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Record W2962015369 · doi:10.1002/chem.201902529

<sup>19</sup>F NMR Monitoring of Reversible Protein Post‐Translational Modifications: Class D β‐Lactamase Carbamylation and Inhibition

2019· article· en· W2962015369 on OpenAlexaff
Emma van Groesen, Christopher T. Lohans, Jürgen Brem, Kristina M. J. Aertker, Timothy D. W. Claridge, Christopher J. Schofield

Bibliographic record

VenueChemistry - A European Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsQueen's University
FundersBiotechnology and Biological Sciences Research CouncilEngineering and Physical Sciences Research CouncilMedical Research CouncilWellcome Trust
KeywordsChemistryClass (philosophy)StereochemistryBiochemistryPhilosophy

Abstract

fetched live from OpenAlex

Abstract Bacterial production of β‐lactamases with carbapenemase activity is a global health threat. The active sites of class D carbapenemases such as OXA‐48, which is of major clinical importance, uniquely contain a carbamylated lysine residue which is essential for catalysis. Although there is significant interest in characterizing this post‐translational modification, and it is a promising inhibition target, protein carbamylation is challenging to monitor in solution. We report the use of 19F NMR spectroscopy to monitor the carbamylation state of 19F‐labelled OXA‐48. This method was used to investigate the interactions of OXA‐48 with clinically used serine β‐lactamase inhibitors, including avibactam and vaborbactam. Crystallographic studies on 19F‐labelled OXA‐48 provide a structural rationale for the sensitivity of the 19F label to active site interactions. The overall results demonstrate the use of 19F NMR to monitor reversible covalent post‐translational modifications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2019
Admission routes1
Has abstractyes

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Same venueChemistry - A European JournalSame topicAntibiotic Resistance in BacteriaFrench-language works237,207