Abstract
Food-derived bioactive peptides are of great interest to science and industry due to evolving drivers of food product innovation, including health and wellness. This study aims to draw attention through a critical study on how bioinformatics analysis is employed in the identification of bioactive peptides in the laboratory. An in silico analysis (PeptideRanker, BIOPEP, AHTpin, and mAHTPred) of a list of peptides from goat casein hydrolysate was performed to predict which sequences could potentially be bioactive. To validate the predictions, the in vitro antihypertensive potential of the five peptides with the highest potential was first measured. Then, for three of these, gastrointestinal digestion was simulated in vitro, followed by the analysis of the resulting ACE inhibitory activity as well as antioxidant capacity. We thus observed that the use of new computational biology technologies to predict peptide sequences is an important research tool, but they should not be used alone and complementarity with various in vitro and in vivo assays is essential.