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Record W4297840445 · doi:10.1039/d2ob01384c

The impact of lysyl-phosphatidylglycerol on the interaction of daptomycin with model membranes

2022· article· en· W4297840445 on OpenAlexafffund
Ryan Moreira, Scott D. Taylor

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2022
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of Canada
KeywordsPhosphatidylglycerolChemistryDaptomycinMembraneBiochemistryPhospholipidVancomycinGeologyBacteria

Abstract

fetched live from OpenAlex

-3-[phospho-1-(3-lysyl(1-glycerol))] (lysyl-PG) and mutations to the proteins directly involved in the synthesis and translocation of lysyl-PG are implicated in resistance mechanisms. To study the interaction of daptomycin with lysyl-DMPG-containing model membranes a new stereospecific and regioselective synthesis of lysyl-DMPG was developed. Studies on model membranes containing lysyl-DMPG demonstrate that: (1) daptomycin is not significantly repelled by the cationic charge of lysyl-DMPG; (2) daptomycin binds less avidly to lysyl-DMPG compared to DMPG; (3) the presence of lysyl-DMPG does not impact the membrane bound backbone conformation of daptomycin in a significant way; (4) lysyl-DMPG increases oligomer formation; (5) lysyl-DMPG does not impact model membrane fluidity at lysyl-PG : PG ratios that are relevant to daptomycin resistance. The results of these studies suggest that increased lysyl-PG content does not confer resistance to daptomycin by altering membrane fluidity or reducing membrane affinity but may confer resistance by altering the structure of daptomycin oligomers.

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 score0.001
Threshold uncertainty score0.002

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 integrity0.0000.000
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 head0.009
GPT teacher head0.260
Teacher spread0.250 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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