Title |
Polypharmacology of Berberine Based on Multi-Target Binding Motifs
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Published in |
Frontiers in Pharmacology, July 2018
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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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Japan | 2 | 18% |
Venezuela, Bolivarian Republic of | 1 | 9% |
Switzerland | 1 | 9% |
Ecuador | 1 | 9% |
Austria | 1 | 9% |
Unknown | 5 | 45% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 7 | 64% |
Scientists | 3 | 27% |
Practitioners (doctors, other healthcare professionals) | 1 | 9% |
Mendeley readers
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% |