Fine-tuning of Voltage Sensitivity of the Kv1.2 Potassium Channel by Interhelix Loop Dynamics
Bibliographic record
Abstract
Many proteins function by changing conformation in response to ligand binding or changes in other factors in their environment. Any change in the sequence of a protein, for example during evolution, which alters the relative free energies of the different functional conformations changes the conditions under which the protein will function. Voltage-gated ion channels are membrane proteins that open and close an ion-selective pore in response to changes in transmembrane voltage. The charged S4 transmembrane helix transduces changes in transmembrane voltage into a change in protein internal energy by interacting with the rest of the channel protein through a combination of non-covalent interactions between adjacent helices and covalent interactions along the peptide backbone. However, the structural basis for the wide variation in the V50 value between different voltage-gated potassium channels is not well defined. To test the role of the loop linking the S3 helix and the S4 helix in voltage sensitivity, we have constructed a set of mutants of the rat Kv1.2 channel that vary solely in the length and composition of the extracellular loop that connects S4 to S3. We evaluated the effect of these different loop substitutions on the voltage sensitivity of the channel and compared these experimental results with molecular dynamics simulations of the loop structures. Here, we show that this loop has a significant role in setting the precise V50 of activation in Kv1 family 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 | 0.000 | 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) | 0.001 | 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".