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Polypharmacology of Berberine Based on Multi-Target Binding Motifs

Overview of attention for article published in Frontiers in Pharmacology, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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11 X users

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Title
Polypharmacology of Berberine Based on Multi-Target Binding Motifs
Published in
Frontiers in Pharmacology, July 2018
DOI 10.3389/fphar.2018.00801
Pubmed ID
Authors

Ming Chu, Xi Chen, Jing Wang, Likai Guo, Qianqian Wang, Zirui Gao, Jiarui Kang, Mingbo Zhang, Jinqiu Feng, Qi Guo, Binghua Li, Chengrui Zhang, Xueyuan Guo, Zhengyun Chu, Yuedan Wang

Abstract

Background: Polypharmacology is emerging as the next paradigm in drug discovery. However, considerable challenges still exist for polypharmacology modeling. In this study, we developed a rational design to identify highly potential targets (HPTs) for polypharmacological drugs, such as berberine. Methods and Results: All the proven co-crystal structures locate berberine in the active cavities of a redundancy of aromatic, aliphatic, and acidic residues. The side chains from residues provide hydrophobic and electronic interactions to aid in neutralization for the positive charge of berberine. Accordingly, we generated multi-target binding motifs (MBM) for berberine, and established a new mathematical model to identify HPTs based on MBM. Remarkably, the berberine MBM was embodied in 13 HPTs, including beta-secretase 1 (BACE1) and amyloid-β1-42 (Aβ1-42). Further study indicated that berberine acted as a high-affinity BACE1 inhibitor and prevented Aβ1-42 aggregation to delay the pathological process of Alzheimer's disease. Conclusion: Here, we proposed a MBM-based drug-target space model to analyze the underlying mechanism of multi-target drugs against polypharmacological profiles, and demonstrated the role of berberine in Alzheimer's disease. This approach can be useful in derivation of rules, which will illuminate our understanding of drug action in diseases.

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The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 23%
Student > Ph. D. Student 5 19%
Researcher 3 12%
Student > Doctoral Student 2 8%
Student > Bachelor 2 8%
Other 3 12%
Unknown 5 19%
Readers by discipline Count As %
Chemistry 6 23%
Biochemistry, Genetics and Molecular Biology 4 15%
Pharmacology, Toxicology and Pharmaceutical Science 3 12%
Medicine and Dentistry 2 8%
Immunology and Microbiology 1 4%
Other 2 8%
Unknown 8 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 14 August 2018.
All research outputs
#5,502,923
of 23,098,660 outputs
Outputs from Frontiers in Pharmacology
#2,133
of 16,458 outputs
Outputs of similar age
#93,241
of 329,805 outputs
Outputs of similar age from Frontiers in Pharmacology
#51
of 398 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 16,458 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,805 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 398 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.