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Record W2330712912 · doi:10.1021/ml500467h

Discovery of a Dual PRMT5–PRMT7 Inhibitor

2015· article· en· W2330712912 on OpenAlexafffund
David Smil, Mohammad S. Eram, Fengling Li, Magdalena M. Szewczyk, Peter J. Brown, Dalia Baršytė-Lovejoy, C.H. Arrowsmith, Masoud Vedadi, Matthieu Schapira

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsPrincess Margaret Cancer CentreStructural Genomics ConsortiumUniversity of Toronto
FundersOntario Genomics InstituteJanssen Research and DevelopmentOntario Ministry of Research, Innovation and ScienceOntario GenomicsWellcome TrustGenome CanadaTakeda Pharmaceutical CompanyAbbVieWellcomeGlaxoSmithKlineMinistero dello Sviluppo EconomicoBayerPfizerEli Lilly and CompanyOntario Ministry of Economic Development and InnovationNovartis FoundationBoehringer Ingelheim
KeywordsProtein arginine methyltransferase 5MethyltransferaseChromatinCell biologyRNA splicingMethylationBiologyChemistryGeneBiochemistryRNA

Abstract

fetched live from OpenAlex

The protein arginine methyltransferases PRMT7 and PRMT5, respectively, monomethylate and symmetrically dimethylate arginine side-chains of proteins involved in diverse cellular mechanisms, including chromatin-mediated control of gene transcription, splicing, and the RAS to ERK transduction cascade. It is believed that PRMT5 and PRMT7 act in conjunction to methylate their substrates, and genetic deletions support the notion that these enzymes derepress cell proliferation and migration in cancer. Using available structures of PRMT5, we designed DS-437, a PRMT5 inhibitor with an IC50 value of 6 μM against both PRMT5 and PRMT7 that is inactive against 29 other human protein-, DNA-, and RNA-methyltransferases and inhibits symmetrical dimethylation of PRMT5 substrates in cells. This compound behaves as a cofactor competitor and represents a valid scaffold to interrogate the potential of the PRMT5-PRMT7 axis as a target for therapy.

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 score0.017
Threshold uncertainty score0.634

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations94
Published2015
Admission routes2
Has abstractyes

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