Exploration of the Structure–Function Relationships of a Novel Frog Skin Secretion-Derived Bioactive Peptide, t-DPH1, through Use of Rational Design, Cationicity Enhancement and In Vitro Studies
Bibliographic record
Abstract
Amphibian skin-derived antimicrobial peptides (AMPs) have attracted increasing attention from scientists because of their excellent bioactivity and low drug resistance. In addition to being the alternative choice of antibiotics or anticancer agents, natural AMPs can also be modified as templates to optimise their bioactivities further. Here, a novel dermaseptin peptide, t-DPH1, with extensive antimicrobial activity and antiproliferative activity, was isolated from the skin secretion of Phyllomedusa hypochondrialis through ‘shotgun’ cloning. A series of cationicity-enhanced analogues of t-DPH1 were designed to further improve its bioactivities and explore the charge threshold of enhancing the bioactivity of t-DPH1. The present data suggest that improving the net charge can enhance the bioactivities to some extent. However, when the charge exceeds a specific limit, the bioactivities decrease or remain the same. When the net charge achieves the limit, improving the hydrophobicity makes no sense to enhance bioactivity. For t-DPH1, the upper limit of the net charge was +7. All the designed cationicity-enhanced analogues produced no drug resistance in the Gram-negative bacterium, Escherichia coli. These findings provide creative insights into the role of natural drug discovery in providing templates for structural modification for activity enhancement.
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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".