Effects of Protein Concentration on the Extent of γ-Ray-Mediated Oxidative Labeling Studied by Electrospray Mass Spectrometry
Bibliographic record
Abstract
Exposure of aqueous protein solutions to gamma-rays results in the formation of *OH radicals that readily react with solvent-exposed amino acid side chains. The incorporation of oxygen leads to peak progressions with a spacing of 16 Da in the mass distribution of the polypeptide chain. Unlike some other oxidative labeling strategies, these radiolysis experiments do not require solution additives that could interfere with the analysis or that might cause secondary oxidation processes. Using myoglobin as a model system, we demonstrate that the level of oxidative labeling depends critically on the protein concentration. If ignored, this effect may lead to ambiguities in the interpretation of experiments employing *OH labeling for monitoring solvent-accessible surface areas, protein folding, and protein-protein interactions. We present a simple analysis that allows oxidation to be interpreted as a process with exponential kinetics, characterized by an apparent rate constant of the form kapp=kRAD/([P]tot+B), where kRAD is the primary rate of hydroxyl radical production, B is a constant, and [P]tot is the total protein concentration. While oxidative labeling may trigger some changes in protein conformation, it is found that the magnitude of this effect is surprisingly small, a result that is consistent with observations previously made by others.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".