Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods

Abstract

Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies’ blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from −5.0 kcal/mol to −9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.

Keywords

• metabolomics
• in silico
• target prediction
• gene
• protein
• metabolites