Characterisation of Calmodulin Structural Transitions by Ion Mobility Mass Spectrometry
Bibliographic record
Abstract
This study demonstrates the ability of travelling wave ion mobility-mass spectrometry to measure collision cross-sections of ions in the negative mode, using a calibration based approach. Here, negative mode ion mobility-mass spectrometry was utilised to understand structural transitions of calmodulin upon Ca2+ binding and complexation with model peptides melittin and the plasma membrane Ca2+ pump C20W peptide. Coexisting calmodulin conformers were distinguished on the basis of their mass and cross-section, and identified as relatively folded and unfolded populations, with good agreement in collision cross-section to known calmodulin geometries. Titration of calcium tartrate to physiologically relevant Ca2+ levels provided evidence for intermediately metalated species during the transition from apo- to holo-calmodulin, with collision cross-section measurements indicating that higher Ca2+ occupancy is correlated with more compact structures. The binding of two representative peptides which exemplify canonical compact (melittin) and extended (C20W) peptide-calmodulin binding models has also been interrogated by ion mobility mass spectrometry. Peptide binding to calmodulin involves intermediates with metalation states from 1–4 Ca2+, which demonstrate relatively collapsed structures, suggesting neither the existence of holo-calmodulin or a pre-folded calmodulin conformation is a prerequisite for binding target peptides or proteins. The biological importance of the different metal unsaturated calmodulin complexes, if any, is yet to be understood.
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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 | 0.001 |
| 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 | 0.001 |
| 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".