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A Mild Protecting-Group Free Strategy for Neoglycoconjugate Synthesis

2025· article· en· W4411549213 on OpenAlexafffund
P. Raju, Chun‐Hua Dong, Craig R. Garen, Michael T. Woodside, Christopher W. Cairo

Bibliographic record

VenueBioconjugate Chemistry · 2025
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryGlycoconjugateGlycanProtecting groupConjugateCombinatorial chemistryAlkeneHydroxylamineBiochemistryGlycoproteinOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.257
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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