Entwicklung einer Methode zur Vorhersage bitterer Peptide in Nahrungsproteinen

The development of bitter peptides from whey proteins or, perhaps the most prominent example, milk casein, reduces consumer acceptance and results in financial damages aside from sustainability issues. The straightforward identification of bitter peptides in food is largely hampered by the enormous complexity of food peptidome. Peptide bitterness was found to be mediated by five bitter taste receptors (TAS2Rs). The overall goal of this project is to decipher the signal code of bitter peptides. By integrating machine learning with functional receptor studies, and sensoproteomics, we will investigate the peptide molecular recognition by TAS2Rs and identify the molecular signature carrying bitter information in peptide sequences. This will allow us to develop an efficient and applicable method to identify key bitter peptides in food.