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Record W1689468366 · doi:10.1186/1471-2105-6-6

Cyclic nucleotide binding proteins in the Arabidopsis thaliana and Oryza sativa genomes

2005· article· en· W1689468366 on OpenAlexafffund
Dave Bridges, M.E. Fraser, Greg B. G. Moorhead

Bibliographic record

VenueBMC Bioinformatics · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsUniversity of CalgaryUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaFondation pour la Recherche Médicale
KeywordsOryza sativaNucleotideBiologyArabidopsis thalianaArabidopsisCyclic nucleotideEffectorProtein kinase AGenomeSecond messenger systemCyclic nucleotide-binding domainOryzaGeneticsBiochemistryCell biologyKinaseSignal transductionGeneMutant

Abstract

fetched live from OpenAlex

BACKGROUND: Cyclic nucleotides are ubiquitous intracellular messengers. Until recently, the roles of cyclic nucleotides in plant cells have proven difficult to uncover. With an understanding of the protein domains which can bind cyclic nucleotides (CNB and GAF domains) we scanned the completed genomes of the higher plants Arabidopsis thaliana (mustard weed) and Oryza sativa (rice) for the effectors of these signalling molecules. RESULTS: Our analysis found that several ion channels and a class of thioesterases constitute the possible cyclic nucleotide binding proteins in plants. Contrary to some reports, we found no biochemical or bioinformatic evidence for a plant cyclic nucleotide regulated protein kinase, suggesting that cyclic nucleotide functions in plants have evolved differently than in mammals. CONCLUSION: This paper provides a molecular framework for the discussion of cyclic nucleotide function in plants, and resolves a longstanding debate about the presence of a cyclic nucleotide dependent kinase in plants.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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 designSimulation or modeling
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

Citations90
Published2005
Admission routes2
Has abstractyes

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