Sortase A as a tool for high‐yield histatin cyclization
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Cyclic peptides are highly valued tools in biomedical research. In many cases, they show higher receptor affinity, enhanced biological activity, and improved serum stability. Technical difficulties in producing cyclic peptides, especially larger ones, in appreciable yields have precluded a prolific use in biomedical research. Here, we describe a novel and efficient cyclization method that uses the peptidyl‐transferase activity of the Staphylococcus aureus enzyme sortase A to cyclize linear synthetic precursor peptides. As a model, we used histatin 1, a 38‐mer salivary peptide with motogenic activity. Chemical cyclization of histatin 1 resulted in ≤3% yields, whereas sortase‐mediated cyclization provided a yield of >90%. The sortase‐cyclized peptide displayed a maximum wound closure activity at 10 nM, whereas the linear peptide displayed maximal activity at 10 μM. Circular dichroism and NMR spectroscopic analysis of the linear and cyclic peptide in solution showed no evidence for conformational changes, suggesting that structural differences due to cyclization only became manifest when these peptides were located in the binding domain of the receptor. The sortase‐based cyclization technology provides a general method for easy and efficient manufacturing of large cyclic peptides.—Bolscher, J. G. M., Oudhoff, M. J., Nazmi, K., Antos, J. M., Guimaraes, C. P., Spooner, E., Haney, E. F., Garcia‐Vallejo, J. J., Vogel, H. J., van't Hof, W., Ploegh, H. L., Veerman. E. C. I. Sortase A as a tool for high‐yield histatin cyclization. FASEB J. 25, 2650–2658 (2011). www.fasebj.org
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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 it