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

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

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, 150108

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

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

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, 150108

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 HA NA is involved in the replication of virions NA Credit: Y-T Wu V. Breton , FCPPL, 150108

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, 150108

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: 500 000 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, 150108

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 > 100.000 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, 150108

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, 150108

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

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

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

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

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, 150108

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, 150108

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 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

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