Snapshot of proteomic changes in Aspergillus oryzae during various stages of fermentative processing of pea protein isolate
Bibliographic record
Abstract
Pea (Pisum sativum) is one of the most abundant and sustainable alternate source of protein. Although pea proteins have good quantities of most of the essential amino acids, they have a limited supply of tryptophan, methionine and cysteine. Moreover, pea proteins have poor techno-functional properties compared to proteins from animal sources, limiting their use in certain food applications. Bioprocessing techniques like solid-state fermentation (SSF) and enzymatic processing have been explored to improve the nutrient profile and functionality of pea proteins. However, there is a lack of information about proteomic changes in the food matrix during fermentation of the pea substrate. In this research, samples during SSF of pea protein isolate with Aspergillus oryzae were used for shotgun mass spectrometry (LC-MS/MS) analysis to identify the underlying functional pathways which play direct or indirect roles in enabling the colonization of the substrate leading to potential improvement of functional and nutritional value of pea protein. Results revealed the identity of A. oryzae proteins involved in different metabolic pathways that differed during various stages of SSF. Among them, methionine synthase was identified as an abundant protein, which catalyzes methionine biosynthesis. This might suggest how fermentation processes could be used to improve the presence of sulfur containing amino acids to rebalance the essential amino acid profile and improve the nutritional quality of pea proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".