Peptide prefractionation is essential for proteomic approaches employing multiple‐reaction monitoring of fruit proteomic research
Bibliographic record
Abstract
Off-gel™ IEF has become a popular tool in proteomics research to fractionate peptides or proteins. We conducted a detailed investigation on the fruit proteomics of apple, banana, and strawberry fruit employing Off-gel™ electrophoresis (OGE) as a crucial step to improve the proteome coverage and quantitative proteomic workflows including multiple-reaction monitoring (MRM). We provide technical details concerning the application of Off-gel™IEF, nano-LC-MS detection, and MRM optimization and analysis. Our results demonstrated that the application of OGE is an effective method for peptide fractionation and increased significantly the number of proteins identified by at least ten times, with more total peptides detected and collected. Furthermore, we developed a protocol combining OGE and MRM studies to identify and quantitatively investigate monodehydroascorbate reductase, a key enzyme in the redox and antioxidant system of apple fruit during fruit ripening. Using this method, the quantitative changes in this protein during ripening and in response to ethylene treatment was investigated. Our results provide direct and comprehensive evidence demonstrating the benefits of OGE and its application for both shotgun and quantitative proteomics research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".