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

Overview of attention for article published in PLoS Computational Biology, December 2011
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Title
Two Birds with One Stone? Possible Dual-Targeting H1N1 Inhibitors from Traditional Chinese Medicine
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002315
Pubmed ID
Authors

Su-Sen Chang, Hung-Jin Huang, Calvin Yu-Chian Chen

Abstract

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@Taiwan (TCM Database@Taiwan) 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.

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Geographical breakdown

Country Count As %
Denmark 1 2%
Unknown 50 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Ph. D. Student 7 14%
Student > Bachelor 6 12%
Student > Postgraduate 4 8%
Professor > Associate Professor 4 8%
Other 15 29%
Unknown 5 10%
Readers by discipline Count As %
Medicine and Dentistry 14 27%
Agricultural and Biological Sciences 5 10%
Biochemistry, Genetics and Molecular Biology 5 10%
Computer Science 4 8%
Veterinary Science and Veterinary Medicine 2 4%
Other 11 22%
Unknown 10 20%