The potential peptides against angiotensin-I converting enzyme through a virtual tripeptide-constructing library. (December 2018)

• Record Type: Journal Article
• Authors: Panyayai, Thitima; Sangsawad, Papungkorn; Pacharawongsakda, Eakasit; Sawatdichaikul, Orathai; Tongsima, Sissades; Choowongkomon, Kiattawee
• Is Part Of: Computational biology and chemistry. Volume 77(2018)
• Page Start: 207
• Page End: 213
• Publication Date: 2018-12
• Subjects: Angiotensin-I-converting enzyme -- Bioactive peptides -- Molecular docking -- Data mining -- ACE-inhibitory peptides; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.10.001
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Integration of in silico tools to explorer potential ACE inhibitory peptides. 8000 possible tripeptides library were ranked by docking scores. -Tryptophan was selected as model from association rules by data mining. The best peptide is WCW peptide with IC50 of 49.50 ± 3.88 μM. This protocol can be used to identify any new active peptides from hydrolysated protein source. Abstract: Peptides derived from food proteins are promising bioactive source for inhibiting Angiotensin-I converting enzyme (ACE) activity. Bioactive peptides (BP) have received much attention, particularly from the pharmaceutical industry. As they not only own potent properties but also possess less side-effects than synthetic drugs. In this work, an 8000 possible tripeptides library was constructed to predict the potential ACE inhibitory peptides by using in silico tools. GOLD molecular docking was then applied to determine the binding mode of action between ACE and each of tripeptide from this in-house library. The first 662 high-ranking tripeptides by ChemScore were chosen to create association rules of tripeptides-ACE complexes. An orientation pattern of amino acid in the binding tunnel of ACE has been examined by frequency analysis. The association rules (confident values over 90%) illustrated that hydrophobic factor has been displayed as main components in the ACE tripeptides inhibitor from four factors in equation, hydrophobic, aromatic, polar, charged. According to in silico output, five tripeptides were chosen to test in vitro study of ACE-inhibitory activity. The half-maximal inhibitory concentration (IC50 ) of these selective five peptides, WCW, IWW, WWW, WWI and WLW for inhibiting ACE were 49.50 ± 3.88 μM, 489.14 ± 8.84 μM, 536.02 ± 38.57 μM, 752.91 ± 41.89 μM and 1783 ± 0.113 μM, respectively. Molecular dynamics simulations approach was applied to study the interaction of WCW (Trp-Cys-Trp) within ACE pocket site. This ligand was stabilized by strong hydrogen bonding interactions with ACE active site, Tyr523-Trp'1 (99.76%) and His353-Trp'1 (95.68%). Our computational protocol could be considered as a new tool for identifying active peptide against ACE from hydrolysated peptides of natural sources.