Genetic polymorphism and protein conformational plasticity in the calmodulin superfamily: Two ways to promote multifunctionality
Bibliographic record
Abstract
Calcium signaling pathways control a variety of cellular events such as gene transcription, protein phosphorylation, nucleotide metabolism, and ion transport. These pathways often involve a large number of calcium-binding proteins collectively known as the calmodulin or EF-hand protein superfamily. Many EF-hand proteins undergo a large conformational change upon binding to Ca(2+) and target proteins. All members of the superfamily share marked sequence homology and similar structural features required to sense Ca(2+). Despite such structural similarities, the functional diversity of EF-hand calcium-binding proteins is extraordinary. Calmodulin itself can bind >300 different proteins, and the many members of the neuronal calcium sensor and S100 protein families collectively recognize a largely different set of target proteins. Recent biochemical and structural studies of many different EF-hand proteins highlight remarkable similarities and variations in conformational responses to the common ligand Ca(2+) and their respective cellular targets. In this review, we examine the essence of molecular recognition activities and the mechanisms by which calmodulin superfamily proteins control a wide variety of Ca(2+) signaling processes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".