Identification of Reactive Cysteines in a Protein Using Arsenic Labeling and Collision-Induced Dissociation Tandem Mass Spectrometry
Bibliographic record
Abstract
Trivalent arsenicals have high affinity for thiols (such as free cysteines) in proteins. We describe here the use of this property to develop a collision-induced dissociation (CID) tandem mass spectrometry (MS/MS) technique for the identification of reactive cysteines in proteins. A trivalent arsenic species, dimethylarsinous acid (DMA (III)), with a residue mass (103.9607) and mass defect distinct from the normal 20 amino acids, was used to selectively label reactive cysteine residues in proteins. The CID fragment ions of the arsenic-labeled sequences shifted away from the more abundant normal fragments that would otherwise overlap with the ions of interest. Along with the internal and immonium ions, the arsenic-labeled fragment ions served as MS/MS signatures for identification of the binding sites and for assessment of the relative reactivity of individual cysteine residues in a protein. Using this method, we have identified two highly reactive binding sites in rat hemoglobin (Hb): Cys-13alpha and Cys-125beta. Cys-13alpha was bound to DMA (III) in the Hb of rats fed with arsenic, and this binding was responsible for arsenic accumulation in rat blood, while Cys-125beta was found to bind to glutathione in rat blood. This study revealed the relative reactivity of the cysteines in rat Hb in the following decreasing order: Cys-13alpha >> Cys-111alpha > Cys-104alpha and Cys-13alpha >> Cys-125beta > Cys-93beta. Arsenic-labeling is easy and fast for identification of active binding sites without enzymatic digestion and acid hydrolysis, and useful for characterization and identification of metal binding sites in other proteins.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".