V. Breton CNRS-IN2P3

France-Korea Particle Physics Laboratory: an International Associated Laboratory for e-science and particle physics

V. Breton CNRS-IN2P3

July 18th 2008 – V. Breton

Table of content

• What is a LIA ?

• Example: the FKPPL

• Introduction to grids

• Grid-enabled virtual screening: the example of WISDOM

• Conclusion


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.


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.


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


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


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)


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


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

Project name Coordinators Partners Status

ILC calorimeter Yongmann Yang, EWHA

Jean-Claude Brient, LLR

EWHA Womans Univ., Kangnung Nat. Univ.,LPC, LLR

Approved

ILC microelectronics

Jongseo Chai, SKKU

Christophe de la Taille, LAL

Sung Kyun Kwan Univ., Korea Institute of Radiological and medical Sciencces, Pohang Accel. Lab. LAL, LLR

Approved

Grid computing S. Hwang, KISTI

D. Boutigny, CC-IN2P3

KISTI, CC-IN2P3 Approved

WISDOM Doman Kim, CNU

V. Breton, LPC

Chonnam Nat. Univ., KISTI, Kangnung Nat. Univ., LPC

Approved

ALICE Yongwook Baek, KNU

Pascal Dupieux, LPC

Kangnung Nat. Univ. LPC Submitted

CDF Kihyeon Cho, KISTI

Aurore Savoy-Navarro, LPNHE

KISTI, LPNHE Submitted


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.


The different kinds of grids

Computing GridFor data crunching applications

Knowledge GridIntelligent use of Data Grid for knowledge creation and tools

provisions to all users

Data GridDistributed and optimized storage of

large amounts of accessible data

ICT for Health, ISTAG WG, March 2004

• 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

V. Breton, July 18th 2008

Enabling Grids for E-sciencE

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EGEE-II INFSO-RI-031688

EGEE Grid Infrastructure

Flagship European grid infrastructure projectNow 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



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• 6 scientific disciplines are routinely using the EGEE grid– >100 applications deployed

6/2006 2/2007 1/2008

Astron. & Astrophysics 2 8 9

Comp. Chemistry 6 27 21

Earth Science 16 16 18

Fusion 2 3 4

High-Energy Physics 9 11 7

Life Sciences 23 39 37

Others 4 14 21

Total 62 118 117

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Scientific Applications on EGEE

Condensed Matter PhysicsComp. Fluid DynamicsComputer Science/ToolsCivil Protection



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Computational Chemistry

• Becoming the second user of the infrastructure after High Energy Physics

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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, [email protected]



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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

July 18th 2008 – V. Breton 17

WISDOM partners

• Laboratories with expertise in grid technology– KISTI in Korea

• “Wet” laboratories for in vitro and in vivo studies– Chonnam national University

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 / CSIR:Bioinformatics, Malaria

biology

Academia Sinica:Grid user interfaceBiological targetsIn vitro testing

HealthGrid:Biomedical grid, Dissemination

CEA, Acamba project:Biological targets, Chemogenomics

Chonnam nat. univ.:In vitro testing

Mahidol Univ.:Biochemistry, in vitro

testing

KISTI:Grid technology

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INFSO-RI-508833 Application to life sciences, J. Montagnat, June 18, 2008


EGEE-II INFSO-RI-031688

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High Throughput Virtual Docking

Chemical compounds :Chembridge – 500,000Drug like – 500,000

Targets :Plasmepsin II (1lee, 1lf2, 1lf3)Plasmepsin IV (1ls5)

Millions of chemicalcompounds availablein laboratories

High Throughput Screening1-10$/compound, nearly impossible

Molecular docking (FlexX, Autodock)~80 CPU years, 1 TB data

Computational data challenge~6 weeks on ~1000/1600 computers

Hits screeningusing assays performed onliving cells

Chemical compounds : ZINCMolecular docking : FlexX, AutodockTargets structures : PDBGrid infrastructure : EGEE

Leads

Clinical testing

Drug



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FLEXX/AUTODOCK

Catalytic aspartic residues

AMBER

CHIMERA

WET LABORATORY

Molecular docking

Molecular dynamics

Complexvisualization

in vitro

in vivo

Virtual screening pipeline

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INFSO-RI-508833 Healthgrid … – March 8th, 2007 – V. Breton


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The present WISDOM architecture

Credit: J. Salzeman, V. Bloch

Major new features: - improved data management - secure storage - migration to web service



INFSO-RI-508833 Healthgrid … – March 8th, 2007 – V. Breton


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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



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MD refinement using Amber

Antechamber Leap Sander Min

SanderMD Ptraj MMPBSA

Mol2

prepi

Target +prepi

top, crd

top, crd

min.rst

min.rst

md.rst Output

top pdb, top

Output

Final output appended into a file

Preparatory Phase Simulation PhaseRe-ranking and Analysis Phase

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.

Best hits from dockingstep based on:• Docking energy• Docked pose

For one complete simulation, all necessary steps are embedded in one single script.



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Grid Performances for MD

25, 000 compounds:• Plasmepsin: 5000 compounds• Pf-DHFR: 15,000 compounds• Pf-GST: 5000 compounds

Number of Jobs 500

Total Number of compounds

simulated

25000

Estimated duration on 1 CPU 347 days

Duration on the grid 25 days

Maximum number of concurrent

running jobs

90

Number of computing elements used 1

Average duration of a job 16.6

hours



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Virtual screening on the grid: deployment status

Dates Target (s) CPU consumed

EGEE AuverGrid

Data

produced

Specific features Status

Summer

2005

Malaria:

plasmepines

80 years 1TB First data

challenge

In vitro tests

In vivo tests

Spring 2006 Avian flu:

Neuraminidase N1

100 years* 800 GB <45 days needed

for preparation

In vitro tests

Winter 2006 Malaria: GST,

DHFR, Tubulin

400 years 1,6TB > 100.000

dockings / hr

Analyzed,ready

for in vitro tests

Fall 2007 Avian flu:

Neuraminidase N1

Estimated 100 CPU

years*

Estimated 800

GB*

Joint deployment

on CNGrid

Under analysis

Spring 2008 Diabetes:

amylase

Estimated 120 CPU

years

Estimated 800

GB

New production

environment

Under way

Summer

2008

Malaria:

DHPS

To be estimated To be

estimated

Joint deployment

on desktop grid

In preparation



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KISTI contribution to WISDOM

• E-science division – Partner of EGEE-III (European project) – Contact point: S. Hwang, [email protected]

• 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,

[email protected]

DrugScreener-G : Architecture


CNU contribution to WISDOM

• Lab. of Functional Carbohydrate Enzyme and Microbial Genomics, led by Prof. D. Kim ([email protected])– 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


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


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


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, [email protected]

• In Korea– First grid school in Seoul currently going on– Deployment of grid nodes foreseen in Korean universities– Contact point: Dr Soonwook Hwang, KISTI, [email protected]


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

Documents

V. Breton CNRS-IN2P3