A quantitative structure-activity relationship (QSAR) model of angiotensin-converting enzyme- (ACE-) inhibitory peptides\r\nwas built with an artificial neural network (ANN) approach based on structural or activity data of 58 dipeptides (including\r\npeptide activity, hydrophilic amino acids content, three-dimensional shape, size, and electrical parameters), the overall correlation\r\ncoefficient of the predicted versus actual data points is R = 0.928, and the model was applied in ACE-inhibitory peptides\r\npreparation from defatted wheat germ protein (DWGP). According to the QSAR model, the C-terminal of the peptide was found\r\nto have principal importance on ACE-inhibitory activity, that is, if the C-terminal is hydrophobic amino acid, the peptide�s ACEinhibitory\r\nactivity will be high, and proteins which contain abundant hydrophobic amino acids are suitable to produce ACEinhibitory\r\npeptides. According to the model, DWGP is a good protein material to produce ACE-inhibitory peptides because it\r\ncontains 42.84% of hydrophobic amino acids, and structural information analysis from the QSAR model showed that proteases of\r\nAlcalase and Neutrase were suitable candidates for ACE-inhibitory peptides preparation from DWGP. Considering higher DH and\r\nsimilar ACE-inhibitory activity of hydrolysate compared with Neutrase, Alcalase was finally selected through experimental study.
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