Abstracts
Immunoinformatics—the new kid in town
Vladimir Brusic and Nikolai Petrovsky
Laboratories for Information Technology, 21 Heng Mui Keng Terrace, Singapore 119613, Centre for Medical
Informatics, Division of Science and Design, University of Canberra, Bruce ACT 2617 and National Health Sciences
Centre, Canberra Clinical School, Woden ACT 2606, Australia
The astounding diversity of immune system components (e.g. immunoglobulins, lymphocyte receptors, or cytokines)
together with the complexity of the regulatory pathways and network-type interactions makes immunology a
combinatorial science. Currently available data represent only a tiny fraction of possible situations and data
continues to accrue at an exponential rate. Computational analysis has therefore become an essential element of
immunology research with a main role of immunoinformatics being the management and analysis of immunological
data. More advanced analyses of the immune system using computational models typically involve conversion of an
immunological question to a computational problem, followed by solving of the computational problem and
translation of these results into biologically meaningful answers. Major immunoinformatics developments include
immunological databases, sequence analysis, structure modelling, mathematical modelling of the immune system,
simulation of laboratory experiments, statistical support for immunological experimentation and immunogenomics.
In this paper we describe the status and challenges within these sub-fields. We foresee the emergence of
immunomics not only as a collective endeavour by researchers to decipher the sequences of T cell receptors,
immunoglobulins, and other immune receptors, but also to functionally annotate the capacity of the immune system
to interact with the whole array of self and non-self entities, including genome-to-genome interactions.
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©2003 The Novartis Foundation
The future for computational modelling and prediction systems in clinical immunology
Nikolai Petrovsky, Diego Silva and Vladimir Brusic
Centre for Medical Informatics, Division of Science and Design, University of Canberra, Bruce ACT 2617,
Australia, Autoimmunity Research Unit, The Canberra Hospital, Woden ACT 2606, John Curtin School of Medical
Research, Canberra ACT 2606, Australia and Laboratories for Information Technology, 21 Heng Mui Keng Terrace,
Singapore 119613
Advances in computational science, despite their enormous potential, have been surprisingly slow to impact on
clinical practice. This paper examines the potential of bioinformatics to advance clinical immunology across a
number of key examples including the use of computational immunology to improve renal transplantation outcomes,
identify novel genes involved in immunological disorders, decipher the relationship between antigen presentation
pathways and human disease, and predict allergenicity. These examples demonstrate the enormous potential for
immunoinformatics to advance clinical and experimental immunology. The acceptance of immunoinformatic techniques
by clinical and research immunologists will need robust standards of data quality, system integrity and properly
validated immunoinformatic systems. Such validation, at a minimum, will require appropriately designed clinical
studies conducted according to Good Clinical Practice standards. This strategy will enable immunoinformatics to
achieve its full potential to advance and shape clinical immunology into the future.
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©2003 The Novartis Foundation
Immunoinformatics in personalized medicine
Kamalakar Gulukota
gvk bioSciences Private Limited, #210, 'My Home Tycoon', 6-3-1192, Begumpet, Hyderabad 500 016, India
Diagnosis of human disease has been undergoing steady improvement over the past few centuries. Many ailments
that were once considered a single entity have been classified into finer categories on the basis of response to
therapy (e.g. type I and type II diabetes), inheritance (e.g. familial and non-familial polyposis coli),
histology (e.g. small cell and adenocarcinoma of lung) and most recently transcriptional profiling (e.g.
leukaemia, lymphoma). The next dimension in this finer categorization appears to be the typing of the patient
rather than the disease i.e. disease X in person of type Y. The problem of personalized medicine is to devise
tests which predict the type of individual, especially where the type is correlated with response to therapy.
Immunology has been at the forefront of personalized medicine for quite a while, even though the term is not
often used in this connection. Blood grouping and cross-matching (for blood transfusion), and anaphylaxis test
(for penicillin) are just two examples. In this paper I will argue that immunological tests have an important
place in the future of personalized medicine. I will describe methods we developed for personalizing vaccines
based on MHC allele frequencies in human populations and methods for predicting peptide binding to class I MHC
molecules. In conclusion, I will argue that immunological tests, and consequently immunoinformatics, will play a
big role in making personalized medicine a reality.
