Proteomic insights into synthesis of isoflavonoids in soybean seeds
Bibliographic record
Abstract
Soybean seeds are the major human dietary source of isoflavonoids, a class of plant natural products almost entirely exclusive to legumes. Isoflavonoids reduce the risk of a number of chronic human illnesses. Biosynthesis and accumulation of this class of compounds is a multigenic and complex trait, with a great deal of variability among soybean cultivars and with respect to the environment. There is a wealth of genomic, transcriptomic, and metabolomics data regarding isoflavonoid biosynthesis, but the connection between multigene families and their cognate proteins is a missing link that could provide us with a great deal of functional information. The changing proteome of the developing seed can shed light on the correlative increase in isoflavonoids, while the maternal seed coat proteome can provide the link with inherited metabolic and signaling machinery. In this effort, 'seed-filling' proteomics has revealed key secondary metabolite enzymes that quantitatively vary throughout seed development. Seed coat proteomics has revealed the existence of metabolic apparatus specific to isoflavonoid biosynthesis (isoflavonoid reductase) that could potentially influence the chemical content of this organ. The future of proteomic analysis of isoflavonoid biosynthesis should be centered on the development of quantitative, tissue-specific proteomes that emphasize low-abundance metabolic proteins to extract the whole suite of factors involved.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".