Metal chelating peptides derived from brewers’ spent grain predicted by bioinformatics: screening and antioxidant activity in 5% fish oil-in-water emulsions

AnOxPePred, a bioinformatics prediction software was used on the proteome of brewers’ spent grain to identify potential metal chelating peptides (MCPs) embedded in the proteins. Based on the prediction score, amino acid sequence and protein abundance, 25 potential MCPs were selected. With the use of switchSENSE®, it was possible to demonstrate that 20 of the peptides showed affinity to Ni 2+ with varying associations constants. Seven peptides were further evaluated for their ability to prevent metal catalyzed oxidation in 5% fish-oil-in-water emulsions stabilized with 1wt% Tween 20. The addition of 0.05wt% peptide did not have any significant influence on the physical stability of the emulsion, however the emulsions containing MCPs showed significantly lower levels of peroxide values, tocopherol consumption and secondary volatile oxidation products compared to control without peptides. The highest antioxidant capacity was found in M25 (MTD), M17 (DTPSSEFNHRFQELGL), M15 (LHPN) and M11 (GQQGYPSTTSPQQSGQGQQLGQGQQPGQGQ). These MCPs all have amino acids with metal chelating activities next to each other, making it possible for them to coordinate multiple times to the metal ion. This study further enhances the knowledge regarding the relationship between peptide sequence and antioxidant activity in MCPs. Moreover, the study demonstrates the applicability of predictive models in identification of novel MCPs based on protein-level data but also highlights that current tools lack predictive accuracy.