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Updated: Sep 11, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
How Do Food Compounds Interact with Their Protein Targets? Alternative Modes for Protein Binding
Mario Astigarraga1, Verónica López-Alejandre1, Andrés Sánchez-Ruiz1
1Cheminformatics & Artificial Intelligence Group, IMDEA Food, CEI UAM+CSIC, Madrid E28049, Spain.
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.
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