A novel mechanism for fine-tuning open-state stability in a voltage-gated potassium channel
Bibliographic record
Abstract
Voltage-gated potassium channels elicit membrane hyperpolarization through voltage-sensor domains that regulate the conductive status of the pore domain. To better understand the inherent basis for the open-closed equilibrium in these channels, we undertook an atomistic scan using synthetic fluorinated derivatives of aromatic residues previously implicated in the gating of Shaker potassium channels. Here we show that stepwise dispersion of the negative electrostatic surface potential of only one site, Phe481, stabilizes the channel open state. Furthermore, these data suggest that this apparent stabilization is the consequence of the amelioration of an inherently repulsive open-state interaction between the partial negative charge on the face of Phe481 and a highly co-evolved acidic side chain, Glu395, and this interaction is potentially modulated through the Tyr485 hydroxyl. We propose that the intrinsic open-state destabilization via aromatic repulsion represents a new mechanism by which ion channels, and likely other proteins, fine-tune conformational equilibria. Voltage-gated potassium channels cycle between closed and open states through poorly-defined transitions. Pless and colleagues incorporate artificial amino acids into Shaker potassium channels and find that that the negative electrostatic surface potential of Phe481, destabilizes the channel open state.
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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 | 0.001 |
| 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".