FIMM, a database of functional molecular immunology: update 2002

HLA3D: an integrated structure-based computational toolkit for immunotherapy

Motivation

The human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting non-self-peptides to T cell receptors. The MHC region has been shown to be associated with a variety of diseases, including autoimmune diseases, organ transplantation and tumours. However, structural analytic tools of HLA are still sparse compared to the number of identified HLA alleles, which hinders the disclosure of its pathogenic mechanism.

Result

To provide an integrative analysis of HLA, we first collected 1296 amino acid sequences, 256 protein data bank structures, 120 000 frequency data of HLA alleles in different populations, 73 000 publications and 39 000 disease-associated single nucleotide polymorphism sites, as well as 212 modelled HLA heterodimer structures. Then, we put forward two new strategies for building up a toolkit for transplantation and tumour immunotherapy, designing risk alignment pipeline and antigenic peptide prediction pipeline by integrating different resources and bioinformatic tools. By integrating 100 000 calculated HLA conformation difference and online tools, risk alignment pipeline provides users with the functions of structural alignment, sequence alignment, residue visualization and risk report generation of mismatched HLA molecules. For tumour antigen prediction, we first predicted 370 000 immunogenic peptides based on the affinity between peptides and MHC to generate the neoantigen catalogue for 11 common tumours. We then designed an antigenic peptide prediction pipeline to provide the functions of mutation prediction, peptide prediction, immunogenicity assessment and docking simulation. We also present a case study of hepatitis B virus mutations associated with liver cancer that demonstrates the high legitimacy of our antigenic peptide prediction process. HLA3D, including different HLA analytic tools and the prediction pipelines, is available at http://www.hla3d.cn/.

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

Databases for T-cell epitopes

Modem immunology and vaccinology incorporate immunoinformatics techniques to give insights into immune systems and accelerate vaccine design. Databases managing epitope data in a structured form with immune-related annotations including sequences, alleles, source organisms, structures, and diseases could be the most crucial part of immunoinformatics offering data sources for the analysis of immune systems and development of prediction methods. This chapter provides an overview of publicly available databases of T-cell epitopes including general databases, pathogen- and tumor-specific databases, and 3D structure databases.

Improving the prediction of HLA class I-binding peptides using a supertype-based method

The computational prediction of peptides that bind to major histocompatibility complex (MHC) molecules has practical importance for the development of epitope-based vaccines. The performance of the prediction methods depends on the verified peptides. However, the available peptide datasets of most alleles contain significant biases. An investigation to the effect of the peptides in the training dataset on the performance of the generated model indicated that there was a discrepancy between the classification of binders from biological data and classification of binders from super-motif-sharing peptides, which was induced by the non-motif-containing peptides. Most human MHC (called HLA) class I molecules could be assigned to supertypes based on their overlapping peptide-binding specificities, therefore, we proposed a supertype-based method for the modeling of the HLA class I-peptide binding: candidates of peptides binding to alleles in a given supertypes were screened using the super-motifs, and then the peptides binding to specific allele in the supertype were predicted by the model trained on the super-motif-sharing peptides. The efficacy of this supertype-based method was examined in two matrix-based methods and one machine learning method for 20 alleles in HLA supertypes A1, A2, A3, A24, B44 and B7. Evaluations on several benchmark datasets indicated that the supertype-based method achieved remarkable success in improving the prediction of HLA-binding peptides.

EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information

Background

Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms.

Results

We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo⁺ and EpicCapo^+REF. Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo⁺ and EpicCapo^+REF outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo^+REF was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments.

Conclusions

Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo^+REF is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.

Predicting MHC class I epitopes in large datasets

Background

Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening.

Results

We have examined the performance of four diverse MHC Class I prediction methods on comparatively large HLA-A and HLA-B allele peptide binding datasets extracted from the Immune Epitope Database and Analysis resource (IEDB). The chosen methods span a representative cross-section of available methodology for MHC binding predictions. Until the development of IEDB, such an analysis was not possible, as the available peptide sequence datasets were small and spread out over many separate efforts. We tested three datasets which differ in the IC₅₀ cutoff criteria used to select the binders and non-binders. The best performance was achieved when predictions were performed on the dataset consisting only of strong binders (IC₅₀ less than 10 nM) and clear non-binders (IC₅₀ greater than 10,000 nM). In addition, robustness of the predictions was only achieved for alleles that were represented with a sufficiently large (greater than 200), balanced set of binders and non-binders.

