Jump to main content
Jump to site search

Issue 3, 2013
Previous Article Next Article

Pathway Pattern-based prediction of active drug components and gene targets from H1N1 influenza's treatment with maxingshigan-yinqiaosan formula

Author affiliations

Abstract

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.

Graphical abstract: Pathway Pattern-based prediction of active drug components and gene targets from H1N1 influenza's treatment with maxingshigan-yinqiaosan formula

Back to tab navigation

Supplementary files

Publication details

The article was received on 13 Sep 2012, accepted on 17 Dec 2012 and first published on 18 Dec 2012


Article type: Paper
DOI: 10.1039/C2MB25372K
Mol. BioSyst., 2013,9, 375-385

  •   Request permissions

    Pathway Pattern-based prediction of active drug components and gene targets from H1N1 influenza's treatment with maxingshigan-yinqiaosan formula

    W. Dai, J. Chen, P. Lu, Y. Gao, L. Chen, X. Liu, J. Song, H. Xu, D. Chen, Y. Yang, H. Yang and L. Huang, Mol. BioSyst., 2013, 9, 375
    DOI: 10.1039/C2MB25372K

Search articles by author

Spotlight

Advertisements