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Record W4401089771 · doi:10.1021/jacsau.4c00442

Paralogue-Selective Degradation of the Lysine Acetyltransferase EP300

2024· article· en· W4401089771 on OpenAlexfundno aff
Xuemin Chen, McKenna C. Crawford, Ying Xiong, Anver Basha Shaik, Kiall F. Suazo, Ludwig G. Bauer, Manini S. Penikalapati, Joycelyn H. Williams, K. Huber, Thorkell Andressen, Rolf E. Swenson, Jordan L. Meier

Bibliographic record

VenueJACS Au · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institutes of HealthInnovative Medicines InitiativeEuropean CommissionMcGill UniversityDiamond Light SourceHorizon 2020 Framework ProgrammeKungliga Tekniska HögskolanDana-Farber Cancer InstituteEuropean Federation of Pharmaceutical Industries and AssociationsCenter for Cancer Research
KeywordsBiologyCREB-binding proteinProteasomeCell biologyBiochemistryGeneTranscription factor

Abstract

fetched live from OpenAlex

The transcriptional coactivators EP300 and CREBBP are critical regulators of gene expression that share high sequence identity but exhibit nonredundant functions in basal and pathological contexts. Here, we report the development of a bifunctional small molecule, MC-1, capable of selectively degrading EP300 over CREBBP. Using a potent aminopyridine-based inhibitor of the EP300/CREBBP catalytic domain in combination with a VHL ligand, we demonstrate that MC-1 preferentially degrades EP300 in a proteasome-dependent manner. Mechanistic studies reveal that selective degradation cannot be predicted solely by target engagement or ternary complex formation, suggesting additional factors govern paralogue-specific degradation. MC-1 inhibits cell proliferation in a subset of cancer cell lines and provides a new tool to investigate the noncatalytic functions of EP300 and CREBBP. Our findings expand the repertoire of EP300/CREBBP-targeting chemical probes and offer insights into the determinants of selective degradation of highly homologous proteins.

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 score0.035
Threshold uncertainty score0.203

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 head0.008
GPT teacher head0.239
Teacher spread0.230 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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