Tapping the translation potential of cAMP signalling: molecular basis for selectivity in cAMP agonism and antagonism as revealed by NMR
Bibliographic record
Abstract
Eukaryotic CBDs (cAMP-binding domains) control multiple cellular functions (e.g. phosphorylation, guanine exchange and ion channel gating). Hence the manipulation of cAMP-dependent signalling pathways has a high translational potential. However, the ubiquity of eukaryotic CBDs also poses a challenge in terms of selectivity. Before the full translational potential of cAMP signalling can be tapped, it is critical to understand the structural basis for selective cAMP agonism and antagonism. Recent NMR investigations have shown that structurally homologous CBDs respond differently to several CBD ligands and that these unexpected differences arise at the level of either binding (i.e. affinity) or allostery (i.e. modulation of the autoinhibitory equilibria). In the present article, we specifically address how the highly conserved CBD fold binds cAMP with markedly different affinities in PKA (protein kinase A) relative to other eukaryotic cAMP receptors, such as Epac (exchange protein directly activated by cAMP) and HCN (hyperpolarization-activated cyclic-nucleotide-modulated channel). A major emerging determinant of cAMP affinity is hypothesized to be the position of the autoinhibitory equilibrium of the apo-CBD, which appears to vary significantly across different CBDs. These analyses may assist the development of selective CBD effectors that serve as potential drug leads for the treatment of cardiovascular diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".