Angiotensin-converting enzyme (ACE) inhibitory peptides derived from food proteins have been widely reported for hypertension treatment. In this paper, a benchmark data set containing 141 unique ACE inhibitory dipeptides was constructed through database mining, and a quantitative structure-activity relationships (QSAR) study was carried out to predict half-inhibitory concentration (IC50) of ACE activity. Sixteen descriptors were tested and the model generated by G-scale descriptor showed the best predictive performance with the coefficient of determination (R2) and cross-validated R2 (Q2) of 0.6692 and 0.6220, respectively. For most other descriptors, R2 were ranging from 0.52 to 0.68 and Q2 were ranging from 0.48 to 0.61. A complex model combining all 16 descriptors was carried out and variable selection was performed in order to further improve the prediction performance. The quality of model using integrated descriptors (R2 0.7340 ± 0.0038, Q2 0.7151 ± 0.0019) was better than that of G-scale. An in-depth study of variable importance showed that the most correlated properties to ACE inhibitory activity were hydrophobicity, steric, and electronic properties and C-terminal amino acids contribute more than N-terminal amino acids. Five novel predicted ACE-inhibitory peptides were synthesized, and their IC50 values were validated through in vitro experiments. The results indicated that the constructed model could give a reliable prediction of ACE-inhibitory activity of peptides, and it may be useful in the design of novel ACE-inhibitory peptides.
Keywords: ACE-inhibitory peptides; QSAR; amino acid descriptors; variable importance; variable selection.