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©2003 The Novartis Foundation
From immunome to vaccine: epitope mapping and vaccine design tools
Anne S. De Groot and William Martin
TB/HIV Research Laboratory, Brown University, International Health Institute, Box GB473, Providence, RI 02912,
and EpiVax Inc, 16 Bassett Street, Providence RI 02903, USA
Since the publication of the complete genome of a pathogenic bacterium in 1995, more than 50 bacterial pathogens
have been sequenced and at least 120 additional projects are currently underway. Faced with the expanding volume
of information now available from genome databases, vaccinologists are turning to epitope mapping tools to screen
vaccine candidates. Bioinformatics tools such as EpiMatrix and Conservatrix, which search for unique or
multi-HLA-restricted (promiscuous) T cell epitopes and can find epitopes that are conserved across variant
strains of the same pathogen, have accelerated the process of epitope mapping. Additional tools for screening
epitopes for similarity to ‘self’ (BlastiMer) and forassembling putative epitopes into strings if they overlap
(EpiAssembler) have been developed at EpiVax. Tools that map proteasome cleavage sites are available on the
Internet. When used together, these bioinformatics tools offer a significant advantage over traditional methods
of vaccine design since high throughput screening and design is performed in
silico, followed by confirmatory
studies in vitro. These new tools are being used to develop novel vaccines and therapeutics for the prevention
and treatment of infectious diseases such as HIV, hepatitis C, tuberculosis, and some cancers. More recent
applications of the tools involve deriving novel vaccine candidates directly from whole genomes, an approach that
has been named ‘genome to vaccine’.
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©2003 The Novartis Foundation
Insights from MHC-bound peptides
Hanah Margalit and Yael Altuvia
Department of Molecular Genetics and Biotechnology, The Hebrew University Hadassah Medical School, Jerusalem
91120, Israel
Cytotoxic T cells recognize short antigenic peptides, the processing products of protein antigens, when they are
bound to major histocompatibility complex (MHC) class I molecules. Peptide binding to MHC molecules has been
studied extensively in numerous laboratories, providing vast amounts of sequence and structure data that have
been used as a rich source for bioinformatic research. MHC-bound peptides and their flanking sequences provide
information about the sequence requirements of the different processing stages, in particular, the cleavage by
the proteasome and the binding to MHC molecules. Elucidation of these sequence requirements sheds light on the
evolutionary forces that have shaped and designed these peptides, and should lead to the development of an
integrative predictive algorithm. Remarkably, the peptide sequence and structure data are also valuable for the
study of biological questions that are apparently unrelated to cellular immunity, namely, sequence–structure
relationship and genome annotation. Here we describe our computational analyses of MHC-bound peptides, applied
to all these biological topics.
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©2003 The Novartis Foundation
Computational vaccinology: quantitative approaches
Darren R. Flower, Helen McSparron, Martin J Blythe, Christianna Zygouri, Deborah Taylor, Pingping Guan, Shouzhan
Wan, Peter Coveney, Valerie Walshe, Persephone Borrow and Irini A. Doytchinova
Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG0 7NN and Centre for
Computational Science, Department of Chemistry, Queen Mary, University of London, Mile End Road, London E1 4NS,
UK
The immune system is hierarchical and has many tiers, exhibiting much emergent behaviour. However, at its heart
are molecular recognition events that are indistinguishable from other types of biomacromolecular interaction.
These can be addressed well by quantitative experimental and theoretical biophysical techniques, and particularly
by methods from drug design. We review here our approach to computational immunovaccinology. In particular, we
describe the JenPep database and two new techniques for T cell epitope prediction. One is based on quantitative
structure–activity relationships (a 3D-QSAR method based on CoMSIA and another 2D method based on the Free–Wilson
approach) and the other on atomistic molecular dynamic simulations using high performance computing. JenPep
(http://www.jenner.ac.uk/JenPep) is a relational database system supporting quantitative data on peptide binding
to major histocompatibility complexes, TAP transporters, TCR-pMHC complexes, and an annotated list of B cell and
T cell epitopes. Our 2D-QSAR method factors the contribution to peptide binding from individual amino acids as
well as 1–2 and 1–3 residue interactions. In the 3D-QSAR approach, the influence of five physicochemical
properties (volume, electrostatic potential, hydrophobicity, hydrogen-bond donor and acceptor abilities) on
peptide affinity were considered. Both methods are exemplified through their application to the well-studied
problem of peptide binding to the human class I MHC molecule HLA-A*0201.
