An in silico analysis of the effect of stressors on Mung bean protein
Bibliographic record
Abstract
Abstract With the world turning its attention towards sustainable protein sources, mung bean, in recent times has garnered significant research acclaim. As an emerging functional food that is rich in protein, little is known about its characteristics during processing. Hence, in this study, an molecular dynamic (MD) simulation approach was performed on 2CV6, the crystal structure of 8Sα globulin. GROMACS software was used to vary input thermal and pressure parameters (i.e., 300, 373, and 398 K at 3, 5, and 7 Kbar). Visual MD interface was used to picture the changes occurring in the protein's secondary structure as an effect of applied stress. The radius of gyration values decreased significantly with increasing pressure while high‐pressure high‐temperature treatment improved packing effects. STRIDE analysis showed that peripheral 2° structures such as α‐helices and β‐sheets underwent conformational changes to form turns and coils, indicating increased randomness. Despite subjecting the protein molecule to high temperature, the pressure applied counteracted the unwinding process, resulting in overall compaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".