Single Molecule Force Spectroscopy Reveals That Iron Is Released from the Active Site of Rubredoxin by a Stochastic Mechanism
Bibliographic record
Abstract
Metal centers in metalloproteins involve multiple metal-ligand bonds. The release of metal ions from metalloproteins can have significant biological consequences, so understanding of the mechanisms by which metal ion dissociates has broad implications. By definition, the release of metal ions from metalloproteins involves the disruption of multiple metal-ligand bonds, and this process is often accompanied by unfolding of the protein. Detailed pathways for metal ion release from metalloproteins have been difficult to elucidate by classical ensemble techniques. Here, we combine single molecule force spectroscopy and protein engineering techniques to investigate the mechanical dissociation mechanism of iron from the active site of the simplest iron-sulfur protein, rubredoxin, at the single molecule level. Our results reveal that the mechanical rupture of this simplest iron center is stochastic and follows multiple, complex pathways that include concurrent rupture of multiple ferric-thiolate bonds as well as sequential rupture of ferric-thiolate bonds that lead to the formation of intermediate species. Our results uncover the surprising complexity of the rupture process of the seemingly simple iron center in rubredoxin and provide the first unambiguous experimental evidence concerning the detailed mechanism of mechanical disruption of a metal center in its native protein environment in aqueous solution. This study opens up a new avenue to investigating the rupture mechanism of metal centers in metalloproteins with unprecedented resolution by using single molecule force spectroscopy techniques.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".