Morphology, Formation Kinetics and Core Composition of Pea and Soy 7S and 11S Globulin Amyloid Fibrils
Bibliographic record
Abstract
Legume seed storage proteins can be induced to form amyloid fibrils upon heating at low pH, which could improve their functionality for use in food and materials. However, the amyloidogenic regions of legume proteins are largely unknown. Here, we used LC-MS/MS to determine the amyloid core regions of fibrils formed by enriched pea and soy 7S and 11S globulins at pH 2, 80 °C, and characterized their hydrolysis, assembly kinetics, and morphology. A lag phase was absent from the fibrillation kinetics of pea and soy 7S globulins, while 11S globulins and crude extracts displayed a similar lag time. Pea and soy protein fibrils differed in morphology, with most pea fibrils being straight and soy fibrils being worm-like. Pea and soy globulins were abundant in amyloid-forming peptides, with over 100 unique fibril-core peptides from pea 7S and around 50 unique fibril-core peptides identified from pea 11S, soy 7S, and soy 11S globulins. Amyloidogenic regions derive predominantly from the homologous core region of 7S globulins and the basic subunit of 11S globulins. Overall, pea and soy 7S and 11S globulins are rich in amyloidogenic regions. This study will help understand their fibrillation mechanism and engineer protein fibrils with specific structures and functionality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".