1. In silico docking on grid infrastructures
Jean Salzemann
LPC of Clermont-Ferrand, France (CNRS/IN2P3)
Embrace Workshop, Helsinki, 2006/06/17
Credit : Nicolas Jacq, Vincent Breton

2. Content WISDOM initiative
Challenges of the high throughput virtual docking
Development of a grid environments for a large-scale deployment
Achieved deployment on EGEE infrastructure
Wide In Silico Docking On Malaria
Accelerate drug design against H5N1 neuraminidase
Perspectives
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

3. WISDOM initiative
WISDOM initiative aims to demonstrate the relevance and the impact of the grid approach to address drug discovery for neglected and emerging diseases.
First achieved experiences:
Summer 2005: Wide In Silico Docking On Malaria (WISDOM)
Spring 2006: Accelerate drug design against H5N1 neuraminidase
Partners:
Grid infrastructures: EGEE, Auvergrid, TWGrid
European projects: Embrace, BioinfoGrid, Share, Simdat
Institutes and association: Fraunhofer SCAI, Academia Sinica of Taiwan, ITB, Unimo University, LPC, CMBA, CERN-ARDA, HealthGrid
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

4. There is a need…
to develop new drugs for the diseases of the developing world
HIV/AIDS, malaria and Tuberculosis account for 5,6 million deaths
Permanent necessity to develop new drugs to fight emerging resistance to drugs (malaria)
Unchanged pharmacopeia for decades against trypanosomiasis, leishmaniasis, Chagas disease, ...
to be able to develop quickly new drugs against emerging diseases
H5N1, SRAS, dengue… are recent examples of emerging diseases
Many factors like world-wide exchanges can help propagation of such diseases at a large scale
Necessity to quickly adapt to emerging resistances
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

5. Combinatorial libraries
Phases of a pharmaceutical development
Molecular Docking: Predict how small molecules, such as substrates or drug candidates, bind to a receptor of known 3D structure
Target discovery
Lead discovery
Target
Identification
Target
Validation
Lead
Identification
Lead
Optimization
Clinical Phases (I-III)
The development of a new drug in pharmaceutical research is extremely time consuming and expensive.
On the average the complete research process from the early screening experiments to the clinical testing
takes 10 to 15 years and costs about million dollars. Recent efforts to minimize development time
and cost led to automatisation and miniaturization of modern drug testing systems. Through the rapid
progress in the fields of combinatorial chemistry and high-throughput screening (HTS) there is an increasing
need for interpretation of large amounts of "noisy" data.
Today it is possible to test the binding affinity of several ten thousands potential drug candidates per day.
The results of these experiments are analyzed with the help of special computer software (CADD).
vHTS
Similarity
analysis
Database
filtering
Computer Aided
Drug Design
(CADD)
de novo design
diversity
selection
Biophores
Alignment
Combinatorial libraries
ADMET
QSAR
Duration: 12 – 15 years, Costs: million US $
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

6. Grid-enabled virtual screening workflow
Grid service customers
Chemist/biologist teams
Biology teams
Data access for expert teams in the world
Grid infrastructure
Check
point
Check
point
Check
point
Selected hits
Hits
Target
MD services
Docking services
Annotation services
Grid service providers
Chimioinformatics teams
Bioinformatics teams
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

7. Challenges for high throughput virtual docking Example: data challenge against H5N1 NA
Millions of chemical
compounds available
in laboratories
In vitro high Throughput Screening
1$/compound, nearly impossible
300,000 Chemical compounds:
ZINC &
Chemical combinatorial library
Molecular docking (Autodock)
~100 CPU years, 600 GB data
Data challenge on EGEE,
Auvergrid, TWGrid
~6 weeks on ~2000 computers
In vitro
screening
of 100 hits
Hits sorting
and refining
Target (PDB) :
Neuraminidase (8 structures)
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

