Development of an algorithm for creating information resources for forecasting milk protein hydrolysis

The dairy industry, like other industries, produces a large number of secondary raw materials, one of the most significant of which is whey. This raw material is rich in proteins with high biological potential, realized during protein hydrolysis and obtaining bioactive peptides. The choice of enzymes for hydrolysis in order to obtain certain bioactive peptides is time-consuming and expensive, which can be optimized using in silico methods. The aim of the research is to develop an algorithm for creating a database for modeling the effect of enzymes on whey proteins, as well as software for optimizing targeted hydrolysis of whey proteins with enzymes acting on β-lactoglobulin and α-lactalbumin to obtain specific biologically active peptides. Whey is a source of β-lactoglobulin and α-lactalbumin proteins. Information for the formation of the database was obtained from well-known sources (BIOPEP-UWM, PeptideCutter, UniProtKB and InterPro). The database was created using the SQLite database management system. The program was developed based on the created database and using the Tkinter graphical widget library using the Python programming language. The program's functionality has been tested; it produces an exact set of enzymes for obtaining a peptide or a chain of peptides depending on the milk protein previously selected for hydrolysis.