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Drug target identification using network analysis: Taking active components in Sini decoction as an example.

Chen S, Jiang H, Cao Y, Wang Y, Hu Z, Zhu Z, Chai Y - Sci Rep (2016)

Bottom Line: The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database.Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level.Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.

ABSTRACT
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

No MeSH data available.


Related in: MedlinePlus

(A) The Component-Target network. The red circles represent the 48 active components in SND, S, J, H and Z refers to alkaloids, gingerols, flavones and saponins in Aconitum carmichaelii, Zingiber officinale and Glycyrrhiza uralensis. The blue hexagons represent the gene names of targets of the three herbs found by text mining, while the green hexagons are the targets found by dock. The yellow hexagons represent the gene names of targets found both by text mining and dock. Targets in the center of network represent the common targets of three herbs, and targets in the curve of S, J or H and Z represent the targets of each kind of active components respectively. (B) The enrichment analysis in biological processes, cellular components and molecular functions of 61 identified target proteins by STRING database.
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f2: (A) The Component-Target network. The red circles represent the 48 active components in SND, S, J, H and Z refers to alkaloids, gingerols, flavones and saponins in Aconitum carmichaelii, Zingiber officinale and Glycyrrhiza uralensis. The blue hexagons represent the gene names of targets of the three herbs found by text mining, while the green hexagons are the targets found by dock. The yellow hexagons represent the gene names of targets found both by text mining and dock. Targets in the center of network represent the common targets of three herbs, and targets in the curve of S, J or H and Z represent the targets of each kind of active components respectively. (B) The enrichment analysis in biological processes, cellular components and molecular functions of 61 identified target proteins by STRING database.

Mentions: We constructed the component-target network (Fig. 2A) based on text mining and docking. This network had 109 nodes and 556 edges, in which red circles and hexagons correspond to active components and target proteins, respectively. Many targets in the middle of Fig. 2A are targeted by components in three medicinal herbs, which meant that these targets are main potential targets. According to the data from CHEMBL, BindingDB and PubMed database, 13 out of 61 potential targets were validated to be exact targets of active components in SND (Supplementary Table S3), which proved the reliability of molecular docking and text mining.


Drug target identification using network analysis: Taking active components in Sini decoction as an example.

Chen S, Jiang H, Cao Y, Wang Y, Hu Z, Zhu Z, Chai Y - Sci Rep (2016)

(A) The Component-Target network. The red circles represent the 48 active components in SND, S, J, H and Z refers to alkaloids, gingerols, flavones and saponins in Aconitum carmichaelii, Zingiber officinale and Glycyrrhiza uralensis. The blue hexagons represent the gene names of targets of the three herbs found by text mining, while the green hexagons are the targets found by dock. The yellow hexagons represent the gene names of targets found both by text mining and dock. Targets in the center of network represent the common targets of three herbs, and targets in the curve of S, J or H and Z represent the targets of each kind of active components respectively. (B) The enrichment analysis in biological processes, cellular components and molecular functions of 61 identified target proteins by STRING database.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837341&req=5

f2: (A) The Component-Target network. The red circles represent the 48 active components in SND, S, J, H and Z refers to alkaloids, gingerols, flavones and saponins in Aconitum carmichaelii, Zingiber officinale and Glycyrrhiza uralensis. The blue hexagons represent the gene names of targets of the three herbs found by text mining, while the green hexagons are the targets found by dock. The yellow hexagons represent the gene names of targets found both by text mining and dock. Targets in the center of network represent the common targets of three herbs, and targets in the curve of S, J or H and Z represent the targets of each kind of active components respectively. (B) The enrichment analysis in biological processes, cellular components and molecular functions of 61 identified target proteins by STRING database.
Mentions: We constructed the component-target network (Fig. 2A) based on text mining and docking. This network had 109 nodes and 556 edges, in which red circles and hexagons correspond to active components and target proteins, respectively. Many targets in the middle of Fig. 2A are targeted by components in three medicinal herbs, which meant that these targets are main potential targets. According to the data from CHEMBL, BindingDB and PubMed database, 13 out of 61 potential targets were validated to be exact targets of active components in SND (Supplementary Table S3), which proved the reliability of molecular docking and text mining.

Bottom Line: The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database.Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level.Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects.

View Article: PubMed Central - PubMed

Affiliation: School of Pharmacy, Second Military Medical University, 325 Guohe Road, Shanghai, 200433, China.

ABSTRACT
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

No MeSH data available.


Related in: MedlinePlus