Plateforme de Calcul pour les Sciences du Vivant V. Breton, IFI, 081107 Addressing emerging...

Plateforme de Calcul pour les Sciences du Vivant

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


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


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


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


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


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


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


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


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!


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


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


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)


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.


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


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


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


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

Plateforme de Calcul pour les Sciences du Vivant V. Breton, IFI, 081107 Addressing emerging...

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