Unified modeling of conductance kinetics for low- and high-conductance potassium ion channels
Bibliographic record
Abstract
A kinetics model is proposed for the description of ion conductance of low- and high-conductance potassium ion channels. The model describes ion permeation through the selectivity filter, which is assumed to be the only conductance determining part of the open channel. The filter occupancy can vary from zero to three ions, affecting the ion entry and exit rates. Ion motion between the binding sites inside the filter is assumed fast compared to the latter rates allowing averaging the equilibrium entry and exit rate constants over the possible ion configurations in the filter with a particular occupancy. Averaged rate constants related to a pair of adjacent occupancy states characterize a particular ion permeation mechanism. An expression for the channel conductance as a function of the symmetrical external ion concentration is derived. It comprises a sum of concentration independent conductance amplitudes for different ion permeation mechanisms weighted by the equilibrium filter occupancy probabilities. It is shown that each amplitude (i.e., maximum contribution to the channel conductance from each conductance mechanism) is proportional to an averaged exit rate constant and to quantities characterizing the effect of the applied electric field on the rate constants and the equilibrium ion distribution in the filter. The conductance expression derived provides a good description of the experimentally observed conductance-concentration curves for low-conductance (e.g., Kir2.1) and high-conductance (e.g., KcsA) potassium channels. It enables one to obtain equilibrium ion binding constants at different filter occupancies and to calculate the average number of ions in the selectivity filter for a given external ion concentration. For KcsA this number (2.0 at 200 mM) is in a good agreement with the available experimental value (2.1 at 200 mM). For the high-conductance potassium channels the net negative electrical charge around the selectivity filter increases the ion binding constants, thereby causing the larger occupancy probabilities to occur at smaller external ion concentrations compared to the low-conductance channels. This substantially increases the contributions of the two- and three-ion permeation mechanisms, with the larger conductance amplitudes leading to increased channel conductance compared to the low-conductance channels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".