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Record W2050897598 · doi:10.1074/jbc.m110.205153

The Dynamics and Mechanism of SUMO Chain Deconjugation by SUMO-specific Proteases

2011· article· en· W2050897598 on OpenAlexaff
Miklós Békés, John Prudden, Tharan Srikumar, Brian Raught, Michael N. Boddy, Guy S. Salvesen

Bibliographic record

VenueJournal of Biological Chemistry · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsOccupational Cancer Research CentreOntario Institute for Cancer Research
FundersNational Institute of Allergy and Infectious DiseasesNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institutes of HealthLeukemia and Lymphoma Society
KeywordsSUMO proteinProteasesSUMO enzymesMechanism (biology)ChemistryUbiquitinBiochemistryCell biologyBiologyEnzymeGene

Abstract

fetched live from OpenAlex

SUMOylation of proteins is a cyclic process that requires both conjugation and deconjugation of SUMO moieties. Besides modification by a single SUMO, SUMO chains have also been observed, yet the dynamics of SUMO conjugation/deconjugation remain poorly understood. Using a non-deconjugatable form of SUMO we demonstrate the underappreciated existence of SUMO chains in vivo, we highlight the importance of SUMO deconjugation, and we demonstrate the highly dynamic nature of the SUMO system. We show that SUMO-specific proteases (SENPs) play a crucial role in the dynamics of SUMO chains in vivo by constant deconjugation. Preventing deSUMOylation in Schizosaccharomyces pombe results in slow growth and a sensitivity to replication stress, highlighting the biological requirement for deSUMOylation dynamics. Furthermore, we present the mechanism of SUMO chain deconjugation by SENPs, which occurs via a stochastic mechanism, resulting in cleavage anywhere within a chain. Our results offer mechanistic insights into the workings of deSUMOylating proteases and highlight their importance in the homeostasis of (poly)SUMO-modified substrates.

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 score0.003
Threshold uncertainty score0.284

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 head0.022
GPT teacher head0.224
Teacher spread0.201 · 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

Citations80
Published2011
Admission routes1
Has abstractyes

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