Development of a QSAR model to predict protein-flavor binding in protein-rich food systems

Protein-flavor binding is a common challenge in food formulation. Prediction models provide a time-, resource-, and cost-efficient way to investigate how the structural and physicochemical properties of flavor compounds affect this binding mechanism. This study presents a Quantitative Structure-Activity Relationship model derived from five commercial plant-based proteins and thirty-three flavor compounds. The results showed that protein-flavor binding is primarily influenced by the structure and physicochemical properties of the flavor compound, with the protein source having a minor contribution. In addition to hydrophobicity, topological, electronic, and geometrical descriptors significantly contribute to the observed protein-flavor binding. The Random Forest model demonstrated a strong correlation between predicted and experimental values (Q² = 0.93) and a high predictive ability for a validation set of flavors and proteins not previously used (Q² = 0.88). The prediction model developed holds promise for customizing flavor combinations and streamlining product design, thereby, optimizing efficiency while reducing the risk of flavor overdose.