Predictive Modeling of Angiotensin I‑Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches

Food-derived angiotensin I-converting
enzyme (ACE) inhibitory peptides
could potentially be used as safe supportive therapeutic products
for high blood pressure. Theoretical approaches are promising methods
with the advantage through exploring the relationships between peptide
structures and their bioactivities. In this study, peptides with ACE
inhibitory activity were collected and curated. Quantitative structure–activity
relationship (QSAR) models were developed by using the combination
of various machine learning approaches and chemical descriptors. The
resultant models have revealed several structure features accounting
for the ACE inhibitions. 14 new dipeptides predicted to lower blood
pressure by inhibiting ACE were selected. Molecular docking indicated
that these dipeptides formed hydrogen bonds with ACE. Five of these
dipeptides were synthesized for experimental testing. The QSAR models
developed were proofed to design and propose novel ACE inhibitory
peptides. Machine learning algorithms and properly selected chemical
descriptors can be promising modeling approaches for rational design
of natural functional food components.