Identification of Novel Cholesterol-binding Regions in Kir2 Channels
Bibliographic record
Abstract
Inwardly rectifying potassium (Kir) channels play an important role in setting the resting membrane potential and modulating membrane excitability. We have recently shown that cholesterol regulates representative members of the Kir family and that in the majority of the cases, cholesterol suppresses channel function. Furthermore, recent data indicate that cholesterol regulates Kir channels by specific sterol-protein interactions, yet the location of the cholesterol binding site in Kir channels is unknown. Using a combined computational-experimental approach, we show that cholesterol may bind to two nonanular hydrophobic regions in the transmembrane domain of Kir2.1 located between adjacent subunits of the channel. The location of the binding regions suggests that cholesterol modulates channel function by affecting the hinging motion at the center of the pore-lining transmembrane helix that underlies channel gating either directly or through the interface between the N and C termini of the channel. Background: Cholesterol modulates inwardly rectifying potassium (Kir) channels. Results: Using a combined computational-experimental approach, we identified two putative cholesterol-binding regions in Kir2.1 that suggest the existence of a novel cholesterol binding motif. Conclusion: Cholesterol binds to nonannular surfaces in the transmembrane domain of Kir2.1. Significance: These findings provide new insights into the mechanisms underlying lipid regulation of ion 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".