Derivatisable Cyanobactin Analogues: A Semisynthetic Approach
Bibliographic record
Abstract
Many natural cyclic peptides have potent and potentially useful biological activities. Their use as therapeutic starting points is often limited by the quantities available, the lack of known biological targets and the practical limits on diversification to fine-tune their properties. We report the use of enzymes from the cyanobactin family to heterocyclise and macrocyclise chemically synthesised substrates so as to allow larger-scale syntheses and better control over derivatisation. We have made cyclic peptides containing orthogonal reactive groups, azide or dehydroalanine, that allow chemical diversification, including the use of fluorescent labels that can help in target identification. We show that the enzymes are compatible and efficient with such unnatural substrates. The combination of chemical synthesis and enzymatic transformation could help renew interest in investigating natural cyclic peptides with biological activity, as well as their unnatural analogues, as therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".