Conclusions

All four methods show good to excellent performance on the comprehensive datasets, with the artificial neural networks based method outperforming the other methods. However, all methods show pronounced difficulties in correctly categorizing intermediate binders.

MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes

Background

Many databases housing the information about MHC binders and non-binders have been developed in the past to help the scientific community working in the field of immunology, immune-informatics or vaccine design. As the information about these MHC binding and non-binding peptides continues to grow with the time and there is a need to keep the databases updated. So, in order to provide the immunological fraternity with the most recent information we need to maintain and update our database regularly. In this paper, we describe the updated version of 4.0 of the database MHCBN.

Findings

MHCBN is a comprehensive database comprising over 25,857 peptide sequences (1053 TAP binding peptides), whose binding affinity with either MHC or TAP molecules has been assayed experimentally. It is a manually curated database where entries are collected & compiled from published literature and existing immunological public databases. MHCBN has a number of web-based tools for the analysis and retrieval of information like mapping of antigenic regions, creation of allele specific dataset, BLAST search, various diseases associated with MHC alleles etc. Further, all entries are hyper linked to major databases like SWISS-PROT, PDB etc. to provide the information beyond the scope of MHCBN. The latest version 4.0 of MHCBN has 6080 more entries than previously published version 1.1.

Conclusion

MHCBN database updating is meant to facilitate immunologist in understanding the immune system and provide them the latest information. We feel that our database will complement the existing databases in serving scientific community.

Novel peptide marker corresponding to salivary protein gSG6 potentially identifies exposure to Anopheles bites

Background

In order to improve malaria control, and under the aegis of WHO recommendations, many efforts are being devoted to developing new tools for identifying geographic areas with high risk of parasite transmission. Evaluation of the human antibody response to arthropod salivary proteins could be an epidemiological indicator of exposure to vector bites, and therefore to risk of pathogen transmission. In the case of malaria, which is transmitted only by anopheline mosquitoes, maximal specificity could be achieved through identification of immunogenic proteins specific to the Anopheles genus. The objective of the present study was to determine whether the IgG response to the Anopheles gambiae gSG6 protein, from its recombinant form to derived synthetic peptides, could be an immunological marker of exposure specific to Anopheles gambiae bites.

Methodology/Principal Findings

Specific IgG antibodies to recombinant gSG6 protein were observed in children living in a Senegalese area exposed to malaria. With the objective of optimizing Anopheles specificity and reproducibility, we designed five gSG6-based peptide sequences using a bioinformatic approach, taking into consideration i) their potential antigenic properties and ii) the absence of cross-reactivity with protein sequences of other arthropods/organisms. The specific anti-peptide IgG antibody response was evaluated in exposed children. The five gSG6 peptides showed differing antigenic properties, with gSG6-P1 and gSG6-P2 exhibiting the highest antigenicity. However, a significant increase in the specific IgG response during the rainy season and a positive association between the IgG level and the level of exposure to Anopheles gambiae bites was significant only for gSG6-P1.

Conclusions/Significance

This step-by-step approach suggests that gSG6-P1 could be an optimal candidate marker for evaluating exposure to Anopheles gambiae bites. This marker could be employed as a geographic indicator, like remote sensing techniques, for mapping the risk of malaria. It could also represent a direct criterion of efficacy in evaluation of vector control strategies.

HLA class I supertypes: a revised and updated classification

BACKGROUND

Class I major histocompatibility complex (MHC) molecules bind, and present to T cells, short peptides derived from intracellular processing of proteins. The peptide repertoire of a specific molecule is to a large extent determined by the molecular structure accommodating so-called main anchor positions of the presented peptide. These receptors are extremely polymorphic, and much of the polymorphism influences the peptide-binding repertoire. However, despite this polymorphism, class I molecules can be clustered into sets of molecules that bind largely overlapping peptide repertoires. Almost a decade ago we introduced this concept of clustering human leukocyte antigen (HLA) alleles and defined nine different groups, denominated as supertypes, on the basis of their main anchor specificity. The utility of this original supertype classification, as well several other subsequent arrangements derived by others, has been demonstrated in a large number of epitope identification studies.

RESULTS

Following our original approach, in the present report we provide an updated classification of HLA-A and -B class I alleles into supertypes. The present analysis incorporates the large amount of class I MHC binding data and sequence information that has become available in the last decade. As a result, over 80% of the 945 different HLA-A and -B alleles examined to date can be assigned to one of the original nine supertypes. A few alleles are expected to be associated with repertoires that overlap multiple supertypes. Interestingly, the current analysis did not identify any additional supertype specificities.

