MétaCan
Menu
Back to cohort
Record W4415967592 · doi:10.3390/biom15111563

Discovery of Drug-like Inhibitors of the Human Caf1/CNOT7 poly(A)-Selective Nuclease Using Compound Screening

2025· article· en· W4415967592 on OpenAlexfundno aff
Ishwinder Kaur, Lubna Hashmi, Peter M. Fischer, G. Sebastiaan Winkler

Bibliographic record

VenueBiomolecules · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council Canada
KeywordsNucleaseRibonucleaseProtein subunitEnzymeGeneTranslation (biology)Messenger RNADrug discovery

Abstract

fetched live from OpenAlex

The human Ccr4–Not complex is a central regulator of post-transcriptional gene regulation, impacting on translation and mRNA degradation. In mRNA degradation, Ccr4–Not participates in the shortening of the mRNA poly(A)-tail via two catalytic subunits. The Caf1 nuclease is encoded by the highly similar paralogues CNOT7 or CNOT8. In addition to its poly(A)-specific ribonuclease activity, this subunit also provides a structural role by binding Ccr4, the second catalytic nuclease subunit encoded by the paralogues CNOT6 or CNOT6L. To facilitate investigations into the roles of the Caf1 subunit, and to complement genetic tools, we set out to identify inhibitors of the enzymatic activity of Caf1/CNOT7. To this end, we screened a library of 10,880 chemically diverse, drug-like compounds using a fluorescence-based biochemical assay. This effort led to the discovery of 15 inhibitors of Caf1/CNOT7 with biochemical IC50 values below 25 μM. Molecular docking was performed to explore potential binding modes of these compounds. The compounds reported here may be useful to differentiate between catalytic and non-catalytic roles of Caf1/CNOT7. In addition, they may be valuable starting points for the development of more potent inhibitors of the Caf1/CNOT7 poly(A)-selective ribonuclease.

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 score0.414

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 head0.013
GPT teacher head0.290
Teacher spread0.277 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBiomoleculesSame topicRNA Research and SplicingFrench-language works237,207