A Mild Protecting-Group Free Strategy for Neoglycoconjugate Synthesis
Bibliographic record
Abstract
The synthesis of neoglycoconjugates has paved the way for the discovery of novel probes that mimic natural glycoconjugates and can provide designed research tools and therapeutics. In some cases, the target protein may not be amenable to harsh conditions; therefore, semisynthetic or chemical methods must be chosen with care. Here, we present a simple and modular chemoselective coupling strategy between an unprotected sugar and an N, O -disubstituted hydroxylamine under mild acidic conditions. This strategy removes any need for protecting groups on the glycan. The terminal alkene group of the conjugate serves as an effective handle to allow facile conjugation to the protein of interest via thiol–ene coupling (TEC), with proteins bearing a cysteine or free thiol to prepare neoglycoconjugates. We demonstrate that the strategy is compatible with both N- and O-linked glycans using protecting-group free strategies and optimize the TEC conditions using a variety of photocatalysts. Finally, we test the method on an aggregation-prone protein, α-synuclein. We envision that this strategy could allow the construction of complex glycoconjugates for biological testing using isolated glycans, or for generation of conjugates where the protein of interest is sensitive to harsh conditions.
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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 | 0.001 |
| 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".