1. Modeling the QSAR of ACE-Inhibitory Peptides with ANN and Its Applied Illustration
- Author
-
Haile Ma, Jiewen Zhao, Wenxue Zhu, Ronghai He, Weirui Zhao, Lin Luo, and Wenjuan Qu
- Subjects
chemistry.chemical_classification ,Quantitative structure–activity relationship ,Proteases ,Article Subject ,business.industry ,Information analysis ,Peptide ,Biochemistry ,Hydrolysate ,Amino acid ,Biotechnology ,Enzyme ,chemistry ,Ace inhibitory ,business ,Research Article - 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 , 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.
- Published
- 2011