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Two Birds with One Stone? Possible Dual-Targeting H1N1 Inhibitors from Traditional Chinese Medicine

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      The H1N1 influenza pandemic of 2009 has claimed over 18,000 lives. During this pandemic, development of drug resistance further complicated efforts to control and treat the widespread illness. This research utilizes traditional Chinese medicine Database@ 123456Taiwan (TCM Database@ 123456Taiwan ) to screen for compounds that simultaneously target H1 and N1 to overcome current difficulties with virus mutations. The top three candidates were de novo derivatives of xylopine and rosmaricine. Bioactivity of the de novo derivatives against N1 were validated by multiple machine learning prediction models. Ability of the de novo compounds to maintain CoMFA/CoMSIA contour and form key interactions implied bioactivity within H1 as well. Addition of a pyridinium fragment was critical to form stable interactions in H1 and N1 as supported by molecular dynamics (MD) simulation. Results from MD, hydrophobic interactions, and torsion angles are consistent and support the findings of docking. Multiple anchors and lack of binding to residues prone to mutation suggest that the TCM de novo derivatives may be resistant to drug resistance and are advantageous over conventional H1N1 treatments such as oseltamivir. These results suggest that the TCM de novo derivatives may be suitable candidates of dual-targeting drugs for influenza.

      Author Summary

      The influenza A subtype H1N1 (H1N1/09) pandemic raised public concerns due to drug resistance strains. Drug resistance occurs from conformational changes causing the original drug to lose binding ability and exhibit biological effects. The world's largest TCM Database@ 123456Taiwan was employed to screen for potential leads that simultaneously bind to H1 and N1. Three de novo compounds derived from Rosemarinus officinalis and Guatteria amplifolia were identified as having dual binding properties to H1 and N1. Structural analysis indicated that the candidates bind to multiple residues in both H1 and N1. In addition, the de novo derivatives were predicted as bioactive using four different computational models. The compounds are validated as potent dual targeting influenza drug candidates through multiple validations. Key advantages of the candidates include (1) binding to H1 and N1 through multiple amino acids, and (2) not binding to known mutation residues in H1 or N1. Such advantages can reduce drug resistance caused by single point mutations. On a broader context, features important for successful H1N1 drug development are discussed in hopes of providing starting templates for drug development and improvements.

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            Author and article information

            [1 ]Laboratory of Computational and Systems Biology, China Medical University, Taichung, Taiwan
            [2 ]Sciences and Chinese Medicine Resources, China Medical University, Taichung, Taiwan
            [3 ]Department of Bioinformatics, Asia University, Taichung, Taiwan
            [4 ]China Medical University Beigang Hospital, Yunlin, Taiwan
            [5 ]Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
            [6 ]Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
            Gordon Life Science Institute, United States of America
            Author notes

            Conceived and designed the experiments: CYCC. Performed the experiments: HJH. Analyzed the data: HJH, SSC. Contributed reagents/materials/analysis tools: CYCC. Wrote the paper: CYCC SSC.

            Role: Editor
            PLoS Comput Biol
            PLoS Computational Biology
            Public Library of Science (San Francisco, USA )
            December 2011
            December 2011
            22 December 2011
            : 7
            : 12
            Chang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Pages: 16
            Research Article
            Computational Biology
            Computer Science
            Computer Modeling
            Drugs and Devices
            Drug Research and Development
            Drug Discovery
            Biophysics Simulations

            Quantitative & Systems biology


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