Crystallographic, kinetic, and calorimetric investigation of PKA interactions with L-type calcium channels and Rad GTPase
Bibliographic record
Abstract
β-Adrenergic signaling activates cAMP-dependent PKA, which regulates the activity of L-type voltage-gated calcium channels such as Ca V 1.2. Several PKA target sites in the C-terminal tail of Ca V 1.2 have been identified, and their phosphorylation has been suggested to increase currents in specific tissues or heterologous expression systems. However, augmentation of Ca V 1.2 currents in the heart is instead mediated by phosphorylation of Rad, a small GTPase that can inhibit Ca V 1.2. It is unclear how each of the proposed target sites in Ca V 1.2 and Rad rank toward their recognition by PKA, which could reveal a preferential phosphorylation. Here, we used quantitative assays on three Ca V 1.2 and four Rad sites. Isothermal titration calorimetry and enzyme kinetics show that there are two Tiers of targets, with Ca V 1.2 residue Ser1981 and Rad residues Ser25 and Ser272 forming tier one substrates for PKA. These share a common feature with two Arginine residues at specific positions that can anchor the peptide into the substrate binding cleft of PKA. In contrast, PKA shows minimal activity for the other, tier two substrates, characterized by low k cat values and undetectable binding via isothermal titration calorimetry. The existence of two tiers suggests that PKA regulation of the Ca V 1.2 complex may occur in a graded fashion. We report crystal structures of the PKA catalytic subunit with and without a Ca V 1.2 and test the importance of several anchoring residues via mutagenesis. Different target sites utilize different anchors, highlighting the plasticity of PKAc to recognize substrates.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".