment of the peptide analogs has many contacts with the polar heads of membrane phospholipids, while the central α helix interacts strongly with the hydrophobic core of the membranes. The peptides also had a marked influence on the wave numbers associated with the phase transition of phospholipids organized as liposomes in both the interface and aliphatic chain regions of the infrared spectra, supporting the interactions observed in the MD trajectories. In addition, interesting links were found between peptide interactions with the aliphatic chains of membrane phospholipids, as determined by FTIR and from the MD trajectories, and the membrane permeabilization capacity of these peptide analogs, as previously demonstrated. To summarize, the combined experimental and computational efforts have provided insights into crucial aspects of the interactions between the investigated peptides and bacterial membranes. This work thus makes an original contribution to our understanding of the molecular interactions underlying the antimicrobial activity of these GAPDH-related antimicrobial peptides from Scombridae.
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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".