Interaction of local anesthetics with the K<sup>+</sup>channel pore domain
Bibliographic record
Abstract
Local anesthetics and related drugs block ionic currents of Na (+) , K (+) and Ca ( 2+) conducted across the cell membrane by voltage-dependent ion channels. Many of these drugs bind in the permeation pathway, occlude the pore and stop ion movement. However channel-blocking drugs have also been associated with decreased membrane stability of certain tetrameric K (+) channels, similar to the destabilization of channel function observed at low extracellular K (+) concentration. Such drug-dependent stability may result from electrostatic repulsion of K (+) from the selectivity filter by a cationic drug molecule bound in the central cavity of the channel. In this study we used the pore domain of the KcsA K (+) channel protein to test this hypothesis experimentally with a biochemical assay of tetramer stability and theoretically by computational simulation of local anesthetic docking to the central cavity. We find that two common local anesthetics, lidocaine and tetracaine, promote thermal dissociation of the KcsA tetramer in a K (+) -dependent fashion. Docking simulations of these drugs with open, open-inactivated and closed crystal structures of KcsA yield many energetically favorable drug-channel complexes characterized by nonbonded attraction to pore-lining residues and electrostatic repulsion of K (+) . The results suggest that binding of cationic drugs to the inner cavity can reduce tetramer stability of K (+) channels.
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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 | 0.000 | 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".