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Record W2970337145 · doi:10.1073/pnas.1905462116

Hydrophobic gasket mutation produces gating pore currents in closed human voltage-gated proton channels

2019· article· en· W2970337145 on OpenAlexafffund
Richard Banh, Vladimir V. Cherny, Deri Morgan, Boris Musset, Sarah Thomas, Kethika Kulleperuma, Susan M. Smith, Régis Pomès, Thomas E. DeCoursey

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersDivision of Molecular and Cellular BiosciencesNational Institute of General Medical SciencesBears CareCanadian Institutes of Health ResearchDeutsche ForschungsgemeinschaftGovernment of CanadaNational Institutes of HealthNational Science Foundation
KeywordsGatingChemistryBiophysicsIon channelProtonKcsA potassium channelHelix (gastropod)StereochemistryBiochemistryBiologyPhysics

Abstract

fetched live from OpenAlex

Significance A large family of membrane proteins, voltage-gated ion channels, regulate a vast array of physiological functions in essentially all life forms. How these molecules sense membrane potential and respond by creating ionic conduction is incompletely understood. The voltage sensors of these channels contain a “hydrophobic gasket,” a ring of hydrophobic amino acids near the center of the membrane, separating internal and external aqueous solutions. Although voltage-gated proton channels, H V 1, resemble voltage-sensing domains of other channels, they differ fundamentally. On depolarization, H V 1 conducts protons, whereas other voltage sensors open a physically distinct pore. We identify Val 109 , Phe 150 , Val 177 , and Val 178 as the hH V 1 hydrophobic gasket. Replacement with less hydrophobic amino acids accelerated channel opening and caused proton-selective leak through closed channels.

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)0.001
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 score0.012
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.307
Teacher spread0.278 · 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

Citations45
Published2019
Admission routes2
Has abstractyes

Explore more

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