Calmodulin-Binding Proteins in Muscle: A Minireview on Nuclear Receptor Interacting Protein, Neurogranin, and Growth-Associated Protein 43
Bibliographic record
Abstract
Calmodulin (CaM) is an important Ca2+-sensing protein with numerous downstream targets that are either CaM-dependant or CaM-regulated. In muscle, CaM-dependent proteins, which are critical regulators of dynamic Ca2+ handling and contractility, include calcineurin (CaN), CaM-dependant kinase II (CaMKII), ryanodine receptor (RyR), and dihydropyridine receptor (DHPR). CaM-regulated targets include genes associated with oxidative metabolism, muscle plasticity, and repair. Despite its importance in muscle, the regulation of CaM—particularly its availability to bind to and activate downstream targets—is an emerging area of research. In this minireview, we discuss recent studies revealing the importance of small IQ motif proteins that bind to CaM to either facilitate (nuclear receptor interacting protein; NRIP) its activation of downstream targets, or sequester (neurogranin, Ng; and growth-associated protein 43, GAP43) CaM away from their downstream targets. Specifically, we discuss recent studies that have begun uncovering the physiological roles of NRIP, Ng, and GAP43 in skeletal and cardiac muscle, thereby highlighting the importance of endogenously expressed CaM-binding proteins and their regulation of CaM in muscle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".