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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 Publisher



Barati M1 ; Javanmardi F2 ; Jabbari M3 ; Mokariyamchi A3 ; Farahmand F4 ; Es I5 ; Farhadnejad H6 ; Davoodi SH7 ; Mousavi Khaneghah A8
Authors
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Authors Affiliations
  1. 1. Student Research Committee, Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  2. 2. Department of Food Science and Technology, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  3. 3. Department of Community Nutrition, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  4. 4. Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. School of Chemical Engineering, Department of Material and Bioprocess Engineering, University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
  6. 6. Nutrition and Endocrine Research Center, Research Institute for Endocrine Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  7. 7. Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  8. 8. Department of Food Science, Faculty of Food Engineering, University of Campinas (UNICAMP), Sao Paulo, Brazil

Source: LWT Published:2020


Abstract

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 αs1-casein, αs2-casein, β-casein, k-casein, β-lactoglobulin, and α-lactalbumin were obtained from UniProt Knowledgebase (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 μmol digestion-resistant peptides; in which 1880.434 μmol out of total peptides have anti-diabetic properties. Of all digestion-resistant peptides, 1978.24, 1955.024, 1700.907, and 1066.07 μmol 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. © 2020 Elsevier Ltd
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