Molecular Strategies to Achieve Selective Conductance in NaK Channel Variants
Bibliographic record
Abstract
A recent crystallization of several ion channels has provided strong impetus for efforts aimed at understanding the different strategies employed by nature for selective ion transport. In this work, we used two variants of the selectivity filter of NaK channel to explore molecular mechanisms that give rise to K(+)-selectivity. We computed one-dimensional (1D) and two-dimensional (2D) potentials of mean force (PMFs) for ion permeation across the channel. The results indicate that the energies for Na(+) and K(+) permeation across the selectivity filter display significant differences in positions of the binding sites and barriers. One characteristic signature of a K(+)-selective channel is the apparent preservation of the site analogous to that of S2 in KcsA. The S2-bound ion can be almost ideally dehydrated and coordinated by 6 to 8 carbonyls. In a striking contrast, the PMFs controlling transport of ions in a nonselective variant show almost identical profiles for either K(+) or Na(+) and significant involvement of water molecules in ion coordination across the entire selectivity filter. An analysis of differences in 1D PMFs for Na(+) and K(+) suggests that coordination number alone is an insufficient predictor of site selectivity, while chemical composition (ratio of carbonyls and water molecules) correlates well with preference for K(+). Multi-ion effects such as dependence of the barriers and wells for permeant ion on the type of copermeant ion were found to play a significant role in the selectivity signature of the channel as well.
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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 | 0.000 | 0.000 |
| 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".