Study of interactions between arsenicals and thioredoxins (human and <i>E. coli</i> ) using mass spectrometry
Bibliographic record
Abstract
Thioredoxin (Trx) plays an important role in achieving redox balances in cells and protecting the cells from oxidative damage. However, little is known about how arsenic affects Trx chemically. It is conceivable that trivalent arsenicals may bind to Trx, which has a highly conserved -CysGlyProCys- sequence. The objective of this study is to characterize the binding of seven arsenic species with Trx from E. coli and humans, using two mass spectrometry techniques. The arsenic-Trx complexes and the free arsenicals were well separated by size-exclusion liquid chromatography (LC) and detected with inductively coupled plasma mass spectrometry (ICPMS). The LC/ICPMS analyses showed that the trivalent arsenic species were able to form complexes with both human and E. coli Trx. Determination of binding constants indicated that affinity to Trx was higher for monomethylarsonous acid (MMA(III)) and phenylarsine oxide (PhAs(III)) than inorganic arsenite (iAs(III)) and dimethylarsinous acid (DMA(III)), probably because MMA(III) and PhAs(III) were able to form stable complexes by binding to two vicinal cysteines in the -CysGlyProCys- region of the Trx. The complexes of arsenicals with both human and E. coli Trx were further characterized by nano-electrospray tandem mass spectrometry. Binding stoichiometries for different arsenic species were consistent with the available cysteine residues in the Trx. Mass spectral evidence also suggests that the pentavalent arsenicals could be reduced by Trx. This study provides the first detailed chemical characterization of the interactions between Trx and arsenic species.
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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".