Predicting Food Protein Hydrolysis Kinetics
Combining high-resolution peptide analytics and AI modeling to predict peptide release kinetics during food protein digestion.
β-Lactoglobulin Hydrolysis by Trypsin
Project outlook demo: We are developing an interactive predictive workflow that starts from amino acid sequence and simulates progressive cleavage events, peptide accumulation, and hydrolysis progression. The demo below illustrates a simplified hydrolysis timeline for β-lactoglobulin under tryptic digestion.
Protein hydrolysis plays a key role in determining the nutritional properties of foods. However, digestion-driven peptide release remains difficult to predict, limiting our ability to fully utilize dietary proteins. This project combines advanced analytical chemistry with data-driven modeling to better understand and predict protein digestion behavior.
We integrate peptide identification and quantification workflows based on LC-MS with kinetic modeling to track how peptides form and evolve during digestion. A comprehensive experimental dataset supports estimation of kinetic parameters from peptide concentration profiles measured across digestion timepoints using untargeted quantitative UHPLC-PDA-ESI-MS. The modeling framework accounts for protease specificity, co-action between digestive enzymes, and structural changes in proteins that influence cleavage accessibility. By combining substrate amino acid sequence information with physico-chemical residue descriptors around cleavage regions, the project aims to uncover sequence-level patterns that govern hydrolysis kinetics and peptide release.