Conjugation of Synthetic Polyproline Moietes to Lipid II Binding Fragments of Nisin Yields Active and Stable Antimicrobials
Bibliographic record
Abstract
Coupling functional moieties to lantibiotics offers exciting opportunities to produce novel derivatives with desirable properties enabling new functions and applications. Here, five different synthetic hydrophobic polyproline peptides were conjugated to either nisin AB (the first two rings of nisin) or nisin ABC (the first three rings of nisin) by using click chemistry. The antimicrobial activity of nisin ABC + O6K3 against Enterococcus faecium decreased 8-fold compared to full-length nisin, but its activity was 16-fold better than nisin ABC, suggesting that modifying nisin ABC is a promising strategy to generate semi-synthetic nisin hybrids. In addition, the resulting nisin hybrids are not prone to degradation at the C-terminus, which has been observed for nisin as it can be degraded by nisinase or other proteolytic enzymes. This methodology allows for getting more insight into the possibility of creating semi-synthetic nisin hybrids that maintain antimicrobial activity, in particular when synthetic and non-proteinaceous moieties are used. The success of this approach in creating viable nisin hybrids encourages further exploring the use of different modules, e.g., glycans, lipids, active peptide moieties, and other antimicrobial moieties.
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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".