Computational Biology Research

1) ImmunoGrid

ImmunoGrid is the European Virtual Human Immune System Project which is funded by the EU Sixth Framework Programme. The goal of this project, which began in February 2006, is to model the Human Immune System by computer simulation. Simulations will model different aspects, from the development of the immune system and its recogniton of self, to the immune response to pathogens. The simulations will take place at different levels (organ, cellular and molecular) according to the problem addressed and the information that is available at each level.

The systems biology project involves eight laboratories in France, Italy, Denmark, Australia and the UK and also a collaboration with the Dana-Farber Cancer Institute at Boston. Here in London we will be constructing a database of information about all aspects of the immune system. This database will be interfaced to the simulator. We shall also be developing new techniques for modelling critical intermolecular interactions in the immune system. We are also developing an educational simulator.

The computational and data resources required for the ImmunoGrid project are found on different hardware and at different sites. In order to integrate these diverse resources we are using Grid technology and observing existing or emerging standards for distributed computing and data access. We have integrated the different middleware components used by our partners' institutions to form a Grid that can be easily used from a Web interface. For example, this Grid has been used to quickly run 160,000 simulations of vaccine administrations, exploring the parameter space of experimental conditions that could never be achieved by experimentation on animals. Details of these projects can be found on the ImmunoGrid website.


Our goal is to provide easy-to-use computational tools that can be assembled together in work-flow patterns suitable for the study of immunological problems such as histocompatibility, vaccine design, autoimmunity and allergy.

One problem is the prediction of MHC:peptide binding affinities, particularly with MHC Class II alleles. We found that results obtained from fairly crude calculations compete well with those obtained by matrix methods for MHC Class II complexes (Matthews et al., ref(vi)). We are now looking at more exact methods using free energy calculations. Preliminary work on automating free energy calculations can be seen in ref (i). Other work in this area is seen in refs (iii) and (iv).

2) Single Nucleotide Polymorphisms

In collaboration with the Anthony Nolan Research Institute and the FP6 Allostem project, we are trying to enhance the host-versus-leukaemia effect to reduce the relapse rate in patients suffering from chronic leukaemia. To this end, we have constructed a database SiPeP of non-synonymous single nucleotide polymorphisms (SNPs) and their frequencies in the human population. This can be used for the discovery of novel minor histocompatibility antigens. We have developed tools to update it regularly as the primary databases evolve.

The database contains all the nonameric peptides that contain a SNP. For each peptide, data is being included on the probability of proteasome cleavage being able to produce that peptide and the likelihood of that peptide binding to certain common class I MHC alleles. It is described in ref (ii).

(i) Toward the atomistic simulation of T-cell epitopes. Automated construction of HLA:peptide structures for free energy calculations. Todman S J, Halling-Brown M D, Davies M N, Flower D R and Moss D S, Journal of Molecular Graphics and Modelling. (2008), 26(6), 957-961.

(ii) SIPEP: A database for the prediction of tissue-specific minor histocompatibility antigens, Halling-Brown, M, Quartey-Papafio, R, Travers P J and Moss D S, Int. J. Immunogenetics, (2006), 33, 289-295. Identification of the HLA-DM/HLA-DR interface. Davies, M N, Lamikanra, A, Sansom, C E, Flower, D R, Moss, D S and Travers, P J, Molecular Immunology, (2007), 45(4), 1063-1070.

(iii) Statistical deconvolution of enthalpic energetics contributions to MHC-peptide binding affinity. Davies, M N, Hattotuwagama, C K, Moss, D S, Drew, M G B and Flower, D R, BMC Structural Biology, (2006), 6, 5.

(iv) Comparative analysis of pKa prediction software. Davies, M N, Toseland, C P, Moss D S and Flower, D R, BMC Biochemistry, (2006), 7, 18.

(v) Fugu-Human synteny viewer: Web software for automatic annotation and display of synteny between Fugu genomic sequence and Human, Halling-Brown, M, Sansom, C, Moss, D S, Elgar, G and Edwards, Y J K, Nucl. Acid Res., (2004), 32(8), 1-5.

(vi) A Novel Predictive Technique for the MHC Class II-peptide binding interaction, Davies M N, Sansom, C E, Beazley C and Moss D S, Mol. Medicine, (2003), 9, 220-225.

(vii) The Bioinformatics Template Library - Generic Components for Biocomputing. W. R. Pitt, M. A. Williams, M. Steven, B. Sweeney, A. J. Bleasby, D. S. Moss. Bioinformatics, (2001), 17(8), 729-737.

Any questions and bug reports to David Moss

Last updated 2 March 2008