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Record W2142612589 · doi:10.1021/jm201417e

Dynamic Combinatorial Mass Spectrometry Leads to Inhibitors of a 2-Oxoglutarate-Dependent Nucleic Acid Demethylase

2012· article· en· W2142612589 on OpenAlexaff
Esther C. Y. Woon, Marina Demetriades, Eleanor A. L. Bagg, Wei Shen Aik, Svetlana M. Krylova, H. Y. Jerome, Mun Chiang Chan, Louise J. Walport, David W. Wegman, Kevin N. Dack, M.A. McDonough, Sergey N. Krylov, Christopher J. Schofield

Bibliographic record

VenueJournal of Medicinal Chemistry · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsYork University
FundersBiotechnology and Biological Sciences Research CouncilPfizer UKWellcome Trust
KeywordsAlkBChemistryNucleic acidSmall moleculeBiochemistryDemethylaseComputational biologyDrug discoveryMass spectrometryEnzymeCombinatorial chemistryDNADNA repairBiology

Abstract

fetched live from OpenAlex

2-Oxoglutarate-dependent nucleic acid demethylases are of biological interest because of their roles in nucleic acid repair and modification. Although some of these enzymes are linked to physiology, their regulatory roles are unclear. Hence, there is a desire to develop selective inhibitors for them; we report studies on AlkB, which reveal it as being amenable to selective inhibition by small molecules. Dynamic combinatorial chemistry linked to mass spectrometric analyses (DCMS) led to the identification of lead compounds, one of which was analyzed by crystallography. Subsequent structure-guided studies led to the identification of inhibitors of improved potency, some of which were shown to be selective over two other 2OG oxygenases. The work further validates the use of the DCMS method and will help to enable the development of inhibitors of nucleic acid modifying 2OG oxygenases both for use as functional probes and, in the longer term, for potential therapeutic use.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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

Citations53
Published2012
Admission routes1
Has abstractyes

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