Immunology on the Grid
Barry R Smith, David S Moss, Matthew Davies
School of Crystallography, Birkbeck College, University of London, Malet Street, London
David Boyd
CCLRC/Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire
Abstract
The binding of peptide fragments from foreign proteins to MHC Class II cell
surface molecules is a process that is central to vaccine design, tissue transfer,
and the understanding of the immune system [1].
The variability of MHC molecules in the human population, and range of
possible peptide fragments, means that the problem is experimentally difficult.
We use molecular dynamics simulations to predict peptide binding affinity.
The simulations are computationally demanding and produce large amounts of
data. Hence, a Grid-based distributed computing approach is ideal.
Immunology Background
When an individual is infected with a virus or bacterium, an immune response is
provoked that recognizes and eliminates the infectious agent [1]. A critical step in the
cellular response of the immune system is the binding of short peptide fragments
(from broken down foreign proteins) to MHC Class II protein molecules (Figure 1).
This complex is then presented to helper T cell receptors for inspection at the cell
surface. The key to peptide binding specificity lies in the highly polymorphic nature
of the MHC binding groove. Identifying peptides that bind to MHC, and hence trigger
a response, may help researchers produce a vaccine. Rheumatoid arthritis and
multiple sclerosis are common diseases where MHC molecules play a role.
Inappropriate immune response can be equally harmful, for example in the treatment
of leukaemia.
Figure 1 MHC class II protein binding groove (red/blue ribbons) with bound peptide (grey ribbon).
Molecular Dynamics
Simulated Annealing is a molecular dynamics technique which involves rapid heating
followed by slow cooling of a system. This allows molecules to sample many
conformations and high energy geometries, then finally settle near the global energy
minimum.
There are many possible peptide sequences. For each chosen peptide, we use the
AMBER [2] and NAMD [3] software packages (with Amber ‘94 force field) to
simulate the complex in solvent. By studying the energetic interactions of each
peptide we can discriminate between good and bad binders. In our work we also make
use of MMTK [4], MMTSB [5], EMBOSS [6] and BTL [7] toolkits for manipulation
of structures (mutation, docking etc) and file processing. The simulations are
computationally demanding and produce large amounts of data:
• Long time scales are required (ideally >1ns duration).
• A full model system is large.
• Each person's immune system comprises a slightly different set of MHC alleles.
Summary of Results
Our initial results (full details in Davies et al [8]) show that we can successfully
predict relative binding affinities of a range of peptides, to an equal or better accuracy
than other prediction methods available (e.g. ‘matrix-based’ methods [9]). Evaluation
of the methods is performed via ROC curves, which compare both specificity and
selectivity. Planned improvements to our simulation method, e.g. free energy
calculations and enlarged model system, should increase accuracy further.
Grid Work
The current work utilises a small cluster of processors and a Globus testbed, as part of
a pilot Grid project with funding from PPARC. Better automation and full use of
distributed computational resources will help achieve our goal of systematically
computing the binding energies of many types of MHC and peptide. Our particular
interest now is to exploit the new Globus 3 Toolkit [10], for example via a distributed
client, and development of an improved results database using mySQL [11] and
OGSA-DAI [12]. We are also collaborators on the UK BioSimGrid project [13].
Acknowledgements
We would like to thank PPARC for funding of the pilot Grid project.
Links and References
[1] ImmunoBiology, Janeway et al, Garland Publishing 1999.
[2] http://amber.scripps.edu/
[3] http://www.ks.uiuc.edu/Research/namd/
[4] http://dirac.cnrs-orleans.fr/MMTK/
[5] http://mmtsb.scripps.edu/software/mmtsbtoolset.html
[6] http://bioinformatics.org/BTL/
[7] http://www.emboss.org/
[8] Davies et al, in print.
[9] Rammensee et al Immunogenetics 50, 213-219.
[10] http://www.globus.org/
[11] http://www.mysql.com/
[12] http://www.ogsadai.org.uk/
[13] http://www.biosimgrid.org/