CamGrid: High Throughput Computing in Science

dfb21@cam.ac.uk

Dr David BurkeAntigenic Cartography Group

Department of ZoologyUniversity of Cambridge

25th June 2008

Modelling the evolution of the influenza virus

Antigenic variation of viruses

Antigenically Stable Pathogens Antigenically Variable Pathogens

Smallpox

Measles

Tuberculosis

Mumps

Tetanus

Influenza Virus

Malaria

HIV

Dengue

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

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.

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.

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

Influenza virus: pandemic and epidemic

Spanish flu

1918

Asian flu19

57Hong Kong flu

1968

40 million deaths 1-4 million deaths 1 million deaths

H1N1 H2N2 H3N2

2008

5-150 million??

H5N1?

Two dimensional Mostly linear Forms Clusters Chronologically ordered Approx equal time between

clusters Approx equal distance between

clustersThese maps are now routinely

used for selection of strains for 'flu vaccine

Features of “antigenic map” of Influenza H3N2 1968-2003

1968

1972

1975

19791987

1989

1992

1995

1997

2002

1977

Why are there distinct

clusters and not slow

progression?

What is the mechanism of

large antigenic changes

Why does ‘flu not evolve

faster?

Questions1968

1972

1975

19791987

1989

1992

1995

1997

2002

1977

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

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

Oseltamivir (tamiflu) bound to neuraminidase

How will strain variation (amino acid changes) affect the specificity for

sialic acid and other inhibitor binding?

Structure Prediction

Comparative modellingBased 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

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

HK68(1968) EN72(1972)

Comparison of Trimer structures

1968

1972

1975

1979

1987

1989

1992

1995

1997

2002

1977

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

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

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

Erasmus Medical CentreRon FouchierJan de JongBjorn KoelVincent MunsterGuus RimmelzwaanWalter BeyerTheo BestebroerRuud van BeekAb 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