Structures of an Eph receptor tyrosine kinase and its potential activation mechanism
Bibliographic record
Abstract
Eph receptor tyrosine kinases (RTKs) and their ephrin ligands play a crucial role in both physiological and pathophysiological processes, including tumourigenesis. A previous study of Eph RTKs established a regulatory role for the juxtamembrane segment (JMS) in kinase activation through the phosphorylation of two tyrosines within the JMS. Here, structures of EphA2 representing various activation states are presented. By determining the unphosphorylated inactive and phosphorylated active structures as well as an alternative conformation, conformational changes during kinase activation have been revealed. It is shown that phosphorylation of a tyrosine residue (Tyr772) in the activation loop without direct involvement of the JMS is sufficient to activate the EphA2 kinase. This mechanistic finding is in contrast to the mechanism of other Eph RTKs, such as EphB2, in which phosphorylation of the two JMS tyrosines initiates the dissociation of the JMS and triggers activation-loop phosphorylation for kinase activation. Furthermore, experiments demonstrate that the EphA2 substrate PTEN, a phosphatase that has been implicated in tumour suppression, acts to regulate the phosphorylation states of EphA2, exemplifying a unique reciprocal enzyme-substrate system. Based on these studies, it is therefore suggested that EphA2 may possess an alternate activation mechanism distinct from other Eph RTKs.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".