Thiol, Disulfide, and Trisulfide Complexes of Ru Porphyrins: Potential Models for Iron–Sulfur Bonds in Heme Proteins
Bibliographic record
Abstract
Thirty-two Ru(porp)L(2) complexes have been synthesized, where porp = the dianion of meso-tetramesitylporphyrin (TMP) or meso-tetrakis(4-methylphenyl)porphyrin (H(2)T-pMe-PP), and L = a thiol, a sulfide, a disulfide, or a trisulfide. Species studied were with RSH [R = Me, Et, (n)Pr, (i)Pr, (t)Bu, Bn (benzyl), and Ph], RSR (R = Me, Bn), RSSR (R = Me, Et, (n)Pr, Bn) and MeSS(t)Bu, and RSSSR (R = Me, Bn). All the species except two, which were the isolated Ru(T-pMe-PP)((t)BuSH)(2) and Ru(TMP)(MeSSMe)(2), were characterized in situ. The disulfide complex was characterized by X-ray analysis. (1)H NMR data for the coordinated thiols are the first reported within metalloporphyrin systems, and are especially informative because of the upfield shifts of the axial sulfur-containing ligands due to the porphyrin π-ring current effect, which is also present in the di- and trisulfide species. The disulfide in the solid state structure of Ru(TMP)(MeSSMe)(2) is η(1)(end-on) coordinated, the first example of such bonding in a nontethered, acyclic dialkyl disulfide; (1)H-(1)H EXSY NMR data in solution show that the species undergoes 1,2-S-metallotropic shifts. Stepwise formation of the bis(disulfide) complex from Ru(TMP)(MeCN)(2) in solution occurs with a cooperativity effect, resembling behavior of Fe(II)-porphyrin systems where crystal field effects dominate, but ligand trans-effects are more likely in the Ru system. The η(1)(end-on) coordination mode is also favored for the trisulfide ligand. Discussed also are the remarkable linear correlations that exist between the ring-current shielding shifts for the axial ligand C(1) protons of Ru(porp)(RS(x)R)(2) and x (the number of S atoms). The Introduction briefly reviews literature on Ru- and Fe porphyrins (including heme proteins) with sulfur-containing ligands or substrates, and relationships between our findings and this literature are discussed throughout the paper.
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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".