Comparing the Calcium Binding Abilities of Two Soybean Calmodulins: Towards Understanding the Divergent Nature of Plant Calmodulins
Bibliographic record
Abstract
The discovery that plants contain multiple calmodulin (CaM) isoforms of variable sequence identity to animal CaM suggested an additional level of sophistication in the intracellular role of calcium regulation in plants. Past research has focused on the ability of conserved or divergent plant CaM isoforms to activate both mammalian and plant protein targets. At present, however, not much is known about how these isoforms respond to the signal of an increased cytosolic calcium concentration. Here, using isothermal titration calorimetry and NMR spectroscopy, we investigated the calcium binding properties of a conserved (CaM1) and a divergent (CaM4) CaM isoform from soybean (Glycine max). Both isoforms bind calcium with a semisequential pathway that favors the calcium binding EF-hands of the C-terminal lobe over those of the N-terminal lobe. From the measured dissociation constants, CaM4 binds calcium with a threefold greater affinity than CaM1 (K(d,Ca,mean) of 5.0 versus 14.9 μM) but has a significantly reduced selectivity against the chemically similar magnesium cation that binds preferentially to EF-hand I of both isoforms. The implications of a potential magnesium/calcium competition on the activation of CaM1 and CaM4 are discussed in context with their ability to respond to stimulus-specific calcium signatures and their known physiological roles.
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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) | 0.000 | 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".