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Record W2261751226 · doi:10.12688/f1000research.7220.1

The business of deubiquitination – location, location, location

2016· preprint· en· W2261751226 on OpenAlexafffund
Erin S. Coyne, Simon S. Wing

Bibliographic record

VenueF1000Research · 2016
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsDeubiquitinating enzymeUbiquitinAllosteric regulationBiologyFunction (biology)Cell biologyEnzymeComputational biologyBiochemistryGene

Abstract

fetched live from OpenAlex

A majority of proteins in the cell can be modified by ubiquitination, thereby altering their function or stability. This ubiquitination is controlled by both ubiquitinating and deubiquitinating enzymes (DUBs). The number of ubiquitin ligases exceeds that of DUBs by about eightfold, indicating that DUBs may have much broader substrate specificity. Despite this, DUBs have been shown to have quite specific physiological functions. This functional specificity is likely due to very precise regulation of activity arising from the sophisticated use of all mechanisms of enzyme regulation. In this commentary, we briefly review key features of DUBs with more emphasis on regulation. In particular, we focus on localization of the enzymes as a critical regulatory mechanism which when integrated with control of expression, substrate activation, allosteric regulation, and post-translational modifications results in precise spatial and temporal deubiquitination of proteins and therefore specific physiological functions. Identification of compounds that target the structural elements in DUBs that dictate localization may be a more promising approach to development of drugs with specificity of action than targeting the enzymatic activity, which for most DUBs is dependent on a thiol group that can react non-specifically with many compounds in large-scale screening.

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)0.002
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 score0.535
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.027
GPT teacher head0.312
Teacher spread0.285 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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