Four drug-sensitive subunits are required for maximal effect of a voltage sensor–targeted KCNQ opener
Bibliographic record
Abstract
KCNQ2-5 (Kv7.2-Kv7.5) channels are strongly influenced by an emerging class of small-molecule channel activators. Retigabine is the prototypical KCNQ activator that is thought to bind within the pore. It requires the presence of a Trp side chain that is conserved among retigabine-sensitive channels but absent in the retigabine-insensitive KCNQ1 subtype. Recent work has demonstrated that certain KCNQ openers are insensitive to mutations of this conserved Trp, and that their effects are instead abolished or attenuated by mutations in the voltage-sensing domain (VSD). In this study, we investigate the stoichiometry of a VSD-targeted KCNQ2 channel activator, ICA-069673, by forming concatenated channel constructs with varying numbers of drug-insensitive subunits. In homomeric WT KCNQ2 channels, ICA-069673 strongly stabilizes an activated channel conformation, which is reflected in the pronounced deceleration of deactivation and leftward shift of the conductance-voltage relationship. A full complement of four drug-sensitive subunits is required for maximal sensitivity to ICA-069673-even a single drug-insensitive subunit leads to significantly weakened effects. In a companion article (see Yau et al. in this issue), we demonstrate very different stoichiometry for the action of retigabine on KCNQ3, for which a single retigabine-sensitive subunit enables near-maximal effect. Together, these studies highlight fundamental differences in the site and mechanism of activation between retigabine and voltage sensor-targeted KCNQ openers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".