Comparative Analysis of Protein Extraction Protocols for Olive Leaf Proteomics: Insights into Differential Protein Abundance and Isoelectric Point Distribution
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Plant proteomics studies face two major challenges: limited databases due to the need for sequenced genomes and the difficulty in obtaining high-quality protein extracts. Olive ( Olea europaea ), a key species in Mediterranean flora known for its rich biochemical content, presents additional complexity due to its lipidic structure and high levels of inhibitory compounds that hinder protein extraction. Consequently, various studies have focused on optimizing the protein extraction methods for olives. While different extraction protocols exist for leaf proteome analysis, their compatibility with LC–MS/MS has been scarcely studied. This work was carried out to compare three protein extraction protocols for LC–MS/MS analysis using olive ( O. europaea L) leaf tissue. Denaturing SDS (Method A), physiological CHAPS (Method B), and phenolic TCA/acetone (Method C) were evaluated with LC–MS/MS data. The quantitative comparisons of the three extraction methods revealed that Protocol A gave the greatest yields. According to the results obtained, Protocol A uniquely identified 77 proteins, Protocol B identified 10 unique proteins, and Protocol C identified 19 unique proteins. Similarly, the peptide sequence analysis showed that Protocol A uniquely identified 208 peptide sequences, Protocol B identified 29, and Protocol C identified 36. Moreover, reversed-phase high-performance liquid chromatography (RP-HPLC) results suggest that Method A may be more efficient in removing and retaining hydrophobic proteins. Overall, Protocol A demonstrated greater sensitivity, efficiency, and reproducibility in LC–MS/MS analysis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".