Interaction of Trivalent Arsenicals with Metallothionein
Bibliographic record
Abstract
Arsenic is a human carcinogen, causing skin, bladder, and lung cancers. Although arsenic in drinking water affects millions of people worldwide, the mechanism(s) of action by which arsenic causes cancers is not known. Arsenic probably exerts some toxic effects by binding with proteins. However, few experimental data are available on arsenic-containing proteins in biological systems. This study reports on arsenic interaction with metallothionein and established binding stoichiometries between metallothionein and the recently discovered trivalent metabolites of arsenic metabolism. Size exclusion chromatography with inductively coupled plasma mass spectrometry analysis of reaction mixtures between trivalent arsenicals and metallothionein clearly demonstrated the formation of complexes of arsenic with metallothionein. Analysis of the complexes using electrospray quadrupole time-of-flight tandem mass spectrometry revealed the detailed binding stoichiometry between arsenic and the 20 Cys residues in the metallothionein molecule. Inorganic arsenite (As(III)) and its two trivalent methylation metabolites, monomethylarsonous acid (MMA(III)) and dimethylarsinous acid (DMA(III)), readily bind with metallothionein. Each metallothionein molecule could bind with up to six As(III), 10 MMA(III), and 20 DMA(III) molecules, consistent with the coordination chemistry of these arsenicals. The findings on arsenic interaction with proteins are useful for a better understanding of arsenic health effects.
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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".