New Quantitative Structure–Activity Relationship Model for Angiotensin-Converting Enzyme Inhibitory Dipeptides Based on Integrated Descriptors

Abstract

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.