CONCLUSION

As a result of this updated analysis, HLA supertype associations have been defined for over 750 different HLA-A and -B alleles. This information is expected to facilitate epitope identification and vaccine design studies, as well as investigations into disease association and correlates of immunity. In addition, the approach utilized has been made more transparent, allowing others to utilize the classification approach going forward.

Comparative modeling of marsupial MHC class I molecules identifies structural polymorphisms affecting functional motifs

Major histocompatibility complex (MHC) class I molecules are transmembrane glycoproteins that present antigenic peptides to CD8+ T cells and are subsequently important for the initiation of an immune response. In this study novel MHC class I sequences from the tammar wallaby (Macropus eugenii) have been characterized. Analysis and comparative modeling of these and existing marsupial molecules reveals potential functional polymorphisms within peptide-binding grooves, MHC assembly motifs and the T cell receptor recognition interface. In addition, we show that a previously identified marsupial-specific insertion is within a region, which is known as a putative NK cell receptor (Ly49A) binding site in the mouse, suggesting that this site may be functionally active in marsupials. Further, the analysis highlighted differences in structural and sequence based grouping of marsupial MHC class I molecules.

Searching and mapping of T-cell epitopes, MHC binders, and TAP binders

This chapter describes searching and mapping tools of MHCBN database, which is a curated database. It comprises over 23,000 peptide sequences, whose binding affinity with major histocompatibility complex (MHC) or transporter associated with antigen processing (TAP) molecules has been assayed experimentally. Each entry of the database provides full information (such as sequence, its MHC- or TAP-binding specificity, and source protein) about peptide whose binding affinity (IC50) and T-cell activity is experimentally determined. MHCBN has number of web-based tools for analyzing and retrieving information. In this chapter, we describe how to use web tools integrated in MHCBN that include (i) mapping of experimentally determined antigenic regions on the query sequence, (ii) creation of allele-specific peptide data set, and (iii) BLAST search against MHC or antigen databases.

Prediction of peptide-MHC binding using profiles

Prediction of peptide binding to major histocompatibility complex (MHC) molecules is a basis for anticipating T-cell epitopes. Peptides that bind to a given MHC molecule are related by sequence similarity. Therefore, a position-specific scoring matrix (PSSM)---also known as profile--derived from a set of aligned peptides known to bind to a given MHC molecule can be used as a predictor of both peptide-MHC binding and T-cell epitopes. In this approach, the binding potential of any peptide sequence (query) to the MHC molecule is determined by its similarity to a set of known peptide-MHC binders and can be obtained by comparing the query to the PSSM. Following structural considerations of the peptide-MHC interaction, we will describe here how to derive alignments and PSSMs that are suitable for the prediction of peptide-MHC binding.

SuperHapten: a comprehensive database for small immunogenic compounds

The immune system protects organisms from foreign proteins, peptide epitopes and a multitude of chemical compounds. Among these, haptens are small molecules, eliciting an immune response when conjugated with carrier molecules. Known haptens are xenobiotics or natural compounds, which can induce a number of autoimmune diseases like contact dermatitis or asthma. Furthermore, haptens are utilized in the development of biosensors, immunomodulators and new vaccines. Although hapten-induced allergies account for 6–10% of all adverse drug effects, the understanding of the correlation between structural and haptenic properties is rather fragmentary. We have developed a manually curated hapten database, SuperHapten, integrating information from literature and web resources. The current version of the database compiles 2D/3D structures, physicochemical properties and references for about 7500 haptens and 25,000 synonyms. The commercial availability is documented for about 6300 haptens and 450 related antibodies, enabling experimental approaches on cross-reactivity. The haptens are classified regarding their origin: pesticides, herbicides, insecticides, drugs, natural compounds, etc. Queries allow identification of haptens and associated antibodies according to functional class, carrier protein, chemical scaffold, composition or structural similarity. SuperHapten is available online at .

