Chemical Tools for K<sup>+</sup> Channel Biology
Bibliographic record
Abstract
K(+) channels are revered for their universal action of suppressing electrical activity in nerve and muscle, as well as regulating salt and water transport in epithelial tissues involved in metabolism and digestion. These multisubunit membrane-embedded proteins carry out their physiological chore, selectively allowing the passage of potassium across the membrane, in response to changes in membrane voltage and ligand concentration. Elucidating the diverse gating properties of K(+) channels is of great biological interest since their molecular motions provide insight into how these structurally similar proteins function in a wide variety of tissues. Armed with patch clamps, chart recorders, and now high-resolution structures, electrophysiologists have been dipping into the top tray of the chemist's tool box: synthesizing cysteine-modifying agents and organic cations and grinding up insects, spiders, and other vermin to isolate natural products to poke, probe, and prod K(+) channels. Recently, there has been further cross-fertilization between chemists and K(+) channelologists, resulting in greater accessibility to more elaborate synthetic methodologies and screening approaches. In this review, we catalogue the evolution of chemical tools and approaches that have been utilized to elucidate the mechanistic underpinnings of K(+) channel biology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".