The WISDOM initiative Wide In Silico Docking On Malaria Yannick - - PowerPoint PPT Presentation

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The WISDOM initiative Wide In Silico Docking On Malaria Yannick - - PowerPoint PPT Presentation

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Enabling Grids for E-sciencE The WISDOM initiative Wide In Silico Docking On Malaria Yannick Legr, CNRS/IN2P3 on behalf oh the WISDOM Consortium Slides credit: N. Jacq & V. Breton, CNRS-IN2P3 Y-T Wu & H-C Lee, Academia Sinica

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INFSO-RI-508833

Enabling Grids for E-sciencE www.eu-egee.org

The WISDOM initiative

Wide In Silico Docking On Malaria

Yannick Legré, CNRS/IN2P3

  • n behalf oh the WISDOM Consortium

Slides credit:

  • N. Jacq & V. Breton, CNRS-IN2P3

Y-T Wu & H-C Lee, Academia Sinica

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The WISDOM application, ISGC 2006 – Taipei – May 1st – 4th, 2006 2

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Content

  • Presentation of the WISDOM initiative
  • Need for new drugs to fight malaria
  • Challenges of the High Throughput Docking
  • Development of the grid environment for a large-scale

deployment

  • Achieved deployment on EGEE infrastructure
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The WISDOM application, ISGC 2006 – Taipei – May 1st – 4th, 2006 3

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  • Biological goal

Proposition of new inhibitors for a family of proteins produced by Plasmodium falciparum

  • 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 => “data challenge”

WISDOM : Wide In Silico Docking On Malaria

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  • Partners

– Fraunhofer SCAI, Germany (Project PI: Martin Hofmann) – LPC Clermont-Ferrand, France (CNRS/IN2P3) – CMBA, France (Center for Bio-Active Molecules screening) – BioSolveIT – HealthGrid

  • Representing different projects:

– EGEE (EU FP6) – Simdat (EU FP6) – AuverGrid (French Regional Grid) – Accamba project (French ACI project)

WISDOM : Wide In Silico Docking On Malaria

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Introduction to the disease : malaria

  • ~300 million people

worldwide are affected

  • 1-1.5 million people

die every year 1 person each 20 seconds !!!

  • Widely spread
  • Caused by protozoan

parasites of the genus Plasmodium

Complex life cycle with multiple stages

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There is a real need for new drugs to fight malaria (WHO)

  • Drug resistance has emerged for all classes of antimalarials

except artemisinins.

– Resistance to chloroquine, the cheapest and the most used drug, is spreading in almost all the endemic countries. – Resistance to the combination of sulfadoxine-pyrimethamine which was already present in South America and in South-East Asia is now emerging in East Africa (65% in Western Tanzania)

  • All countries experiencing resistance to

conventional monotherapies should use ACTs (artemisinin-based combination therapies)

  • But there is even the threat of resistance to artemisinin too,

as it is already observed in murine Plasmodium yoelii

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Identification of new malarial targets

  • The available drugs focus on a limited number of biological

targets => cross-resistance to antimalarials

  • There is a consensus that substantial scientific effort is needed to

identify new targets for antimalarials

  • With the advent of the plasmodium genome, many targets came

into light

  • The potential antimalarial drug targets are broadly classified into

three categories, and each category has many individual targets.

– Targets involved in human hemoglobin degradation (proteases) – Targets involved in parasite metabolism (Folate, phospholipid… ) – Targets engaged in parasite membrane transport and signalling (choline carrier etc).

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Plasmepsins role in human hemoglobin degradation

  • Plasmepsins are involved in

the hemoglobin degradation inside the food vacuole during the erythrocytic phase of the life cycle.

  • The sequence homology

between the plasmepsins is high (65-70%)

  • The sequence homology with

its nearest human aspartic protease is fortunately low (35%)

  • Presence of X-

crystallographic data in Protein Data Base

Plasmepsins (I, II, IV, and HAP)

Amino acids

HEMOGLOBIN

Small Peptides Smaller Peptides Heme Hematin Hemozoin (malarial pigment) Falcipain and plasmepsin

  • xidation

polymerization Aminopepdidases

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Target Identification Target Validation Lead Identification Lead Optimization

Target discovery Lead discovery

Phases of a pharmaceutical development

Clinical Phases (I-III)

