1. Interoperable HT-BAC for Personalised Medicine Peter V Coveney & team Shantenu Jha & team With Special thanks to Dieter Kranzlmuller

2. Monomer B 101 - 199 Monomer A 1 - 99 Flaps Leucine - 90, 190 Glycine - 48, 148 Catalytic Aspartic Acids - 25, 125 Saquinavir P2 Subsite N-terminalC-terminal Drug Binding Affinity Ranking Application in HIV Drug Resistance Enzyme of HIV responsible for protein maturation Target for 9 FDA approved Anti- retroviral Inhibitors Example of Structure Assisted Drug Design So what’s the problem? Emergence of drug resistant mutations in protease Render drug ineffective Drug resistant mutants have emerged for all FDA inhibitors Mutations frequently interact We want to predict the binding affinity of inhibitors to any sequence 2 HIV Drug Resistance

3. BAC can reliably predict binding affinities of compounds with their target proteins, and be used potentially as a drug ranking tool in clinical application or a virtual screening tool in pharmaceutical lead discovery. Blackbox-like BAC Ranking of binding affinities Binding affinity calculator (BAC) 3 S. K. Sadiq, D. Wright, S. J. Watson, S. J. Zasada, I. Stoica, Ileana, and P. V. Coveney, "Automated Molecular Simulation-Based Binding Affinity Calculator for Ligand-Bound HIV-1 Proteases", Journal of Chemical Information and Modeling, 48, (9), 1909-1919, (2008), DOI: 10.1021/ci8000937.DOI: 10.1021/ci8000937.

4. Computational Application to Drug Affinity Ranking – Single MD simulation 4 PROTEIN DRUGS SINGLE MD Drug Affinity Ranking Errors uncontrolled Results unreproducible

5. Computational Application to Drug Affinity Ranking – Ensemble Simulations 5 Drug Affinity Ranking Errors fully under control; Results reproducible.

6. Ensemble Molecular Dynamics Protocol Run 50 ‘replica’ simulations Vary only initial velocities 4 ns of production trajectory per replica More efficient sampling compared to single long simulation Allows us to examine reproducibility of results The workflow can be completed within nine hours of wallclock time, provided the required number of cores is available. To compute more than one binding affinity concurrently, one needs to multiply the requirement by the number of molecules of interest. 6 Sadiq, S.K, Wright, D.W., Kenway, O.A. and Coveney, P.V. “Accurate Ensemble Molecular Dynamics Binding Free Energy Ranking of Multidrug-Resistant HIV-1 Proteases.” Journal of Chemical Information and Modeling 2010 50 (5), 890-905. Wan, S., Knapp, B., Wright, D.W., Deane, C.M., Coveney, P.V., “Rapid and Accurate Peptide-MHC Binding Affinity Predictions from in silico Molecular Dynamics” 2015, preprint submitted for publication.

7. 7 Single vs Ensemble MD Simulations The binding free energy can vary widely (up to 12 kcal/mol) between two single simulations. Single simulation: not reproducible, unscientific! Drug – EGFR Drug – HIV-1 protease Wan & Coveney, J. R. Soc. Interface, 8, 1114-1127, (2011). Wright, Hall, Kenway, Jha & Coveney, JCTC, (2014), DOI: 10.1021/ct4007037.

8. 8 Ensemble MD Simulations The MM/PBSA results follow well defined Gaussian distributions. Configurational entropies, obtained from normal mode estimates, closely resemble normal distributions. Drug – HIV-1 protease Wright, Hall, Kenway, Jha & Coveney, JCTC, (2014), DOI: 10.1021/ct4007037.

9. Length of Simulations in an Ensemble Run 9 Wright, DW, Hall, BA,Kenway, OA, Jha, S and Coveney, PV, "Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors.” J. Chem. Theory Comput., 2014, DOI: 10.1021/ct4007037 The variations of the bootstrap statistics with replica simulation length and the sampling rate used for the averages of  G MMPBSA and –T  S NM for 50 replica ensemble simulations.  G MMPBSA is converged at 4ns with a  boot of less than 0.3kcal/mol. -T  S NM also converged at 4ns with a  boot of less than 0.3kcal/mol. All of the production runs are therefore limited to 4ns.

