Characterization of wheat gluten proteins by hplc and MALDI TOF mass spectrometry
Bibliographic record
Abstract
We have performed a detailed characterization and identification of wheat gluten proteins obtained from the Teal variety of Canadian hard red spring wheat. RP-HPLC separation of the sample into 35 fractions has reduced the spectral complexity; this was followed by MALDI mass spectrometry (MS), which showed the presence of six or fewer resolved protein components above 20 kDa in each RP-HPLC fraction, giving a total of 93 MS resolved peaks. These included 17 peaks in the omega-gliadin fractions (F1-4), 12 in the high molecular weight (HMW) glutenin subunit fractions (F5-8), 59 in the alpha- and beta-gliadins and low molecular weight (LMW) glutenin subunit fractions (F9-31) and 5 peaks in the gamma-gliadin fractions (F32-35). Peptide maps of tryptic digests of HPLC fractions were obtained from a tandem quadrupole time-of-flight mass spectrometer (MALDI QqTOF MS) and were submitted to the ProFound search engine. HMW glutenin subunits including Ax2*, Dx5, Bx7, and Dy10 (consistent with the known profile of Teal), and LMW glutenin subunits including six from group 3 type II and 1 from group 2 type I, were identified with reasonable sequence coverage from HPLC fraction 5, 7, 17, and 18. The identities of the peptides attributed to selected gluten proteins were confirmed using MS/MS with BioMultiView to match the predicted and measured partial amino acid sequences. Because of incomplete wheat DNA databases, many wheat gluten proteins could not be identified. These results suggest that the combination of RP-HPLC with MS and MS/MS techniques is a promising approach for the characterization of wheat gluten proteins.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".