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Record W2000200816 · doi:10.1073/pnas.1314557110

Crystallographic insights into sodium-channel modulation by the β4 subunit

2013· article· en· W2000200816 on OpenAlexaff
John M. Gilchrist, Samir Das, Filip Van Petegem, Frank Bosmans

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Neurological Disorders and Stroke
KeywordsProtein subunitSodium channelBiophysicsAllosteric regulationExtracellularChemistryDocking (animal)CysteineSmall moleculeCell biologyBiologyBiochemistrySodiumReceptorEnzymeMedicine

Abstract

fetched live from OpenAlex

Voltage-gated sodium (Nav) channels are embedded in a multicomponent membrane signaling complex that plays a crucial role in cellular excitability. Although the mechanism remains unclear, β-subunits modify Nav channel function and cause debilitating disorders when mutated. While investigating whether β-subunits also influence ligand interactions, we found that β4 dramatically alters toxin binding to Nav1.2. To explore these observations further, we solved the crystal structure of the extracellular β4 domain and identified (58)Cys as an exposed residue that, when mutated, eliminates the influence of β4 on toxin pharmacology. Moreover, our results suggest the presence of a docking site that is maintained by a cysteine bridge buried within the hydrophobic core of β4. Disrupting this bridge by introducing a β1 mutation implicated in epilepsy repositions the (58)Cys-containing loop and disrupts β4 modulation of Nav1.2. Overall, the principles emerging from this work (i) help explain tissue-dependent variations in Nav channel pharmacology; (ii) enable the mechanistic interpretation of β-subunit-related disorders; and (iii) provide insights in designing molecules capable of correcting aberrant β-subunit behavior.

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 score0.166
Threshold uncertainty score0.189

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 studies0.0000.001
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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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

Citations80
Published2013
Admission routes1
Has abstractyes

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