Radiometal-Containing Aryl Diazonium Salts for Chemoselective Bioconjugation of Tyrosine Residues
Bibliographic record
Abstract
Abstract Tyrosine is an attractive target for chemo- and site-selective protein modification. The particular chemical nature of tyrosine residues allows bioconjugation chemistry with reactive aryl diazonium salts via electrophilic aromatic substitution to produce diazo compounds. In this work, we describe the preparation of 64Cu- and 68Ga-labeled 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA)-diazonium salts as building blocks for azo coupling chemistry with tyrosine and tyrosine-containing peptides and proteins under mild conditions. 2-S-(4-aminobenzyl)-1,4,7-triazacyclononane-1,4,7-triacetic acid (p-NH2-Bn-NOTA) was used to form the corresponding 64Cu- and 68Ga-labeled complexes, followed by diazotization with NaNO2 in the presence of HCl. 64Cu- and 68Ga-labeled NOTA complexes were prepared in high radiochemical yields >80% starting from 20 μg of p-NH2-Bn-NOTA. Conversion of p-NH2-Bn-NOTA complexes into diazonium salts followed by azo coupling with l-tyrosine afforded 64Cu- and 68Ga-labeled tyrosine in radiochemical yields of 80 and 56%, respectively. Azo coupling with tyrosine-containing hexapeptide neurotensin NT(8–13) afforded 64Cu- and 68Ga-labeled NT(8–13) in radiochemical yields of 45 and 11%, respectively. Azo coupling of 64Cu-labeled NOTA-diazonium salt with human serum albumin (HSA) gave 64Cu-labeled HSA in radiochemical yields of 20%. The described azo coupling chemistry represents an innovative and versatile bioconjugation strategy for selective targeting of tyrosine residues in peptides and proteins.
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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 | 0.001 |
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".