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Record W2008640926 · doi:10.1002/cbic.201300739

Simultaneous Inhibition Assay for Human and Microbial Kinases via MALDI‐MS/MS

2014· article· en· W2008640926 on OpenAlexafffund
Anne Marie Smith, John D. Brennan

Bibliographic record

VenueChemBioChem · 2014
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinaseChemistryEnzymeBiochemistryAntimicrobialSmall molecule

Abstract

fetched live from OpenAlex

Selective inhibition of one kinase over another is a critical issue in drug development. For antimicrobial development, it is particularly important to selectively inhibit bacterial kinases, which can phosphorylate antimicrobial compounds such as aminoglycosides, without affecting human kinases. Previous work from our group showed the development of a MALDI-MS/MS assay for the detection of small molecule modulators of the bacterial aminoglycoside kinase APH3'IIIa. Herein, we demonstrate the development of an enhanced kinase MALDI-MS/MS assay involving simultaneous assaying of two kinase reactions, one for APH3'IIIa, and the other for human protein kinase A (PKA), which leads to an output that provides direct information on selectivity and mechanism of action. Specificity of the respective enzyme substrates were verified, and the assay was validated through generation of Z'-factors of 0.55 for APH3'IIIa with kanamycin and 0.60 for PKA with kemptide. The assay was used to simultaneously screen a kinase-directed library of mixtures of ten compounds each against both enzymes, leading to the identification of selective inhibitors for each enzyme as well as one non-selective inhibitor following mixture deconvolution.

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 score0.034
Threshold uncertainty score0.891

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)0.0010.000

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 head0.247
Teacher spread0.238 · 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

Citations5
Published2014
Admission routes2
Has abstractyes

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