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Record W4397023402 · doi:10.1126/sciadv.adm7907

Probing the energy barriers and stages of membrane protein unfolding using solid-state NMR spectroscopy

2024· article· en· W4397023402 on OpenAlexaff
Peng Xiao, Philip Drewniak, Dylan Dingwell, Leonid S. Brown, Vladimir Ladizhansky

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLipid bilayerNuclear magnetic resonance spectroscopyMembrane proteinBiophysicsProtein foldingTransmembrane domainCrystallographyTransmembrane proteinNative stateSolid-state nuclear magnetic resonanceFolding (DSP implementation)ChemistryMembraneProtein structureForce spectroscopyBiochemistryNuclear magnetic resonanceBiologyPhysicsMoleculeStereochemistry

Abstract

fetched live from OpenAlex

Understanding how the amino acid sequence dictates protein structure and defines its stability is a fundamental problem in molecular biology. It is especially challenging for membrane proteins that reside in the complex environment of a lipid bilayer. Here, we obtain an atomic-level picture of the thermally induced unfolding of a membrane-embedded α-helical protein, human aquaporin 1, using solid-state nuclear magnetic resonance spectroscopy. Our data reveal the hierarchical two-step pathway that begins with unfolding of a structured extracellular loop and proceeds to an intermediate state with a native-like helical packing. In the second step, the transmembrane domain unravels as a single unit, resulting in a heterogeneous misfolded state with high helical content but with nonnative helical packing. Our results show the importance of loops for the kinetic stabilization of the whole membrane protein structure and support the three-stage membrane protein folding model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.317
Teacher spread0.305 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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