SNO spectral counting (SNOSC), a label-free proteomic method for quantification of changes in levels of protein S-nitrosation
Bibliographic record
Abstract
S-Nitrosation plays an important role in regulation of protein function and signal transduction. Discovering S-nitrosated targets is a prerequisite for further functional study. However, current proteomic methods used to quantify S-nitrosation are limited in their applicability to certain types of samples, or by the need for special reagents and complex procedures to obtain the results. Here we devised a label-free proteomic method for quantification of changes in the level of protein S-nitrosation on the basis of a spectral counting strategy, called S-nitrosothiol (SNO) spectral counting (SNOSC). With this method, samples can be from any source (cells, tissues); there is no need for labelling reagents or procedures, and the results yield quantitative information. Moreover, as it is based on the irreversible biotinylation procedure (IBP) for S-nitrosation protein enrichment, false positive targets caused by the interference of intermolecular disulphide bonds are ruled out. Using SNOSC we studied S-nitrosation in the cell line RAW264.7 induced exogenously with S-nitrosoglutathione (GSNO), or induced endogenously by lipopolysaccharides/interferon-gamma (LPS/IFN-γ). We detected a significant increase in S-nitrosation of 50 proteins after exogenous induction and 17 proteins after endogenous induction. We thus demonstrate that SNOSC is a widely applicable proteomic method for fast screening of SNO 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".