Structural Investigation into the Differential Target Enzyme Regulation Displayed by Plant Calmodulin Isoforms
Bibliographic record
Abstract
The conserved calmodulin (CaM) isoform SCaM-1 and the divergent SCaM-4 from soybean bind to many of the same target enzymes, but differentially activate or competitively inhibit them. Class 1 target enzymes are activated by both calcium (Ca(2+))-bound SCaM-1 (Ca(2+)-SCaM-1) and Ca(2+)-bound SCaM-4 (Ca(2+)-SCaM-4), while class 2 enzymes are activated by Ca(2+)-SCaM-1 but competitively inhibited by Ca(2+)-SCaM-4, and class 3 enzymes are activated by Ca(2+)-SCaM-4 but competitively inhibited by Ca(2+)-SCaM-1. To determine whether these differences can be attributed to unique interactions with the CaM-binding domains (CaMBD) of these enzymes, we have studied the binding of each protein to peptides derived from the CaMBD of a representative target enzyme from each of these three classes. Using a combination of NMR spectroscopy and isothermal titration calorimetry, we demonstrate that the N- and C-domains of either Ca(2+)-SCaM bind to each peptide to form structurally compact complexes driven by the burial of hydrophobic surfaces. Interestingly, the interactions with the CaMBD peptides from classes 1 and 2 are similar for the two proteins; however, binding to the peptide from class 3 is structurally and thermodynamically distinct for Ca(2+)-SCaM-1 and -4. We also demonstrate that both calcium-free SCaM-1 (apo-SCaM-1) and calcium-free SCaM-4 (apo-SCaM-4) bind to the CaMBD from cyclic nucleotide phosphodiesterase, and that the interactions are similar to each other and to the interactions with apo-mammalian CaM. Therefore, the apo-SCaMs are also capable of binding to the same target enzymes, which could provide an additional mechanism for CaM-dependent signaling in plants.
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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".