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Record W2772559207 · doi:10.1021/acs.jmedchem.7b01674

Discovery of Potent and Selective Allosteric Inhibitors of Protein Arginine Methyltransferase 3 (PRMT3)

2017· article· en· W2772559207 on OpenAlexaff
H. Ümit Kanıskan, Mohammad S. Eram, Kehao Zhao, Magdalena M. Szewczyk, Xiaobao Yang, Keith T. Schmidt, Xiao Luo, Sean Xiao, Miao Dai, Feng He, Irene Zang, Ying Lin, Fengling Li, E. Dobrovetsky, David Smil, Sun‐Joon Min, Jennifer Lin‐Jones, Matthieu Schapira, Peter Atadja, En Li, Dalia Baršytė-Lovejoy, C.H. Arrowsmith, Peter J. Brown, Feng Liu, Zhengtian Yu, Masoud Vedadi, Jian Jin

Bibliographic record

VenueJournal of Medicinal Chemistry · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsPrincess Margaret Cancer CentreStructural Genomics ConsortiumUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Cancer InstituteNational Institute of Child Health and Human DevelopmentNational Institute of General Medical SciencesNational Natural Science Foundation of ChinaWellcome Trust
KeywordsAllosteric regulationChemistryStructure–activity relationshipBiochemistryEnzymeArginineDrug discoveryChemical synthesisStereochemistryIn vitroAmino acid

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide PRMT3 catalyzes the asymmetric dimethylation of arginine residues of various proteins. It is crucial for maturation of ribosomes and has been implicated in several diseases. We recently disclosed a highly potent, selective, and cell-active allosteric inhibitor of PRMT3, compound 4 . Here, we report comprehensive structure–activity relationship studies that target the allosteric binding site of PRMT3. We conducted design, synthesis, and evaluation of novel compounds in biochemical, selectivity, and cellular assays that culminated in the discovery of 4 and other highly potent (IC 50 values: ∼10–36 nM), selective, and cell-active allosteric inhibitors of PRMT3 (compounds 29, 30, 36, and 37 ). In addition, we generated compounds that are very close analogs of these potent inhibitors but displayed drastically reduced potency as negative controls (compounds 49 – 51 ). These inhibitors and negative controls are valuable chemical tools for the biomedical community to further investigate biological functions and disease associations of PRMT3.

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 score0.004
Threshold uncertainty score0.393

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 head0.006
GPT teacher head0.248
Teacher spread0.241 · 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

Citations41
Published2017
Admission routes1
Has abstractyes

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