SNO spectral counting (SNOSC), a label-free proteomic method for quantification of changes in levels of protein S-nitrosation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.003 |
| 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 | 0.001 | 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 it