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Computational study of the molecular mechanism of Lonicera japonica organic acids against influenza

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      Abstract

      Highlights:

      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.

      Abstract

      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.

      Translated abstract

      目的 流感病毒的高突变的特点和多样性导致其对人类健康具有很大的危害。近年来,研究表明中药金银花能够有效的抗流感病毒感染。然而,由于中药的多成分和多靶点特点,其物质基础和分子机理不能被清楚认识。本研究中,我们使用计算机模拟的方法研究金银花中有机酸的抗流感病毒分子机制。 方法 本研究中分子对接方法被用来预测有机酸的靶点。然后构建成分-靶点网络,阐明金银花中的有机酸与其相应靶点的关系。 结果 研究表明有机酸中的每个成分对预测得到的靶点都具有潜在的抑制效果。而且我们预测发现金银花中有机酸的3个成分(化合物D、E、F)对于流感病毒N1和N9的突变型具有潜在的抑制作用。 结论 我们的研究为金银花抗流感病毒感染提供了分子层面的支持,同时也为中药治疗流感提供了新的研究思路。我们认为金银花能够通过其多个成分间的协同效应抗流感病毒。其中3个成分(D、E、F) 被选择,并且预测其能够作为流感病毒N1和N9及其突变型的抑制剂。

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      Most cited references 35

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      On April 15 and April 17, 2009, novel swine-origin influenza A (H1N1) virus (S-OIV) was identified in specimens obtained from two epidemiologically unlinked patients in the United States. The same strain of the virus was identified in Mexico, Canada, and elsewhere. We describe 642 confirmed cases of human S-OIV infection identified from the rapidly evolving U.S. outbreak. Enhanced surveillance was implemented in the United States for human infection with influenza A viruses that could not be subtyped. Specimens were sent to the Centers for Disease Control and Prevention for real-time reverse-transcriptase-polymerase-chain-reaction confirmatory testing for S-OIV. From April 15 through May 5, a total of 642 confirmed cases of S-OIV infection were identified in 41 states. The ages of patients ranged from 3 months to 81 years; 60% of patients were 18 years of age or younger. Of patients with available data, 18% had recently traveled to Mexico, and 16% were identified from school outbreaks of S-OIV infection. The most common presenting symptoms were fever (94% of patients), cough (92%), and sore throat (66%); 25% of patients had diarrhea, and 25% had vomiting. Of the 399 patients for whom hospitalization status was known, 36 (9%) required hospitalization. Of 22 hospitalized patients with available data, 12 had characteristics that conferred an increased risk of severe seasonal influenza, 11 had pneumonia, 8 required admission to an intensive care unit, 4 had respiratory failure, and 2 died. The S-OIV was determined to have a unique genome composition that had not been identified previously. A novel swine-origin influenza A virus was identified as the cause of outbreaks of febrile respiratory infection ranging from self-limited to severe illness. It is likely that the number of confirmed cases underestimates the number of cases that have occurred. 2009 Massachusetts Medical Society
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        Despite preventive efforts, influenza epidemics are responsible for substantial morbidity and mortality every year in the United States (US). Vaccination strategies to reduce disease burden have been implemented. However, no previous studies have systematically estimated the annual economic burden of influenza epidemics, an estimate necessary to guide policy makers effectively. We estimate age- and risk-specific disease burden, and medical and indirect costs attributable to annual influenza epidemics in the United States. Using a probabilistic model and publicly available epidemiological data we estimated the number of influenza-attributable cases leading to outpatient visits, hospitalization, and mortality, as well as time lost from work absenteeism or premature death. With data from health insurance claims and projections of either earnings or statistical life values, we then estimated healthcare resource utilization associated with influenza cases as were their medical and productivity (indirect) costs in $2003. Based on 2003 US population, we estimated that annual influenza epidemics resulted in an average of 610,660 life-years lost (undiscounted), 3.1 million hospitalized days, and 31.4 million outpatient visits. Direct medical costs averaged $10.4 billion (95% confidence interval [C.I.], $4.1, $22.2) annually. Projected lost earnings due to illness and loss of life amounted to $16.3 billion (C.I., $8.7, $31.0) annually. The total economic burden of annual influenza epidemics using projected statistical life values amounted to $87.1 billion (C.I., $47.2, $149.5). These results highlight the enormous annual burden of influenza in the US. While hospitalization costs are important contributors, lost productivity from missed work days and lost lives comprise the bulk of the economic burden of influenza.
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          Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine.

           N Jain (2003)
          Surflex is a fully automatic flexible molecular docking algorithm that combines the scoring function from the Hammerhead docking system with a search engine that relies on a surface-based molecular similarity method as a means to rapidly generate suitable putative poses for molecular fragments. Results are presented evaluating reliability and accuracy of dockings compared with crystallographic experimental results on 81 protein/ligand pairs of substantial structural diversity. In over 80% of the complexes, Surflex's highest scoring docked pose was within 2.5 A root-mean-square deviation (rmsd), with over 90% of the complexes having one of the top ranked poses within 2.5 A rmsd. Results are also presented assessing Surflex's utility as a screening tool on two protein targets (thymidine kinase and estrogen receptor) using data sets on which competing methods were run. Performance of Surflex was significantly better, with true positive rates of greater than 80% at false positive rates of less than 1%. Docking time was roughly linear in number of rotatable bonds, beginning with a few seconds for rigid molecules and adding approximately 10 s per rotatable bond.
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            Author and article information

            Affiliations
            1 College of pharmacy, Shanxi university of Chinese medicine, Xi'an, 712046, Shanxi, China.
            2 College of Life Sciences, Northwest A&F University, Yangling, 712100, Shanxi, China.
            Author notes
            *Correspondence to: Xia Shen, PhD, College of pharmacy, Shanxi university of Chinese medicine, Xi’an, 712046, Shanxi, China. E-mail: shenxtgyx123@ 123456126.com
            Submitted: 10 March 2016

            Executive Editor: Cui-Hong Zhu English Editor: Xue-Ning Niu, Zhen-Yan Liu, Ting Shuai, Zong-Shi Qin, Xiao-Hua Zhang, Yi-Cheng Shi Chelsea Williams.

            * Xia Shen and Xuan Luo contributing equally to this work.

            Contributors
            Journal
            Traditional Medicine Research
            Traditional Medicine Research
            TMR Editorial Board (Jintang road, 99, Hedong district Tianjin,China, 300170. )
            2413-3973
            January 2016
            5 January 2016
            : 1
            : 3
            : 128
            2413-3973-1-3-128

            This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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            Categories
            Orginal Article
            Medicine
            Traditional Medicine

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