Artifactual Sulfation of Silver-stained Proteins
Bibliographic record
Abstract
Sulfation and phosphorylation are post-translational modifications imparting an isobaric 80-Da addition on the side chain of serine, threonine, or tyrosine residues. These two post-translational modifications are often difficult to distinguish because of their similar MS fragmentation patterns. Targeted MS identification of these modifications in specific proteins commonly relies on their prior separation using gel electrophoresis and silver staining. In the present investigation, we report a potential pitfall in the interpretation of these modifications from silver-stained gels due to artifactual sulfation of serine, threonine, and tyrosine residues by sodium thiosulfate, a commonly used reagent that catalyzes the formation of metallic silver deposits onto proteins. Detailed MS analyses of gel-separated protein standards and Escherichia coli cell extracts indicated that several serine, threonine, and tyrosine residues were sulfated using silver staining protocols but not following Coomassie Blue staining. Sodium thiosulfate was identified as the reagent leading to this unexpected side reaction, and the degree of sulfation was correlated with increasing concentrations of thiosulfate up to 0.02%, which is typically used for silver staining. The significance of this artifact is discussed in the broader context of sulfation and phosphorylation site identification from in vivo and in vitro experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".