V. Breton CNRS-IN2P3

1. France-Korea Particle Physics Laboratory: an International Associated Laboratory for e-science and particle physics V. Breton CNRS-IN2P3

2. Table of content • What is a LIA ? • Example: the FKPPL • Introduction to grids • Grid-enabled virtual screening: the example of WISDOM • Conclusion

3. International Associated Laboratory – LIA (1/3) • An LIA is a "laboratory without walls" and is not a legal entity. • It brings together laboratories from CNRS and from one other country. • These laboratories contribute human and material resources to a common, jointly-defined project designed to "add value" to their individual pursuits. • An LIA agreement is for 4 years, renewable twice.

4. LIA(2/3) • The laboratories comprising an LIA retain their independence, their regular status, their director and their separate locations. • Co-directors of the LIA are appointed if so desired. • An LIA receives earmarked funding from the CNRS and the partner institutions, for equipment, scientific missions, associate research positions, etc. • It is coordinated by a scientific management committee, which determines the research program to be submitted to the steering committee. The latter is composed of representatives of the two partner institutions as well as established scientists from outside the LIA.

5. LIA(3/3) • When to apply for LIA status? • Proposals for the creation of an LIA may be filled at any time with a laboratory's scientific department. • Who makes the decision to approve an LIA proposal? • The decision to create an LIA is made by the CNRS and its foreign partner institution. • When the proposal has been accepted, an agreement is established between the Director General of the CNRS and the supervisory board of the partner institution

6. A brief history • 2005 • December: first contacts between François Le Diberder, Do-Won Kim and Marianne Noël • François Le Diberder: deputy director of CNRS Institute of Nuclear and Particle Physics • Do-Won Kim: professor of physics at Kangnung University • Marianne Noël: Attache for science and technology at French embassy in Seoul • 2007 • April : signature of CNRS – KISTI MoU during the 3rd session of the Korea-France joint committee for scientific and technological cooperation • November : Visit to Korea of Blaise Pascal University president • December : François le Diberder visit to Korea - addition of new partners and of a new project on ILC microelectronics • 2008 • March 20th 2008: signature of the LIA creation document at the French Embassy in Seoul

7. Partners • Korean partners • KISTI (Daejeon) • Chonnam National University (Gwangju) • EWHA Womans University (Seoul) • Kangnung National University • Korea Institute of Radiological and Medical Sciences (Seoul) • Pohang Accelerator Laboratory (Seoul) • Sung Kyun Kwan University • French partners • CNRS • Blaise Pascal University (Clermont-Ferrand) • University Paris XI (Orsay) • Ecole Polytechnique (Palaiseau)

8. FKPPL management • Steering Committee members • Professor Dong-Pil Min (Seoul National University), co-chairman • Professor François Le Diberder (Stanford University), co-chairman • Professor YungE Earm, Seoul National University • Doctor Jysoo Lee, KISTI • Doctor Mannque Rho, CEA • Doctor Jean-Eudes Augustin, CNRS-IN2P3 • Professor Alain Baldit, University Blaise Pascal • Doctor Dominique Boutigny, CC-IN2P3 • Co-directors • Doctor O. Byeon, KISTI • V.B., CNRS-IN2P3

9. FKPPL scientific projects • FKPPL focusses on particle physics and e-science • Both require international collaboration • Particle physics is the first user community to have completely adopted the grid technology

10. What is a grid ? • a fully distributed, dynamically reconfigurable, scalable and autonomous infrastructure to provide location independent, pervasive, reliable, secure and efficient access to a coordinated set of services encapsulating and virtualizing resources (computing power, storage, instruments, data, etc.) in order to support problem solving and knowledge generation across multiple administrative domains.

