The potential peptides against angiotensin-I converting enzyme through a virtual tripeptide-constructing library

Area of Science:

• Biochemistry and Molecular Biology
• Computational Chemistry
• Food Science and Technology

Background:

• Food-derived bioactive peptides (BP) are recognized for their potential to inhibit Angiotensin-I converting enzyme (ACE) activity.
• These peptides offer a promising alternative to synthetic drugs due to their potent properties and reduced side-effects.
• The pharmaceutical industry shows significant interest in exploring bioactive peptides for therapeutic applications.

Purpose of the Study:

• To computationally predict and identify novel tripeptides with Angiotensin-I converting enzyme (ACE) inhibitory potential.
• To elucidate the binding interactions between predicted tripeptides and the ACE active site using molecular docking and dynamics simulations.
• To validate the in silico findings through in vitro assessment of ACE inhibitory activity.

Main Methods:

• Construction of an 8000-tripeptide library for in silico screening.
• Application of GOLD molecular docking to determine tripeptide-ACE binding modes and identify high-ranking candidates based on ChemScore.
• Frequency analysis of amino acid orientation within the ACE binding tunnel and in vitro testing of selected tripeptides' ACE-inhibitory activity (IC50 determination) and molecular dynamics simulations.

Main Results:

• Association rules revealed that hydrophobic factors are key components in ACE tripeptide inhibitors.
• Five tripeptides (WCW, IWW, WWW, WWI, WLW) were selected for in vitro testing, with WCW exhibiting the lowest IC50 value (49.50 ± 3.88 μM).
• Molecular dynamics simulations confirmed strong hydrogen bonding interactions between WCW and ACE active site residues (Tyr523 and His353).

Conclusions:

• The study successfully identified potent ACE-inhibitory tripeptides using a computational approach.
• The WCW tripeptide demonstrated significant ACE inhibitory activity, highlighting its therapeutic potential.
• The developed computational protocol serves as an effective tool for discovering active peptides against ACE from natural sources.