SiPep: a system for the prediction of tissue-specific minor histocompatibility antigens

Approximately 50 years ago it was found that inbred strains of mice were able to reject tumours and skin grafts from major histocompatibility complex (MHC) identical donors. They proposed that additional transplantation antigens must exist outside the MHC. These were described as minor histocompatibility antigens (mHAgs). Since then, related studies in humans have identified 16 human mHAgs. The aim of this work is to increase the number of known mHAgs by prediction of candidate minor histocompatibility loci by identifying coding single nucleotide polymorphisms (SNPs) where the amino acid variation lies within an MHC-binding peptide and alters the ability of that peptide to bind. We have developed an algorithm called SiPep which uses peptide sequences derived from the flanking regions of known non-synonymous SNPs, various MHC-binding and proteolytic cleavage evaluation methods and protein expression data to predict mHAgs. We have processed 45094 SNPs using the SiPep algorithm and have stored the results in a database called SNPBinder. The facilities to process submitted proteins through the SiPep algorithm as well as the SNPBinder database are available to the public. A set of peptides that are predicted as possible mHAgs by the SiPep algorithm have been tested using refolding assays and gel filtration and the results are presented in this paper. The SiPep tools and SNPBinder database are available free of charge via the internet. An HTML interface providing search facilities can be found at the following address: http://www.sipep.org/.

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

MOTIVATION

Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules.

RESULTS

We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801) and A24 supertype (A*2301 and A*2402), whose data were collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.

Production of a novel class of polyreactive pathogenic autoantibodies in BXD2 mice causes glomerulonephritis and arthritis

OBJECTIVE

The BXD2 mouse strain spontaneously develops glomerulonephritis and erosive arthritis. The goal of this study was to identify the antigenic target proteins and epitopes and to unravel the mechanisms by which the related conditions arise in BXD2 mice.

METHODS

Individual hybridomas isolated from the spleen of a 10-month-old BXD2 mouse were injected intraperitoneally into nonautoimmune mice for evaluation of pathogenicity of each autoantibody. Autoantigens were immunoprecipitated with the pathogenic autoantibody L3A4. Autoantigens were identified using enzyme-linked immunosorbent assay, Western blotting, 2-dimensional gel electrophoresis, and matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MS) and tandem MS. Antigenic epitopes were determined using a high-throughput epitope mapping method.

RESULTS

The production of autoantibodies in BXD2 mice occurred in an orderly progression, with peak levels of autoantibodies to nitrotyrosine (NT)-modified enolase, Ro, alpha-actin, and heat-shock proteins (HSPs) preceding peak levels of antihistone, anti-DNA, and rheumatoid factor. Two monoclonal autoantibodies, L3A4 and T56G10, were identified that could induce immune complexes, renal disease, and/or arthritis. Both L3A4 and T56G10 were polyreactive, and each reacted with separate sets of autoantigens. The antigenic targets of L3A4 consisted of NT-modified enolase, ATP5b, alpha-actin, and Hsp70 family proteins including Hspa5 and Hsp74. The antigenic epitopes of NT-modified enolase and Hspa5 exhibited sequence homology and cross-reactivity, suggesting that epitope spreading may occur through a molecular mimicry mechanism.

CONCLUSION

The polyreactivity of autoantibodies that target a novel class of autoantigens may enable these autoantibodies to induce erosive arthritis or glomerulonephritis either by direct pathogenic mechanisms or indirectly via Fc or immune complex deposition.

AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data

AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries. AntiJen provides the most complete and up-to-date dataset of its kind. While AntiJen v2.0 retains a focus on both T cell and B cell epitopes, its greatest novelty is the archiving of continuous quantitative data on a variety of immunological molecular interactions. This includes thermodynamic and kinetic measures of peptide binding to TAP and the Major Histocompatibility Complex (MHC), peptide-MHC complexes binding to T cell receptors, antibodies binding to protein antigens and general immunological protein-protein interactions. The database also contains quantitative specificity data from position-specific peptide libraries and biophysical data, in the form of diffusion co-efficients and cell surface copy numbers, on MHCs and other immunological molecules. The uses of AntiJen include the design of vaccines and diagnostics, such as tetramers, and other laboratory reagents, as well as helping parameterize the bioinformatic or mathematical in silico modeling of the immune system. The database is accessible from the URL: .

An ontology for immune epitopes: application to the design of a broad scope database of immune reactivities

Background

Epitopes can be defined as the molecular structures bound by specific receptors, which are recognized during immune responses. The Immune Epitope Database and Analysis Resource (IEDB) project will catalog and organize information regarding antibody and T cell epitopes from infectious pathogens, experimental antigens and self-antigens, with a priority on NIAID Category A-C pathogens () and emerging/re-emerging infectious diseases. Both intrinsic structural and phylogenetic features, as well as information relating to the interactions of the epitopes with the host's immune system will be catalogued.

Description

To effectively represent and communicate the information related to immune epitopes, a formal ontology was developed. The semantics of the epitope domain and related concepts were captured as a hierarchy of classes, which represent the general and specialized relationships between the various concepts. A complete listing of classes and their properties can be found at .

