Effect of Point Mutations on the Secondary Structure and Membrane Interaction of Antimicrobial Peptide Anoplin
Bibliographic record
Abstract
Anoplin (GLLKRIKTLL-NH 2 ) is the smallest linear α-helical antimicrobial peptide found naturally to date. Antibacterial and hemolytic properties of anoplin depend strongly on physicochemical properties. Two anoplin derivatives, anoplin-8K (Ano8K, GLLKTIKKLL-NH 2 ) and anoplin-1K5 V8K (Ano1K5 V8K, KLLKVIKLL-NH 2 ), were found to have increased bacterial and low hemolytic activity. In the present work physicochemical properties of these three peptides were studied by UV resonance Raman (UVRR) spectroscopy, Langmuir−Blodgett monolayer technique, and carboxyfluorescein (CF) leakage assay. UVRR data indicated that all three peptides adopt predominantly unordered conformation in aqueous buffer solution. In membrane-mimicking trifluoroethanol, the α-helical content increases for all three peptides with Ano1K5 V8K having the highest α-helix percentage, followed by Ano8K and anoplin. Critical micelle concentrations were found to be similar for all three peptides, and the saturation pressure decreases in the sequence Ano1K5 V8K, anoplin, Ano8K. Critical pressure of insertion was found to be greater for anionic lipid monolayer DPPG than for zwitterionic lipid DPPC indicating preferential adsorption of all three peptides to DPPG. Finally, membrane lytic activities of all three peptides toward various model lipid vesicles were compared through CF leakage assay. Overall the data indicate that antimicrobial activity of anoplin increases with charge, whereas membrane lytic activity correlates with peptides helicity and amphipathicity.
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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".