Metallothionein‐3: <scp><sup>63</sup>Cu</scp>(I) binds to human <scp><sup>68</sup>Zn<sub>7</sub></scp>‐βα <scp>MT3</scp> with no preference for Cu<sub>4</sub>‐β cluster formation
Bibliographic record
Abstract
Human metallothioneins (MTs) are involved in binding the essential elements, Cu(I) and Zn(II), and the toxic element, Cd(II), in metal‐thiolate clusters using 20 reduced cysteines. The brain‐specific MT3 binds a mixture of Cu(I) and Zn(II) in vivo . Its metallation properties are critically important because of potential connections between Cu, Zn and neurodegenerative diseases. We report that the use of isotopically pure 63 Cu(I) and 68 Zn(II) greatly enhances the element resolution in the ESI‐mass spectral data revealing species with differing Cu:Zn ratios but the same total number of metals. Room temperature phosphorescence and circular dichroism spectral data measured in parallel with ESI‐mass spectral data identified the presence of specific Cu(I)‐thiolate clusters in the presence of Zn(II). A series of Cu(I)‐thiolate clusters form following Cu(I) addition to apo MT3: the two main clusters that form are a Cu 6 cluster in the β domain followed by a Cu 4 cluster in the α domain. 63 Cu(I) addition to 68 Zn 7 ‐MT3 results in multiple species, including clustered Cu 5 Zn 5 ‐MT3 and Cu 9 Zn 3 ‐MT3. We assign the domain location of the metals for Cu 5 Zn 5 ‐MT3 as a Cu 5 Zn 1 ‐β cluster and a Zn 4 ‐α cluster and for Cu 9 Zn 3 ‐MT3 as a Cu 6 ‐β cluster and a Cu 3 Zn 3 ‐α cluster. While many reports of the average MT3 metal content exist, determining the exact Cu,Zn stoichiometry has proven very difficult even with native ESI‐MS. The work in this paper solves the ambiguity introduced by the overlap of the naturally abundant Cu(I) and Zn(II) isotopes. Contrary to other reports, there is no indication of a major fraction of Cu 4 ‐β‐Zn n ‐α‐MT3 forming.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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; both teacher heads agree on what is shown here.
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".