Development and Applications of In-Gel CNBr/Tryptic Digestion Combined with Mass Spectrometry for the Analysis of Membrane Proteins
Bibliographic record
Abstract
Hydrophobic membrane proteins often have complex functions and are thus of great interest. However, their analysis presents a challenge because they are not readily soluble in polar solvents and often undergo aggregation. We present a sequential CNBr and trypsin in-gel digestion method combined with mass spectrometry for membrane protein analysis. CNBr selectively cleaves methionine residues. But due to the low number of methionines in proteins, CNBr cleavage produces a small number of large peptide fragments with MWs typically >2000, which are difficult to extract from gel pieces. To produce a larger number of smaller peptides than that obtained by using CNBr alone, we demonstrate that trypsin can be used to further digest the sample in gel. The use of n-octyl glucoside (n-OG) to enhance the digestion efficiency and peptide recovery was also studied. We demonstrate that the sensitivity of this membrane protein identification method is in the tens of picomole regime, which is compatible to the Coomassie staining gel-spot visualization method, and is more sensitive than other techniques reported in the literature. This CNBr/trypsin in-gel digestion method is also found to be very reproducible and has been successfully applied for the analysis of complex protein mixtures extracted from biological samples. The results are presented from a study of the analysis of bacteriorhodopsin, nitrate reductase 1 gamma chain, and a complex protein mixture extracted from the endoplasmic recticulum membrane of mouse liver.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".