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GRID Middleware for Biomolecular Science Applications: A User’s Perspective

GRID Middleware for Biomolecular Science Applications: A User’s Perspective. Kashif Sadiq Centre for Computational Science, University College London. Overview. Scientific Motivation HIV-1 Protease Computational Techniques Ensemble MD Thermodynamic Integration Computational Requirements

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GRID Middleware for Biomolecular Science Applications: A User’s Perspective

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  1. GRID Middleware for Biomolecular Science Applications: A User’s Perspective Kashif Sadiq Centre for Computational Science, University College London

  2. Overview • Scientific Motivation • HIV-1 Protease • Computational Techniques • Ensemble MD • Thermodynamic Integration • Computational Requirements • HPC Requirements • Middleware Requirements: MOCAS • Conclusion

  3. HIV-1 Protease Monomer B 101 - 199 Monomer A 1 - 99 • Enzyme of HIV responsible for protein maturation • Target for Anti-retroviral Inhibitors • Example of Structure Assisted Drug Design • 8 FDA inhibitors of HIV-1 protease Flaps Glycine - 48, 148 Saquinavir So what’s the problem ? P2 Subsite Catalytic Aspartic Acids - 25, 125 • Emergence of drug resistant mutations in protease • Render drug ineffective • Drug Resistant mutants have emerged for all FDA inhibitors C-terminal N-terminal Leucine - 90, 190

  4. Molecular Dynamics Simulations of HIV-1 Protease AIMS • Study the differential interactions between wild-type and mutant proteases with an inhibitor • Gain insight at molecular level into dynamical cause of drug resistance • Determine conformational differences of the drug in the active site • Calculate drug binding affinities Mutant 1: G48V (Glycine to Valine) Inhibitor: Saquinavir Mutant 2: L90M (Leucine to Methionine)

  5. Computational Techniques Ensemble MD is suited for HPC GRID • Simulate each system many times from same starting position • Each run has randomized atomic energies fitting a certain temperature • Allows conformational sampling End Conformations Start Conformation Series of Runs Launch simultaneous runs (60 sims, each 1.5 ns) C1 C2 Cx C3 Equilibration Protocols C4 eq1 eq2 eq3 eq4 eq5 eq6 eq7 eq8 S.K. Sadiq, S. Wan and P.V. Coveney Biophys J., (submitted)

  6. Calculating drug affinities Thermodynamic integration is ideally suited for a HPC Grid Use steering to launch, spawn and terminate - jobs t Run each independent job on the Grid V Starting conformation Check for convergence Combine and calculate integral =0.1 ∂V/∂ time =0.2 =0.3 … Seed successive simulations (10 sims, each 2ns) =0.9  P.W. Fowler, S. Jha and P.V. Coveney, Phil. Trans. R. Soc. A., 363,1999-2015 (2005)

  7. HPC Requirements • Simultaneous use of multiple and heterogeneous Supercomputing Resources • Improved policies • resource co-allocation • capability computing vs task farming EMD and TI are techniques that can really take advantage of a HPC GRID ! But also…… Require suitable middleware to make simulations manageable !

  8. Middleware Requirements • Monitoring - Checking resource status and job progression • Optimizing - Determining best resources to launch specific jobs on • Chaining - Launching a series of jobs in a sequential chain • Automating - Launching multiple jobs automatically based on definable prerequisites, automatic staging retrieval and clean up • Steering - Interacting and changing the direction of simulation during job execution

  9. GSISSH vs AHE GSISSH • Globus installation difficult – does it effect the user ? • Resource awareness • Manual file staging, retrieval and monitoring for each resource • Chaining is simple • Multiple job submission is simple but limited to single resource AHE • Client can be installed by user – no admin necessary • Does some monitoring, centralized staging and retrieval • Single interface to multiple GRID resources • No queue/resource availability information • Chaining is currently limited • Multiple job submission is simple and can be implemented across multiple resources • Potential for MOCAS

  10. Conclusion • Several techniques exist and are still emerging that take advantage of HPC GRID resources • Robust and easy to use middleware assists the scientist to implement these techniques in a feasible manner

  11. Acknowledgements Collaborators Department of Infection & Immunity, UCL • Paul Kellam • Deenan Pillay • Robert Gifford • Simon Watson MRC HIV Clinical Trials Unit, UCL • David Dunn CCS • Peter Coveney • Radhika Saksena • Stefan Zasada • Shunzhou Wan • Shantenu Jha • Philip Fowler

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