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Record W1963550395 · doi:10.1371/journal.pone.0071077

Geometric Attributes of Retaining Glycosyltransferase Enzymes Favor an Orthogonal Mechanism

2013· article· en· W1963550395 on OpenAlexafffund
Brock Schuman, Stephen V. Evans, Thomas M. Fyles

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsGlycosyltransferaseGlycosidic bondAnomerChemistryStereochemistryEnzymeAcceptorGlycoside hydrolaseMoleculeCarbohydrate conformationBiochemistryNuclear magnetic resonance spectroscopyOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Retaining glycosyltransferase enzymes retain the stereochemistry of the donor glycosidic linkage after transfer to an acceptor molecule. The mechanism these enzymes utilize to achieve retention of the anomeric stereochemistry has been a matter of much debate. Re-analysis of previously released structural data from retaining and inverting glycosyltransferases allows competing mechanistic proposals to be evaluated. The binding of metal-nucleotide-sugars between inverting and retaining enzymes is conformationally unique and requires the donor substrate to occupy two different orientations in the two types of glycosyltransferases. The available structures of retaining glycosyltransferases lack appropriately positioned enzymatic dipolar residues to initiate or stabilize the intermediates of a dissociative mechanism. Further, available structures show that the acceptor nucleophile and anomeric carbon of the donor sugar are in close proximity. Structural features support orthogonal (front-side) attack from a position lying ≤ 90° from the C1-O phosphate bond for retaining enzymes. These structural conclusions are consistent with the geometric conclusions of recent kinetic and computational studies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.008
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0090.000

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 head0.050
GPT teacher head0.208
Teacher spread0.157 · 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

Citations34
Published2013
Admission routes2
Has abstractyes

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