Design of new plasmepsin inhibitors: a virtual high throughput screening approach on the EGEE grid.

Though different species of the genus Plasmodium may be responsible for malaria, the variant caused by P. falciparum is often very dangerous and even fatal if untreated. Hemoglobin degradation is one of the key metabolic processes for the survival of the Plasmodium parasite in its host. Plasmepsins, a family of aspartic proteases encoded by the Plasmodium genome, play a prominent role in host hemoglobin cleavage. In this paper we demonstrate the use of virtual screening, in particular molecular docking, employed at a very large scale to identify novel inhibitors for plasmepsins II and IV. A large grid infrastructure, the EGEE grid, was used to address the problem of large computation resources required for docking hundreds of thousands of chemical compounds on different plasmepsin targets of P. falciparum. A large compound library of about 1 million chemical compounds was docked on 5 different targets of plasmepsins using two different docking software, namely FlexX and AutoDock. Several strategies were employed to analyze the results of this virtual screening approach including docking scores, ideal binding modes, and interactions to key residues of the protein. Three different classes of structures with thiourea, diphenylurea, and guanidino scaffolds were identified to be promising hits. While the identification of diphenylurea compounds is in accordance with the literature and thus provides a sort of "positive control", the identification of novel compounds with a guanidino scaffold proves that high throughput docking can be effectively used to identify novel potential inhibitors of P. falciparum plasmepsins. Thus, with the work presented here, we do not only demonstrate the relevance of computational grids in drug discovery but also identify several promising small molecules which have the potential to serve as candidate inhibitors for P. falciparum plasmepsins. With the use of the EGEE grid infrastructure for the virtual screening campaign against the malaria causing parasite P. falciparum we have demonstrated that resource sharing on an eScience infrastructure such as EGEE provides a new model for doing collaborative research to fight diseases of the poor.

Grid enabled high throughput virtual screening against four different targets implicated in malaria.

After having deployed a first data challenge on malaria and a second one on avian flu, respectively in summer 2005 and spring 2006, we are demonstrating here again how efficiently the computational grids can be used to produce massive docking data at a high-throughput. During more than 2 months and a half, we have achieved at least 140 million dockings, representing an average throughput of almost 80,000 dockings per hour. This was made possible by the availability of thousands of CPUs through different infrastructures worldwide. Through the acquired experience, the WISDOM production environment is evolving to enable an easy and fault-tolerant deployment of biological tools; in this case it is the FlexX commercial docking software which is used to dock the whole ZINC database against 4 different targets.

Roadmap for a European healthgrid.

This paper proposes a 10-year roadmap to achieve the goal to offer to healthcare professionals an environment created through the sharing of resources, in which heterogeneous and dispersed health data as well as applications can be accessed by all users as a tailored information providing system according to their authorisation and without loss of information. The paper identifies milestones and presents short term objectives on the road to this healthgrid.

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

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.

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.

Demonstration of in silico docking at a large scale on grid infrastructure.

WISDOM stands for World-wide In Silico Docking On Malaria. First step toward enabling the in silico drug discovery pipeline on a grid infrastructure, this CPU consuming application generating large data flows was deployed successfully on EGEE, the largest grid infrastructure in the world, during the summer 2005. 46 million docking scores were computed in 6 weeks. The proposed demonstration presents the submission of in silico docking jobs at a large scale on the grid. The demonstration will use the new middleware stack gLite developed within the EGEE project.