Electronic Structure Contributions to Electron-Transfer Reactivity in Iron−Sulfur Active Sites: 3. Kinetics of Electron Transfer
Bibliographic record
Abstract
The kinetics of electron transfer for rubredoxins are examined using density functional methods to determine the electronic structure characteristics that influence and allow for fast electron self-exchange in these electron-transport proteins. Potential energy surfaces for [FeX(4)](2-,1-) models confirm that the inner-sphere reorganization energy is inherently small for tetrathiolates ( approximately 0.1 eV), as evidenced by the only small changes in the equilibrium Fe-S bond distance during redox (Deltar(redox) approximately 0.05 A). It is concluded that electronic relaxation and covalency in the reduced state allow for this small in this case relative to other redox couples, such as the tetrachloride. Using a large computational model to include the protein medium surrounding the [Fe(SCys)(4)](2-,1-) active site in Desulfovibrio vulgaris Rubredoxin, the electronic coupling matrix element for electron self-exchange is defined for direct active-site contact (H0(DA)). Simple Beratan-Onuchic model is used to extend coupling over the complete surface of the protein to provide an understanding of probable electron-transfer pathways. Regions of similar coupling properties are grouped together to define a surface coupling map, which reveals that very efficient self-exchange occurs only within 4 sigma-bonds of the active site. Longer-range electron transfer cannot support the fast rates of electron self-exchange observed experimentally. Pathways directly through the two surface cysteinate ligands dominate, but surface-accessible amides hydrogen-bonded to the cysteinates also contribute significantly to the rate of electron self-exchange.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".