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Record W2345852749 · doi:10.1021/jacs.6b03036

Global Profiling of Acetyltransferase Feedback Regulation

2016· article· en· W2345852749 on OpenAlexaff
David C. Montgomery, Julie M. Garlick, Rhushikesh A. Kulkarni, Abdellah Allali‐Hassani, Yin‐Ming Kuo, Andrew J. Andrews, Hong Wu, Masoud Vedadi, Jordan L. Meier

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersNational Cancer InstituteNational Institutes of HealthNational Institute of General Medical SciencesWellcome Trust
KeywordsChemistryAcetyltransferaseComputational biologyAcetyltransferasesProfiling (computer programming)MetaboliteLysineProteomeBiochemistryCombinatorial chemistryAcetylationComputer scienceBiologyGeneAmino acid

Abstract

fetched live from OpenAlex

Lysine acetyltransferases (KATs) are key mediators of cell signaling. Methods capable of providing new insights into their regulation thus constitute an important goal. Here we report an optimized platform for profiling KAT-ligand interactions in complex proteomes using inhibitor-functionalized capture resins. This approach greatly expands the scope of KATs, KAT complexes, and CoA-dependent enzymes accessible to chemoproteomic methods. This enhanced profiling platform is then applied in the most comprehensive analysis to date of KAT inhibition by the feedback metabolite CoA. Our studies reveal that members of the KAT superfamily possess a spectrum of sensitivity to CoA and highlight NAT10 as a novel KAT that may be susceptible to metabolic feedback inhibition. This platform provides a powerful tool to define the potency and selectivity of reversible stimuli, such as small molecules and metabolites, that regulate KAT-dependent signaling.

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 score0.013
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.238
Teacher spread0.232 · 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
Published2016
Admission routes1
Has abstractyes

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