1. http://www.itb.cnr.it/bioinfogrid Grid Enabled High Throughput Virtual Screening Against Four Different Targets Implicated in Malaria Presented by Vinod Kasam CLADE workshop, HPDC conference, June 25, 2007, Monterey Bay

2. 25-06-2007, Monterey Bay 2 Outline Wisdom introduction Biological targets Resources used in wisdom Production environment Results Issues Conclusions

3. 25-06-2007, Monterey Bay 3 Introduction to the disease : malaria ~300 million people worldwide are affected 1-1.5 million people die every year Widely spread Caused by protozoan parasites of the genus Plasmodium Complex life cycle with multiple stages

4. 25-06-2007, Monterey Bay 4 WISDOM-II, second large scale docking deployment against malaria Parasite DNA synthesis Parasite cell replication Parasite DNA synthesis Parasite detoxification CEA, Acamba project, France U. of Modena, Italia U. of Los Andes, Venezuela U. of Modena, Italia U. of Pretoria, South-Africa Biology partners Tubulin from Plasmodium/plant/ mamal DHFR from Plasmodium falciparum DHFR from Plasmodium vivax GST from Plasmodium falciparum Malaria target Involved in

5. 25-06-2007, Monterey Bay 5 Biological goal Proposition of new inhibitors for a family of proteins produced by Plasmodium Biomedical informatics goal Deployment of in silico virtual docking on the grid Grid goal Deployment of a CPU consuming application generating large data flows to test the grid operation and services => “data challenge” WISDOM : Wide In Silico Docking On Malaria

6. 25-06-2007, Monterey Bay 6 High Throughput Virtual Docking Compounds: ZINC- 4,3M Chembridge - 500 000 Targets: 3D structures in PDB One homology model Millions of chemical compounds available High Throughput Screening 1-10$/compound, several hours Molecular docking (FlexX, Autodock) 20 cents/compound, 1 minute Data challenge on EGEE ~ 3 months on ~2000 computers Hits screening using assays performed on living cells Leads Clinical testing Drug

7. 25-06-2007, Monterey Bay 7 Objective of the WISDOM development Objective –Dock a whole compound database in a limited time with a minimal human involvement during the data challenge. Need an optimized environment –Production in Limited time –Performance are important Need a fault tolerant environment –Stress usage of the grid during the DC –Grid is heterogeneous and dynamic –Data produced are important and can’t be easily reproduced Need an automatic production environment –Grid API are not fully adapted for a bulk use at a large scale –Ease the execution –User-friendly hi-level services

8. 25-06-2007, Monterey Bay 8 Use of a production system Managing thousands of jobs and files is a manually labor- intensive task –Job preparation, submission and monitoring, output retrieval, failure identification and resolution, job resubmission… –In order to efficiently use the resources The amount of transferred data impacts on grid performance –The data must be installed on the grid –The database is stored into subsets Grid process introduces significant delays –The submitted jobs must be sufficiently long in order to reduce the impact of this middleware overhead The production system will provide automated and fault- tolerant jobs and files management

9. 25-06-2007, Monterey Bay 9 Grid added value for international collaboration on neglected and emerging diseases Grids offer unprecedented opportunities for sharing information and resources world wide Grids are unique tools for : -Collecting and sharing information (Epidemiology, Genomics) -Networking experts -Mobilizing resources routinely or in emergency (vaccine & drug discovery)

10. 25-06-2007, Monterey Bay 10 Grid added value of EGEE for a large scale in silico experimentation Large computing and storage resources 24 hours a day availability of resources, user support Workload Management Service Information and Monitoring Services Data Management Services Security Reliability of services

11. 25-06-2007, Monterey Bay 11 Simplified grid workflow FlexX license server : –6000 floating licenses offered by BioSolveIT to SCAI –Maximum number of concurrent used licenses was 5000 StorageElement ComputingElement Site1 Site2 StorageElement User interface ComputingElement Compounds database Parameter settings Target structures Compounds sub lists Results Statistics Compounds list ResourceBroker Software

12. 25-06-2007, Monterey Bay 12 User Interface HealthGrid Server Web Site WMS SEsCEs &WNs FlexLM Schema of the current WISDOM production environment User Interface WISDOM production system WMS Submits the jobs Checks job status Resubmits CEs &WNs FlexX job SEs Structure file Compounds file inputs outputs Output file Local server Web Site WISDOM DB Statistics FLEXlm license FlexX Statistics DMS/GFTPDMS/GFTP

