An in silico model to predict and estimate digestion-resistant and bioactive peptide content of dairy products: A primarily study of a time-saving and affordable method for practical research purposes

摘要

The purpose of this study is to estimate the concentration of digestion-resistant and bioactive peptides in dairy products using an in silico method. The major contributors of milk protein sequences including alpha(s1)-casein, alpha(s)(2)-casein, beta-casein, k-casein, beta-lactoglobulin, and alpha-lactalbumin were obtained from UniProt Knowledge-base (UniProtKB). In silico digestion and bioactive fragment, findings were analyzed using the BIOPEP tool. Bioactive peptide content of the dairy products was estimated based on molecular weight, percent of major proteins existing in the food items, and the number of peptides obtained after in silico digestion from each protein. The results showed that 100 g milk contains 6700.241 mu mol digestion-resistant peptides; in which 1880.434 mu mol out of total peptides have anti-diabetic properties. Of all digestion-resistant peptides, 1978.24, 1955.024, 1700.907, and 1066.07 mu mol belong to very low, low, medium, and high bioactivity sub-groups, respectively. Using the data introduced here, risk assessment could be done for dairy originated bioactive peptides and chronic disease.