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©2003 The Novartis Foundation
IMGT, the international ImMunoGeneTics database®,
http://imgt.cines.fr
Marie-Paule Lefranc
Université Montpellier II, Laboratoire d'ImmunoGénétique Moléculaire, LIGM, UPR CNRS 1142, Institut de Génétique
Humaine, Montpellier, France
IMGT, the international ImMunoGeneTics database® (http://imgt.cines.fr), is a high quality integrated information
system specializing in immunoglobulins (Ig), T cell receptors (TCR) and major histocompatibility complexes (MHC)
of human and other vertebrates, created in 1989 by LIGM at the Université Montpellier II, CNRS, Montpellier,
France. IMGT provides a common access to standardized data which include nucleotide and protein sequences,
oligonucleotide primers, gene maps, genetic polymorphisms, specificities, and 2D and 3D structures. IMGT includes
four databases (IMGT/LIGM-DB, IMGT/3Dstructure-DB, IMGT/HLA-DB, IMGT/PRIMER-DB,) Web resources (‘IMGT Marie-Paule
page’) and interactive tools (IMGT/V-QUEST, IMGT/JunctionAnalysis, IMGT/PhyloGene, IMGT/LocusView, IMGT/Geneview,
IMGT/GeneSearch). IMGT data are expertly annotated according to the rules of the IMGT scientific chart based on
IMGT-ONTOLOGY. IMGT tools are particularly useful for the analysis of the Ig and TCR repertoires in physiological
normal and pathological situations. IMGT has important applications in medical research (autoimmune diseases,
AIDS, leukaemias, lymphomas, myelomas), biotechnology related to antibody engineering (phage displays,
combinatorial libraries) and therapeutic approaches (graft, immunotherapy). IMGT is freely available at
http://imgt.cines.fr.
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©2003 The Novartis Foundation
Generating data for databases—the peptide repertoire of HLA molecules
Stefan Stevanović, Claudia Lemmel, Maik Häntschel and Ute Eberle
Eberhard-Karls-Universität Tübingen, Institut für Zellbiologie, Abteilung Immunologie, Auf der Morgenstelle 15,
D-72076 Tübingen, German
During the past few years, a huge amount of information about HLA-presented peptides has been compiled: several
thousand naturally processed ligands of such cell surface receptors are already known. Nevertheless, our
knowledge covers only a minute proportion of the total peptide repertoire. The overall amount of different
peptides presented by one given HLA class I molecule lies between 1000 and 10000 individual sequences per
cell. There is, however, no HLA molecule of which more than 100 ligands have been published so far. The situation
is further complicated by the fact that different cells present different sets of peptides by the same HLA
molecules, a feature that provides great hope for immunotherapy. We have been analysing HLA-presented peptides
for many years for three reasons. First, the basic rules of peptide presentation (the ‘peptide motifs’) had to be
established. Second, the listing of individual peptides presented by HLA molecules is steadily continuing,
although a comprehensive catalogue of all possible HLA-presented peptides is utopical in our days. Third,
quantitative differences in the presentation of individual HLA ligands provide information about the dynamic
state of the host cells. Comprehensive information about HLA-presented peptides enables accurate epitope
prediction and provides a basis for diagnostic assessment and therapeutic intervention.
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©2003 The Novartis Foundation
HLA nomenclature and the IMGT/HLA Sequence Database
Steven G. E. Marsh
Anthony Nolan Research Institute and Department of Haematology, Royal Free & University College Medical School,
Hampstead, London NW3 2QG, UK
Early in their study it was recognized that the genes encoding the HLA molecules were highly polymorphic and that
there was a need for a systematic nomenclature. The result was the WHO Nomenclature Committee for Factors of the
HLA System, which first met in 1968, and laid down the criteria for successive meetings. This committee meets
regularly to discuss issues of nomenclature and has published 16 major reports documenting firstly the HLA
antigens and more recently the genes and alleles. The standardization of HLA antigenic specificities has been
controlled by the exchange of typing reagents and cells in the International Histocompatibility Workshops. Since
1989 when a large number of HLA allele sequences were first analysed and named, the job of curating and
maintaining a database of sequences has been of prime importance. In 1998 the IMGT/HLA database became the
official repository for HLA sequences. In addition to the nucleotide and protein sequences the database contains
information of the cell from which the sequence was obtained. The database which provides tools for sequence
analysis and the submission of new data, is updated quarterly and now contains over 1500 HLA allele sequences.
