Laminar Flow Effects During Laser-Induced Oxidative Labeling for Protein Structural Studies by Mass Spectrometry
Bibliographic record
Abstract
Laser-induced oxidative labeling of proteins provides insights into biomolecular structures and interactions. In these experiments, the hydroxyl radical ((*)OH) formed by photolysis of H(2)O(2) generates covalent modifications that are detectable by mass spectrometry. Under conditions where individual protein molecules are irradiated only once, the short (*)OH lifetime (approximately 1 micros) ensures that covalent modifications are formed before any oxidation-induced conformational changes take place. This feature implies that the method should be free of structural artifacts. It has been proposed that single-exposure conditions can be achieved by passing the solution through a capillary where successive laser pulses generate a string of irradiated flow segments that are well separated from one another. The current work explores the convection phenomena within the labeling capillary in more detail. The experiments are conducted at Reynolds numbers <<2000, resulting in laminar flow. The associated parabolic velocity profile causes a portion of each irradiated segment to remain in the labeling window during the subsequent laser pulse. Achieving a genuine single-exposure regime is, therefore, not possible. We estimate the fraction of labeled protein formed under laminar flow conditions, as well as the occurrence of multiple exposure events for any combination of experimental parameters (laser spot width, pulse frequency, and solution flow rate). A proper choice of these parameters provides extensive labeling, while keeping multiple exposure events at an acceptably low level. The theoretical framework developed here is supported by experimental data. Overall, this study reaffirms the feasibility of the use of flow devices for meaningful laser-induced oxidative labeling studies. At the same time, we provide a theoretical underpinning of this technique that goes beyond previously suggested plug flow models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".