Conclusion

The IEDB's ontology is the first ontology specifically designed to capture both intrinsic chemical and biochemical information relating to immune epitopes with information relating to the interaction of these structures with molecules derived from the host immune system. We anticipate that the development of this type of ontology and associated databases will facilitate rigorous description of data related to immune epitopes, and might ultimately lead to completely new methods for describing and modeling immune responses.

Textmining in support of knowledge discovery for vaccine development

Complete genome data of infectious microorganisms permit systematic computational sequence-based predictions and experimental testing of candidate vaccine epitopes. Both, predictions and the interpretation of experiments rely on existing information in the literature which is mostly manually extracted and curated. The growing amount of data and literature information has created a major bottleneck for the interpretation of results and maintenance of curated databases. The lack of suitable free-text information extraction, processing, and reporting tools prompted us to develop a knowledge discovery support system that enhances the understanding of immune response and vaccine development. The current prototype system, Gene expression/epitpopes/protein interaction (GEpi), focuses on molecular functions of HIV-infected T-cells and HIV epitope information, using textmining, and interrelation of biomolecular data from domain-specific databases with MEDLINE abstract-inferred information. Results showed that extraction and processing of molecular interaction, disease associations, and gene ontology-derived functional information generate intuitive knowledge reports that aid the interpretation of host-pathogen interaction. In contrast, epitope (word and sequence) information in MEDLINE abstracts is surprisingly sparse and often lacks necessary context information, such as HLA-restriction. Since the majority of epitope information is found in tables, figures, and legends of full-text articles, its extraction may not require sophisticated natural language processing techniques. Support of vaccine development through textmining requires therefore the timely development of domain-specific extraction rules for full-text articles, and a knowledge model for epitope-related information.

EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology

SUMMARY

EPIMHC is a relational database of MHC-binding peptides and T cell epitopes that are observed in real proteins. Currently, the database contains 4867 distinct peptide sequences from various sources, including 84 tumor-associated antigens. The EPIMHC database is accessible through a web server that has been designed to facilitate research in computational vaccinology. Importantly, peptides resulting from a query can be selected to derive specific motif-matrices. Subsequently, these motif-matrices can be used in combination with a dynamic algorithm for predicting MHC-binding peptides from user-provided protein queries.

AVAILABILITY

The EPIMHC database server is hosted by the Dana-Farber Cancer Institute at the site http://immunax.dfci.harvard.edu/bioinformatics/epimhc/

An Update on the Functional Molecular Immunology (FIMM) Database

Data on the major histocompatibility complex, T-cell epitopes, B-cell epitopes, antigens and diseases are heterogeneous and scattered among different databases and the literature. Since it has become increasingly difficult to obtain an integrated view of functional immune response components, we have developed and updated over several years the Functional molecular IMMunology (FIMM) database (http:// research.i2r.a-star.edu.sg/fimm/). FIMM contains integrated expert-curated data on protein antigens, and on human immunological receptors that recognise and bind them in healthy or disease states. Interfaces with multiple, intuitive query options and query reports provide immunologists with prioritised information that aids data interpretation, vaccine target discovery and immune disease research.

Candidate epitope identification using peptide property models: application to cancer immunotherapy

Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic peptide-MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on peptide property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments - data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures - offer hope for the future.

Computational tools for the study of allergens

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

JenPep: a novel computational information resource for immunobiology and vaccinology

JenPep is a relational database containing a compendium of thermodynamic binding data for the interaction of peptides with a range of important immunological molecules: the major histocompatibility complex, TAP transporter, and T cell receptor. The database also includes annotated lists of B cell and T cell epitopes. Version 2.0 of the database is implemented in a bespoke postgreSQL database system and is fully searchable online via a perl/HTML interface (URL: http://www.jenner.ac.uk/JenPep).

Computational immunology: The coming of age

The explosive growth in biotechnology combined with major advances in information technology has the potential to radically transform immunology in the postgenomics era. Not only do we now have ready access to vast quantities of existing data, but new data with relevance to immunology are being accumulated at an exponential rate. Resources for computational immunology include biological databases and methods for data extraction, comparison, analysis and interpretation. Publicly accessible biological databases of relevance to immunologists number in the hundreds and are growing daily. The ability to efficiently extract and analyse information from these databases is vital for efficient immunology research. Most importantly, a new generation of computational immunology tools enables modelling of peptide transport by the transporter associated with antigen processing (TAP), modelling of antibody binding sites, identification of allergenic motifs and modelling of T-cell receptor serial triggering.