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Record W2039131752 · doi:10.12688/f1000research.1-4.v1

Rapamycin-insensitive mTORC1 activity controls eIF4E:4E-BP1 binding

2012· preprint· en· W2039131752 on OpenAlexafffund
Mark Livingstone, Michael Bidinosti

Bibliographic record

VenueF1000Research · 2012
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersTerry Fox FoundationPasteur Foundation
KeywordsmTORC1PI3K/AKT/mTOR pathwaymTORC2Mechanistic target of rapamycinRPTORPhosphorylationFKBPSerineCell biologyKinaseP70-S6 Kinase 1BiologyChemistrySignal transduction

Abstract

fetched live from OpenAlex

The recent development of mammalian target of rapamycin (mTOR) kinase domain inhibitors and genetic dissection of rapamycin-sensitive and -insensitive mTOR protein complexes (mTORC1 and mTORC2) have revealed that phosphorylation of the mTOR substrate 4E-BP1 on amino acids Thr37 and/or Thr46 represents a rapamycin-insensitive activity of mTORC1. Despite numerous previous reports utilizing serine (Ser)-to-alanine (Ala) and threonine (Thr)-to-Ala phosphorylation site mutants of 4E-BP1 to assess which post-translational modification(s) directly regulate binding to eIF4E, an ambiguous understanding persists. This manuscript demonstrates that the initial, rapamycin-insensitive phosphorylation event at Thr46 is sufficient to prevent eIF4E:4E-BP1 binding. This finding is relevant, particularly as mTOR kinase domain inhibitors continue to be assessed for clinical efficacy, since it clarifies a difference between the action of these second-generation mTOR inhibitors and those of rapamycin analogues.

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 score0.004
Threshold uncertainty score0.012

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.052
GPT teacher head0.359
Teacher spread0.307 · 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

Citations40
Published2012
Admission routes2
Has abstractyes

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