Exploring metalation pathways of metallothionein through competition with carbonic anhydrase
Bibliographic record
Abstract
The cysteine-rich, metal-binding proteins called metallothioneins (MT) are ubiquitous throughout Nature, including humans, and are important for metal homeostasis, heavy metal detoxification and the reduction of oxidative stress. Following overexpression of recombinant human MT1a in Escherichia coli, we partially metalated the protein and characterized its binding properties using native electrospray ionization mass spectrometry (ESI-MS). Metallothionein 1a is found to follow a metalation pathway involving metal-thiolate cluster formation at low pH and a pathway dominated by beaded formation involving terminal thiolates at high pH. The degree to which depends on the type of metal bound. The different conformations are confirmed using circular dichroism (CD) and metalation experiments at physiological pH using ESI-MS. In order to further explore the binding patterns of metallothionein, a competition experiment was performed against bovine carbonic anhydrase for the exact binding constants of cadmium. Metallothionein was found to bind to cadmium strongly in comparison to that of carbonic anhydrase and shows evidence of direct interaction to carbonic anhydrase, different from what was previously seen with zinc1. This discovery assists in explaining the role of metallothionein in heavy metal detoxification and metal homeostasis in the body.
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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 | 0.001 |
| 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 | 0.001 | 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".