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Record W3190823582 · doi:10.1101/2021.07.31.454604

Molecular Dynamics Simulation of a Model Brain Membrane with and without a 14-3-3 tau Protein

2021· preprint· en· W3190823582 on OpenAlexaff
Jorge Alfonso Arvayo-Zatarain, Fernando Favela-Rosales, Angel D. Reyes Figueroa, Pavel Castro-Villarreal, Claudio Contreras-Aburto

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsMembraneTransmembrane proteinMembrane proteinDiffusionMolecular dynamicsDeuteriumBiophysicsChemistryIntegral membrane proteinTransmembrane domainPhysicsBiochemistryBiologyComputational chemistryThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract An unbalanced composition of lipids and proteins in brain membranes is related to the appearance neurodegenrative diseases and recent investigations show that the 14-3-3 tau protein might relate to some of these diseases. This article reports results from a coarse-grained model brain membrane with and without a 14-3-3 τ/θ protein inside the membrane. We investigated the symmetrized partial density, thickness, diffusion coefficients, and deuterium order parameters of the membrane with and without protein. We observe a slight increase in heads and linkers in the symmetrized partial density of the membrane with the protein inserted and higher values of the deuterium order parameters for the brain model membrane with protein. We observe a reduction in the diffusion coefficient of the fluid membrane in the presence of the transmembrane tau protein. Our findings show that the protein can modify the structural and dynamical properties of the membrane. This work will serve as a guide for future investigations on the interactions of tau proteins with brain membrane models and their relation to neurodegenerative diseases.

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 categoriesMeta-epidemiology (narrow)
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 score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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