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Record W2766278911 · doi:10.1038/s41598-017-12777-7

Phosphorylable tyrosine residue 162 in the double-stranded RNA-dependent kinase PKR modulates its interaction with SUMO

2017· article· en· W2766278911 on OpenAlexaff
Carlos F. de la Cruz-Herrera, Maite Baz-Martínez, Ahmed El Motiam, Santiago Vidal, Manuel Collado, Anxo Vidal, Manuel S. Rodríguez, Mariano Estéban, Carmen Rivas

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsCanada Research Chairs
FundersInstituto de Salud Carlos IIIMinisterio de Economía y CompetitividadConsejo Nacional de Ciencia y Tecnología
KeywordsProtein kinase RPhosphorylationEIF-2 kinaseCell biologyBiologyTyrosineSUMO proteinProtein kinase ARNAPactRNA silencingChemistryBiochemistryRNA interferenceMitogen-activated protein kinase kinaseCyclin-dependent kinase 2Ubiquitin

Abstract

fetched live from OpenAlex

Activated dsRNA-dependent serine/threonine kinase PKR phosphorylates the alpha subunit of eukaryotic initiation factor 2 (eIF2α), resulting in a shut-off of general translation, induction of apoptosis, and inhibition of virus replication. PKR can be activated by binding to dsRNA or cellular proteins such as PACT/RAX, or by its conjugation to ISG15 or SUMO. Here, we demonstrate that PKR also interacts with SUMO in a non-covalent manner. We identify the phosphorylable tyrosine residue 162 in PKR (Y162) as a modulator of the PKR-SUMO non-covalent interaction as well as of the PKR SUMOylation. Finally, we show that the efficient SUMO-mediated eIF2α phosphorylation and inhibition of protein synthesis induced by PKR in response to dsRNA depend on this residue. In summary, our data identify a new mechanism of regulation of PKR activity and reinforce the relevance of both, tyrosine phosphorylation and SUMO interaction in controlling the activity of PKR.

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)0.002
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 score0.039
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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
Published2017
Admission routes1
Has abstractyes

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