Overlapping Binding Sites of Structurally Different Antiarrhythmics Flecainide and Propafenone in the Subunit Interface of Potassium Channel Kv2.1*
Bibliographic record
Abstract
Kv2.1 channels, which are expressed in brain, heart, pancreas, and other organs and tissues, are important targets for drug design. Flecainide and propafenone are known to block Kv2.1 channels more potently than other Kv channels. Here, we sought to explore structural determinants of this selectivity. We demonstrated that flecainide reduced the K(+) currents through Kv2.1 channels expressed in Xenopus laevis oocytes in a voltage- and time-dependent manner. By systematically exchanging various segments of Kv2.1 with those from Kv1.2, we determined flecainide-sensing residues in the P-helix and inner helix S6. These residues are not exposed to the inner pore, a conventional binding region of open channel blockers. The flecainide-sensing residues also contribute to propafenone binding, suggesting overlapping receptors for the drugs. Indeed, propafenone and flecainide compete for binding in Kv2.1. We further used Monte Carlo-energy minimizations to map the receptors of the drugs. Flecainide docking in the Kv1.2-based homology model of Kv2.1 predicts the ligand ammonium group in the central cavity and the benzamide moiety in a niche between S6 and the P-helix. Propafenone also binds in the niche. Its carbonyl group accepts an H-bond from the P-helix, the amino group donates an H-bond to the P-loop turn, whereas the propyl group protrudes in the pore and blocks the access to the selectivity filter. Thus, besides the binding region in the central cavity, certain K(+) channel ligands can expand in the subunit interface whose residues are less conserved between K(+) channels and hence may be targets for design of highly desirable subtype-specific K(+) channel drugs.
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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".