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©2003 The Novartis Foundation
From immunogenetics to immunomics: functional prospecting of genes and transcripts
Christian Schönbach
Biomedical Knowledge Discovery Team, Bioinformatics Group, RIKEN Genomic Sciences Center (GSC), 1-7-22
Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan
Human and mouse genome and transcriptome projects have expanded the field of ‘immunogenetics’ beyond the
traditional study of the genetics and evolution of MHC, TCR and Ig loci into the new interdisciplinary area of
‘immunomics’. Immunomics is the study of the molecular functions associated with all immune-related coding and
non-coding mRNA transcripts. To unravel the function, regulation and diversity of the immunome requires that we
identify and correctly categorize all immune-related transcripts. The importance of intercalated genes, antisense
transcripts and non-coding RNAs and their potential role in regulation of immune development and function are
only just starting to be appreciated. To better understand immune function and regulation, transcriptome projects
(e.g. Functional Annotation of the Mouse, FANTOM), that focus on sequencing full-length transcripts from multiple
tissue sources, ideally should include specific immune cells (e.g. T cell, B cells, macrophages, dendritic cells)
at various states of development, in activated and unactivated states and in different disease contexts. Progress
in deciphering immune regulatory networks will require the cooperative efforts of immunologists,
immunogeneticists, molecular biologists and bioinformaticians. Although primary sequence analysis remains useful
for annotation of new transcripts it is less useful for identifying novel functions of known transcripts in a new
context (protein interaction network or pathway). The most efficient approach to mine useful information from the
vast a priori knowledge contained in biological databases and the scientific literature, is to use a combination
of computational and expert-driven knowledge discovery strategies. This paper will illustrate the challenges
posed in attempts to functionally infer transcriptional regulation and interaction of immune-related genes from
text and sequence-based data sources.
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©2003 The Novartis Foundation
Mathematical models of HIV and the immune system
Dominik Wodarz
Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP-665, Seattle, WA 98109-1024, USA
I describe how mathematical models have been used to elucidate the principles which govern HIV and immune system
dynamics in relation to antiviral drug therapy. The review starts by introducing a basic model of virus infection
and demonstrates how it was used to study HIV dynamics and to measure crucial parameters which lead to a new
understanding of the disease process. Since this analysis indicates that eradication of the virus is not feasible
during the lifetime of the patient, I continue to discuss mathematical models with the aim to explore how drug
therapy can be used to induce long-term immunological control of the infection.
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©2003 The Novartis Foundation
Immunogenomics: towards a digital immune system
Stephan Beck
Wellcome Trust Sanger Institute, Hinxton Genome Campus, Cambridge CB10 1SA, UK
One of the major differences that set apart vertebrates from non-vertebrates is the presence of a complex immune
system. Over the past 400–500 million years, many novel immune genes and gene families have emerged and their
products form sophisticated pathways providing protection against most pathogens. The Human Genome Project has
laid the foundation to study these genes and pathways in unprecedented detail. Members of the immunoglobulin (Ig)
superfamily alone were found to make up over 2% of human genes possibly constituting the largest gene family in
the human genome. A subgroup of these human immune genes, those (among others) involved in antigen processing and
presentation, are encoded in a single region, the major histocompatibility complex (MHC) on the short arm of
chromosome 6. My laboratory has a long-standing interest in understanding the molecular organization and
evolution of the MHC. To this end, we have been generating a range of MHC genomic resources that we make
available in form of maps and databases. Much of the complex data of the immune system can be reduced to binary
(on/off) information that can easily be made available and analysed by bioinformatics approaches, thus
contributing to better understand immune function via a 'digital immune system'.
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©2003 The Novartis Foundation
Viral bioinformatics: computational views of host and pathogen
Paul Kellam, Ria Holzerlandt, Eva Gramoustianou*, Richard Jenner and Antonia Kwan
Viral Genomics and Bioinformatics Group, Wohl Virion Centre, Department of Immunology and Molecular Pathology and
Department of Virology, University College, Windeyer Institute of Medical Sciences, 46 Cleveland Street, London
W1T 4JF, UK
Wherever cellular life occurs, viruses are also found. As a result, complex organism and cellular antiviral
responses co-evolve with virally encoded countermeasures. Since viruses co-opt or interfere with specific
cellular pathways during their replication, knowledge of viral genome sequences has helped fundamental
understanding of host biology. During viral infection, shifts in the balance between host and viral biological
processes result in acute or chronic viral disease pathology accompanied with either active viral replication,
viral containment/persistence or viral clearance. Studying host–virus interactions at the level of single gene
effects however, fails to produce a global systems-level understanding. This should now be achievable in the
context of complete host and pathogen genome sequences. New experimental methods and computational approaches are
rapidly developing, allowing global views of dynamic viral and cellular molecular mechanisms. Systems level
virology using DNA microarrays and specific viral data resources will reveal the detailed cellular context in
which viruses replicate, highlighting common and distinct antiviral mechanisms, the effect of different host cell
gene expression programs and the response of cells to similar or diverse virus types. Ultimately, microbiology
and immunology will tend towards a systems-level view of how host and pathogen interact.
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©2003 The Novartis Foundation