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Record W4408185330 · doi:10.1016/j.foodhyd.2025.111328

Molecular dynamics simulation allows mechanistic understanding of natural deep eutectic solvents action on rapeseed proteins

2025· article· en· W4408185330 on OpenAlexaff
Grace Chidimma James, Stephen R. Euston

Bibliographic record

VenueFood Hydrocolloids · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMolecular dynamicsEutectic systemRapeseedChemistryAction (physics)Biochemical engineeringBiological systemComputational biologyComputational chemistryBiologyOrganic chemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.017
GPT teacher head0.277
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2025
Admission routes1
Has abstractyes

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