1. CamGrid: High Throughput Computing in Science dfb21@cam.ac.uk Dr David Burke Antigenic Cartography Group Department of Zoology University of Cambridge 25 th June 2008 Modelling the evolution of the influenza virus

2. Antigenic variation of viruses Antigenically Stable PathogensAntigenically Variable Pathogens Smallpox Measles Tuberculosis Mumps Tetanus Influenza Virus Malaria HIV Dengue

3. The Influenza Virus Annually, 'flu infects 7-14% of the population (400-800 million people globally ) Virus genome contains 8 RNA segments which code 11 proteins RNA polymerase makes a single nucleotide error roughly every 10 thousand nucleotides Nearly every new influenza virus has multiple mutations

4. Hemagglutinin (HA) is found on the surface of the influenza viruses. There are ~500 HA copies per virus It is responsible for binding the virus, to the cell that is being infected, via sugars (sialic acid) on the surface of the cells. Haemagglutinin There are at least 16 different HA antigens. These subtypes are labelled H1 through H16. Only the first three hemagglutinins, H1, H2, and H3, are found in human influenza viruses.

5. Haemagglutinin-Antibody Complex HA is the major target for an individuals antigenic response Over time, mutations build up and antibodies lose the ability to bind. For this reason, the 'flu vaccine has had to be updated more than 20 times over the last 40 years.

6. Nine subtypes of influenza neuraminidase are known. Subtypes N1 and N2 have been linked to epidemics in man This is the target for several drugs (tamiflu, relenza) Neuraminidase cleaves terminal sialic acid residues from carbohydrate moieties on the surfaces of infected cells. This promotes the release of viruses from the cells. Neuraminidase Influenza strains are classified according their HA/NA subtypes ie H3N2 There are 100 molecules of neuraminidase per virion

7. Influenza virus: pandemic and epidemic Spanish flu 1918 Asian flu 1957 Hong Kong flu 1968 40 million deaths1-4 million deaths1 million deaths H1N1 H2N2 H3N2 2008 5-150 million?? H5N1?

8. Two dimensional Mostly linear Forms Clusters Chronologically ordered Approx equal time between clusters Approx equal distance between clusters These maps are now routinely used for selection of strains for 'flu vaccine Features of “antigenic map” of Influenza H3N2 1968-2003 1968 1972 1975 1979 1987 1989 1992 1995 1997 2002 1977

9. Why are there distinct clusters and not slow progression? What is the mechanism of large antigenic changes Why does ‘flu not evolve faster? Questions 1968 1972 1975 1979 1987 1989 1992 1995 1997 2002 1977

10. 5-N-acetylneuraminic acid- alpha 2,6-galactose 5-N-acetylneuraminic acid alpha- 2,3-galactose oseltamivir (tamiflu) Determinants of ligand specificity for HA and NA Human & Pig adapted influenza viruses Avian, Equine & Pig adapted influenza viruses Neuraminidase Inhibitor

11. Sialic acid binding to haemagglutinin How will strain variation change the affinity and specificity of sialic acid binding? gal-  -2-3-sia gal-  -2-6-sia

12. Oseltamivir (tamiflu) bound to neuraminidase How will strain variation (amino acid changes) affect the specificity for sialic acid and other inhibitor binding?

13. Structure Prediction Comparative modelling Based on xray structure of a strain of HA from 1968 Molecular Dynamics Monte Carlo simulations Which features of the protein structure change as the virus evolves? Can we quantify the antigenic change given the amino acid substitutions and subsequent structure prediction In silico predictions of the structure of the virus

14. Multiple strains >300 HA strains >100 NA strains Multiple simulation conditions Use of CamGrid resource Both MD and MC methods are computationally expensive Each simulation takes >5 days single cpu Total simulations to date 222,000 cpu hrs = 25.3 CPU years This is only made possible by CamGrid

15. HK68(1968) EN72(1972) Comparison of Trimer structures

16. 1968 1972 1975 1979 1987 1989 1992 1995 1997 2002 1977

17. This is the technique of using a GPU, which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the CPU. A GPU is actually 100s of individual processors. GPGPU is made possible by the addition of code which allows software developers to use the graphics card for non-graphics data. Usually this requires a high level of programming General-purpose computing on graphics processing units (GPGPU) Contains standard numerical libraries for FFT (Fast Fourier Transform) and BLAS (Basic Linear Algebra Subroutines) Support for Linux 32/64-bit and Windows XP 32/64-bit operating systems NVIDIA CUDA™ is a C language environment for application development on the GPU

18. Accelerating Molecular Modelling Applications with Graphics Processors Folding@Home use molecular dynamics to fold proteins in silico. Since 2006, their code uses GPUs from ATI X1900 class of graphics cards as well as the new Cell processor in Sony's PlayStation 3. John Stone and colleagues (J Comput Chem 28: 2618–2640) rewrote NAMD to use CUDA on a NVIDIA 8800GTX card (128 processor cores). They produced a 5X increase in speed reaching 269 GFLOPS performance. The 2.6-GHz Intel quad core CPU reached 5.3 GFLOPS

19. The future of CamGrid? Nvidia have released Tesla, a version specifically for GPGPU,which has no graphics output Tesla cards have up to 240 cores per processor Tesla C1060 has 1 GPU achieving ~1000 GFLOPS of processing power Tesla S1060 (1U rack) has 4 GPU reaching ~4000 GFLOPS An 8 GPU version of the Tesla S870 is planned for the future

20. Erasmus Medical Centre Ron Fouchier Jan de Jong Bjorn Koel Vincent Munster Guus Rimmelzwaan Walter Beyer Theo Bestebroer Ruud van Beek Ab Osterhaus Santa Fe Institute Alan Lapedes University of Cambridge Derek Smith David Burke Terry Jones Colin Russell Nicola Lewis (& AHT) Dan Horton (& VLA) Ana Mosterin Eugene Skepner Yan Wong (& Leeds) Margaret Mackinnon (& KEMRI) David Wales (Chemistry) Chris Whittleston (Chemistry) Birgit Strodel (Chemistry) Mike Payne (Cavendish labs) Sebastian Ahnert (Cavendish labs) CamGrid sys admins WHO global influenza surveillance CDC: Nancy Cox, Sasha Klimov, Michael Shaw MELB: Ian Gust, Ian Barr, Aeron Hurt, Alan Hampson NIMR: Alan Hay, Y-P Lin, Vicky Gregory NIID: Tashiro Masato, Takato Odagiri WHO: Wenqing Zhang, Klaus Stohr NICs: Critical and enormously valuable Funding NIH Director’s Pioneer Award, Fogarty International Center, HFSP, IFPMA, CIDC, EU Framework 5 Novaflu, EU Framework 6 Virgil