Molecular dynamics simulation allows mechanistic understanding of natural deep eutectic solvents action on rapeseed proteins
Bibliographic record
Abstract
There is growing demand for alternative sources of nutritional protein to replace animal protein in the diet. This is driven partly by the concern over environmental sustainability of animal agriculture and partly by consumer health concerns. Animal proteins (e.g. milk and egg protein) are relatively easy to isolate and purify, whilst this is not always true for alternative proteins sources including those from agricultural co-product streams such as the press cake from oil production from oil seeds. These can require relatively harsh organic solvent conditions that may alter protein functionality. In the search for alternative, more gentle solvents, natural deep eutectic solvents (NADES) are receiving significant attention. In this study, a combination of experimental and molecular dynamics simulation studies was used to explore the efficiency of NADES made from glycerol and choline chloride and betaine and citric acid at extracting protein from rapeseed press cake. Whilst extraction with water alone gave the highest protein purity, NADES formulations were able to significantly increase the protein yield compared to both water and alkaline extraction, although at reduced purity of the extract. MD simulation highlighted that glycerol-choline chloride NADES were more efficient at disrupting protein-water interactions which may facilitate extraction. Overall, the study suggests NADES have potential as mild protein extractants, but more research is required on optimizing both the choice of components and concentration of species to maximise yield and purity of protein extracts. • Molecular dynamics simulations reveal underlying molecular mechanism for NADES action • Natural deep eutectic solvents are efficient at extracting protein from rapeseed cake • Water improves efficacy of glycerol+choline chloride and betaine+citric acid NADES • Yield is higher than for water or alkaline extraction, but purity is lower
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".