11. The different kinds of grids • Computing grids provide resources for intensive calculations • Particularly intestesting for embarassingly parallel computations (Monte-Carlo) • Currently used for docking and molecular dynamics • Data Grids allow distributed and secured storage and access to biochemical data • Databases (Zinc, PDB) • Knowledge grid are about information management • Goal: allow end users to access all the computing and data resources of the grids while manipulating concepts they are familiar with • Requirements: data interoperability and ontologies Computing Grid For data crunching applications Knowledge Grid Intelligent use of Data Grid for knowledge creation and tools provisions to all users Data Grid Distributed and optimized storage of large amounts of accessible data ICT for Health, ISTAG WG, March 2004

12. Enabling Grids for E-sciencE EGEE Grid Infrastructure Flagship European grid infrastructure project Now in 3rd phase with more than 100 partners • Size of the infrastructure today: • > 250 sites in 48 countries • One in Korea (KISTI) • > 70 000 CPU cores • ~ 5 PB disk + tape MSS • > 150 000 jobs/day • > 9000 registered users 12 EGEE-II INFSO-RI-031688

13. 6 scientific disciplines are routinely using the EGEE grid >100 applications deployed Scientific Applications on EGEE Condensed Matter Physics Comp. Fluid Dynamics Computer Science/Tools Civil Protection 13

14. Computational Chemistry Becoming the second user of the infrastructure after High Energy Physics 14

15. Computational chemistry on EGEE • Software deployed on the grid • Free sofware: GAMESS, COLUMBUS, DL_POLY, RWAVEP or ABCtraj • Licensed software: Amber, Gaussian, Turbomole and Wien2K • Contact point: Mariusz Sterzel, CYFRONET, m.sterzel@cyf-kr.edu.pl

16. Enabling Grids for E-sciencE WISDOM In silico Drug Discovery • WISDOM: http://wisdom.healthgrid.org/ • Goal: find new drugs for neglected and emerging diseases • Neglected diseases lack R&D • Emerging diseases require very rapid response time • Need for an optimized environment • To achieve production in a limited time • To optimize performances • Method: grid-enabled virtual docking • Cheaper than in vitro tests • Faster than in vitro tests 16

17. SCAI Fraunhofer: Knowledge extraction, Chemoinformatics LPC Clermont-Ferrand: Biomedical grid CEA, Acamba project: Biological targets, Chemogenomics Chonnam nat. univ.: In vitro testing Univ. Modena: Biological targets, Molecular Dynamics KISTI: Grid technology HealthGrid: Biomedical grid, Dissemination ITB CNR: Bioinformatics, Molecular modelling Academia Sinica: Grid user interface Biological targets In vitro testing Univ. Los Andes: Biological targets, Malaria biology Univ. Pretoria / CSIR: Bioinformatics, Malaria biology Mahidol Univ.: Biochemistry, in vitro testing WISDOM partners • Laboratories with expertise in grid technology • KISTI in Korea • “Wet” laboratories for in vitro and in vivo studies • Chonnam national University

18. Enabling Grids for E-sciencE High Throughput Virtual Docking Chemical compounds : ZINC Molecular docking : FlexX, Autodock Targets structures : PDB Grid infrastructure : EGEE Millions of chemical compounds available in laboratories Chemical compounds : Chembridge – 500,000 Drug like – 500,000 High Throughput Screening 1-10$/compound, nearly impossible Molecular docking (FlexX, Autodock)‏ ~80 CPU years, 1 TB data Computational data challenge ~6 weeks on ~1000/1600 computers Targets : Plasmepsin II (1lee, 1lf2, 1lf3)‏ Plasmepsin IV (1ls5)‏ Hits screening using assays performed on living cells Leads Clinical testing Drug 18 EGEE-II INFSO-RI-031688 Application to life sciences, J. Montagnat, June 18, 2008

19. Virtual screening pipeline Molecular docking FLEXX/ AUTODOCK Molecular dynamics AMBER Complex visualization CHIMERA Catalytic aspartic residues in vitro WET LABORATORY in vivo 19

20. Enabling Grids for E-sciencE The present WISDOM architecture Major new features: - improved data management - secure storage - migration to web service Credit: J. Salzeman, V. Bloch 20 Healthgrid … – March 8th, 2007 – V. Breton INFSO-RI-508833

