1. Addressing emerging diseases on the grid
Vincent Breton, CNRS-IN2P3, LPC Clermont-Ferrand
Credits: Ying-Ta Wu (Academia Sinica, Taïwan)
Doman Kim (Chonnam National University, Korea)
« Communication is the key to controlling communicable diseases »
Anita Barry, director of Communicable Disease Control, Boston Public Health Commission
V. Breton, IFI,

2. Emerging diseases, a growing burdeon on public health
Several new diseases have emerged in the last decades (HIV/AIDS, SRAS, Bird Flu)
They constitute a growing threat to public health due to world wide exchanges and circulation of persons
Bird flu status on
January 15th 2008:
- 86 human cases in 2007, 58 deaths
- 1 lethal case in 2008
- 30 countries infected by H5N1 in 2007
V. Breton , FCPPL,

3. Addressing emerging diseases
International collaboration is required for:
Prevention (common health policies)
Epidemiological watch
Early detection and warning
Search for new drugs
Search for vaccines
Dimension internationale requise pour la détection précoce: informer les autres pays de l’existence de foyers, précautions pour les voyageurs
Pour la surveillance épidémiologique: les données doivent être partagées entre toutes les équipes de recherche
Pour la prévention: il ne sert à rien de prendre des précautions dans un pays si le pays limitrophe multiplie les risques de déclencher une pandémie
Pour la recherche de nouveaux médicaments: il est essentiel de connaître l’évolution de la résistance aux traitements
V. Breton , FCPPL,

4. Searching for new drugs
Drug development is a long (10-12 years) and expensive (~800 MDollars) process
In silico drug discovery opens new perspectives to speed it up and reduce its cost
V. Breton , FCPPL,

5. Screening
Biologists identify a protein involved in the metabolism of the virus: the target
The goal is to find molecules to prevent the protein from playing its role in the virus life cycle: the hits
Hits dock in the active site of the protein
in silico vs in vitro screening
In silico: computational evaluation of binding energy
In vitro: optical measurement of chemical reaction constant
V. Breton , FCPPL,

6. Virtual screening workflow
Molecular docking
Molecular dynamics
Re-ranking
MMPBSA-GBSA
Complex
visualization
In vitro
tests
Catalytic aspartic residues
4 H bonds
Amber
Ligand
2 Hydrogen
Bonds
AMBER
CHIMERA
WET LABORATORY
Millions
5000
180 30
FLEXX
AUTODOCK
Credit: D. Kim
V. Breton , FCPPL,

7. First large scale grid deployment on avian flu
Goal n°1: find new drug-like molecules with inhibition activity on neuraminidase N1, target of the existing drugs (Tamiflu) against avian flu
Method: large scale docking of selected compounds against a neuraminidase N1 structure published in PDB HA
NA is involved in the replication of virions NA
Credit: Y-T Wu
V. Breton , FCPPL,

8. Anticipate the mutations
Emerging diseases are characterized by rapidly mutating viruses
Mutations can be predicted
Structures can be modified
Goal n°2: quantify the impact of 8 mutations on known drugs and find new hits on mutated targets
: Predicted mutation site by
structure overlay and
sequence alignment
: Reported mutation site
V. Breton , FCPPL,

9. Grid-enabled virtual docking
Millions of potential
drugs to test against
interesting proteins!
High Throughput Screening
1-10$/compound, several hours
Molecular docking (FlexX, Autodock)
~1 to 15 minutes
Targets:
PDB: 3D structures
Compounds:
ZINC: 4.3M
Chembridge:
Data challenge on EGEE
~ 2 to 30 days on ~5000 computers
Changer l’ordre er réorganiser.
Définir le screening
ZINC -> 2 jours sur 5000 computers
Cheap and fast!
Hits screening
using assays
performed on
living cells
Leads
Clinical testing
Drug
Selection of the best hits
V. Breton , FCPPL,

10. Data challenges on avian flu and malaria
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*
Only 45 days needed for preparation
Winter 2006
GST, DHFR, Tubulin
400 years
1,6TB
> dockings / hr
Under analysis
Fall 2007
Estimated 100 CPU years*
Estimated 800 GB*
Joint deployment on CNGrid
Data Challenge under way
Winter 2007
DHPS
To be estimated
Joint deployment on desktop grid
In preparation
*: use of DIANE/GANGA and WISDOM production environments
V. Breton , FCPPL,

