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Record W4416759365 · doi:10.1038/s42004-025-01795-6

Fragment-based drug design of a bacterial kinase inhibitor capable of increasing the antibiotic sensitivity of clinical isolates

2025· article· en· W4416759365 on OpenAlexafffund
Julien Kowalewski, Robin C. E. Deutscher, Marion Richardoz, Mathilde Tomaszczyk, Muriel Gelin, Gilles Labesse, Felix Hausch, Gerard D. Wright, Catherine Dunyach‐Remy, Jean‐François Guichou, Corinne Lionne

Bibliographic record

VenueCommunications Chemistry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsMcMaster University
FundersBundesministerium für Bildung und ForschungAgence Nationale de la RechercheAssociation Vaincre la MucoviscidoseFrench Infrastructure for Integrated Structural BiologyMitacsGovernment of Canada
KeywordsPseudomonas aeruginosaAntibioticsStaphylococcus aureusAntibiotic resistanceAminoglycosideDrug resistanceDrugAntimicrobial

Abstract

fetched live from OpenAlex

According to the World Health Organization (WHO), antimicrobial resistance is a serious global health issue. Overcoming antibiotic resistance involves several strategies, including the inhibition of resistance mechanisms. Among the various resistance mechanisms, aminoglycoside phosphotransferases (APHs) catalyze the transfer of the γ-phosphate from a nucleotide donor to various aminoglycosides, leading to their inactivation. In this work, using a fragment-based drug design (FBDD) approach, we have identified and characterized a promising APH inhibitor capable of increasing the sensitivity of Pseudomonas aeruginosa and Staphylococcus aureus resistant to aminoglycosides. It is therefore a good candidate for the future development of APH inhibitors to be prescribed in combination with aminoglycosides. This molecule is a competitive inhibitor of adenosine 5'-triphosphate (ATP), the phosphate donor of APHs. Further studies are required to optimize this molecule to improve its specificity for APHs and its bioavailability in bacteria.

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 score0.005
Threshold uncertainty score0.383

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.293
Teacher spread0.272 · 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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