How Do Food Compounds Interact with Their Protein Targets? Alternative Modes for Protein Binding
Bibliographic record
Abstract
A large body of research is oriented toward the determination of the mechanism of action of food compounds at the molecular level in order to rationalize the important role of these molecules in health and as a source of new drugs. In this work, we perform a systematic analysis of all the food-protein complexes at atomic resolution present in the Protein Data Bank. We analyze both the interaction types used in their binding as well as the functional groups involved in these; from the protein side, we also analyze the partner amino acid types and their interaction types, as well as the corresponding protein classes. For the analysis, food compounds are divided into a set of molecules derived from fatty lipids (FoodFL, which includes glycerolipids, glycerophospholipids, and fatty acyls) and the rest of the molecules (FoodnoFL), since these correspond to highly dissimilar chemical spaces. As a control, a set of drugs is used. From this analysis, it is found that the three compound sets establish protein-ligand complexes through alternative binding modes. Thus, although the most dominant interaction in the three sets is the hydrophobic one, each compound set displays some characteristic interaction types compared with the others. FoodnoFL compounds have a characteristically high content of hydrogen bonds, salt bridges, cation-π interactions, and metal coordinations, while FoodFL compounds have characteristically high hydrophobic interactions. In turn, drugs stem for their characteristically high π-π, cation-π, and halogen bond interactions. These differences result from differences in the types and relative abundances of functional groups, differential usage of interaction types by the same functional groups, and differential usage of interaction types by the partner amino acids. This new knowledge can be exploited in the design of new drugs inspired by food compounds.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".