New Quantitative Structure-Activity Relationship Model for Angiotensin-Converting Enzyme Inhibitory Dipeptides Based...

Area of Science:

• Biochemistry
• Pharmacology
• Computational Chemistry

Background:

• Food-derived peptides show potential for hypertension management by inhibiting angiotensin-converting enzyme (ACE).
• Developing predictive models for ACE inhibitory activity is essential for efficient drug discovery.

Purpose of the Study:

• To construct a benchmark dataset of ACE inhibitory dipeptides.
• To develop and validate a quantitative structure-activity relationship (QSAR) model for predicting ACE inhibitory activity.
• To identify key molecular descriptors influencing ACE inhibition.

Main Methods:

• Database mining to create a dataset of 141 ACE inhibitory dipeptides.
• Quantitative structure-activity relationship (QSAR) analysis using 16 molecular descriptors.
• Model development, including single descriptor models and a combined descriptor model with variable selection.
• In vitro experimental validation of five novel predicted ACE-inhibitory peptides.

Main Results:

• A QSAR model using integrated descriptors achieved high predictive performance (R² = 0.7340, Q² = 0.7151), outperforming models based on single descriptors like G-scale.
• Hydrophobicity, steric, and electronic properties were identified as key factors influencing ACE inhibitory activity.
• C-terminal amino acids demonstrated a greater contribution to ACE inhibition than N-terminal amino acids.
• Synthesized peptides showed validated in vitro ACE inhibitory activity, confirming the model's reliability.

Conclusions:

• The developed QSAR model provides a reliable method for predicting ACE inhibitory activity of peptides.
• The model can significantly aid in the rational design and discovery of novel antihypertensive peptides.
• Understanding the contribution of specific amino acid properties and positions enhances peptide design strategies.