Hydrogen Bond Strength Modulates the Mechanical Strength of Ferric-Thiolate Bonds in Rubredoxin
Bibliographic record
Abstract
It has long been recognized that hydrogen bonds formed by protein backbone amides with cysteinyl S(γ) atoms play important roles in modulating the functional and structural properties of the iron-sulfur centers in proteins. Here we use single molecule atomic force microscopy, cyclic voltammetry, and protein engineering techniques to investigate directly how the strength of N-H···S(γ) hydrogen bonds in the secondary coordination sphere affects the mechanical stability of Fe(III)-thiolate bonds of rubredoxin. Our results show that the mechanical stability of Fe(III)-thiolate bonds in rubredoxin correlates with the strength of N-H···S(γ) hydrogen bonds as reflected by the midpoint reduction potential, providing direct evidence that N-H···S(γ) hydrogen bonds play important roles in modulating the mechanical and kinetic properties of the Fe(III)-thiolate bonds of iron-sulfur proteins and corroborating the important roles of the protein environment in tuning the properties of metal-thiolate bonds.
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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".