Activation and Function of the MAPKs and Their Substrates, the MAPK-Activated Protein Kinases
Bibliographic record
Abstract
The mitogen-activated protein kinases (MAPKs) regulate diverse cellular programs by relaying extracellular signals to intracellular responses. In mammals, there are more than a dozen MAPK enzymes that coordinately regulate cell proliferation, differentiation, motility, and survival. The best known are the conventional MAPKs, which include the extracellular signal-regulated kinases 1 and 2 (ERK1/2), c-Jun amino-terminal kinases 1 to 3 (JNK1 to -3), p38 (α, β, γ, and δ), and ERK5 families. There are additional, atypical MAPK enzymes, including ERK3/4, ERK7/8, and Nemo-like kinase (NLK), which have distinct regulation and functions. Together, the MAPKs regulate a large number of substrates, including members of a family of protein Ser/Thr kinases termed MAPK-activated protein kinases (MAPKAPKs). The MAPKAPKs are related enzymes that respond to extracellular stimulation through direct MAPK-dependent activation loop phosphorylation and kinase activation. There are five MAPKAPK subfamilies: the p90 ribosomal S6 kinase (RSK), the mitogen- and stress-activated kinase (MSK), the MAPK-interacting kinase (MNK), the MAPK-activated protein kinase 2/3 (MK2/3), and MK5 (also known as p38-regulated/activated protein kinase [PRAK]). These enzymes have diverse biological functions, including regulation of nucleosome and gene expression, mRNA stability and translation, and cell proliferation and survival. Here we review the mechanisms of MAPKAPK activation by the different MAPKs and discuss their physiological roles based on established substrates and recent discoveries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".