AI Enables De Novo Design of Bitter Peptides for Taste Research

Summary

Researchers at the Leibniz Institute for Food Systems Biology at the Technical University of Munich and collaborators developed an AI-based method to predict whether peptides taste bitter and to design new bitter peptides from scratch. The approach combines a protein language model trained on about 500 known bitter peptides with BitterPep-GCN, a graph convolutional network for peptide prediction. The team generated 161 previously uncharacterized peptide sequences and used the model to identify candidates predicted to be bitter or non-bitter. The most promising candidates were synthesized and assessed by a trained sensory panel. Of 31 peptides tested, the panel correctly classified 25 as bitter or non-bitter, and the study also identified previously unknown bitter and non-bitter peptides. The researchers say the method could support more deliberate control of bitter peptide formation during production of fermented foods and protein hydrolysates, particularly plant-based, protein-rich foods whose consumer acceptance can be affected by unwanted flavor notes. The work was published in NPJ Science of Food on June 25, 2026.