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Record W1872801834 · doi:10.3934/molsci.2015.4.382

SUMO modulation of protein aggregation and degradation

2015· article· en· W1872801834 on OpenAlexaff
Marco Feligioni, Serena Marcelli, Erin Knock, Urooba Nadeem, Ottavio Arancio, Paul E. Fraser

Bibliographic record

VenueAIMS molecular science · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsSUMO proteinProtein aggregationNeurodegenerationAmyotrophic lateral sclerosisUbiquitinCell biologyProteasomeProtein degradationBiologyIntracellularNeuroscienceChemistryDiseaseBiochemistryMedicinePathology

Abstract

fetched live from OpenAlex

Small ubiquitin-like modifier (SUMO) conjugation and binding to target proteins regulate a wide variety of cellular pathways. The functional aspects of SUMOylation include changes in protein-protein interactions, intracellular trafficking as well as protein aggregation and degradation. SUMO has also been linked to specialized cellular pathways such as neuronal development and synaptic transmission. In addition, SUMOylation is associated with neurological diseases associated with abnormal protein accumulations. SUMOylation of the amyloid and tau proteins involved in Alzheimer's disease and other tauopathies may contribute to changes in protein solubility and proteolytic processing. Similar events have been reported for α-synuclein aggregates found in Parkinson's disease, polyglutamine disorders such as Huntington's disease as well as protein aggregates found in amyotrophic lateral sclerosis (ALS). This review provides a detailed overview of the impact SUMOylation has on the etiology and pathology of these related neurological diseases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2015
Admission routes1
Has abstractyes

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