Noncovalent labeling of myoglobin for capillary electrophoresis with laser‐induced fluorescence detection by reconstitution with a fluorescent porphyrin
Bibliographic record
Abstract
Traditional protein labeling reactions for capillary electrophoresis (CE) with laser-induced fluorescence (LIF) detection suffer from a variety of disadvantages. The reactions can be nonquantitative on a reasonable time scale, require relatively high concentrations of protein and fluorophore, and can give multiple reaction products that can not be separated. Herein, we describe a new noncovalent labeling technique that is rapid, selective for myoglobin, and gives a simple reaction product. Myoglobin is denatured with either 5.4 M urea or low pH (2.0). The denatured myoglobin releases its nonfluorescent heme group. A fluorescent porphyrin (protoporphyrin IX (PPIX) or its zinc (II) complex, Zn-PPIX), is added to the mixture and the solution conditions are altered (dilute to 0.54 M urea or adjust pH to 7.0) to allow myoglobin refolding. Upon refolding, the protein incorporates PPIX from solution, thus making the reaction product fluorescent. The experimental conditions have been optimized for both urea and low-pH denaturation of myoglobin. The latter procedure produces a detection limit of 50 nM. Alternatively, the reaction can be performed without denaturation by a simple exchange of the porphyrins. The use of Zn-PPIX yields the most efficient reaction. The low-pH reaction is unaffected by a 2000-fold excess of bovine serum albumin.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".