BUDE. A General Purpose Molecular Docking Program Using OpenCL. Richard B Sessions

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1 BUDE A General Purpose Molecular Docking Program Using OpenCL Richard B Sessions 1

2 The molecular docking problem receptor ligand Proteins typically O(1000) atoms Ligands typically O(100) atoms predicted complex 1 Sampling (6-degrees of freedom) EMC 2 Binding affinity prediction EFE-FF 2

3 An atom-atom based forcefield parameterised according to atom type, analagous to standard molecular mechanics 3

4 Empirical Free Energy Forcefield McIntosh-Smith, S., et al., Benchmarking Energy Efficiency, Power Costs and Carbon Emissions on Heterogeneous Systems. Computer Journal, (2): p soft core

5 Re-docking a ligand into the Xray Structure (good prediction == low RMSD) 1CIL (Human carbonic anhydrase II) RMSD ~ 0.2 Å 5

6 Another example 1EZQ (Human Factor XA) RMSD ~ 1.2 Å 6

7 Accuracy of Pose Prediction (re-docking the BindingDB validation set, 84 complexes) 7

8 Binding Energy Prediction: is BUDE any better? Mike Hann s 2006 test of docking software Yes better but not perfect! 8

9 BUDE Simplified Flow Diagram (C++/OpenCL) Start BUDE Enter Initial Data End BUDE Yes Print Help Error(s)? Yes Data Reading Error(s)? No Write Control File Info No Docking Type Prepare Data for Docking End BUDE Act on Option Docking Small Large No Error(s)? Yes Site Docking Surface Docking Print Results Calculate Energies Rank Energies Host Job Do Docking No Generate Surface Pairs Do Generation Score Results EMC Accelerated Job Yes Parallel Code? Yes Last Generation No 9

10 BUDE s heterogeneous approach 1. Discover all OpenCL platforms/devices, inc. both CPUs and GPUs 2. Run a micro benchmark on each device, ideally a short piece of real work 3. Load balance using micro benchmark results 4. Re-run micro benchmark at regular intervals in case load changes 10

11 BUDE s Three Docking Modes Virtual Screening by Docking Binding Site Prediction Protein-Protein Docking in real space 11

12 Virtual Screening by Docking 12

13 Virtual Screening by Docking of NDM-1 New Delhi metallo-β-lactamase-1 8 million ZINC8 candidate drug molecules 20 conformers each 160M dockings EMERALD (STFC funded machine in Oxford) 372 GPU 2.4x10 17 atom-atom energies calculated ~60 hours actual wall-time 13

14 BUDE s EMC in Action 14

15 Virtual Screening for Ligands to Stabilise a Protein Screened 160 million conformations of the 8 million ZINC database against 5 different conformations of the protein on EMERALD Selected and tested 58 compounds with two types of experimental assays and found 18 compounds binding between 10 and 100 µm 31% hit rate 15

16 A New Virtual Screen against a key protein from the Malaria Parasite BlueCrystal P3 EMERALD 76 Nvidia K20s 372 Nvidia M2090s 16

17 Binding Site Identification Full rotation and limited translation of the ligand at each receptor surface vector

18 Location of the Binding Site of PI3P to a Protein (homology model) Involved in Insulin Signalling Thomas & Tavare 18

19 Protein-Protein Docking (in real space) Each point on ligand offered to each point on receptor with a local mini-dock: complete rotation in Z, rock in X & Y, small translations in X, Y & Z 19

20 Protein-Protein Docking Example the leucine zipper coiled coil Best energy -> RMSD = 0.2 Å In a real case with Pete Cullen s group we have mapped a proteinprotein interface using BUDE and confirmed it experimentally. This took only 20 site-directed mutations, instead of the hundreds required by full alanine-scanning mutagenesis 20

21 Performance across devices GHz High performance in silico virtual drug screening on many-core processors. Simon McIntosh-Smith, James Price, Richard B. Sessions & Amaurys A. Ibarra International Journal of High Performance Computing Applications (accepted for publication) 21

22 Main Optimisations Conditional accumulation Predicated accumulation Instruction mix in the innermost loop of the energy calculation High performance in silico virtual drug screening on many-core processors. Simon McIntosh-Smith, James Price, Richard B. Sessions & Amaurys A. Ibarra International Journal of High Performance Computing Applications (accepted for publication) 22

23 Optimisations High performance in silico virtual drug screening on many-core processors. Simon McIntosh-Smith, James Price, Richard B. Sessions & Amaurys A. Ibarra International Journal of High Performance Computing Applications (accepted for publication) 23

24 Summary GPUs and machines like Emerald are enabling new science BUDE is promising a step-change in Molecular Docking But plenty more developments and improvements are possible! 24

25 Acknowledgements Amaurys Avila Ibarra Simon N McIntosh-Smith James Price Debbie K Shoemark On the shoulders of giants... Emil Fischer ( ) Lock and Key Willard Gibbs ( ) Gibbs Free Energy G = H T S EMERALD and the einfrastructure South Consortium UK BlueCrystal and the Advanced Computing Research Centre (Bristol) 25

26 Supplementary Slides 26

27 Structure and Binding Energy Prediction speed vs accuracy tradeoff Speed Accuracy Typical docking scoring functions Empirical Free Energy Forcefield BUDE Free Energy calculations MM 1,2 QM/MM 3 Entropy: solvation configurational Electrostatics All atom Explicit solvent No Yes Yes Approx Approx Yes? Approx Yes No Yes Yes No No Yes 1. MD Tyka, AR Clarke, RB Sessions, J. Phys. Chem. B (2006) 2. MD Tyka, RB Sessions, AR Clarke, J. Phys. Chem. B (2007) 3. CJ Woods, FR Manby, AJ Mulholland, J. Chem. Phys (2008) 27

28 EMC Genetic Algorithm minimiser 28

29 On the shoulders of giants... Emil Fischer ( ) Lock and Key Willard Gibbs ( ) Gibbs Free Energy G = H T S

30 Receptor and Ligand Flexibility Full flexibility: would be Molecular Dynamics Limited flexibility: is appropriate for Molecular Docking: Protein: Backbone dock to selected Xray or MD structures Sidechains sample side chain rotamers during docking Small molecule: generate and dock many different conformations e.g. ZINC database of 8 M drug-like compounds 160 M conformers 30

31 EMC Genetic Algorithm Seed Parents Selected By Flag Generation Size Output Output Mutation Parameter Parameter Parameter Descriptors Coordinates Method N M R * True X Y Z U K% R* R*

32 BUDE Algorithm 32

33 33

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