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Critical Role of Ser-520 Phosphorylation for Membrane Recruitment and Activation of the ZAP-70 Tyrosine Kinase in T Cells

2003· article· en· W2171214509 on OpenAlexafffund
Yaoming Yang, Patricia Villain, Tomas Mustelin, Clément Couture

Bibliographic record

VenueMolecular and Cellular Biology · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNational Cancer InstituteNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesCancer Research Society
KeywordsBiologyPhosphorylationCell biologyTyrosine phosphorylationTyrosine kinaseKinasePhosphorylation cascadeTyrosineSignal transductionBiochemistryProtein phosphorylationProtein kinase A

Abstract

fetched live from OpenAlex

Regulation of protein tyrosine kinases (PTKs) by tyrosine phosphorylation is well recognized; in fact, nearly all PTKs require phosphorylation of tyrosine residues in their "activation loop" for catalytic activity. In contrast, the phosphorylation of PTKs on serine and threonine residues has not been studied nearly as much. We report that the ZAP-70 PTK contains predominately phosphoserine in normal T lymphocytes as well as in Jurkat T leukemia cells. We have identified one site of phosphorylation as Ser-520 and find this site to be important for the recruitment and activation of ZAP-70 in T cells. Mutant ZAP-70-S520A had reduced ability to autophosphorylate and to mediate antigen receptor-induced interleukin 2 gene activation and was not enriched at the plasma membrane. These defects were rescued by addition of a myristylation signal to the N terminus of ZAP-70-S520A to force its plasma membrane and lipid raft localization. We conclude that phosphorylation of ZAP-70 at Ser-520 plays an important role in the correct localization of ZAP-70 and in priming ZAP-70 for its acute recruitment and activation upon antigen receptor ligation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.244
Teacher spread0.233 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2003
Admission routes2
Has abstractyes

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