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Record W3191319644 · doi:10.3390/ijms22158211

The EphB6 Receptor: Kinase-Dead but Very Much Alive

2021· review· en· W3191319644 on OpenAlexaff
Timothy G. Strozen, Jessica C. Sharpe, Evelyn D. Harris, Maruti Uppalapati, Behzad M. Toosi

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2021
Typereview
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsErythropoietin-producing hepatocellular (Eph) receptorAllosteric regulationBiologyProtein kinase domainEphrinCell biologySignal transductionReceptor tyrosine kinaseSH2 domainTyrosine kinaseReceptorBiochemistryGene

Abstract

fetched live from OpenAlex

The Eph receptor tyrosine kinase member EphB6 is a pseudokinase, and similar to other pseudoenzymes has not attracted an equivalent amount of interest as its enzymatically-active counterparts. However, a greater appreciation for the role pseudoenzymes perform in expanding the repertoire of signals generated by signal transduction systems has fostered more interest in the field. EphB6 acts as a molecular switch that is capable of modulating the signal transduction output of Eph receptor clusters. Although the biological effects of EphB6 activity are well defined, the molecular mechanisms of EphB6 function remain enigmatic. In this review, we use a comparative approach to postulate how EphB6 acts as a scaffold to recruit adaptor proteins to an Eph receptor cluster and how this function is regulated. We suggest that the evolutionary repurposing of EphB6 into a kinase-independent molecular switch in mammals has involved repurposing the kinase activation loop into an SH3 domain-binding site. In addition, we suggest that EphB6 employs the same SAM domain linker and juxtamembrane domain allosteric regulatory mechanisms that are used in kinase-positive Eph receptors to regulate its scaffold function. As a result, although kinase-dead, EphB6 remains a strategically active component of Eph receptor signaling.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0030.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.374
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2021
Admission routes1
Has abstractyes

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