Activation loop phosphorylation of the atypical MAP kinases ERK3 and ERK4 is required for binding, activation and cytoplasmic relocalization of MK5
Bibliographic record
Abstract
Mitogen-activated protein (MAP) kinases are typical examples of protein kinases whose enzymatic activity is mainly controlled by activation loop phosphorylation. The classical MAP kinases ERK1/ERK2, JNK, p38 and ERK5 all contain the conserved Thr-Xxx-Tyr motif in their activation loop that is dually phosphorylated by members of the MAP kinase kinases family. Much less is known about the regulation of the atypical MAP kinases ERK3 and ERK4. These kinases display structural features that distinguish them from other MAP kinases, notably the presence of a single phospho-acceptor site (Ser-Glu-Gly) in the activation loop. Here, we show that ERK3 and ERK4 are phosphorylated in their activation loop in vivo. This phosphorylation is exerted, at least in part, in trans by an upstream cellular kinase. Contrary to classical MAP kinases, activation loop phosphorylation of ERK3 and ERK4 is detected in resting cells and is not further stimulated by strong mitogenic or stress stimuli. However, phosphorylation can be modulated indirectly by interaction with the substrate MAP kinase-activated protein kinase 5 (MK5). Importantly, we found that activation loop phosphorylation of ERK3 and ERK4 stimulates their intrinsic catalytic activity and is required for the formation of stable active complexes with MK5 and, consequently, for efficient cytoplasmic redistribution of ERK3/ERK4-MK5 complexes. Our results demonstrate the importance of activation loop phosphorylation in the regulation of ERK3/ERK4 function and highlight differences in the regulation of atypical MAP kinases as compared to classical family members.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".