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Record W2908877984 · doi:10.1021/acs.jctc.8b00933

Cholesterol Flip-Flop in Heterogeneous Membranes

2019· article· en· W2908877984 on OpenAlexafffund
Ruo‐Xu Gu, Svetlana Baoukina, D. Peter Tieleman

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institutes of Health ResearchAlberta Innovates - Health Solutions
KeywordsMembraneFlipCholesterolBilayerMolecular dynamicsWork (physics)Chemical physicsMoleculeFlip-flopMaterials scienceChemistryBiophysicsNanotechnologyComputational chemistryPhysicsOptoelectronicsBiochemistryBiologyThermodynamics

Abstract

fetched live from OpenAlex

Cholesterol is the most abundant molecule in the plasma membrane of mammals. Its distribution across the two membrane leaflets is critical for understanding how cells work. Cholesterol trans-bilayer motion (flip-flop) is a key process influencing its distribution in membranes. Despite extensive investigations, the rate of cholesterol flip-flop and its dependence on the lateral heterogeneity of membranes remain uncertain. In this work, we used atomistic molecular dynamics simulations to sample spontaneous cholesterol flip-flop events in a DPPC:DOPC:cholesterol mixture with heterogeneous lateral distribution of lipids. In addition to an overall flip-flop rate at the time scale of sub-milliseconds, we identified a significant impact of local environment on flip-flop rate. We discuss the atomistic details of the flip-flop events observed in our simulations.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.250
Teacher spread0.244 · 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 designSimulation or modeling
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

Citations112
Published2019
Admission routes2
Has abstractyes

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