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Record W4416998133 · doi:10.1038/s42004-025-01794-7

Targeting bacterial kinases as a strategy to counteract antibiotic resistance

2025· article· en· W4416998133 on OpenAlexafffund
Vanessa Buffa, Julien Kowalewski, Robin C. E. Deutscher, Matijas Cica, Marion Richardoz, Mathilde Tomaszczyk, Andreas Krämer, Stefan Knapp, Catherine Dunyach‐Remy, Katharina Rox, Jean‐François Guichou, Corinne Lionne, Felix Hausch

Bibliographic record

VenueCommunications Chemistry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsStructural Genomics Consortium
FundersAssociation Vaincre la MucoviscidoseFrench Infrastructure for Integrated Structural BiologyMitacsBundesministerium für Bildung und ForschungAgence Nationale de la RechercheAssociation Nationale de la Recherche et de la TechnologieDeutsches Zentrum für Infektionsforschung
KeywordsADMEKinaseAntibioticsAminoglycosideAntibiotic resistanceDrug resistanceKinome

Abstract

fetched live from OpenAlex

Antibiotic resistance is rapidly emerging as one of the most critical health threats, with resistant microorganisms progressively diminishing the effectiveness of established antibiotics. As a result, the development of therapeutic approaches that effectively target resistant pathogens is of utmost importance. In this study, we developed inhibitors for APH(2")-IVa, a bacterial kinase conveying resistance to aminoglycoside antibiotics. Starting from a hit of a fragment-based screening, we explored the inhibitory motif by structure-based design, ultimately leading to a series of triazole analogues. Advanced analogues displayed promising ADME properties, emerging selectivity vs a panel of human kinases, permeability in both Gram-positive and Gram-negative bacteria, and a moderate antibiotic efficacy for clinical strains of P. aeruginosa. Taken together, our results suggest inhibition of bacterial kinases could be a promising option to reinstall the efficacy of aminoglycoside antibiotics.

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 score0.276
Threshold uncertainty score0.710

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 science0.0010.001
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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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