21. Enabling Grids for E-sciencE WISDOM-II: A huge international effort Significant contributions from several International grid infrastructures Over 420 CPU years in 10 weeks A record throughput of 100.000 docked compounds per hour 21 Healthgrid … – March 8th, 2007 – V. Breton INFSO-RI-508833

22. MD refinement using Amber Re-ranking and Analysis Phase Preparatory Phase Simulation Phase Target +prepi top pdb, top Mol2 top, crd min.rst Sander MD Antechamber Leap Sander Min MMPBSA Ptraj prepi top, crd min.rst md.rst Output Output Final output appended into a file • Best hits from docking • step based on: • Docking energy • Docked pose For one complete simulation, all necessary steps are embedded in one single script. A. Ferrari, G. Degliesposti, M. Sgobba, G. Rastelli. Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors. Bioorganic & Medicinal Chemistry. 2007. In Press.

23. Grid Performances for MD • 25, 000 compounds: • Plasmepsin: 5000 compounds • Pf-DHFR: 15,000 compounds • Pf-GST: 5000 compounds

24. Virtual screening on the grid: deployment status

25. KISTI contribution to WISDOM • E-science division • Partner of EGEE-III (European project) • Contact point: S. Hwang, hwang@kisti.re.kr • Improve data management services on EGEE • Improvement to the WISDOM production environment • Allow the use of several biochemical data servers • Deploy large scale docking computations on amylase • Collaboration with CNU and LPC Clermont-Ferrand • DrugScreener-G • Goal: provide a user-friendly integrated environment for Grid-based large-scale virtual screening for users without much knowledge of Grid computing • Target users: Bioinformaticians, Biologists, Drug Chemists • Contact point: Jincheol Kim, e-science division, KISTI, jin.cheol.kim@kisti.re.kr

26. DrugScreener-G : Architecture

27. CNU contribution to WISDOM • Lab. of Functional Carbohydrate Enzyme and Microbial Genomics, led by Prof. D. Kim (dmkim@jnu.ac.kr) • Partner of EGEE-III (European project) • Partner of STAR project (Korea-France funding program) • In vitro test of the best compounds selected in silico on the grid • Malaria: 6/30 compounds similar or better than PepstatinA on plasmepsin target • Avian flu: 20% of compounds better than Tamiflu on neuraminidase N1 target • Best compounds patented in Korea • Search for new drugs against diabetes • First target studied: amylase

28. Summary of the existing collaborations on grids • Bilateral agreements • Memorandum of Understanding between Chonnam Nat. Univ. and Univ. B. Pascal, Kangnung Nat. Univ. and Univ. B. Pascal • STAR project( CNU, KNU, CNRS) • International Associated Laboratory FKPPL (CNU, KISTI, CNRS, Univ. Blaise Pascal) • EU project • KISTI, CNU involved in the EGEE-III project (FP7) with CNRS and Univ. B. Pascal • Participation to the Life Sciences cluster of EGEE-III

29. Conclusion • The FKPPL LIA offers a framework for collaboration on particle physics and e-science between Korea and France • July 14 – 25: Grid school at Seoul National University • Installation of grid services • User tutorial • Advanced tools for data analysis • July 21 : first FKPPL Steering Committee meeting • Grids are under adoption by the Computational Chemistry community • Popular software like Amber, Gaussian deployed on EGEE grid • Well fitted for embarrassingly parallel applications (Monte-Carlo) • Potential limitations: • Licensing issues • Memory < 2GB / node

30. Perspectives • In France • French Ministry of Research has organized a wide consultation for the deployment of a multidisciplinary grid for scientific production • Contribution of the Computational Chemistry community is important • Contact point: Guy Wormser, director of Institut des Grilles, wormser@lal.in2P3.fr • In Korea • First grid school in Seoul currently going on • Deployment of grid nodes foreseen in Korean universities • Contact point: Dr Soonwook Hwang, KISTI, hwang@kisti.re.kr

31. PCSV group at LPC Clermont-Ferrand (together with HealthGrid association)