Duration: 12 – 15 years, Costs: 500 - 800 million US $

Molecular Docking: Predict how small molecules, such as substrates

  • r drug candidates, bind to a receptor of known 3D structure
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High Throughput Virtual Docking

Chemical compounds (ZINC): Chembridge – 500,000 Drug like – 500,000 Targets (PDB): Plasmepsin II (1lee, 1lf2, 1lf3) Plasmepsin IV (1ls5) Millions of chemical compounds available in laboratories High Throughput Screening 1-10$/compound, nearly impossible Molecular docking (FlexX, Autodock) ~80 CPU years, 1 TB data Data challenge on EGEE ~6 weeks on ~1700 computers Hits screening using assays performed on living cells Leads Clinical testing Drug

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Molecular docking and modeling

  • Target scenarios

– number of water molecules in the active site

  • Software scenarios

– Docking methods (Autodock) – Water molecules place and max

  • verlapping volume (FlexX)
  • Compounds preparation

– Yet drug like – Hydrogens added

  • Target preparation

– X-ray crystal structures of 5 plasmepsins (PDB) – Active site created from native crystal ligand

Ligand Loops Active site

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EGEE, international project of grid infrastructure

  • Started in 2004, >70 partners in the world
  • Project leader : CERN
  • 7 scientific domains with >20 applications deployed
  • ~200 grid nodes, ~20.000 CPUs, several PetaBytes of data, 10.000

concurrent jobs

Countries with nodes contributing to the data challenge WISDOM

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Simplified grid workflow

  • FlexX license server :

– 3000 floating licenses offered by BioSolveIT to SCAI – Maximum number of concurrent used licenses was 1008

Storage Storage Element Element Computing Computing Element Element

Site1 Site2

Storage Storage Element Element

User interface User interface

Computing Computing Element Element Compounds database Parameter settings Target structures Compounds sub lists Results Results Statistics Compounds list Resource Resource Broker Broker Software

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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

– Grid is heterogeneous and dynamic – Stress usage of the grid during the DC

  • Need an automatic production environment

– Execution with the Biomedical Task Force – Grid API are not fully adapted for a bulk use at a large scale

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WISDOM architecture

GRID

LCG components EGEE resources Application components

wisdom_install Installer Tester wisdom_test wisdom_execution

Workload definition Job submission Job monitoring Job bookkeeping Fault tracking Fault fixing Job resubmission

Set of jobs User wisdom_collect Accounting data Superviser wisdom_site wisdom_db License server

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Deployment preparation on AuverGrid, a French regional project

  • Started in 2005 for 3 years
  • Interconnecting the main laboratories of the Auvergne region

using EGEE middleware

  • Share technologies, competences and resources

2,5 minutes Data transfer time for 1 job 30 minutes Grid overhead time for 1 job 9 hours CPU time for 1 job 150 Crunching factor 40 hours Duration 188 days (6,3 months) Total CPU time

100,000 docking runs in 500 jobs

Metrics

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Number of docked ligands vs time

1: Intensive submission of FlexX jobs with Chembridge ligands base 2: Resubmission 3: Intensive submission of FlexX jobs with drug like ligands base 4: Resubmission 5: Intensive submission of Autodock jobs with Chembridge ligands base 6: Resubmission 1 2 3 4 5 6

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Number of waiting and runing jobs vs time

1 2 3 4 5 6

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INFSO-RI-508833 SouthEasternEurope, 10% SouthWesternEurope, 12% Italy, 16% France, 18% UKI, 29% NorthernEurope, 7% CentralEurope, 4% AsiaPacific, 2% GermanySwitzerland, 1% Russia, 1%

Total amount of CPU provided by EGEE federation

The following institutes contributed computing resources to the data challenge: IPP-BAS, IMBM-BAS and IPP-ISTF (Bulgaria); CYFRONET (Poland); ICI (Romania); CEA-DAPNIA, CGG, IN2P3-CC, IN2P3-LAL, IN2P3- LAPP and IN2P3-LPC (France); SCAI (Germany); INFN (Italy); NIKHEF, SARA and Virtual Laboratory for e-Science (Netherlands); IMPB RAS (Russia); UCY (Cyprus); AUTH FORTH-ICS and HELLASGRID (Greece); RBI (Croatia); ASCC (Taiwan); TAU (Israel); CESGA, CIEMAT, CNB- UAM, IFCA, INTA, PIC and UPV- GryCAP (Spain); BHAM, University of Bristol, IC, Lancaster University, MANHEP, University of Oxford, RAL and University of Glasgow (United Kingdom).

