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Record W4408215385 · doi:10.1126/sciadv.adr7012

The conductance of KCNQ2 and its pathogenic variants is determined by individual subunit gating

2025· article· en· W4408215385 on OpenAlexaff
Andy Hinojo-Perez, Jodene Eldstrom, Ying Dou, Allan Marinho-Alcara, Michaela Edmond, Alicia de la Cruz, Marta E. Perez Rodriguez, Maykelis Dı́az, Derek M. Dykxhoorn, David Fedida, René Barro-Soria

Bibliographic record

VenueScience Advances · 2025
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 subunitGatingAllosteric regulationConductancePotassium channelBiophysicsChannel (broadcasting)Membrane potentialMutationNeuroscienceIon channelChemistryBiologyPhysicsBiochemistryGeneComputer scienceEnzyme

Abstract

fetched live from OpenAlex

KCNQ2 channel subunits form part of the M-current and underlie one of the major potassium currents throughout the human nervous system, regulating resting membrane potentials, shaping action potentials, and impeding repetitive neuronal firing. However, how individual subunits within tetramers control channel functionality remains unresolved. Here, we investigate (i) whether opening of KCNQ2 channels requires a concerted step or can result from independent subunit activation and (ii) how individual subunits regulate gate opening and conductance. The E140R mutation in the S2 segment prevents activated voltage sensor conformations, but concatemeric constructs containing up to three E140R subunits retain KCNQ2-like currents. The underlying single-channel currents show subconductance levels resulting from limitations in inner gate dimensions, determined by the number of activated subunits and their spatial arrangement. Channel opening is allosteric and requires activation of only a single subunit, which can accentuate the influence of clinically relevant heterozygous mutations at threshold voltages.

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 score0.021
Threshold uncertainty score0.198

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 head0.013
GPT teacher head0.280
Teacher spread0.268 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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