Hybrid Strategy of Bioinformatics Modeling (in silico): Biologically Active Peptides of Milk Protein - Alexandr G. Kruchinin; Ekaterina I. Bolshakova (Техника и технология пищевых производств, 2022)
Bioinformatic analysis methods are an auxiliary tool in the preliminary stage of research into biocatalytic conversion of proteins with predicted release of biologically active peptides. However, there are a number of factors ignored in current strategies for designing biologically active peptides, which prevents the complete prediction of their biological properties. This determines the relevance of the research objective, i.e. developing a hybrid strategy for bioinformatic modeling to study biologically active peptides of milk protein. The new strategy ranks key criteria based on high-performance algorithms of proteomic database. The research featured the scientific publications on in silico methods applied to biologically active peptides. Modern taxonometric methods of information retrieval were applied using the RSCI, Scopus and Web of Science databases. The article introduces and describes step by step the optimal in silico hybrid strategy algorithm for studying biologically active milk protein peptides. The algorithm takes into account the safety assessment of all hydrolysis products, their physicochemical and technological properties. The strategy algorithm relies on analytical data on the protein profile, the amino acid sequence of proteins that make up the raw material, taking into account their polymorphism, and the subsequent identification of bioactive amino acid sites in the protein structure. The algorithm selects optimal enzyme preparations, as well as models the hydrolysis and assesses the peptide bioactivity using proteomic databases. At the preliminary stage of protein hydrolysis, the new in silico strategy scientifically predicts the targeted release of stable peptide complexes of biologically active peptides with proven bioactivity, safety and sensory characteristics. The hybrid algorithm contributes to accumulation of the necessary primary data so as to reduce the time and cost of laboratory experiments.