A computational investigation into the catalytic activity of a diselenolene sulfite oxidase biomimetic complex
Bibliographic record
Abstract
Molybdenum is the only 4d metal found in almost all life. One such molybdenum-containing enzyme is sulfite oxidase, which also contains the dithiolene-molybdopterin ligand. Sulfite oxidase is essential in the degradation of sulfur-containing compounds such as cysteine and methionine. Past work has shown parallels in the chemistry of dithiolene–metal and diselenolene–metal complexes. Thus, in this present work, the oxygen atom transfer mechanism for a diselenolene sulfite oxidase biomimetic complex was investigated using computational tools, the results of which were compared to the analogous dithiolene biomimetic complex. From the results obtained, the molybdenum-diselenolene sulfite oxidase biomimetic complex is able to catalyse the oxygen atom transfer and does so with a marginally lower value of ΔrG‡ than that for the analogous dithiolene complex. In particular, it was found that on average, the diselenolene complex had an activation energy 1.2 kJ mol–1 lower in energy than the analogous dithiolene complex. However, the calculated value of ΔrG suggests that the oxidation of sulfite is more favourable for the dithiolene complex where the average difference in reaction aqueous Gibbs reaction energy was –9.4 kJ mol–1 relative to the diselenolene complex. It is noted that with the use of D3 and D3BJ corrections in combination with the B3LYP functional, the barrier for oxygen atom transfer is lowered by more than 30.0 kJ mol–1 for both the diselenolene and dithiolene complexes. Such results suggest that to study such oxo-transfer reactions, the proper treatment of dispersion interaction is necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".