Phenolic-protein interactions: insight from in-silico analyses – a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it