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Record W2166054787 · doi:10.1021/bi011262w

Inhibition of CMP-Sialic Acid Transport into Golgi Vesicles by Nucleoside Monophosphates

2001· article· en· W2166054787 on OpenAlexaff
Molly Chiaramonte, Jennifer L. Koviach, Chad L. Moore, Vidhya V. Iyer, Carston R. Wagner, Randall L. Halcomb, Wayne H. Miller, Paul Melançon, Robert D. Kuchta

Bibliographic record

VenueBiochemistry · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical Sciences
KeywordsVesicleGolgi apparatusChemistryNucleosideSialic acidBiochemistryBiophysicsMembraneBiologyCell

Abstract

fetched live from OpenAlex

We examined the interactions of nucleotides with the CMP-sialic acid transporter in order to better understand which features play a role in binding and to investigate the relationship between binding and subsequent transport. With respect to the sugar, the transporter requires a complete ribose ring for tight binding, and the 2'-ara hydrogen makes an important contact. The enzyme exhibits little specificity with respect to the 2'- and 3'-hydroxyls, as it tolerated substitutions ranging from fluorine to an azido group. In the base, the C4 amine and C2 carbonyl groups make important contacts, while the N3 nitrogen does not. However, adding a methyl group to N3 dramatically reduced binding, indicating that mass at this position sterically hinders binding. Adding a group at C5 had either no effect or slightly enhanced binding. To determine if the transporter recognizes these CMP analogues as substrates, we assayed them for their ability to trans stimulate CMP-sialic acid import. These data suggest that the enzyme transports a wide variety of NMPs, and the rate of transport is inversely proportional to the K(I) of the analogue. The importance of our findings for understanding the specificities of the different nucleotide-sugar tranlocators and the design of novel glycosylation inhibitors are discussed.

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 score0.012
Threshold uncertainty score0.699

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations21
Published2001
Admission routes1
Has abstractyes

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