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

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

HIV-1 Protease Enzyme of HIV responsible for protein maturation 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 Leucine - 90, 190 C-terminal N-terminal

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)

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 Start Conformation Series of Runs End Conformations Launch simultaneous runs (60 sims, each 1.5 ns) C1 C2 Cx C3 Equilibration Protocols C4 S.K. Sadiq, S. Wan and P.V. Coveney Biophys J., (submitted) eq1 eq2 eq3 eq4 eq5 eq6 eq7 eq8

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

HPC Requirements Computational Technique Total N.o. Simulations Required Nanoseconds/sim Total number of cpus for optimal efficiency Ensemble MD 60 1.5 2560 Thermodynamic Integration 20 2 640 Each Simulation ideally uses 32 cpus (~ 30000 atoms) NAMD - scaled performance currently 8hrs/ns 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 !

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

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

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

Acknowledgements Collaborators CCS 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