γ-Ray-Mediated Oxidative Labeling for Detecting Protein Conformational Changes by Electrospray Mass Spectrometry
Bibliographic record
Abstract
Exposure of proteins to hydroxyl radicals induces the incorporation of oxygen atoms into solvent-exposed side chains. Earlier studies have employed this approach for mapping protein-protein interactions in mass spectrometry-based footprinting experiments. This work explores whether the overall level of gamma-ray mediated oxidative labeling can be used for monitoring large-scale conformational changes. According to a recently developed kinetic model (Tong, X.; Wren, J. C.; Konermann, L. Anal. Chem. 2007, 79, 6376-6382), the apparent first-order rate constant for oxidative labeling can be approximated as k(app) = k(RAD)/([P](tot) + C/k(u)), where k(RAD) is the primary rate of *OH formation, [P](tot) is the protein concentration, C reflects the presence of competing radical deactivation channels, and ku is the rate constant at which hydroxyl radicals react with the protein. The current study introduces conformational effects into this model by proposing that k(u) = [see text for formula] , where N is the number of amino acids, alphai is a measure for the solvent exposure of residue i, and k(ch)(i) is the oxidation rate constant that would apply for a completely solvent-exposed side chain. Using myoglobin and cytochrome c as model systems, it is demonstrated that unfolding by addition of H(3)PO(4) increases k(app) by up to 30% and 70%, respectively. Unfolding by other commonly used denaturants such as organic acids or urea results in dramatically lower oxidation levels than for the native state, a behavior that is due to the radical scavenging activity of these substances (corresponding to an increased value of C). Control experiments on model peptides are suitable for identifying such "secondary" effects, i.e., factors that modify oxidation levels without being related to conformational changes. In conclusion, the overall *OH labeling level represents a viable probe of large-scale protein conformational changes only under conditions where secondary effects are known to be minimal and where [P](tot) is constant.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".