Addressing emerging diseases on the grid

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, 081107

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, 150108

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 V. Breton , FCPPL, 150108

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, 150108

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, 150108

6. Molecular docking Millions Molecular dynamics 5000 AMBER Re-ranking MMPBSA-GBSA Ligand CHIMERA Amber 4 H bonds Complex visualization 180 Ligand Catalytic aspartic residues Catalytic aspartic residues Ligand 2 Hydrogen Bonds Catalytic aspartic residues In vitro tests 30 WET LABORATORY Virtual screening workflow FLEXX AUTODOCK Credit: D. Kim V. Breton , FCPPL, 150108

7. NA HA NA is involved in the replication of virions 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 300.000 selected compounds against a neuraminidase N1 structure published in PDB Credit: Y-T Wu V. Breton , FCPPL, 150108

8. : Predicted mutation site by structure overlay and sequence alignment : Reported mutation site 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 V. Breton , FCPPL, 150108

9. Compounds: ZINC: 4.3M Chembridge: 500 000 Molecular docking (FlexX, Autodock) ~1 to 15 minutes Data challenge on EGEE ~ 2 to 30 days on ~5000 computers Targets: PDB: 3D structures Cheap and fast! Hits screening using assays performed on living cells Leads Selection of the best hits Clinical testing Drug Grid-enabled virtual docking Millions of potential drugs to test against interesting proteins! High Throughput Screening 1-10$/compound, several hours V. Breton , FCPPL, 150108

10. Data challenges on avian flu and malaria *: use of DIANE/GANGA and WISDOM production environments V. Breton , FCPPL, 150108

11. Point mutations do impact inhibitory effectiveness T01:E119A T05:R293K Variation of docking score on wild type (T06) and mutated targets potential hits T01 E119A V. Breton , FCPPL, 150108

12. 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. 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 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, 150108

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, 150108

16. Public Public Public Public Public Public Private Private Private Private Private Private 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 Collaboration started with IHEP and CNIC within FCPPL: - Definition of data model - Implementation using AMGA metadata catalogue V. Breton , FCPPL, 150108

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 Academica 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 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 V. Breton , FCPPL, 150108

18. Perspectives • Avian flu • 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 Africa@home • Diabetes • Large scale docking started 2 days ago on amylase (CNU, KISTI, LPC) • AIDS • Collaboration between Univ. Cyprus and ITB-CNR V. Breton , FCPPL, 150108

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, 150108