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Record W1208303706 · doi:10.1038/ncomms9116

Atomic basis for therapeutic activation of neuronal potassium channels

2015· article· en· W1208303706 on OpenAlexafffund
Robin Y. Kim, Michael C. Yau, Jason D. Galpin, Guiscard Seebohm, Christopher A. Ahern, Stephan A. Pless, Harley T. Kurata

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNovo Nordisk FondenNational Institutes of HealthMichael Smith Health Research BCUniversities Space Research Association
KeywordsPotassiumPotassium channelBasis (linear algebra)NeuroscienceComputational biologyBiophysicsChemistryBiologyMathematics

Abstract

fetched live from OpenAlex

Retigabine is a recently approved anticonvulsant that acts by potentiating neuronal M-current generated by KCNQ2-5 channels, interacting with a conserved Trp residue in the channel pore domain. Using unnatural amino-acid mutagenesis, we subtly altered the properties of this Trp to reveal specific chemical interactions required for retigabine action. Introduction of a non-natural isosteric H-bond-deficient Trp analogue abolishes channel potentiation, indicating that retigabine effects rely strongly on formation of a H-bond with the conserved pore Trp. Supporting this model, substitution with fluorinated Trp analogues, with increased H-bonding propensity, strengthens retigabine potency. In addition, potency of numerous retigabine analogues correlates with the negative electrostatic surface potential of a carbonyl/carbamate oxygen atom present in most KCNQ activators. These findings functionally pinpoint an atomic-scale interaction essential for effects of retigabine and provide stringent constraints that may guide rational improvement of the emerging drug class of KCNQ channel activators.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.266

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 head0.045
GPT teacher head0.312
Teacher spread0.267 · 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

Citations92
Published2015
Admission routes2
Has abstractyes

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