MétaCan
Menu
Back to cohort
Record W2078154375 · doi:10.1074/jbc.m111.331553

Identification of Residues That Underpin Interactions within the Eukaryotic Initiation Factor (eIF2) 2B Complex

2012· article· en· W2078154375 on OpenAlexaff
Xuemin Wang, Noel C. Wortham, Rui Liu, Christopher G. Proud

Bibliographic record

VenueJournal of Biological Chemistry · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsUniversity of British Columbia
FundersBiotechnology and Biological Sciences Research Council
KeywordseIF2BiologyProtein subunitGeneticsBiochemistryGeneRNA

Abstract

fetched live from OpenAlex

Eukaryotic initiation factor 2B (eIF2B) plays a key role in protein synthesis and in its control. It comprises five different subunits, α-ε, of which eIF2Bε contains the catalytic domain. Formation of the complete complex is crucial for full activity and proper control of eIF2B. Mutations in the genes for eIF2B cause an often severe neurological disorder, "vanishing white matter." eIF2Bγ and eIF2Bε contain homologous and conserved domains with sequence similarity to nucleotidyl transferases (NTs) and acyl transferases and can form a binary complex. The latter contain a hexad repeat that mainly comprises isoleucyl residues (hence termed the "I-patch" region). These data reveal that certain residues in the NT domains of eIF2Bγ/ε, which are highly conserved throughout eukaryotes, play key roles in the interactions between subunits in the eIF2B complex. Our data show that the I-patch regions are important in the interactions between the catalytic eIF2Bγε complex and the other subunits. We also studied the functional effects of vanishing white matter mutations in the NT and I-patch domains. Lastly, our data show that eIF2Bγ promotes the expression of eIF2Bε, providing a mechanism for achieving correct stoichiometry of these eIF2B subunits in the cell.

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 score0.277
Threshold uncertainty score0.217

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 head0.072
GPT teacher head0.317
Teacher spread0.246 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biological ChemistrySame topicRNA regulation and diseaseFrench-language works237,207