Fluorine‐18 labeling of phosphopeptides: A potential approach for the evaluation of phosphopeptide metabolism in vivo
Bibliographic record
Abstract
Abstract Phosphopeptides are very useful reagents to study signal transduction pathways related with cellular protein phosphorylation/dephosphorylation. Phosphopeptides have also been identified as important drug candidates to modulate intracellular signaling mechanisms through targeting phosphotyrosine, phosphoserine, or phosphothreonine residue‐binding protein domains. In this report, we describe the development of a convenient method for the mild and sufficient radiolabeling of phosphopeptides with the short‐lived positron emitter fluorine‐18 (18F) to allow radiopharmacological studies on phosphopeptide metabolism in vivo by means of positron emission tomography (PET). Radiolabeling was accomplished via conjugation of the N‐terminus of polo‐box domain (PBD)‐binding phosphopeptide H‐Met‐Gln‐Ser‐pThr‐Pro‐Leu‐OH with the bifunctional labeling agent N‐succinimidyl‐4‐[18F]fluorobenzoate ([18F]SFB) in reproducible isolated radiochemical yields of 25–28%. Radiopharmacological evaluation in vitro and in vivo of radiolabeled phospheptide [18F]FB‐Met‐Gln‐Ser‐pThr‐Pro‐Leu‐OH [18F]‐3 and its non‐phosphorylated analog [18F]FB‐Met‐Gln‐Ser‐Thr‐Pro‐Leu‐OH [18F]‐4 involved metabolic stability, cell uptake studies, and small animal PET experiments. Radiolabeled phosphopeptide [18F]‐3 showed a remarkable high metabolic stability in vivo compared to the corresponding non‐phosphorylated peptide [18F]‐4. The presented method indicates that radiolabeling of phosphopeptides with [18F]SFB is a promising approach for studying phosphopeptide metabolism in vivo. © 2009 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 92: 479–488, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
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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) | 0.001 | 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".