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Record W2890503353 · doi:10.1021/acs.jpcb.8b09064

Does α-Tocopherol Flip-Flop Help to Protect Membranes Against Oxidation?

2018· article· en· W2890503353 on OpenAlexafffund
Phansiri Boonnoy, Mikko Karttunen, Jirasak Wong-ekkabut

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsWestern University
FundersFaculty of Science, Kasetsart UniversityThailand Research FundKasetsart University Research and Development InstituteNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMembraneLipid bilayerChemistryBilayerRadicalMoleculeAldehydeBiophysicsOxidative stressOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

α-Tocopherols (α-toc) are crucial in protecting biological membranes against oxidation by free radicals. We investigate the behavior of α-toc molecules in lipid bilayers containing oxidized lipids by molecular dynamics (MD) simulations. To verify the approach, the location and orientation of α-toc are first shown to be in agreement with previous experimental results. The simulations further show that α-toc molecules stay inside the lipid bilayer with their hydroxyl groups in contact with the bilayer surface. Interestingly, interbilayer α-toc flip-flop was observed in both oxidized and nonoxidized bilayers with significantly higher frequency in aldehyde lipid bilayer. Free-energy calculations were performed, and estimates of the flip-flop rates across the bilayers were determined. As the main finding, our results show that the presence of oxidized lipids leads to a significant decrease of free-energy barriers and that the flip-flop rates depend on the type of oxidized lipid present. Our results suggest that α-toc molecules could potentially act as high-efficacy scavengers of free radicals to protect membranes from oxidative attack and help stabilize them under oxidative stress.

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 score0.012
Threshold uncertainty score0.337

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 science0.0000.000
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 head0.006
GPT teacher head0.256
Teacher spread0.249 · 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

Citations33
Published2018
Admission routes2
Has abstractyes

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