Phenolic-protein interactions: insight from in-silico analyses – a review
Bibliographic record
Abstract
Abstract Phenolic compounds are ubiquitous plant secondary metabolites that possess various biological activities and are known to interact with proteins, altering their structure and properties. Therefore, interactions between these compounds and proteins has gained increasing attention due to their potential benefits to human health and for exploitation by the food industry. Phenolic compounds and proteins can form complexes via covalent linkages and/or non-covalent interactions through hydrophobic, electrostatic, van der Waals forces and hydrogen bonding. This review describes possible mechanisms of phenol-protein complex formation, their physiological action and activities that are important in the food industry, and possible outcomes in the terms of molecular docking and simulation analysis. The conformational changes of the protein upon binding with polyphenols can lead to the folding or unfolding of the protein molecules, forming insoluble or soluble complexes. The concentration of polyphenols, their molecular weight and structure, ions/cofactors and conditions of the system determine the precipitation or solubilization of the complex, affecting their nutritional and functional properties as well as their bioactivities. In this regard, molecular docking and simulation studies of phenolic-protein interactions allows comprehensive virtual screening of competitive/non-competitive and site-specific/non-specific conjugation of phenolics with different protein targets and facilitates understanding the observed effects. The docking analysis of flavonoids with enzymes and milk proteins has indicated their potential application in producing nutraceuticals and functional foods. Thus, combining molecular docking and simulation studies with experimental techniques is vital for better understanding the reactions that take place during digestion to engineer and manufacture novel food ingredients with desirable pharmacological properties and as potential food additives. Graphical Abstract
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".