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Food chemistry

Improved short peptide identification using HILIC-MS/MS: retention time prediction model based on the impact of amino acid position in the peptide sequence.


PMID 25466098

Abstract

Short peptides can have interesting beneficial effects but they are difficult to identify in complex mixtures. We developed a method to improve short peptide identification based on HILIC-MS/MS. The apparent hydrophilicity of peptides was determined as a function of amino acid position in the sequence. This allowed the differentiation of peptides with the same amino acid composition but with a different sequence (homologous peptides). A retention time prediction model was established using the hydrophilicity and peptide length of 153 di- to tetrapeptides. This model was proven to be reliable (R(2)=0.992), it was validated using statistical methods and a mixture of 14 synthetic peptides. A whey protein hydrolysate was analysed to assess the ability of the model to identify unknown peptides. In parallel to milk protein database and de novo searches, the retention time prediction model permitted reduction and ranking of potential short peptides, including homologous peptides, present in the hydrolysate.