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Record W2090901060 · doi:10.1074/jbc.275.2.1405

The Noncatalytic Domain of Protein-tyrosine Phosphatase-PEST Targets Paxillin for Dephosphorylation in Vivo

2000· article· en· W2090901060 on OpenAlexaff
Yu Shen, Patrick D. Lyons, Marion A. Cooley, Dominique Davidson, André Veillette, Ravi Salgia, James D. Griffin, Michael D. Schaller

Bibliographic record

VenueJournal of Biological Chemistry · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Institutes of Health
KeywordsDephosphorylationPaxillinIn vivoProtein tyrosine phosphatasePhosphorylationCell biologyPhosphataseChemistryTyrosineBiochemistryDomain (mathematical analysis)BiologyBiotechnologyFocal adhesion

Abstract

fetched live from OpenAlex

The noncatalytic domain of protein-tyrosine phosphatase (PTP)-PEST contains a binding site for the focal adhesion-associated protein paxillin. This binding site has been narrowed to a 52-residue sequence that is composed of two nonoverlapping, weak paxillin binding sites. The PTP-PEST binding site on paxillin has been mapped to the two carboxyl-terminal LIM (lin11, isl-1, and mec-3) domains. Transient expression of PTP-PEST reduced tyrosine phosphorylation of p130(cas), as anticipated. A PTP-PEST mutant defective for binding p130(cas) does not cause a reduction in its tyrosine phosphorylation in vivo. Expression of PTP-PEST also caused a reduction of phosphotyrosine on paxillin. Expression of mutants of PTP-PEST with deletions in the paxillin-binding site did not associate with paxillin in vivo and failed to cause a reduction in the phosphotyrosine content of paxillin. These results demonstrate that paxillin can serve as a PTP-PEST substrate in vivo and support the model that a noncatalytic domain interaction recruits paxillin to PTP-PEST to facilitate its dephosphorylation.

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)0.001
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 score0.037
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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

Citations69
Published2000
Admission routes1
Has abstractyes

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