Contributions of Intracellular Ions to Kv Channel Voltage Sensor Dynamics
Bibliographic record
Abstract
Voltage-sensing domains (VSDs) of Kv channels control ionic conductance through coupling of the movement of charged residues in the S4 segment to conformational changes at the cytoplasmic region of the pore domain, that allow K(+) ions to flow. Conformational transitions within the VSD are induced by changes in the applied voltage across the membrane field. However, several other factors not directly linked to the voltage-dependent movement of charged residues within the voltage sensor impact the dynamics of the voltage sensor, such as inactivation, ionic conductance, intracellular ion identity, and block of the channel by intracellular ligands. The effect of intracellular ions on voltage sensor dynamics is of importance in the interpretation of gating current measurements and the physiology of pore/voltage sensor coupling. There is a significant amount of variability in the reported kinetics of voltage sensor deactivation kinetics of Kv channels attributed to different mechanisms such as open state stabilization, immobilization, and relaxation processes of the voltage sensor. Here we separate these factors and focus on the causal role that intracellular ions can play in allosterically modulating the dynamics of Kv voltage sensor deactivation kinetics. These considerations are of critical importance in understanding the molecular determinants of the complete channel gating cycle from activation to deactivation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".