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Record W2801808374 · doi:10.1111/febs.14489

Structural basis for nucleotide recognition by the ectoenzyme <scp>CD</scp>203c

2018· article· en· W2801808374 on OpenAlexafffund
A. Gorelik, Antsa Randriamihaja, Katalin Illes, Bhushan Nagar

Bibliographic record

VenueFEBS Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsMcGill University
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Research Council CanadaCanada Foundation for InnovationUniversity of Saskatchewan
KeywordsNucleotideEnzymeBiochemistryPhosphodiesteraseExtracellularChemistryBiologyCell biologyGene

Abstract

fetched live from OpenAlex

The ecto-nucleotide pyrophosphatase/phosphodiesterase (NPP) enzyme family modulates purinergic signaling by degrading extracellular nucleotides. CD203c (NPP3, ENPP3) regulates the inflammatory response of basophils via ATP hydrolysis and is a marker for allergen sensitivity on the surface of these cells. Multiple other roles and substrates have also been proposed for this protein. In order to gain insight into its molecular functions, we determined the crystal structure of human NPP3 as well as its complex with an ATP analog. The enzyme exhibits little preference for nucleobase type, and forms specific contacts with the alpha and beta phosphate groups of its ligands. Dimerization of the protein does not affect its catalytic activity. These findings expand our understanding of substrate recognition within the NPP family. DATABASE: Structural data are available in the Protein Data Bank under the accession numbers 6C01 (human NPP3) and 6C02 (human NPP3 T205A N594S with AMPCPP).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.019
GPT teacher head0.256
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 source (direct Gemma or distilled Codex), 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

Citations42
Published2018
Admission routes2
Has abstractyes

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