Domain Selection in Metallothionein 1A: Affinity-Controlled Mechanisms of Zinc Binding and Cadmium Exchange
Bibliographic record
Abstract
Mammalian metallothioneins (MTs) are small, metal binding proteins implicated in cellular metal ion homeostasis and heavy metal detoxification. Divalent, metal-saturated MTs form two distinct domains; the N-terminal β domain binds three metals using nine Cys residues, and the C-terminal α domain binds four metals with 11 Cys residues. Domain selection during zinc binding and cadmium exchange to human MT1A was examined using a series of competition reactions with mixtures of the isolated domain fragments. These experiments were conducted at two biologically significant pH conditions where MTs exist in vivo. Neither zinc binding nor cadmium exchange showed any significant degree of specificity or selectivity based on detailed analysis of electrospray ionization mass spectrometric and circular dichroic data. Under acidic conditions, zinc binding and cadmium exchange showed slight α domain selectivity because of the increased preference for cooperative clustering of the α domain. Modeling of the reactions showed that at both physiological (7.4) and acidic (5.8) pHs, zinc binding and cadmium exchanges occur essentially randomly between the two fragments. The metal binding affinity distributions between the domain fragments are comingled and not significantly separated as required for a domain specific mechanism. The models show rather that the order of the binding events follows the order of the binding affinities that are distributed across both domains and that this can be considered quantitatively by the KF(Cd)/KF(Zn) binding constant ratio for each metal bound.
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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".