Bibliographic record
Abstract
High-resolution two-dimensional gel electrophoresis and in-gel digestion are routinely used for large-scale protein separation and peptide generation in mass spectrometry-based proteomics, respectively. However, the combination of isoelectric focusing in the first dimension and polyacrylamide slab gel electrophoresis in the second dimension is not suitable for the proper separation of integral proteins and high-molecular-mass proteins. In addition, in-gel trypsination may not result in a high degree of efficient digestion levels for the production of large numbers of peptides in the case of certain protein species. The application of gradient one-dimensional gel electrophoresis and on-membrane digestion can overcome these technical problems and be extremely helpful for the comprehensive identification of proteins that are underrepresented in routine two-dimensional gel electrophoretic approaches. This review critically examines the general application of on-membrane digestion techniques in proteomics and its recent application for the identification of very large integral membrane proteins from skeletal muscle by mass spectrometry. This includes the discussion of proteomic studies that have focused on the proteomic characterization of the membrane cytoskeletal protein dystrophin from sarcolemma vesicles and the ryanodine receptor calcium release channel of the sarcoplasmic reticulum from skeletal muscle.
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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) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".