(P04-008-24) Quantitative Metabolomics-Driven Protein Target Prediction: A Computational Workflow for Precision Nutrition Discovery

Objectives: Metabolomics explores complex relationships between small-molecule metabolites and biological systems, and has evolved into a potent tool for unraveling the intricacies of cellular processes. Even though it offers profound insights into complex relationships between metabolites and health, a crucial hurdle remains in identifying the associated protein companions that interact with these metabolites, especially in the vast, uncharted territory of dietary phytochemicals. In the present study, our main objective is to employ a structure-based target prediction workflow by introducing an innovative in-silico approach that systematically and efficiently predicts protein targets for metabolites.

Methods: We develop a workflow to determine the protein targets for the dietary metabolites and utilize docking to predict the binding of these metabolites to the protein targets. Although this methodology encompasses a multi-faceted process, ranging from the analysis of untargeted and targeted metabolomics data to molecular modeling and exploration of interaction networks, our focus is directed towards a specific set of blood serum metabolites derived from nutrition intervention studies feeding diets rich in blueberries, which are known to have health benefits. In addition to engaging with multiple protein targets, the findings indicate that gut metabolites derived from blueberry consumption predominantly interact with carbonic anhydrase family proteins and play a crucial role in various physiological processes, including acid-base regulation, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism.

Results: The molecular docking of these dietary metabolites with predicted carbonic anhydrase protein family as major protein targets and has revealed a common binding pocket, exhibiting binding energies comparable to those of experimentally validated compounds.

Conclusions: This research sheds additional light on the molecular mechanisms underlying the health benefits of blueberries and provided a novel insights into precision nutrition strategies by integrating a structural and computational metabolomics workflow.

Funding Sources: This work was supported in part through USDA-ARS project 6026-51000-012-000D, and Kansas IDeA Network of Biomedical Research Excellence (K-INBRE) P20GM103418.