Gold Nanoparticle Enrichment Method for Identifying <i>S</i>-Nitrosylation and <i>S</i>-Glutathionylation Sites in Proteins
Bibliographic record
Abstract
We present a simple method by which gold nanoparticles (AuNPs) are used to simultaneously isolate and enrich for free or modified thiol-containing peptides, thus facilitating the identification of protein S-modification sites. Here, protein disulfide isomerase (PDI) and dual specificity phosphatase 12 (DUSP12 or hYVH1) were S-nitrosylated or S-glutathionylated, their free thiols differentially alkylated, and subjected to proteolysis. AuNPs were added to the digests, and the AuNP-bound peptides were isolated by centrifugation and released by thiol exchange. These AuNP-bound peptides were analyzed by MALDI-TOF mass spectrometry revealing that AuNPs result in a significant enrichment of free thiol-containing as well as S-nitrosylated, S-glutathionylated, and S-alkylated peptides, leading to the unequivocal assignment of thiols susceptible to modification.
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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) | 0.001 | 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".