Single-molecule force spectroscopy reveals force-enhanced binding of calcium ions by gelsolin
Bibliographic record
Abstract
Force is increasingly recognized as an important element in controlling biological processes. Forces can deform native protein conformations leading to protein-specific effects. Protein–protein binding affinities may be decreased, or novel protein–protein interaction sites may be revealed, on mechanically stressing one or more components. Here we demonstrate that the calcium-binding affinity of the sixth domain of the actin-binding protein gelsolin (G6) can be enhanced by mechanical force. Our kinetic model suggests that the calcium-binding affinity of G6 increases exponentially with force, up to the point of G6 unfolding. This implies that gelsolin may be activated at lower calcium ion levels when subjected to tensile forces. The demonstration that cation–protein binding affinities can be force-dependent provides a new understanding of the complex behaviour of cation-regulated proteins in stressful cellular environments, such as those found in the cytoskeleton-rich leading edge and at cell adhesions. The application of force can influence biological processes such as ligand and protein–protein binding, with mechanical stress typically hindering such interactions. Here, the authors use atomic force microscopy to show that the binding of calcium to gelsolin can be improved under stress.
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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 | 0.001 | 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".