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Record W2124951428 · doi:10.1093/glycob/cwv017

Sialyltransferases with enhanced legionaminic acid transferase activity for the preparation of analogs of sialoglycoconjugates

2015· article· en· W2124951428 on OpenAlexaff
David C. Watson, Warren W. Wakarchuk, Sonia Leclerc, M. Schur, Ian C. Schoenhofen, N. Martin Young, Michel Gilbert

Bibliographic record

VenueGlycobiology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsToronto Metropolitan UniversityNational Research Council Canada
Fundersnot available
KeywordsTransferaseChemistryBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Legionaminic acids (Leg) are bacterial analogs of neuraminic acid, with the same stereochemistry but different substituents at C5, C7 and C9. Hence they may be incorporated into useful analogs of sialoglycoconjugates, and we previously reported two sialyltransferases that could utilize cytidine monophosphate (CMP)-Leg5Ac7Ac for preparation of Leg glycoconjugates, which were resistant to sialidases [Watson DC, Leclerc S, Wakarchuk WW, Young NM. 2011. Enzymatic synthesis and properties of glycoconjugates with legionaminic acid as a replacement for neuraminic acid. Glycobiology. 21:99-108.]. These were the porcine ST3Gal1 and Pasteurella multocida sialyltransferases. We now report two additional sialyltransferases with superior Leg-transferase properties to the previous two. These are (i) a truncated form of a Photobacterium α2,6-sialyltransferase with an Ala-Met mutation in its active site, and (ii) an α2,3-sialyltransferase from Neisseria meningitidis MC58 with a higher transferase activity than the P. multocida enzyme, with either CMP-Neu5Ac or CMP-Leg5Ac7Ac as the donor. These enzymes will enable the production of useful Leg5Ac7Ac glycoconjugate derivatives with either α2,6 or α2,3 linkages and unique biological properties.

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 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 score0.071
Threshold uncertainty score0.379

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)0.0000.000

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 head0.029
GPT teacher head0.317
Teacher spread0.288 · 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

Citations28
Published2015
Admission routes1
Has abstractyes

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