Quantitative Redox Proteomics: The NOxICAT Method
Bibliographic record
Abstract
Because of its versatile chemical properties, the amino acid cysteine plays a variety of vital roles in proteins. It can form structure-stabilizing elements (e.g., disulfide bonds), coordinate metal cofactors and is part of the catalytic center of many enzymes. Recently, a new role has been discovered for cysteine: so-called redox-sensitive proteins use the thiol group of cysteine as a specific sensor for Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS). The oxidation of such a redox-active cysteine, e.g., under conditions of elevated cellular ROS or RNS levels (oxidative or nitrosative stress), often results in a reversible thiol modification. This, in turn, might lead to structural changes and altered protein activity. When the oxidative stress subsides, cellular antioxidant systems, including thioredoxin and glutathione can reduce the redox-active cysteine and restore the original structure and activity of the redox-sensitive protein. This makes oxidative thiol modifications an attractive mechanism for cellular redox sensing and signaling.To study the target cysteines of oxidative and nitrosative stress and to quantify the extent of the thiol modifications generated under these conditions, we have recently developed a thiol trapping technique using isotope coded affinity tag (ICAT) chemistry (1). With this method, reduced cysteines are selectively labeled with the isotopically light form of ICAT and oxidized cysteines with the isotopically heavy form of ICAT. Thus we could globally quantify the ratio of reduced and oxidized cysteines in cellular proteins based on the modified peptide masses. Here, we present an expansion of this method, which we term NOxICAT, because it uses ICAT chemistry to detect changes in thiol modifications of proteins upon Nitrosative and Oxidative stress. The NOxICAT-method is a highly specific and quantitative method to study the global changes in the thiol redox state of cellular proteins under a variety of physiological and pathological stress conditions.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".