Ion permeation through a narrow cavity constriction in KCNQ1 channels: Mechanism and implications for pathogenic variants
Bibliographic record
Abstract
KCNQ1 potassium channels play a pivotal role in the physiology and pathophysiology of several human excitable and epithelial tissues. The latest cryo-electron microscopy (cryo-EM) structures provide unique insights into channel function and pharmacology, opening avenues for different therapeutic strategies against human diseases associated with KCNQ1 mutations. However, these structures also raise fundamental questions about the mechanisms of ion permeation. Cryo-EM structures thought to represent the open state of the channel feature a cavity region not wide enough for accommodation of hydrated K + . To understand how K + passes through the cavity constriction, we utilized microsecond-scale molecular dynamics (MD) simulations using the KCNQ1/KCNE3 cryo-EM structure, characterized mutants at the G345 residue situated at the narrowest point of the cavity, and recorded single channels. The findings indicate that ions become partially dehydrated at the constriction, which enables permeation. MD simulations demonstrate that the constriction can impede the flow of ions through the channel’s pore, a finding that is corroborated by mutational screening and single-channel recordings. Reduced channel conductance is the key mechanism underlying reported pathological KCNQ1 mutations at or near the constriction site.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".