Modeling the Hydrolysis of Iron–Sulfur Clusters
Bibliographic record
Abstract
Iron-sulfur (FeS) clusters are essential metal cofactors involved in a wide variety of biological functions. Their catalytic efficiency, biosynthesis, and regulation depend on FeS stability in aqueous solution. Here, molecular modeling is used to investigate the hydrolysis of an oxidized (ferric) mononuclear FeS cluster by bare dissociation and water substitution mechanisms in neutral and acidic solution. First, approximate electronic structure descriptions of FeS reactions by density functional theory are validated against high-level wave function CCSD(T) calculations. Solvation contributions are included by an all-atom model with hybrid quantum chemical/molecular mechanical (QM/MM) potentials and enhanced sampling molecular dynamics simulations. The free energy profile obtained for FeS cluster hydrolysis indicates that the hybrid functional M06 together with an implicit solvent correction capture the most important aspects of FeS cluster reactivity in aqueous solution. Then, 20 reaction channels leading to two consecutive Fe-S bond ruptures were explored with this calibrated model. For all protonation states, nucleophilic substitution with concerted bond breaking and forming to iron is the preferred mechanism, both kinetic and thermodynamically. In neutral solution, proton transfer from water to the sulfur leaving group is also concerted. Dissociative reactions show higher barriers and will not be relevant for FeS reactivity when exposed to solvent. These hydrolysis mechanisms may help to explain the stability and catalytic mechanisms of FeS clusters of multiple sizes and proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".