Grid-enabled high-throughput in silico screening against influenza A neuraminidase

Hurng-Chun Lee; Jean Salzemann; Nicolas Jacq; Hsin-Yen Chen; Li-Yung Ho; Ivan Merelli; Luciano Milanesi; Vincent Breton; Simon C Lin; Ying-Ta Wu

doi:10.1109/tnb.2006.887943

Grid-enabled high-throughput in silico screening against influenza A neuraminidase

IEEE Trans Nanobioscience. 2006 Dec;5(4):288-95. doi: 10.1109/tnb.2006.887943.

Authors

Hurng-Chun Lee¹, Jean Salzemann, Nicolas Jacq, Hsin-Yen Chen, Li-Yung Ho, Ivan Merelli, Luciano Milanesi, Vincent Breton, Simon C Lin, Ying-Ta Wu

Affiliation

¹ Academia Sinica, Taipei 115, Taiwan, ROC. hclee@gate.sinica.edu.tw

PMID: 17181029
DOI: 10.1109/tnb.2006.887943

Abstract

Encouraged by the success of the first EGEE biomedical data challenge against malaria (WISDOM), the second data challenge battling avian flu was kicked off in April 2006 to identify new drugs for the potential variants of the influenza A virus. Mobilizing thousands of CPUs on the Grid, the six-week-long high-throughput screening activity has fulfilled over 100 CPU years of computing power and produced around 600 gigabytes of results on the Grid for further biological analysis and testing. In the paper, we demonstrate the impact of a worldwide Grid infrastructure to efficiently deploy large-scale virtual screening to speed up the drug design process. Lessons learned through the data challenge activity are also discussed.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Binding Sites
Drug Design*
Drug Evaluation, Preclinical / methods*
Enzyme Inhibitors / chemistry*
Influenza A virus / enzymology*
Internet*
Neuraminidase / chemistry*
Protein Binding
Protein Interaction Mapping / methods
Sequence Analysis, Protein / methods*

Substances

Enzyme Inhibitors
Neuraminidase