Unravelling the Skin Secretion Peptides of the Gliding Leaf Frog, Agalychnis spurrelli (Hylidae)
Bibliographic record
Abstract
Frog skin secretions contain medically-valuable molecules, which are useful for the discovery of new biopharmaceuticals. The peptide profile of the skin secretion of Agalychnis spurrelli has not been investigated; therefore, the structural and biological characterization of its compounds signify an inestimable opportunity to acquire new biologically-active chemical scaffolds. In this work, skin secretion from this amphibian was analysed by molecular cloning and tandem mass spectrometry. Although the extent of this work was not exhaustive, eleven skin secretion peptides belonging to five peptide families were identified. Among these, we report the occurrence of two phyllokinins, and one medusin-SP which were previously reported in other related species. In addition, eight novel peptides were identified, including four dermaseptins, DRS-SP2 to DRS-SP5, one phylloseptin-SP1, and three orphan peptides. Phylloseptin-SP1 and dermaseptins-SP2 were identified in HPLC fractions based on their molecular masses determined by MALDI-TOF MS. Among the antimicrobial peptides, dermaseptin-SP2 was the most potent, inhibiting Escherichia coli, Staphylococcus aureus, and ORSA with a minimum inhibitory concentration (MIC) of 2.68 μM, and Candida albicans with an MIC of 10.71 μM, without haemolytic effects. The peptides described in this study represent but a superficial glance at the considerable structural diversity of bioactive peptides produced in the skin secretion of A. spurrelli.
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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 | 0.001 |
| 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".