PIP2 mediates functional coupling and pharmacology of neuronal KCNQ channels
Bibliographic record
Abstract
Retigabine (RTG) is a first-in-class antiepileptic drug that suppresses neuronal excitability through the activation of voltage-gated KCNQ2-5 potassium channels. Retigabine binds to the pore-forming domain, causing a hyperpolarizing shift in the voltage dependence of channel activation. To elucidate how the retigabine binding site is coupled to changes in voltage sensing, we used voltage-clamp fluorometry to track conformational changes of the KCNQ3 voltage-sensing domains (VSDs) in response to voltage, retigabine, and PIP2. Steady-state ionic conductance and voltage sensor fluorescence closely overlap under basal PIP2 conditions. Retigabine stabilizes the conducting conformation of the pore and the activated voltage sensor conformation, leading to dramatic deceleration of current and fluorescence deactivation, but these effects are attenuated upon disruption of channel:PIP2 interactions. These findings reveal an important role for PIP2 in coupling retigabine binding to altered VSD function. We identify a polybasic motif in the proximal C terminus of retigabine-sensitive KCNQ channels that contributes to VSD-pore coupling via PIP2, and thereby influences the unique gating effects of retigabine.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".