Structural and Dynamical Basis of the Cross Talk between cAMP and cGMP Signaling Pathways
Bibliographic record
Abstract
Protein kinases A and G (PKA and PKG) are respectively activated by the cAMP and cGMP second messengers and regulate signaling pathways that are often distinct. Nevertheless, due to the typically higher intracellular concentrations of cAMP vs. cGMP, cAMP contributes to the partial activation of PKG and to the modulation of the cGMP‐dependence of PKG activity, resulting in cAMP vs. cGMP signaling cross talk. However, the structural and dynamical basis for such cAMP vs. cGMP cross regulation is only limitedly understood. While it is known that the partial activation of PKG by cAMP arises from a combination of reduced cAMP vs. cGMP affinity (potency) and activation (efficacy) [1], the contributions of different functional groups in the cyclic nucleotide bases to such variations in potency and efficacy have not been fully dissected. Here, we show how the distinct contributions to potency and efficacy of the carbonyl at position 6 and the amino at position 2 of the guanine base are dissected by mapping through NMR [2–8] the interactions of a library of cyclic‐nucleotides with the C‐terminal cyclic nucleotide‐binding domain (CNB) of PKG, which is primarily responsible for the selective activation of PKG by cGMP. A model is proposed to explain the non‐additive contributions of these two functional groups to the potency and efficacy of PKG activation, providing insight into the molecular basis of cAMP vs. cGMP signaling cross talk. Support or Funding Information This work was supported by Grant (MOP‐68897) from the Canadian Institutes of Health Research (to G. M.). The authors declare that they have no conflicts of interest with the contents of this article.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".