title: Modeling the QSAR of ACE-Inhibitory Peptides with ANN and Its Applied Illustration.
abstract: A quantitative structure-activity relationship (QSAR) model of angiotensin-converting enzyme- (ACE-) inhibitory peptides was built with an artificial neural network (ANN) approach based on structural or activity data of 58 dipeptides (including peptide activity, hydrophilic amino acids content, three-dimensional shape, size, and electrical parameters), the overall correlation coefficient of the predicted versus actual data points is R = 0.928, and the model was applied in ACE-inhibitory peptides preparation from defatted wheat germ protein (DWGP). According to the QSAR model, the C-terminal of the peptide was found to have principal importance on ACE-inhibitory activity, that is, if the C-terminal is hydrophobic amino acid, the peptide's ACE-inhibitory activity will be high, and proteins which contain abundant hydrophobic amino acids are suitable to produce ACE-inhibitory peptides. According to the model, DWGP is a good protein material to produce ACE-inhibitory peptides because it contains 42.84% of hydrophobic amino acids, and structural information analysis from the QSAR model showed that proteases of Alcalase and Neutrase were suitable candidates for ACE-inhibitory peptides preparation from DWGP. Considering higher DH and similar ACE-inhibitory activity of hydrolysate compared with Neutrase, Alcalase was finally selected through experimental study.
authors.affiliation: School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu 212013, China.
authors.forename: Ronghai
authors.initials: R
authors.lastname: He
authors.forename: Haile
authors.initials: H
authors.lastname: Ma
authors.forename: Weirui
authors.initials: W
authors.lastname: Zhao
authors.forename: Wenjuan
authors.initials: W
authors.lastname: Qu
authors.forename: Jiewen
authors.initials: J
authors.lastname: Zhao
authors.forename: Lin
authors.initials: L
authors.lastname: Luo
authors.forename: Wenxue
authors.initials: W
authors.lastname: Zhu