Grid Enabled High Throughput Virtual Screening Against Four Different Targets Implicated in Malaria

1
Grid Enabled High Throughput Virtual Screening
Against Four Different Targets Implicated in
Malaria

• Presented by
• Vinod Kasam

HealthGrid Conference, April 2007, Geneva
2
Outline

• Wisdom introduction
• Biological targets
• Resources used in wisdom
• Production environment
• Results
• Issues
• Conclusions

3
WISDOM Wide In Silico Docking On Malaria

• Biological goal
• Proposition of new inhibitors for a family of
  proteins produced by Plasmodium
• Biomedical informatics goal Deployment of in
  silico virtual docking on the grid
• Grid goal
• Deployment of a CPU consuming application
  generating large data flows to test the grid
  operation and services gt data challenge

4
Objective of the WISDOM development

• Objective
• Producing a large amount of data in a limited
  time with a minimal human cost during the data
  challenge.
• Need an optimized environment
• Limited time
• Performance goal
• Need a fault tolerant environment
• Stress usage of the grid during the DC
• Need an automatic production environment
• Grid API are not fully adapted for a bulk use at
  a large scale

5
Introduction to the disease malaria

• 300 million people worldwide are affected
• 1-1.5 million people die every year
• Widely spread
• Caused by protozoan parasites of the genus
  Plasmodium

Complex life cycle with multiple stages
6
WISDOM-II, second large scale docking deployment
against malaria
Involved in
Biology partners
Malaria target
Parasite detoxification
U. of Pretoria, South-Africa
GST from Plasmodium falciparum
Parasite DNA synthesis
U. of Los Andes, Venezuela U. of Modena, Italia
DHFR from Plasmodium vivax
Parasite DNA synthesis
U. of Modena, Italia
DHFR from Plasmodium falciparum
Parasite cell replication
CEA, Acamba project, France
Tubulin from Plasmodium/plant/mamal
7
High Throughput Virtual Docking
Millions of chemical compounds available in
laboratories
High Throughput Screening 1-10/compound, nearly
impossible

• Chemical compounds (ZINC) 4.3 million

Molecular docking (FlexX) 413 CPU years, 1.738
TB data
Data challenge on EGEE 90 days on 5000 computers
Hits screening using assays performed on living
cells
Leads
Targets (PDB) Plm, PvDHFR PfDHFR, GST, tubulin
Clinical testing
Drug
8
Use of a production system

• Managing thousands of jobs and files is a
  manually labor-intensive task
• Job preparation, submission and monitoring,
  output retrieval, failure identification and
  resolution, job resubmission
• In order to efficiently use the resources
• The amount of transferred data impacts on grid
  performance
• The data must be installed on the grid
• The database is stored into subsets
• Grid process introduces significant delays
• The submitted jobs must be sufficiently long in
  order to reduce the impact of this middleware
  overhead
• The production system will provide automated and
  fault-tolerant jobs and files management
• The system requires tools providing global
  statistic data and figures

9
Simplified grid workflow
Results
Compounds list
Site1
Parameter settings Target structures Compounds
sub lists
Statistics
Resource Broker
User interface
Site2
Compounds database
Storage Element
Software
Results

• FlexX license server
• 6000 floating licenses offered by BioSolveIT to
  SCAI
• Maximum number of concurrent used licenses was
  5000

10
Schema of the current WISDOM production
environment
User Interface
User Interface
CEs WNs
SEs
Submits the jobs
SEs
CEs WNs
D M S / G F T P
WMS
WMS
WISDOM production system
FlexX job
FlexX
Checks job status Resubmits
Statistics
Structure file
FLEXlm
FlexLM
Compounds file
Statistics
license
license
Output file
Local server
HealthGrid Server
Web Site
Web Site
inputs
WISDOM DB
outputs
11
Grid infrastructures and projects contributing to
WISDOM-II
EMBRACE
BioinfoGrid
SHARE
EGEE
Auvergrid
EUMedGrid
EUChinaGrid
TWGrid
EELA
European grid project
European grid infrastructure
Regional/national grid infrastructure
12
Instances on different infrastructures
Instances deployed on the different
infrastructures during the WISDOM-II data
challenge
13
Deployment on different infrastrucures

• Up to 5000 computers in more than17 countries
  mobilized in Autumn 2007 to provide CPU
• 1.738 TB of data produced

Distribution of jobs
14
Statistics of deployment

• First DC
• 80 CPU years
• 1 TB
• 1700 CPUs used in parallel
• July 1st - August 15th 2005
• 2nd DC
• 100 CPU years
• 800 GB
• 1700 CPUs used used in parallel
• May 1st -April 15th 2006
• 3rd DC
• 413 CPU years
• 1.7 TB
• Up to 5000 CPUs in parallel
• 1st October 2006 - 31 January 2007

15
Biological results

• The repartition of docking energies of the ZINC
  database against GST A structure.
• (The red column represents a score of -24kj/Mol,
  the docking score of a co-crystallized ligand
  (GTX) of GST A chain)

16
Issues

• Scheduling efficiency of the grid is still a
  major issue
• The resource broker is still the main bottleneck
• This deployment also shows that it is not
  possible to do a naive blacklisting of the
  failing resources, for the simple fact that
  virtually all the grid resources have produced
  aborted jobs, and this blacklisting should also
  take care of the site scheduled downtimes.
• Store and treat the data in a relational database
  

17
Conclusions

• Demonstrated the relevance of computational grids
  in life science applications
• Manual intervention is reduced
• Future steps
• Analysis of the biological results
• Address the issue of RB

18
The long term vision a grid for malaria

• Use the grid technology to foster research and
  development on malaria and other neglected
  diseases
• To provide services to research laboratories
• To collect and analyze epidemiological data
• To build a chemogenomic knowledge space

19
Acknowledments
Auvergrid Accamba BioInfoGRID EGEE EMBRACE EUChina
GRID EUMedGRID SHARE TWGrid Conseil Regional
dAuvergne European Union
Academia Sinica BioSolveIT CNR-ITB CNRS CEA Health
grid IN2P3 LPC SCAI Fraunhofer Università di
Modena e Reggio Emilia Université Blaise
Pascal University of Pretoria University of Los
Andes
wisdom.healthgrid.org