Peptide mass fingerprinting by matrix‐assisted laser desorption ionization mass spectrometry of proteins detected by immunostaining on nitrocellulose
Bibliographic record
Abstract
We have developed an approach that allows peptide mass mapping by matrix-assisted laser desorption ionization-mass spectrometry of proteins visualized on a nitrocellulose membrane by immunochemical detection. Proteins are separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), electroblotted onto a nitrocellulose membrane and after blocking with a nonprotein-containing polymer such as polyvinylpyrrolidone 40 (PVP-40) or Tween 20, the proteins are stained with fount India ink. After incubation with primary and, if required, secondary peroxidase-coupled antibodies, immunochemically reactive proteins can be visualized using conventional enhanced chemiluminescence detection and assigned to the India ink-stained membrane by simple superposition. The proteins of interest are excised, submitted to "on-membrane" cleavage and the peptides are analyzed by mass spectrometry. Protein-based blocking reagents normally used in standard immunodetection protocols, such as skimmed milk, can be employed. We have obtained high-quality mass spectra of bovine serum albumin (BSA) detected on an immunoblot with an estimated amount of 100 fmol applied onto the gel, indicating the sensitivity of the present method. In addition, the approach is demonstrated with two other commercially available proteins, a serum protein, the successful identification of a tyrosine phosphorylated protein from total rat liver homogenate and serine phosphorylated proteins from an EcR 293 nuclear extract separated by two-dimensional (2-D) SDS-PAGE.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".