<scp>TMT</scp>‐labeled quantitative proteomic analysis to identify proteins associated with the stability of peanut milk
Bibliographic record
Abstract
BACKGROUND: Peanut milk benefits human health mainly due to its high protein content and suitable amino acid composition. To reveal the molecular mechanism affecting the quality of peanut milk, tandem mass tag (TMT)-labeled proteomic analysis was applied to identify the proteome variation between two peanut cultivars that produced peanut milk with the best and worst stability. RESULTS: A total of 478 differentially abundant proteins (fold change >1.2 or <0.83, P < 0.05) were identified. Most of these proteins were located in the cytoplasm and chloroplasts. Correlation analysis showed that RNA recognition motif (RRM) domain-containing protein (17.1 kDa) had a negative relationship with the sedimentation rate of peanut milk and that 22.0 kDa class IV heat shock protein was negatively correlated with the creaming index (P < 0.05). Bioinformatic analysis showed that the molecular function of RRM domain-containing protein (17.1 kDa) was associated with RNA binding and nucleotide binding, and 22.0 kDa class IV heat shock protein was involved in the pathway of protein processing in the endoplasmic reticulum. CONCLUSION: Overall, the differentially abundant proteins in the biological metabolic pathway might offer some potential markers to guide future peanut breeding, especially for the production of peanut milk. © 2021 Society of Chemical Industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".