10. Number of replicas in an Ensemble Simulation 10 Wright, DW, Hall, BA,Kenway, OA, Jha, S and Coveney, PV, "Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors.” J. Chem. Theory Comput., 2014, DOI: 10.1021/ct4007037 Variations of the bootstrap statistics with number of replicas within an ensemble simulation on the Spearman rank coefficient.  Larger ensembles make for more reproducible ranking with lower  boot.  Minor changes in  boot after approximately 25 replicas.  Decrease slows in  boot after 25 replicas included in the ensemble. 25 or more replicas needed in an ensemble study.

11. Calculating Clinically Relevant Binding Affinities 11 Wright, DW, Hall, BA,Kenway, OA, Jha, S and Coveney, PV, "Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors.” J. Chem. Theory Comput., 2014, DOI: 10.1021/ct4007037 This work used several of the most powerful supercomputers in the USA, UK, and EU. FDA-approved drugs to wild-type HIV-1 protease

12. Validation and Verification of the Available Free Energy Methodologies 12 Wright, DW, Hall, BA,Kenway, OA, Jha, S and Coveney, PV, "Computing Clinically Relevant Binding Free Energies of HIV-1 Protease Inhibitors.” J. Chem. Theory Comput., 2014, DOI: 10.1021/ct4007037 Spearman rank coefficient for each of the studied computational free energy methodologies compared to the two experimental datasets and their average Improved rankings and estimates obtained for the relative binding strengths of the drugs by using a novel combination of:  MMPBSA/MMGBSA  Normal mode entropy estimate  Free energy of association. Free Energy Methodologies

13. A Pore Man’s View of the TeraGrid/XSEDE 2005-09: Tried running many simulations on many supercomputers. Did not work (well)! Why? What has changed?

14. RADICAL Cybertools http://radical-cybertools.github.com Abstractions-based, Standards-driven approach to HPDC Manage heterogeneity –Middleware variants (syntax) –Infrastructure utilization (semantics) –(some) Architectural features Flexible execution and resource management techniques –“Static resource” execution versus “Dynamic resource” execution –Using Pilot Concept as “higher-level” resource management Serve as building blocks upon which other components can be built –Use other RADICAL-Cybertools components –Application/Domain specific Toolkits: –BAC + RADICAL-Cybertools  HT-BAC –RADICAL-Cybertools used on ARCHER and SuperMuc + XSEDE Interoperability for free, more flexible resource utilization modes

15. Resource Access Layer Resource Management Layer Application Toolkit Layer

16. RADICAL SAGA RADICAL-SAGA: –Native Python implementation of Open Grid Forum GFD.90. –Allows access to different middleware / services through a unified interface –Provides access via different backend plug-ins (“adaptors”). –SAGA-Python provides both a common API, but also unified semantics across heterogeneous middleware: Transparent Remote operations (SSH / GSISSH tunneling). Asynchronous operations. Callbacks. Error Handling.

17. RADICAL-Pilot http://radical-cybertools.github.io/radical-pilot Lightweight, portable, fast, scalable pilot framework Implements P*, well defined state models (for pilots and units) Scalability (up and out) –Lightweight data model –Bulk operations –Notifications / support for async programming Portability –Modular Pilot agent adaptable for different architectures –Pure Python, SAGA-Python as plumbing layer Supports Research whilst supporting production scalable science! –Pluggable schedulers; High degree of introspection, provenance; consistent and verifiable performance

18. Heterogeneous Resource: Localized to Agent

19. So why the focus on Pilot-Jobs? Conceptual basis for dynamic execution models and resource management –Predicting Tq Difficult: “Can’t beat ‘em, Join ‘em” –Difficult only because of static utilization Case for flexibile distributed resource utilization Unifying abstraction across HPC and DCI and others.. –Task-level parallelism has very strong application drivers! –For the foreseeable future we will use task-level parallelism for extreme sale computing -- whether 1 machine or many smaller machines “I think that the community's focus on only scaling SPMD computations is misguided..” Ian Foster Act as a building blocks: Provides the resource management layer for application-level tools, libraries and services

20. Project Status Thanks to Nancy/XSEDE Allocation on XSEDE resources Thanks to Dieter/PRACE –1M on SuperMuc Through a combination of many other allocation sources –ARCHER, HecTOR, SuperMuc, XSEDE and others (Hartree Center) Collaboration ongoing: both infrastructure and science dimension –Ability to scale & support greater number of ensembles