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In silico target fishing and pharmacological profiling for the isoquinoline alkaloids of Macleaya cordata (Bo Luo Hui)

Overview of attention for article published in Chinese Medicine, December 2015
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Title
In silico target fishing and pharmacological profiling for the isoquinoline alkaloids of Macleaya cordata (Bo Luo Hui)
Published in
Chinese Medicine, December 2015
DOI 10.1186/s13020-015-0067-4
Pubmed ID
Authors

Qifang Lei, Haibo Liu, Yong Peng, Peigen Xiao

Abstract

Some isoquinoline alkaloids from Macleaya cordata (Willd). R. Br. (Bo Luo Hui) exhibited antibacterial, antiparasitic, antitumor, and analgesic effects. The targets of these isoquinoline alkaloids are undefined. This study aims to investigate the compound-target interaction network and potential pharmacological actions of isoquinoline alkaloids of M. cordata by reverse pharmacophore database screening. The targets of 26 isoquinoline alkaloids identified from M. cordata were predicted by a pharmacophore-based target fishing approach. Discovery Studio 3.5 and two pharmacophore databases (PharmaDB and HypoDB) were employed for the target profiling. A compound-target interaction network of M. cordata was constructed and analyzed by Cytoscape 3.0. Thirteen of the 65 predicted targets identified by PharmaDB were confirmed as targets by HypoDB screening. The targets in the interaction network of M. cordata were involved in cancer (31 targets), microorganisms (12 targets), neurodegeneration (10 targets), inflammation and autoimmunity (8 targets), parasitosis (5 targets), injury (4 targets), and pain (3 targets). Dihydrochelerythrine (C6) was found to hit 23 fitting targets. Macrophage migration inhibitory factor (MIF) hits 15 alkaloids (C1-2, C11-16, C19-25) was the most promising target related to cancer. Through in silico target fishing, the anticancer, anti-inflammatory, and analgesic effects of M. cordata were the most significant among many possible activities. The possible anticancer effects were mainly contributed by the isoquinoline alkaloids as active components.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 19%
Student > Ph. D. Student 8 17%
Student > Bachelor 6 13%
Researcher 6 13%
Student > Doctoral Student 2 4%
Other 5 10%
Unknown 12 25%
Readers by discipline Count As %
Chemistry 7 15%
Medicine and Dentistry 6 13%
Agricultural and Biological Sciences 4 8%
Engineering 3 6%
Computer Science 3 6%
Other 10 21%
Unknown 15 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 December 2015.
All research outputs
#5,832,456
of 6,818,838 outputs
Outputs from Chinese Medicine
#175
of 217 outputs
Outputs of similar age
#239,693
of 295,531 outputs
Outputs of similar age from Chinese Medicine
#9
of 10 outputs
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So far Altmetric has tracked 217 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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