Proteomic Analysis of Common Bean (Phaseolus vulgaris L.) by Two-Dimensional Gel Electrophoresis and Mass Spectrometry
Bibliographic record
Abstract
The modern cultivated common bean (Phaseolus vulgaris) has evolved from wild common beans distributed in Central America, Mexico and the Andean region of South America. It has been reported that wild common bean accessions have higher levels of protein content than the domesticated dry bean cultivars. However, there is limited proteomic analysis of wild or domesticated common beans. In this investigation, we reported a proteomic analysis of a wild bean from Mexico. We utilized the TCA/Acetone method for protein extraction, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) for protein separation, and subsequent mass spectrometry (MS) for protein identification. Proteins were separated in the first dimension using pH range from 4-7. A total of 237 protein spots from the second dimension were isolated, digested with trypsin, and analyzed by MALDI/TOF/TOF mass spectrometry. We identified 141 protein spots by searching NCBI non redundant databases using the Mascot search engine and found a total of 43 unique proteins. Gene Ontology (GO) analysis was employed to understand the molecular processes in which the identified common bean proteins are involved. The majority of proteins are involved in binding (41.5%) and catalytic activity (35.8%), followed by nutrient reservoir activity (7.5%), antioxidant activity (1.9%), transporter activity (3.8%), enzyme regulator activity (3.8%), structural molecule activity (1.9%), and electron carrier activity (3.8%). The results indicate that TCA/Acetone extraction and 2D-PAGE is efficient in separating common bean proteins. Further identification of these proteins by MS demonstrates the quality of this protein extraction method. These results will be useful for the development of value added common beans by alteration of protein components.
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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 | 0.001 |
| 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 | 0.000 |
| 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".