Modeling interactions between voltage-gated Ca<sup>2+</sup>channels and KCa1.1 channels
Bibliographic record
Abstract
High voltage-activated (HVA) Cav channels form complexes with KCa1.1 channels, allowing reliable activation of KCa1.1 current through a nanodomain interaction. We recently found that low voltage-activated Cav3 calcium channels also create KCa1.1-Cav3 complexes. While coimmunoprecipitation studies again supported a nanodomain interaction, the sensitivity to calcium chelating agents was instead consistent with a microdomain interaction. A computational model of the KCa1.1-Cav3 complex suggested that multiple Cav3 channels were necessary to activate KCa1.1 channels, potentially causing the KCa1.1-Cav3 complex to be more susceptible to calcium chelators. Here, we expanded the model and compared it to a KCa1.1-Cav2.2 model to examine the role of Cav channel conductance and kinetics on KCa1.1 activation. As found for direct recordings, the voltage-dependent and kinetic properties of Cav3 channels were reflected in the activation of KCa1.1 current, including transient activation from lower voltages than other KCa1.1-Cav complexes. Substantial activation of KCa1.1 channels required the concerted activity of several Cav3.2 channels. Combined with the effect of EGTA, these results suggest that the Ca (2+) domains of several KCa1.1-Cav3 complexes need to cooperate to generate sufficient [Ca (2+)]i, despite the physical association between KCa1.1 and Cav3 channels. By comparison, Cav2.2 channels were twice as effective at activating KCa1.1 channels and a single KCa1.1-Cav2.2 complex would be self-sufficient. However, even though Cav3 channels generate small, transient currents, the regulation of KCa1.1 activity by Cav3 channels is possible if multiple complexes cooperate through microdomain interactions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".