Using Redox Proteomics to Gain New Insights into Neurodegenerative Disease and Protein Modification
Bibliographic record
Abstract
Antioxidant defenses in biological systems ensure redox homeostasis, regulating baseline levels of reactive oxygen and nitrogen species (ROS and RNS). Oxidative stress (OS), characterized by a lack of antioxidant defenses or an elevation in ROS and RNS, may cause a modification of biomolecules, ROS being primarily absorbed by proteins. As a result of both genome and environment interactions, proteomics provides complete information about a cell's proteome, which changes continuously. Besides measuring protein expression levels, proteomics can also be used to identify protein modifications, localizations, the effects of added agents, and the interactions between proteins. Several oxidative processes are frequently used to modify proteins post-translationally, including carbonylation, oxidation of amino acid side chains, glycation, or lipid peroxidation, which produces highly reactive alkenals. Reactive alkenals, such as 4-hydroxy-2-nonenal, are added to cysteine (Cys), lysine (Lys), or histidine (His) residues by a Michael addition, and tyrosine (Tyr) residues are nitrated and Cys residues are nitrosylated by a Michael addition. Oxidative and nitrosative stress have been implicated in many neurodegenerative diseases as a result of oxidative damage to the brain, which may be especially vulnerable due to the large consumption of dioxygen. Therefore, the current methods applied for the detection, identification, and quantification in redox proteomics are of great interest. This review describes the main protein modifications classified as chemical reactions. Finally, we discuss the importance of redox proteomics to health and describe the analytical methods used in redox proteomics.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".