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Record W4285086132 · doi:10.1021/acs.jpcb.2c02768

Interaction between Antimicrobial Peptide Magainin 2 and Nonlipid Components in the Bacterial Outer Envelope

2022· article· en· W4285086132 on OpenAlexafffund
Sheyla Montero Vega, Valerie Booth, Christopher N. Rowley

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsMemorial University of NewfoundlandCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsMagaininCell envelopeAntimicrobial peptidesMembraneBiophysicsPeptideChemistryBacterial outer membraneBacterial cell structureBacteriaBiochemistryBiologyEscherichia coli

Abstract

fetched live from OpenAlex

Antimicrobial peptides (AMPs) offer advantages over conventional antibiotics; for example, bacteria develop more resistance to small-molecule antibiotics than to AMPs. The interaction of the AMPs with the lipopolysaccharide (LPS) layer of the Gram-negative bacteria cell envelope is not well understood. A MARTINI model was constructed of a Gram-negative bacterial outer membrane interacting with the AMP Magainin 2. In a 20 μs molecular dynamics (MD) simulation, the AMP diffused to the LPS layer of the cell envelope and remained there, suggesting interactions between the Magainin 2 and the LPS layer, causing the AMP to concentrate at that position. The free energy profile for the insertion of the Magainin 2 into the membrane was also calculated using umbrella sampling, which showed that the AMP positioned such that the cationic side chains of the AMP coordinated to the negatively charged phosphate groups of the LPS layer. These simulations indicate that the AMP Magainin 2 partition into the LPS layer of a bacterial membrane.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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