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Record W2053711280 · doi:10.1139/v04-125

The synthesis of some mechanistic probes for sialic acid processing enzymes and the labeling of a sialidase from <i>Trypanosoma rangeli</i>

2004· article· en· W2053711280 on OpenAlexvenueno aff
Andrew G. Watts, Stephen G. Withers

Bibliographic record

VenueCanadian Journal of Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsnot available
Fundersnot available
KeywordsSialidaseSialic acidChemistryBiochemistryResidue (chemistry)EnzymeGlycosylN-Acetylneuraminic acidCovalent bondGlycanStereochemistryNeuraminidaseGlycoproteinOrganic chemistry

Abstract

fetched live from OpenAlex

Sialyl hydrolases, trans-sialidases, and sialyl transferases are biologically important enzymes that are responsible for the incorporation and removal of sialic acid residues, which decorate many cell surface glycocongugates. Two fluorinated sialic acid derivatives have been synthesized as mechanism-based inactivators, to probe the catalytic mechanisms through which sialidases and trans-sialidases operate. Both compounds are known to be covalent inactivators of a trans-sialidase from Trypanosoma cruzi. Here, 3-fluorosialosyl fluoride has been found to covalently label the catalytic nucleophile of a sialidase from T. rangeli, and the residue involved is shown to be Tyr346 within the sequence DENSGYSSVL. This is the first demonstration that sialidases operate through a covalent glycosyl-enzyme intermediate, strongly suggesting a common catalytic mechanism amongst all members of the sialidase superfamily. CMP-3-fluoro sialic acid is a competitive inhibitor of sialyl transferases and was synthesized via a two-step enzymatic process from commercially available N-acetyl mannosamine, 3-fluoropyruvic acid, and cytidine triphosphate in around 84% yield.Key words: sialidase, mechanism, labeling, nucleophile, inhibitor.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.018
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2004
Admission routes1
Has abstractyes

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