Describe the scientific/technical community and the scientific/technical activity using (planning to use) the EGEE infrastructure. A high-level description is needed (neither a detailed specialist report nor a list of references).
The community targeted is the drug discovery community. The WISDOM initiative aims at lowering the cost for finding new drugs by developing the use of grid technologies in the drug discovery process. Its first success was to demonstrate how grids can speed up and reduce cost for large scale in vitro screening. But the scientific impact of this approach depends on its biological relevance which is addressed in this abstract.
Report on the experience (or the proposed activity). It would be very important to mention key services which are essential for the success of your activity on the EGEE infrastructure.
We report here on the in vitro tests which were carried on to validate the results obtained in silico. The recombinant NA from H5N1 influenza virus strain A/Vietnam/1203/04 was successfully expressed and purified in this experiment. Neuraminidase activity was determined using (MU-Neu5Ac) as a fluorogenic substrate. Inhibition activity of NA was determined by incubating enzyme solution with 40 mM sodium phosphate buffer (pH 7.2), MU-Neu5Ac [2’-(4-methylumberlliferyl)--D-N-acetylneuraminic acid], and with or without target compounds as following the measuring the fluorescence using excitation at 365 nm and emission at 450 nm. Compared with Grid score, target compounds were ranked by the degree of inhibition of NA. The results of in vitro analysis demonstrate the relevance of the approach adopted in silico and the potential grid impact to reduce the cost and time of structure-based drug design.
Describe the added value of the Grid for the scientific/technical activity you (plan to) do on the Grid. This should include the scale of the activity and of the potential user community and the relevance for other scientific or business applications
Virtual screening is about selecting and ranking in silico the best molecules which could impact the target biochemical activity. Screening millions of compounds which are made available due to advances in the combinatorial chemistry takes years and terabytes of storage.
Grid infrastructures are solutions for such large scale experiments.
In April 2006, data challenge for influenza neuraminidase (H5N1) was carried out by Grid-enabled high throughput in-silico screening based on AutoDock and Python programs against 308,585 compounds. As a result, a subset of compounds was identified with putative inhibition activity on neuraminidase.