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Record W4298894219 · doi:10.17615/x53z-fd51

Discovery of a Selective, Substrate-Competitive Inhibitor of the Lysine Methyltransferase SETD8

2020· article· en· W4298894219 on OpenAlexfundno aff
Peter J. Brown, Jian Jin, Rachel M. Bleich, Anqi Ma, Fengling Li, Stephen V. Frye

Bibliographic record

VenueUNC Libraries · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesUniversity of North Carolina at Chapel HillNational Institutes of HealthMinistero dello Sviluppo EconomicoWellcome TrustEli Lilly CanadaGlaxoSmithKlinePfizerEli Lilly and CompanyOntario Ministry of Economic Development and InnovationV Foundation for Cancer Research
KeywordsMethyltransferaseLysineSubstrate (aquarium)ChemistryBiochemistryBiologyMethylationDNAAmino acid

Abstract

fetched live from OpenAlex

The lysine methyltransferase SETD8 is the only known methyltransferase that catalyzes monomethylation of histone H4 lysine 20 (H4K20). Monomethylation of H4K20 has been implicated in regulating diverse biological processes including the DNA damage response. In addition to H4K20, SETD8 monomethylates non-histone substrates including proliferating cell nuclear antigen (PCNA) and promotes carcinogenesis by deregulating PCNA expression. However, selective inhibitors of SETD8 are scarce. The only known selective inhibitor of SETD8 to date is nahuoic acid A, a marine natural product, which is competitive with the cofactor. Here, we report the discovery of the first substrate-competitive inhibitor of SETD8, UNC0379 (1). This small-molecule inhibitor is active in multiple biochemical assays. Its affinity to SETD8 was confirmed by ITC (isothermal titration calorimetry) and SPR (surface plasmon resonance) studies. Importantly, compound 1 is selective for SETD8 over 15 other methyltransferases. We also describe structure–activity relationships (SAR) of this series.

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 score0.015
Threshold uncertainty score0.303

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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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
Published2020
Admission routes1
Has abstractyes

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