Bi(III) Binding Stoichiometry and Domain‐Specificity Differences Between Apo and Zn(II)‐bound Human Metallothionein 1a
Bibliographic record
Abstract
Abstract Bismuth is a xenobiotic metal with a high affinity to sulfur that is used in a variety of therapeutic applications. Bi(III) induces the cysteine‐rich metallothionein (MT), a protein known to form two‐domain cluster structures with certain metals such as Zn(II), Cd(II), or Cu(I). The binding of Bi(III) to MTs has been previously studied, but there are conflicting reports on the stoichiometry and binding pathway, which appear to be highly dependent on pH and initial metal‐loading status of the MT. Additionally, domain specificity has not been thoroughly investigated. In this paper, ESI‐MS was used to determine the binding constants of [Bi(EDTA)]− binding to apo‐MT1a and its individual αMT fragment. The results were compared to previous experiments using βMT1a and βαMT3. Domain specificity was investigated using proteolysis methods and the initial cooperatively formed Bi2MT was found to bind to cysteines that spanned across the traditional metal binding domain regions. Titrations of [Bi(EDTA)]− into Zn7MT were performed and were found to result in a maximum stoichiometry of Bi7MT, contrasting the Bi6MT formed when [Bi(EDTA)]− was added to apo‐MT. These results show that the initial structure of the apo‐MT determines the stoichiometry of new incoming metals and explains the previously observed differences in stoichiometry.
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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".