Rapid separation and characterization of grain water-soluble proteins in bread wheat cultivars (<i>Triticum aestivum</i> L.) by capillary electrophoresis
Bibliographic record
Abstract
Water-soluble (WS) proteins in wheat grain are considered to represent the suite of biologically active enzymes and enzyme inhibitors in the grain. In this study, a rapid capillary electrophoresis (CE) method for WS protein separations was developed using untreated fused-silica columns and an acidic phosphate-glycine buffer system. In order to optimize the resolution and reproducibility of CE separation, different protein extraction methods, organic modifiers in phosphate-glycine buffer and capillary electrophoresis conditions, including capillary length and inner diameter (ID), operating temperature, performance voltages, sample injection times, etc., were investigated. High resolution and reproducibility of WS proteins were achieved using 20% ethanol as the extracting buffer. The optimal condition to separate these proteins was 50 μm ID × 31.5 cm (26.5 cm to the detector) capillary at 11.0 kV and 35°C. The optimum buffer was 0.1 M phosphate-glycine (pH 2.5) containing 20% acetonitrile (ACN) and 0.05% hydroxylpropylmethylcellulose. Using this method, the WS proteins were well separated in less than 10 min. A total of 120 Chinese bread wheat cultivars were analyzed. The CE patterns of most bread wheat cultivars showed a higher level of polymorphisms compared with SDS-PAGE patterns. All cultivars analyzed could be readily differentiated based on their WS protein profiles. Results indicate that the WS proteins are useful biochemical markers for wheat genetics and breeding research and CE is expected to become a new and powerful tool for the separation and characterization of grain WS proteins in bread wheat. Key words: Triticum aestivum, bread wheat, water-soluble proteins, capillary electrophoresis, biochemical markers
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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 | 0.000 |
| 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".