Network pharmacology study of Curcuma longa L. : potential target proteins and their functional enrichment analysis
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(2020) 13:468 Kumari and Subramanya BMC Res Notes https://doi.org/10.1186/s13104-020-05301-0
Open Access
RESEARCH NOTE
Network pharmacology study of Curcuma longa L.: potential target proteins and their functional enrichment analysis Sangeeta Kumari* and Hosahalli S. Subramanya*
Abstract Objective: This study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein–protein interaction network. Results: We used target proteins to create protein–protein interaction network (PPIN) and identified significant node and edge attributes of PPIN. We identified the cluster of proteins in the PPIN, which were used to identify enriched pathways. We identified closeness centrality and jaccard score as most important node and edge attribute of the PPIN respectively. The enriched pathways of various clusters were overlapped suggesting synergistic mechanism of action. The three pathways found to be common among three clusters were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway. Keywords: Markov clustering, Protein clusters, Centrality measure, Synergistic mechanism Introduction The Curcuma longa L. has been studied for antiinflammatory and anticancer effects [1]. The exact mechanism remains largely unexplored. Bioactives have shown the multi-components and multi-targets effect by using protein–protein interaction network (PPIN). A target protein usually carries out a typical function by regulating other molecules; thus, the study of PPIN helps to understand relationship between target proteins and other interacting proteins in a systematic way.. Earlier study has shown that the target proteins indeed have some special topological features that are significantly different than the normal proteins [2]. Thus, we decided to do a comparison study of a true PPIN and a false PPIN to identify discriminating topological attributes. Further, we used those attributes to select importantnodes and edges in the PPIN. *Correspondence: [email protected]; [email protected] Institute of Bioinformatics and Applied Biotechnology, Biotech Park, Electronics City Phase 1, Bengaluru 560100, Karnataka, India
Main text Methods
The four bioactive compounds namely curcumin, Desmethoxycurcumin, Bisdemethoxycurcumin and Turmerone of C. longa were studied. We used similarity ensemble approach (http://sea.bkslab.org/) to identify the potential target proteins of all these four bioactive compounds [3]. Further, we queried the target proteins to StringDB (human protein interaction database) to retrieve all the listed interactions involving the target proteins. A small set of target proteins (TP) was found to have interaction (biological or physiochemical) with many other interacting proteins (IP). We used NetworkX library in python to build and study the true and false PPIN. To create the true PPIN, we created an undirected graph having edge indicating the interaction between the
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