Qualitative and Quantitative Evaluation of Protein Extraction Protocols for Apple and Strawberry Fruit Suitable for Two-Dimensional Electrophoresis and Mass Spectrometry Analysis
Bibliographic record
Abstract
A modified phenol-based protocol and a phenol-free protocol that involves hot SDS extraction followed by TCA precipitation in acetone were qualitatively and quantitatively compared and evaluated on apple peel and strawberry fruit. The phenol protocol resulted in significantly higher protein yields of 2.35 +/- 0.1 and 0.46 +/- 0.06 mg/g of FW from apple and strawberry fruit, respectively, compared to the SDS protocol, which produced 0.74 +/- 0.1 and 0.27 +/- 0.02 mg/g of FW, respectively. 2-DE analysis of apple protein extracts revealed 1422 protein spots associated with the phenol protocol and 849 spots associated with the SDS protocol. Of these, 761 were present only in phenol gels, whereas 23 were exclusive to SDS samples. For strawberry, SDS extraction produced poor-quality spots with a high degree of streaking, indicating possible contamination. The application of a cleanup procedure resulted in a purified protein extract with high-quality spots. 2-DE analysis of strawberry protein extracts revealed 1368 spots for the phenol protocol and 956 spots for the SDS protocol accompanied by the cleanup procedure. Of these, 599 spots were present only in phenol gels, whereas 109 were present only in SDS samples. Spots from each fruit tissue and extraction procedure were selected, and a total of 26 were identified by LC-MS/MS. Overall, this study demonstrates the complexity of protein extraction of fruit tissues and suggests that a phenol-based protein extraction protocol should be used as a standard procedure for recalcitrant fruit tissues, whereas a SDS protocol with or without a cleanup procedure may be used as an alternative protocol.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".