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Bioactive Peptides from Corn (Zea mays L.) with the Potential to Decrease the Risk of Developing Non-Communicable
Caroline Cagnin1, Bianca de Fátima Garcia1, Thais de Souza Rocha1
1Department of Food Science and Technology, State University of Londrina, Celso Garcia Cid Road, PR-445, Km 380, University Campus, Londrina 86057-970, PR, Brazil.
Abstract:
Studies have shown that corn (Zea mays L.) proteins, mainly α-zein, have the potential to act on therapeutic targets related to non-communicable chronic diseases, such as high blood pressure and type 2 diabetes. Enzymatic hydrolysis of proteins present in foods can result in a great diversity of peptides with different structures and possible bioactivities. A review of recent scientific research papers was performed to show evidence of the bioactive properties of corn peptides by in vitro assays. The α-zein amino acid sequences were identified in the UniProtKB protein database and then analyzed in the BIOPEP database to simulate enzymatic digestion and verify the potential biological action of the resulting peptides. The peptides found in the BIOPEP database were categorized according to the probability of presenting biological action using the PeptideRanker database. The aim was to use existing data to identify in silico the potential for obtaining biologically active peptides from α-zein, the main storage protein of corn. The analysis showed that the majority of peptide fragments were related to the inhibition of angiotensin-converting enzyme, followed by the inhibition of dipeptidyl peptidase IV and dipeptidyl peptidase III. Many drugs used to treat high blood pressure and type 2 diabetes work by inhibiting these enzymes, suggesting that corn peptides could be potential alternative agents. In vitro studies found that the primary bioactivity observed was antioxidative action. Both in vitro and in silico approaches are valuable for evaluating the bioactive properties resulting from protein hydrolysis, such as those found in α-zein. However, conducting in vitro studies based on prior in silico evaluation can be more efficient and cost-effective.
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