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Record W4220987554 · doi:10.1016/j.slasd.2022.03.007

Development of a high-throughput assay to identify inhibitors of the ubiquitin-conjugating enzyme UBCH10

2022· article· en· W4220987554 on OpenAlexafffund
Patrick Cléroux, Laure Voisin, Sylvain Meloche

Bibliographic record

VenueSLAS DISCOVERY · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCanadian Cancer Society Research Institute
KeywordsUbiquitin ligaseUbiquitinFörster resonance energy transferEnzymeChemistryUbiquitin-conjugating enzymeBiochemistryRegulatorCancer cellHigh-throughput screeningBiologyMolecular biologyCell biologyCancerFluorescenceGeneGenetics

Abstract

fetched live from OpenAlex

UBCH10 is an ubiquitin-conjugating enzyme (E2) of the anaphase-promoting complex E3 ligase, a key regulator of the cell cycle. The UBCH10 gene and protein are frequently upregulated in multiple solid tumors, associated with an unfavorable outcome. Accumulating evidence from studies of human cancer cell lines, mouse transgenic models, and analyses of clinical samples suggest that UBCH10 is a potential cancer drug target. No small molecule inhibitor of UBCH10 has been reported in the literature. Here, we described the development and optimization of a novel time-resolved fluorescence resonance energy transfer (TR-FRET) UBCH10 assay based on the self-polyubiquitination of the enzyme in the absence of E3. The homogenous assay is robust, sensitive, and scalable to different multi-well formats for high-throughput screening (HTS). We demonstrate the suitability of the TR-FRET assay to identify chemical inhibitors of UBCH10 in a pilot HTS campaign.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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Same venueSLAS DISCOVERYSame topicUbiquitin and proteasome pathwaysFrench-language works237,207