8. Issues for the grid-enabled high throughput virtual docking
Computer-based in-silico screening can help to identify the most promising leads for biological tests
systematic and productive
reduces the cost of trail-and-error approach
In silico docking is well-fitted for grid deployment
CPU intensive application
Huge amount of output
No communication between tasks
Issues of a large scale grid deployment
The rate of submitted jobs must be carefully monitored
The amount of transferred data impacts on grid performance
Grid process introduces significant delays
Licensed software requires licenses distribution strategy on grid
The rate of submitted jobs must be carefully monitored
Robust and fault-tolerant environment
Testing services and resources for the application
The amount of transferred data impacts on grid performance
Install application on grid
Subsets of the database instead of large unique compound files
Grid process introduces significant delays
Submission is limited by the number of Resources Brokers
All resources must be filled but saturation must be avoided
Licensed software requires licenses distribution strategy on grid
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

9. Grid tools of the data challenges
WISDOM
a workflow of grid job handling: automated job submission, status check and report, error recovery
push model job scheduling
batch mode job handling
DIANE
a framework for applications with master-worker model
pull mode job scheduling
interactive mode job handling with flexible failure recovery feature
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

10. WISDOM components Installer Tester User wisdom_install wisdom_test
Set of jobs
wisdom_execution
Workload definition
Job submission
Job monitoring
Job bookkeeping
Fault tracking
Fault fixing
Job resubmission
GRID
Grid services (RB, RLS…)
Grid resources (CE, SE)
Application components
(Software, database)
Superviser
License server
Accounting data
wisdom_collect
wisdom_db
wisdom_site
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

11. Simplified grid workflow for WISDOM
Results
Storage
Element
Subsets
Computing
Element
WISDOM production system
Site1
Statistics
Jobs
Parameter settings
Target structures
Resource
Broker
Computing
Element
User interface
Site2
Subsets
Compounds database
Storage
Element
Software
Results
FlexX license server :
3000 floating licenses given by BioSolveIT to SCAI
Maximum number of used licenses was 1008
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

12. Grid resources of the data challenges
EGEE-II
AuverGrid
TWGrid
a world-wide infrastructure providing freely over than 5,000 CPUs and 21 TB for biomedical applications
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

13. First biomedical data challenge: World-wide In Silico Docking On Malaria (WISDOM)
Significant biological parameters
2 different docking applications (Autodock and FlexX)
About 1 million virtual compounds selected
Target proteins from the parasite responsible for malaria
Significant numbers
Total of about 46 million ligands docked in 6 weeks
1TB of data produced
Up 1700 computers in 15 countries used simultaneously
About 80 CPU years
Average crunching factor ~600
Number of docked compounds vs time
Number of running and waiting jobs vs time
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

14. Second biomedical data challenge: Accelerate drug design against H5N1 neuraminidase
Significant biological parameters
1 docking application (Autodock)
About 300,000 virtual compounds selected
Target proteins with predicted mutations involved in the virus multiplication
Significant numbers
Total of about 2,5 million ligands docked in 6 weeks
600 GB of data produced
Up 2000 computers in 17 countries used simultaneously corresponding to about 105 CPU years
Average crunching factor ~900
France,25%
UKI,23%
SouthWestern Europe,16%
SouthEastern Europe,14%
Italy,6%
Northern Europe,4%
Asia Pacific,5%
Germany-Switzerland,1%
Central Europe,2%
Russia,4%
Rate of jobs by EGEE federation
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

15. Selecting the promising compounds
The in-silico screening provides not only the docking poses of a compound against the target but also the docking energy
By ranking the information, chemist can select the promising compounds to go on the structure-based drug design for potential drugs
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In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01

16. Perspectives Second large scale docking on EGEE in fall 2006
Several new foreseen targets on malaria, dengue and other neglected diseases.
Resources needed: ~80 CPU years per target
Supported by EGEE-II and EELA european projects, Swiss BioGrid initiative
Collaboration is open for new targets,software & infrastructures
Reranking of WISDOM hits by Molecular Dynamics simulations
Supported by BioinfoGrid & EGEE-II european projects
Interest for ressources on supercomputers (contact with DEISA)
Best hits further processed through in vitro testing and structure activity relationships
mardi 2 avril 2019
In silico docking on grid infrastructures, NGN Helsinki, 2006/06/01