GSK-3: tricks of the trade for a multi-tasking kinase
Bibliographic record
Abstract
Glycogen synthase kinase 3 (GSK-3) is a multifunctional serine/threonine kinase found in all eukaryotes. The enzyme is a key regulator of numerous signalling pathways, including cellular responses to Wnt, receptor tyrosine kinases and G-protein-coupled receptors and is involved in a wide range of cellular processes, ranging from glycogen metabolism to cell cycle regulation and proliferation. GSK-3 is unusual in that it is normally active in cells and is primarily regulated through inhibition of its activity. Another peculiarity compared with other protein kinases is its preference for primed substrates, that is, substrates previously phosphorylated by another kinase. Several recent advances have improved our understanding of GSK-3 regulation in multiple pathways. These include the solution of the crystal structure of GSK-3, which has provided insight into GSK-3's penchant for primed substrates and the regulation of GSK-3 by serine phosphorylation, and findings related to the involvement of GSK-3 in the Wnt/beta-catenin and Hedgehog pathways. Finally, since increased GSK-3 activity may be linked to pathology in diseases such as Alzheimer's disease and non-insulin-dependent diabetes mellitus, several new GSK-3 inhibitors, such as the aloisines, the paullones and the maleimides, have been developed. Although they are just starting to be characterized in cell culture experiments, these new inhibitors hold promise as therapeutic agents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".