addressing emerging diseases on the grid
DESCRIPTION
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 » - PowerPoint PPT PresentationTRANSCRIPT
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http://clrwww.in2p3.fr/PCSV
Addressing emerging diseases on the grid
Vincent Breton, CNRS-IN2P3, LPC Clermont-FerrandCredits: 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
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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
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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
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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
TargetIdentification and validation- 2/5 years- 30% success rate
Leadidentification- 0.5 year- 65% success rate
Leadoptimization- 2/4 years- 55% success rate
Target discovery Lead discovery
Gene expression analysis,Target function prediction,Target structure prediction
De novo design,Virtual screening
Virtual screening,QSAR
TargetIdentification and validation- 2/5 years- 30% success rate
Leadidentification- 0.5 year- 65% success rate
Leadoptimization- 2/4 years- 55% success rate
Target discovery Lead discovery
Gene expression analysis,Target function prediction,Target structure prediction
De novo design,Virtual screening
Virtual screening,QSAR
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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
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Virtual screening workflow
FLEXXAUTODOCK
Molecular docking
Molecular dynamics
Re-ranking MMPBSA-GBSA
Complexvisualization
In vitro tests
Catalytic aspartic residuesCatalytic aspartic residues4 H bonds
AmberLigand
Ligand2 Hydrogen Bonds
Ligand
Catalytic aspartic residues
AMBER
CHIMERA
WET LABORATORY
Millions
5000
180
30
Credit: D. Kim
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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
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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
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Grid-enabled virtual docking
Millions of potential drugs to test againstinteresting proteins!
High Throughput Screening1-10$/compound, several hours
Data challenge on EGEE~ 2 to 30 days on ~5000 computers
Hits screeningusing assays performed onliving 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: 500 000
Cheap and fast!
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Data challenges on avian flu and malaria
*: use of DIANE/GANGA and WISDOM production environments
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
In vitro tests
Winter 2006
Malaria:
GST, DHFR, Tubulin
400 years 1,6TB > 100.000 dockings / hr
Under analysis
Fall 2007 Avian 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 To be estimated
Joint deployment on desktop grid
In preparation
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Point mutations do impact inhibitory effectiveness
cpd
E119A E119D H275F R293K E119A_o Y344_oOrig.
cpd
E119A E119D H275F R293K E119A_o Y344_oOrig.
T01E119A
T01:E119A T05:R293K
pote
ntial
hits
Variation of docking score on wild type(T06) and mutated targets
전남대학전남대학교교
http://altair.chonnam.ac.kr/http://altair.chonnam.ac.kr/~carboenz~carboenz
기능성 탄수화물 효소 및 미생물 유전체 연구실기능성 탄수화물 효소 및 미생물 유전체 연구실
Spectrofluorometric detector RF-551362 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
BlueInhibition
First screening
(200 nmol)
Second screening (2 nmol)
Kinetic study
전남대학전남대학교교
http://altair.chonnam.ac.kr/http://altair.chonnam.ac.kr/~carboenz~carboenz
기능성 탄수화물 효소 및 미생물 유전체 연구실기능성 탄수화물 효소 및 미생물 유전체 연구실
Measure at excitation 362 nm andemission at 448 nm
4MU-NANA: 20 M/RM
Neuraminidase: 10 mU/reaction
Rank Compounds Relative activity of Neu1
1 113 67
2 16 72
3 6 73
4 155 74
5 78 78
63 Tamiflu 100
On UV
Results on 308 compounds tested in vitro
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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)
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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.
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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
PublicPrivate
Public
Private
Public
Private
Public
Collaboration started with IHEP and CNIC within FCPPL: - Definition of data model - Implementation using AMGA metadata catalogue
V. Breton , FCPPL, 150108 17
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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 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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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
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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