Computational study of the molecular mechanism of Lonicera japonica organic acids against influenza
Journal Title: Traditional Medicine Research - Year 2016, Vol 1, Issue 3
Abstract
1. Molecular docking was applied for determining the potential targets of Lonicera japonica organic acids. 2. Finding relationships between the organic acids and proteins of influenza virus NA, HA, M2 and PB2. 3. Lonicera japonica against influenza virus by multiple components with synergistic effect. 4. Provided molecular evidences on treatment of influenza virus infection with Lonicera japonica and a modern theory for anti-influenza virus treatment by natural herbs. Objective: High evolutionary rate by mutation or by re-assortment makes influenza virus a threat to human health. Recently, many studies have shown that herbal medicine Lonicera japonica is effective against influenza virus infection. However, owing to the natural herb’s complexity in both components and functions, its molecular mechanism is not fully understood. Here, we use computational methods to study the molecular mechanism of anti-influenza activity of organic acids in Lonicera japonica. Methods: Molecular docking was applied for determining the potential targets of the organic acids. Moreover, the compound-target networks were constructed to clarify the relationship between organic acids and their relative targets. Results: Each compound had inhibitory activity against all tested targets. In addition, three molecules (D, E and F) from Lonicera japonica were predicted as potential inhibitors of the mutants of N1 and N9 subtypes of influenza viruses. Conclusions: Our study provided molecular evidences on treatment of influenza virus infection with Lonicera japonica and a modern theory for anti-influenza virus treatment by natural herbs. We confirmed that Lonicera japonica acts synergistically with multiple components against influenza virus. Three molecules (D, E, and F) were selected and predicted as potential inhibitors of N1 and N9 as well as their mutants.
Authors and Affiliations
Luo Xuan, Shen Xia, Hu Ben-Xiang, Wang Hao, Zhao Zhen-Yu, Guo Zi-Hu
Keywords
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- EP ID EP45288
- DOI 10.12032/TMR201603018
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