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Discovery of New Bromodomain Scaffolds by Biosensor Fragment Screening

2016· article· en· W2522007658 on OpenAlexfundno aff
Iva Navrátilová, Tonia Aristotelous, S. Picaud, A. Chaikuad, Stefan Knapp, Panagis Filappakopoulos, Andrew L. Hopkins

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsnot available
FundersMinistero dello Sviluppo EconomicoFundação de Amparo à Pesquisa do Estado de São PauloStructural Genomics ConsortiumOntario Ministry of Economic Development and InnovationGenome CanadaNovartis PharmaWellcome TrustEuropean Federation of Pharmaceutical Industries and AssociationsWellcomeMerckPfizer
KeywordsBromodomainBRD4PCAFFragment (logic)ChemistryComputational biologyDrug discoverySurface plasmon resonanceCombinatorial chemistryDNABiochemistryNanotechnologyComputer scienceBiologyMaterials scienceAlgorithmChromatinNanoparticleHistone

Abstract

fetched live from OpenAlex

The discovery of novel bromodomain inhibitors by fragment screening is complicated by the presence of dimethyl sulfoxide (DMSO), an acetyl-lysine mimetic, that can compromise the detection of low affinity fragments. We demonstrate surface plasmon resonance as a primary fragment screening approach for the discovery of novel bromodomain scaffolds, by describing a protocol to overcome the DMSO interference issue. We describe the discovery of several novel small molecules scaffolds that inhibit the bromodomains PCAF, BRD4, and CREBBP, representing canonical members of three out of the seven subfamilies of bromodomains. High-resolution crystal structures of the complexes of key fragments binding to BRD4(1), CREBBP, and PCAF were determined to provide binding mode data to aid the development of potent and selective inhibitors of PCAF, CREBBP, and BRD4.

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 score0.069
Threshold uncertainty score0.511

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 head0.222
Teacher spread0.215 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

Explore more

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