Predicting milk-derived hydrogel-forming peptides with TANGO

The uncovering of single peptides derived from food sources that can form hydrogels is of great relevance for several applications. However, identifying single peptide hydrogels from food is a daunting task given the complex nature of the food systems. The proof of concept of the applicability of TANGO, a statistical mechanical-based algorithm that predicts the β-aggregate propensity of peptides, as a tool to uncover peptides derived from milk that can form hydrogels is reported. Using TANGO in conjunction with a set of defined criteria we discovered that from a group of thirteen peptides derived from milk proteins, seven formed hydrogels at a concentration of 2 wt% and pH 7 at room temperature. Three more peptides formed aggregates and appeared to go through the syneresis process, and three additional peptides remained liquid under the experimental conditions. This result sets the basis of a simple methodology for unveiling peptide hydrogels from food and other natural sources.