11. Point mutations do impact inhibitory effectiveness
Variation of docking score on wild type
(T06) and mutated targets
T01:E119A
T05:R293K
potential hits
T01
E119A
V. Breton , FCPPL,

12. Second screening (2 nmol)
In vitro tests at Chonnam National University
4-Methylumbeliferyl-N-acetyl-a-D-neuramininic acid ammonium salt
[4MU-NANA]; Substrate
First screening
(200 nmol)
Recombinant Neuraminidase
Spectrofluorometric detector RF-551
362 nm excitation and 448 nm emission wavelengths
Second screening (2 nmol)
Red
Kinetic study
Inhibition
Blue

13. Relative activity of Neu1
Results on 308 compounds tested in vitro
4MU-NANA
: 20 mM/RM
Neuraminidase
: 10 mU/reaction
Measure at excitation 362 nm and
emission at 448 nm
Rank
Compounds
Relative activity of Neu1 1
113 67 2 16 72 3 6 73 4
155 74 5 78 63
Tamiflu
100
On UV

14. The second data challenge
N1 targets
PDB structures: open and close conformations (2HU0, 2HU4)
wild type + 3 mutations (H274, R293, E119)
prepared by Italian and Taiwanese teams (Dr. Luciano Milanesi and Dr. Ying-Ta Wu)
Compounds
300,000 lab-ready compounds from Dr. Ying-Ta Wu (Academia SInica, Taiwan)
200,000 compounds from Dr. Kun-Qian Yu (Shanghaï Institute of Materia Medica, CAS, China)
V. Breton , FCPPL,

15. Grids for early warning network
Critical importance of global early warning and rapid response
SARS
Identified keys to set up successful warning network
increased political will
resources for reporting
improved coordination and sharing of information
raising clinicians' awareness,
additional research to develop more rigorous triggers for action.
V. Breton , FCPPL,

16. A data grid to monitor avian flu
Each database to collect at a national level
Genomics data on virus and targets
Epidemiological data: information on human and bird cases
Geographical data: maps of outbreaks
Chemical data: focussed compound libraries
Private
Public
Private
Public
Private
Public
Private
Public
Private
Public
Collaboration started with
IHEP and CNIC within FCPPL:
- Definition of data model
- Implementation using
AMGA metadata catalogue
Private
Public
V. Breton , FCPPL,

17. Conclusion
The grid provides the centuries of CPU cycles required for in silico drug discovery
20% of the compounds selected in silico show better inhibition activity on H5N1 than Tamiflu during in vitro tests
The grid offers a collaborative environment for the sharing of data in the research community on emerging 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 / CSIR:
Bioinformatics, Malaria biology
Academica Sinica:
Grid user interface
Biological targets
In vitro testing
HealthGrid:
Biomedical grid, Dissemination
CEA, Acamba project:
Biological targets, Chemogenomics
Chonnam nat. univ.:
Mahidol Univ.:
Biochemistry, in vitro testing
KISTI:
Grid technology
Collaboration a débutée avec LPC et SCAI, et a trouvé de nombreux utilisateurs (biologistes). Preuve que cela remplit un role.
V. Breton , FCPPL,

18. Perspectives Avian flu Other diseases
In vitro tests of the compounds selected in silico for mutated targets
Second data challenge under way to be analyzed in Taïwan
Set-up of data repositories with grid data management services
Other diseases
Malaria
already 2 compounds identified with strong inhibition activity on the parasite -> patent
In vitro tests planned for in silico selected compounds on 2 targets docked in the winter of 2006
New target ready to be deployed both on EGEE and
Diabetes
Large scale docking started 2 days ago on amylase (CNU, KISTI, LPC)
AIDS
Collaboration between Univ. Cyprus and ITB-CNR
V. Breton , FCPPL,

19. Credits Development of the WISDOM environment
ASGC: Yu-Hsuan Chen, Li-Yung Ho, Hurng-Chun Lee
ITB-CNR: G. Trombetti
CNRS-IN2P3: V. Bloch, M. Diarena, J. Salzemann
HealthGrid: B. Grenier, N. Spalinger, N. Verhaeghe
Biochemical preparation and analysis
ASGC: Y-T Wu
Chonnam National University: D. Kim & al
CNRS-IN2P3: A. Da Costa, V. Kasam
ITB-CNR: L. Milanesi & al
Projects supporting WISDOM
Projects providing human resources: BioinfoGRID, EGEE, Embrace
Projects providing computing resources: AuverGRID, EELA, EGEE, EUMedGRID, EUChinaGRID, TWGrid
V. Breton , FCPPL,