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Record W2046813153 · doi:10.1002/psc.702

Headgroup structure and fatty acid chain length of the acidic phospholipids modulate the interaction of membrane mimetic vesicles with the antimicrobial peptide protegrin‐1

2005· article· en· W2046813153 on OpenAlexaff
Weiguo Jing, Elmar J. Prenner, Hans J. Vogel, Alan J. Waring, Robert I. Lehrer, Karl Lohner

Bibliographic record

VenueJournal of Peptide Science · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLiposomeChemistryVesiclePeptidePhospholipidMembraneThermotropic crystalBiophysicsAntimicrobial peptidesBiological membraneBiochemistryStereochemistryPhase (matter)Organic chemistryBiology

Abstract

fetched live from OpenAlex

The interaction of protegrin-1 (PG-1), a small beta-sheet antimicrobial peptide with acidic phospholipid model membranes was investigated by differential scanning calorimetry. We found that PG-1 can distinguish between liposomes of the anionic phospholipids DPPG, DPPS and DPPA, eventhough the headgroups of these phospholipids all have the same net charge and they carry the same hydrocarbon chains. Specifically, PG-1 had only a minor effect on the thermotropic phase behavior of DPPA liposomes, while it interacted preferentially with the fluid phase of DPPS. Furthermore, PG-1 could induce a phase separation in DPPG liposomes resulting in the formation of peptide-rich domains even at low concentrations of the peptide. However, this peptide-rich domain was not evident when the fatty acyl chains were longer or shorter by two carbon atoms. In addition, PG-1 can also form peptide-rich domains in DPPS vesicles but only at high concentrations of the peptide. These results suggest that in addition to an overall negative charge, the structural features of the phospholipid headgroups, lipid packing and thus membrane fluidity will influence the interaction with PG-1, thereby modulating its biological activity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.015
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.212 · 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.

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

Citations29
Published2005
Admission routes1
Has abstractyes

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