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Plateforme de Calcul pour les Sciences du Vivant V. Breton, IFI, 081107 Addressing emerging diseases on the grid Vincent Breton,

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Presentation on theme: "Plateforme de Calcul pour les Sciences du Vivant V. Breton, IFI, 081107 Addressing emerging diseases on the grid Vincent Breton,"— Presentation transcript:

1 Plateforme de Calcul pour les Sciences du Vivant V. Breton, IFI, 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

2 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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 countries infected by H5N1 in 2007

3 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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

4 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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

5 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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

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

7 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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

8 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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

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

10 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, Data challenges on avian flu and malaria *: use of DIANE/GANGA and WISDOM production environments DatesTarget (s)CPU consumed EGEE AuverGrid Data produced Specific features Status Summer 2005 Malaria: plasmepines 80 years1TBFirst data challenge In vitro tests In vivo tests Spring 2006 Avian flu: Neuraminidase N1 100 years*800 GB*Only 45 days needed for preparation In vitro tests Winter 2006 Malaria: GST, DHFR, Tubulin 400 years1,6TB> dockings / hr Under analysis Fall 2007Avian flu: Neuraminidase N1 Estimated 100 CPU years* Estimated 800 GB* Joint deployment on CNGrid Data Challenge under way Winter 2007 Malaria: DHPS To be estimated Joint deployment on desktop grid In preparation

11 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, Point mutations do impact inhibitory effectiveness T01 E119A T01:E119AT05:R293K potential hits Variation of docking score on wild type (T06) and mutated targets

12 전남대학 교 기능성 탄수화물 효소 및 미생물 유전체 연구실 Spectrofluorometric detector RF nm excitation and 448 nm emission wavelengths 4-Methylumbeliferyl-N-acetyl-  -D-neuramininic acid ammonium salt [4MU-NANA]; Substrate Recombinant Neuraminidase In vitro tests at Chonnam National University Red Blue Inhibition First screening (200 nmol) Second screening (2 nmol) Kinetic study

13 전남대학 교 기능성 탄수화물 효소 및 미생물 유전체 연구실 Measure at excitation 362 nm and emission at 448 nm 4MU-NANA : 20  M/RM Neuraminidase : 10 mU/reaction RankCompounds Relative activity of Neu Tamiflu100 On UV Results on 308 compounds tested in vitro

14 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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)

15 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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.

16 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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 Private Public Collaboration started with IHEP and CNIC within FCPPL: - Definition of data model - Implementation using AMGA metadata catalogue

17 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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.: In vitro testing Mahidol Univ.: Biochemistry, in vitro testing KISTI: Grid technology

18 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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 – Diabetes  Large scale docking started 2 days ago on amylase (CNU, KISTI, LPC) –AIDS  Collaboration between Univ. Cyprus and ITB-CNR

19 Plateforme de Calcul pour les Sciences du Vivant V. Breton, FCPPL, 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


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