Prediction of ETA oligopeptides antagonists from Glycine max based on in silico proteolysis.
10.19540/j.cnki.cjcmm.20161222.060
- Author:
Lian-Sheng QIAO
1
;
Lu-di JIANG
1
;
Gang-Gang LUO
1
;
Fang LU
1
;
Yan-Kun CHEN
1
;
Ling-Zhi WANG
1
;
Gong-Yu LI
1
;
Yan-Ling ZHANG
1
Author Information
1. Key Laboratory of Traditional Chinese Medicine Foundation and New Drug Research, School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100102, China.
- Publication Type:Journal Article
- Keywords:
ETA;
Glycine max;
docking;
homology modeling;
in silico proteolysis;
oligopeptides;
pharmacophore
- From:
China Journal of Chinese Materia Medica
2017;42(4):746-751
- CountryChina
- Language:Chinese
-
Abstract:
Oligopeptides are one of the the key pharmaceutical effective constituents of traditional Chinese medicine(TCM). Systematic study on composition and efficacy of TCM oligopeptides is essential for the analysis of material basis and mechanism of TCM. In this study, the potential anti-hypertensive oligopeptides from Glycine max and their endothelin receptor A (ETA) antagonistic activity were discovered and predicted based on in silico technologies.Main protein sequences of G. max were collected and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, the pharmacophore of ETA antagonistic peptides was constructed and included one hydrophobic feature, one ionizable negative feature, one ring aromatic feature and five excluded volumes. Meanwhile, three-dimensional structure of ETA was developed by homology modeling methods for further docking studies. According to docking analysis and consensus score, the key amino acid of GLN165 was identified for ETA antagonistic activity. And 27 oligopeptides from G. max were predicted as the potential ETA antagonists by pharmacophore and docking studies.In silico proteolysis could be used to analyze the protein sequences from TCM. According to combination of in silico proteolysis and molecular simulation, the biological activities of oligopeptides could be predicted rapidly based on the known TCM protein sequence. It might provide the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides.
