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Record W4320491661 · doi:10.1021/acs.jproteome.2c00825

Interactomes of Glycogen Synthase Kinase-3 Isoforms

2023· article· en· W4320491661 on OpenAlexafffund
Kevin W. Cormier, Brett Larsen, Anne‐Claude Gingras, James R. Woodgett

Bibliographic record

VenueJournal of Proteome Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersCanadian Institutes of Health ResearchTerry Fox Research Institute
KeywordsGene isoformGSK-3InteractomeBiologyFunction (biology)KinaseBiochemistryHEK 293 cellsThreonineProteomicsProtein–protein interactionSerineComputational biologyPhosphorylationCell biologyGene

Abstract

fetched live from OpenAlex

Functional differentiation of the two isoforms of the protein-serine/threonine kinase, glycogen synthase kinase-3 (GSK-3), is an unsettled area of research. The isoforms are highly similar in structure and are largely redundant, though there is also evidence for specific roles. Identification of isoform-specific protein interactors may elucidate the differences in function and provide insight into isoform-selective regulation. We therefore sought to identify novel GSK-3 interaction partners and to examine differences in the interactomes of the two isoforms using both affinity purification and proximity-dependent biotinylation (BioID) mass spectrometry methods. While the interactomes of the two isomers are highly similar in HEK293 cells, BioID in HeLa cells yielded a variety of preys that are preferentially associated with one of the two isoforms. DCP1B, which favored GSK-3α, and MISP, which favored GSK-3β, were evaluated for reciprocal interactions. The differences in interactions between isoforms may help in understanding the distinct functions and regulation of the two isoforms as well as offer avenues for the development of isoform-specific strategies.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.398
Teacher spread0.344 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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