Document Detail


Pathway Pattern-based prediction of active drug components and gene targets from H1N1 influenza's treatment with maxingshigan-yinqiaosan formula.
MedLine Citation:
PMID:  23348248     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Traditional Chinese Medicine (TCM) remedies are composed of different chemical compounds. To understand the underlying pharmacological basis, we need to explore the active components, which function systematically against multiple gene targets to exert efficacy. Predicting active component-gene target interactions could help us decipher the mechanism of action of TCM. Here, we introduce a Pathway Pattern-based method to prioritize the 153 candidate compounds and 7895 associated genes using the extracted Pathway Pattern, which is made up of groups of pathways. The gene prioritization result is compared to previous literature findings to demonstrate the top ranked genes' roles in the pathogenesis of H1N1 influenza. Further, molecular docking is utilized to validate compounds' effects through docking compounds into drug targets of oseltamivir. After setting thresholds, 16 active components, 29 gene targets and 162 active component-gene target interactions are finally identified to elucidate the pharmacology of maxingshigan-yinqiaosan formula. This novel strategy is expected to serve as a springboard for the efforts to standardize and modernize TCM.
Authors:
Wen Dai; Jianxin Chen; Peng Lu; Yibo Gao; Lin Chen; Xi Liu; Jianglong Song; Haiyu Xu; Di Chen; Yiping Yang; Hongjun Yang; Luqi Huang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-25
Journal Detail:
Title:  Molecular bioSystems     Volume:  -     ISSN:  1742-2051     ISO Abbreviation:  Mol Biosyst     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-25     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101251620     Medline TA:  Mol Biosyst     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
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