<i>p</i>‐Azidophenylarsenoxide: An Arsenical “Bait” for the In Situ Capture and Identification of Cellular Arsenic‐Binding Proteins
Bibliographic record
Abstract
Identification of arsenic-binding proteins is important for understanding arsenic health effects and for developing arsenic-based therapeutics. We report here a strategy for the capture and identification of arsenic-binding proteins in living cells. We designed an azide-labeled arsenical, p-azidophenylarsenoxide (PAzPAO), to serve bio-orthogonal functions: the trivalent arsenical group binds to cellular proteins in situ, and the azide group facilitates click chemistry with dibenzylcyclooctyne. The selective and efficient capture of arsenic-binding proteins enables subsequent enrichment and identification by shotgun proteomics. Applications of the technique are demonstrated using the A549 human lung carcinoma cells and two in vitro model systems. The technique enables the capture and identification of 48 arsenic-binding proteins in A549 cells incubated with PAzPAO. Among the identified proteins are a series of antioxidant proteins (e.g., thioredoxin, peroxiredoxin, peroxide reductase, glutathione reductase, and protein disulfide isomerase) and glyceraldehyde-3-phosphate dehydrogenase. Identification of these functional proteins, along with studies of arsenic binding and enzymatic inhibition, points to these proteins as potential molecular targets that play important roles in arsenic-induced health effects and in cancer treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".