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Pharmacological mechanism analysis of oligopeptide from Pinctada fucata based on in silico proteolysis and protein interaction network / 中国中药杂志
Article in Chinese | WPRIM | ID: wpr-335839
Responsible library: WPRO
ABSTRACT
Pinctada fucata oligopeptide is one of key pharmaceutical effective constituents of P. fucata. It is significant to analyze its pharmacological effect and mechanism. This study aims to discover the potential oligopeptides from P. fucata and analyze the mechanism of P. fucata oligopeptide based on in silico technologies and protein interaction network(PIN). First, main protein sequences of P. fucata were collected, and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, key potential targets of P. fucata oligopeptides were obtained through pharmacophore screening. The protein-protein interaction(PPI) of targets was achieved and implemented to construct PIN and analyze the mechanism of P. fucata oligopeptides. P. fucata oligopeptide database was constructed based on in silico technologies, including 458 oligopeptides. Twelve modules were identified from PIN by a graph theoretic clustering algorithm Molecular Complex Detection(MCODE) and analyzed by Gene ontology(GO) enrichment. The results indicated that P. fucata oligopeptides have an effect in treating neurological diseases, such as Alzheimer's disease. In silico proteolysis could be used to analyze the protein sequences of traditional Chinese medicine(TCM). According to the combination of in silico proteolysis and PIN, the biological activity of oligopeptides could be interpreted rapidly based on the known TCM protein sequence. The study provides the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides.

Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: China Journal of Chinese Materia Medica Year: 2017 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: China Journal of Chinese Materia Medica Year: 2017 Type: Article