Potassium Channel Block by a Tripartite Complex of Neutral Ligands with a Potassium Ion
Bibliographic record
Abstract
K + channels are blocked by structurally diverse compounds. While hydrophilic cations like TEA block Kv channels with a stoichiometry of 1:1, many uncharged lipophilic compounds like the novel immunosuppressant PAP‐1 (Kv1.3 IC 50 2 nM) exhibit Hill coefficients of 2. To determine the mechanism of PAP‐1 block, we first explored the SAR around PAP‐1 and found that the coumarin ring carbonyl group is indispensable, but does not accept an H‐bond from the channel. We next demonstrated that block by PAP‐1 is voltage‐dependent, a feature expected for cationic but not neutral ligands. Through molecular modeling we then proposed a model in which the carbonyl groups of two PAP‐1 molecules coordinate a K + ion in the permeation pathway, while the hydrophobic phenoxyalkoxy side‐chains extend into the intrasubunit interfaces between helices S5 and S6 and reach the L45 linker. To test the model we generated 58 point mutants and then determined their biophysical properties and their sensitivity to PAP‐1. We found excellent agreement between the atomic‐scale model and the experimental studies. Besides the known drug‐binding locus in the inner pore, which is rather conserved between different Kv channels, the PAP‐1 receptor involves low homology loci. These loci constitute attractive targets for the design of subtype‐specific K + channel drugs and offer new directions for structure‐based drug design. Supported by CIHR, NIH, and HHMI.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".