Functional interactions of voltage sensor charges with an S2 hydrophobic plug in hERG channels
Bibliographic record
Abstract
Human ether-à-go-go-related gene (hERG, Kv11.1) potassium channels have unusually slow activation and deactivation kinetics. It has been suggested that, in fast-activating Shaker channels, a highly conserved Phe residue (F290) in the S2 segment forms a putative gating charge transfer center that interacts with S4 gating charges, i.e., R362 (R1) and K374 (K5), and catalyzes their movement across the focused electric field. F290 is conserved in hERG (F463), but the relevant residues in the hERG S4 are reversed, i.e., K525 (K1) and R537 (R5), and there is an extra positive charge adjacent to R537 (i.e., K538). We have examined whether hERG channels possess a transfer center similar to that described in Shaker and if these S4 charge differences contribute to slow gating in hERG channels. Of five hERG F463 hydrophobic substitutions tested, F463W and F463Y shifted the conductance-voltage (G-V) relationship to more depolarized potentials and dramatically slowed channel activation. With the S4 residue reversals (i.e., K525, R537) taken into account, the closed state stabilization by F463W is consistent with a role for F463 that is similar to that described for F290 in Shaker. As predicted from results with Shaker, the hERG K525R mutation destabilized the closed state. However, hERG R537K did not stabilize the open state as predicted. Instead, we found the neighboring K538 residue to be critical for open state stabilization, as K538R dramatically slowed and right-shifted the voltage dependence of activation. Finally, double mutant cycle analysis on the G-V curves of F463W/K525R and F463W/K538R double mutations suggests that F463 forms functional interactions with K525 and K538 in the S4 segment. Collectively, these data suggest a role for F463 in mediating closed-open equilibria, similar to that proposed for F290 in Shaker channels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".