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Exploitation metrics

6302 GB Volume of transferred data (input+output) 946 GB Volume of output data 1643 Number of jobs running in parallel on the grid 58 Number of grid nodes 72751 Number of jobs 80 years Total CPU time FlexX + Autodock phases Metrics

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Performance metrics

62,1 MB/s Peak rate 0,8 MB/s Average transfer rate 662 Crunching factor 37 days Effective duration 67,15 years Effective CPU time 46475 Number of docked ligands / h 41,27 Cumulated millions number of docked ligands FlexX + Autodock phases Metrics

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Efficiency metrics (1/2)

46,2% Success rate after results checking 63 % Success rate after results checking without WISDOM failures 77 % Success rate FlexX + Autodock phases Metrics

  • Efficiency depends on :

– Heterogeneous and dynamic nature of the grid – Stress usage – Automatic jobs (re)submission (“sink-hole” effect)

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Score results Browser

  • Quick overview on

very large log-files

  • Sorting and merging
  • f files
  • Storing and retrieval

in databases

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Searching identified key interactions

ASP 214 ASP 34

  • Example : Ligand plot
  • f 1lee (Plasmepsin II)

with inhibitors R36 500

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Preliminary results of the first data challenge

  • Score of an output is independent of

the grid resource where the job runs (conditions controlled)

  • 10% compounds of Chembridge (ZINC)

may are hits

– Top scoring compounds possess basic chemical groups like thiourea, guanidino, andamino acroleinas core structure. – Identified compounds are non peptidic and low molecular weight compounds – But the identified compounds look like human thrombin inhibitors

WISDOM-375228 WISDOM-113696

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Perspectives

  • WISDOM (Wide In-Silico Docking On Malaria) is the first large

scale drug discovery initiative on an open grid infrastructure

– About 80 CPU years to produce TB of data – http://wisdom.eu-egee.fr

  • Future works on the results

– Qualitative comparisons of docking tools – Ligand similarity based clustering of results

  • Future works on the hits

– simulation on 1000 hits for reranking (EU BioinfoGrid FP6 project)

100 CPU years Docking well fitted for cluster grids, Molecular Dynamics well fitted for supercomputers

– Finally in vitro testing and structure activity relationships

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Data challenge on avian flu: grid facts

  • A collaboration of 5 grid projects: Auvergrid,

BioinfoGrid, EGEE-II, Embrace, TWGrid

  • Data challenge parameters:

– One docking software: autodock – 8 conformations of the target (N1) – 300.000 selected compounds – 100 year CPU to dock all configurations on all compounds

  • Timescale:

– First contacts: March 1st 2006 – Kick-off: April 1st 2006 – Targeted duration: 4 weeks

  • See Hurng-Chun Lee's

talk

Credit: Y-T Wu

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Perspectives

  • Extension of in silico workflow (Embrace)

– Virtual docking service at a large scale on gLite (EGEE) with Taverna

  • Second Data Challenge is ongoing (see Hurng-Chun Lee's talk)
  • Third large scale docking on EGEE in fall 2006

– Several new foreseen targets on malaria, dengue and other neglected diseases. – Resources needed: up to 80 years CPU per target – Supported by EGEE-II and EELA European projects, Swiss BioGrid initiative, AuverGrid, … – Any other Asian-Pacific project interested in?

  • We will be pleased to welcome you in the WISDOM initiative!
  • Grid-enabled In Silico Drug Discovery Workshop June 6th 2006 in

Valencia (Spain) within the HealthGrid'06 conference

– http://valencia2006.healthgrid.org/registration.php

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Credits

LPC (CNRS/IN2P3)

– V. Breton – N. Jacq – J. Salzemann – Y. Legré – M. Reichstadt – F. Jacq

EGEE

– Biomed Task Force – EIS team – JRA2 team – SA1 sites

Fraunhofer SCAI

– M. Hofmann – M. Zimmermann – A. Maaß – M. Sridhar – K. Vinod-Kusam – H. Schwichtenberg

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"The only thing necessary for the triumph of Evil is for good men to do nothing!" Edmund Burke

Questions ?

4th HealthGrid conference

6th – 9th June Valencia (Spain) http://valencia2006.healthgrid.org