↓ Skip to main content

PLOS

Molecular Dynamics Simulations Suggest that Electrostatic Funnel Directs Binding of Tamiflu to Influenza N1 Neuraminidases

Overview of attention for article published in PLoS Computational Biology, September 2010
Altmetric Badge

Mentioned by

blogs
1 blog
wikipedia
1 Wikipedia page
f1000
1 research highlight platform

Citations

dimensions_citation
54 Dimensions

Readers on

mendeley
101 Mendeley
citeulike
2 CiteULike
Title
Molecular Dynamics Simulations Suggest that Electrostatic Funnel Directs Binding of Tamiflu to Influenza N1 Neuraminidases
Published in
PLoS Computational Biology, September 2010
DOI 10.1371/journal.pcbi.1000939
Pubmed ID
Authors

Ly Le, Eric H. Lee, David J. Hardy, Thanh N. Truong, Klaus Schulten

Abstract

Oseltamivir (Tamiflu) is currently the frontline antiviral drug employed to fight the flu virus in infected individuals by inhibiting neuraminidase, a flu protein responsible for the release of newly synthesized virions. However, oseltamivir resistance has become a critical problem due to rapid mutation of the flu virus. Unfortunately, how mutations actually confer drug resistance is not well understood. In this study, we employ molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, as well as graphics processing unit (GPU)-accelerated electrostatic mapping, to uncover the mechanism behind point mutation induced oseltamivir-resistance in both H5N1 "avian" and H1N1pdm "swine" flu N1-subtype neuraminidases. The simulations reveal an electrostatic binding funnel that plays a key role in directing oseltamivir into and out of its binding site on N1 neuraminidase. The binding pathway for oseltamivir suggests how mutations disrupt drug binding and how new drugs may circumvent the resistance mechanisms.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 7%
Indonesia 2 2%
Denmark 2 2%
France 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
Russia 1 <1%
China 1 <1%
Unknown 85 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 27%
Student > Ph. D. Student 19 19%
Student > Master 14 14%
Professor 6 6%
Student > Postgraduate 6 6%
Other 20 20%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 36%
Chemistry 22 22%
Biochemistry, Genetics and Molecular Biology 13 13%
Physics and Astronomy 5 5%
Computer Science 4 4%
Other 10 10%
Unknown 11 11%