13. 25-06-2007, Monterey Bay 13 Grid infrastructures and projects contributing to WISDOM-II : European grid infrastructure : European grid project EELA EUMedGridEUChinaGrid : Regional/national grid infrastructure Auvergrid EGEE TWGrid EMBRACEBioinfoGrid SHARE

14. 25-06-2007, Monterey Bay 14 Instances on different infrastructures Instances deployed on the different infrastructures during the WISDOM-II data challenge

15. 25-06-2007, Monterey Bay 15 Deployment on different infrastrucures Up to 5000 computers in more than17 countries mobilized from october 2006 – Jan 2007 to provide CPU 1.738 TB of data produced Distribution of jobs

16. 25-06-2007, Monterey Bay 16 Statistics of deployment First DC: –80 CPU years –1 TB –1700 CPUs used in parallel –July 1st - August 15th 2005 2nd DC –100 CPU years –800 GB –1700 CPUs used used in parallel –May 1st -April 15th 2006 3rd DC –413 CPU years –1.7 TB –Up to 5000 CPUs in parallel –1st October 2006 - 31 January 2007 Number of Jobs77,504 Total Number of completed dockings156,407,400 Estimated duration on 1 CPU413 years Duration of the experiment76 days Average throughput78,400 dockings/hour Maximum number of loaded licences (concurrent running jobs) 5,000 Number of used computing elements98 Average duration of a job41 hours Average crunching factor1,986 Volume of output results1,738 TB The crunching factor is the ratio of the total CPU time over the duration of the experiment. It represents the average number of CPUs used simultaneously all along the data challenge and is a metric of the parallelization gain.

17. 25-06-2007, Monterey Bay 17 Biological results The repartition of docking energies of the ZINC database against GST A structure. (The red column represents a score of -24kj/Mol, the docking score of a co-crystallized ligand (GTX) of GST A chain)

18. 25-06-2007, Monterey Bay 18 Issues Scheduling efficiency of the grid is still a major issue The resource broker is still the main bottleneck This deployment also shows that it is not possible to do a naive blacklisting of the failing resources, for the simple fact that virtually all the grid resources have produced aborted jobs, and this blacklisting should also take care of the site scheduled downtimes. Store and treat the data in a relational database

19. 25-06-2007, Monterey Bay 19 Interactive Web Portal User Friendly Interface for biologists Real Time output of the results –3D views of the docking poses and structures Resubmission of docking jobs

20. 25-06-2007, Monterey Bay 20 Conclusion Take advantage of the EGEE services, APIs and resources. Demonstrated the relevance of computational grids in life science applications Manual intervention is reduced (automatic resubmission of jobs) Use of AMGA to store results and statistics immediately. Interoperable Web Service Interface WSDL following the WS-I profile Improved flexibility to deploy other bioinformatics applications.

21. 25-06-2007, Monterey Bay 21 The next steps To address the issue of resource brokers, we are trying to submit the jobs by bypassing resource brokers Docking step still requires a lot of manual intervention –Task: improve output data collection and post-docking analysis The next step after docking is Molecular Dynamics –Task: deploy Molecular Dynamics computations on grid infrastructures (successfully deployed already on one target, plasmepsin) –Contribution from CNRS-IN2P3, within the framework of BioinfoGRID, Fraunhofer SCAI and University of Modena Beyond virtual screening, the long term vision: building a grid for malaria –To provide services to research labs working on malaria –To collect and analyze epidemiological data

22. 25-06-2007, Monterey Bay 22 Long term vision: a grid for malaria Use the grid technology to foster research and development on malaria and other neglected diseases Univ. Los Andes: Biological targets, Malaria biology LPC Clermont-Ferrand: Biomedical grid SCAI Fraunhofer: Knowledge extraction, Chemoinformatics Univ. Modena: Biological targets, Molecular Dynamics ITB CNR: Bioinformatics, Molecular modelling Univ. Pretoria: Bioinformatics, Malaria biology Academica Sinica: Grid user interface Contacts also established with WHO, Microsoft, TATRC, Argonne, SDSC, SERONO, NOVARTIS, Sanofi- Aventis, Hospitals in subsaharian Africa, HealthGrid: Biomedical grid, Dissemination CEA, Acamba project: Biological targets, Chemogenomics

23. 25-06-2007, Monterey Bay 23 Acknowledments Academia Sinica BioSolveIT CNR-ITB CNRS CEA Healthgrid IN2P3 LPC SCAI Fraunhofer Università di Modena e Reggio Emilia Université Blaise Pascal University of Pretoria University of Los Andes Auvergrid Accamba BioInfoGRID EGEE EMBRACE EUChinaGRID EUMedGRID SHARE TWGrid Conseil Regional d’Auvergne European Union wisdom.healthgrid.org