Stapled ghrelin peptides as fluorescent imaging probes
Bibliographic record
Abstract
Abstract Fluorescently labelled ghrelin is an effective imaging probe for ex vivo biopsy analysis, in vivo distribution studies, and cell‐based analyses. The objective of our study was to improve the receptor affinity and stability of this ghrelin probe through cyclization, thereby providing a chemical probe with advantages in specificity and sensitivity as compared to immunohistochemical approaches. Truncation of ghrelin to its first 20 essential binding amino acids simplifies chemical synthesis, but reduces the α‐helical content of the peptide, which is important for receptor recognition. To overcome this limitation, we used a “staple scan” to synthesize stable α‐helical cyclic ghrelin(1‐20) analogues using a lactam bridge in either the i, i + 4 or i, i + 7 position. Stapling improved helicity in every case when compared to the linear sequence; however, the binding affinity to the receptor was dependent on the staple position. The peptide with the greatest improvement resulted in a [θ]222/[θ]208 ratio of 0.84, and an IC50 of 7.85 nM. The lead analogue was fluorescently labeled on the C‐terminal lysine of the peptide and microscopy experiments confirmed receptor binding in cells expressing GHS‐R1a. We postulate that the lead stapled peptide can be used as a cancer cell‐specific fluorescent stain with potential research and clinical applications.
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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".