Revised Efficient and Reproducible Synthesis of an Fmoc-protected Tn Antigen
Bibliographic record
Abstract
In 2021, our team reported a concise synthesis of Thomsen-Nouveau (Tn) antigen, a tumour-associated O-linked mucin glycopeptide. We have since realized that our characterization of the reported glycoside was mistaken. Instead of the intended ether-bonded α anomer, the β-anomer containing an ester glycosidic bond was formed using palladium catalysis and characterised incorrectly as the spectra are remarkably similar. We demonstrated this conclusively be repeating Danishefky’s synthesis, with some required modifications of the protocol, of Fmoc-Tn for confirmation. The error is too significant for a correction as it does affect the conclusions of the work, and the original paper has been retracted, though its work is included in this manuscript. In this replacement we report a successful synthesis of the Tn antigen using adjusted glycosylation conditions: a different glycosyl acceptor, N-Fmoc serine benzyl ester, using TMSOTf as the catalyst. This remains, to the best of our knowledge, the shortest Tn antigen synthesis reported from galactose, although it provides the Tn antigen now. Furthermore, this route gives ready access to an essential Tn antigen building block that can be used for large-scale solid phase peptide synthesis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".