Cross-talk between amino acid residues and flavonoid derivatives: insights into their chemical recognition
Bibliographic record
Abstract
Currently, there is a general consensus that flavonoids exert their antioxidant activity through their ability to interact with a broad range of proteins, enzymes and transcription factors rather than acting as conventional hydrogen-donating antioxidants. For this, the effect of different chemical groups of the conjugated flavonoid metabolites is apparently playing a pivotal role. Yet, many questions concerning the relevant molecular mechanisms still remain open. It is therefore crucial to gain a deeper insight into the amino acid residue-flavonoid interaction. Here we show extensive theoretical thermodynamic data and structural characteristics of the interaction of chalcone, genistein, epigallocatechin gallate, and quercetin and some of its metabolites with amino acid residues. By correlating (a) the binding energies of flavonoids-amino acid residues, (b) the hydrophobicity of amino acids, and (c) the abundance of amino acid residues in the binding sites of proteins, we can conclude that flavonoids appear to be strongly bonded to only few charged hydrophilic amino acids in the protein pockets, and rather weakly bonded to the majority of amino acid residues in the binding sites. This finding strongly impacts the understanding of the chemical recognition of flavonoids and their metabolites in their interaction with proteins and would contribute to a better design of further experimental studies. Particularly, the amino acids Phe, Leu, Ile and Trp seem to play a crucial role in the dynamics of flavonoid ligands in the binding sites of proteins.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".