Quantitative approaches to computational vaccinology

CD171 Multi-epitope peptide design based on immuno-informatics approach as a cancer vaccine candidate for glioblastoma

Glioblastoma (GB) is a common primary malignancy of the central nervous system, and one of the highly lethal brain tumors. GB cells can promote therapeutic resistance and tumor angiogenesis. The CD171 is an adhesion molecule in neuronal cells that is expressed in glioma cells as a regulator of brain development during the embryonic period. CD171 is one of the immunoglobulin-like CAMs (cell adhesion molecules) families that can be associated with prognosis in a variety of human tumors. The multi-epitope peptide vaccines are based on synthetic peptides with a combination of both B-cell epitopes and T-cell epitopes, which can induce specific humoral or cellular immune responses. Moreover, Cholera toxin subunit B (CTB), a novel TLR agonist was utilized in the final construct to polarize CD4+ T cells toward T-helper 1 to induce strong cytotoxic T lymphocytes (CTL) responses. In the present study, several immune-informatics tools were used for analyzing the CD171 sequence and studying the important characteristics of a designed vaccine. The results included molecular docking, molecular dynamics simulation, immune response simulation, prediction and validation of the secondary and tertiary structure, physicochemical properties, solubility, conservancy, toxicity as well as antigenicity and allergenicity of the promising candidate for a vaccine against CD171. The immuno-informatic analyze suggested 12 predicted multi-epitope peptides, whose construction consists of 582 residues long. Therewith, cloning adaptation of the designed vaccine was performed, and eventually sequence was inserted into pET30a (+) vector for the application of the anti-glioblastoma vaccine development.Communicated by Ramaswamy H. Sarma.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

A qualitatively validated mathematical-computational model of the immune response to the yellow fever vaccine

Background

Although a safe and effective yellow fever vaccine was developed more than 80 years ago, several issues regarding its use remain unclear. For example, what is the minimum dose that can provide immunity against the disease? A useful tool that can help researchers answer this and other related questions is a computational simulator that implements a mathematical model describing the human immune response to vaccination against yellow fever.

Methods

This work uses a system of ten ordinary differential equations to represent a few important populations in the response process generated by the body after vaccination. The main populations include viruses, APCs, CD8+ T cells, short-lived and long-lived plasma cells, B cells and antibodies.

Results

In order to qualitatively validate our model, four experiments were carried out, and their computational results were compared to experimental data obtained from the literature. The four experiments were: a) simulation of a scenario in which an individual was vaccinated against yellow fever for the first time; b) simulation of a booster dose ten years after the first dose; c) simulation of the immune response to the yellow fever vaccine in individuals with different levels of naïve CD8+ T cells; and d) simulation of the immune response to distinct doses of the yellow fever vaccine.

Conclusions

This work shows that the simulator was able to qualitatively reproduce some of the experimental results reported in the literature, such as the amount of antibodies and viremia throughout time, as well as to reproduce other behaviors of the immune response reported in the literature, such as those that occur after a booster dose of the vaccine.

The quantitative prediction of HLA-B*2705 peptide binding affinities using Support Vector Regression to gain insights into its role for the Spondyloarthropathies

Computational methods are increasingly utilised in many immunoinformatics problems such as the prediction of binding affinity of peptides. The peptides could provide valuable insight into the drug design and development such as vaccines. Moreover, they can be used to diagnose diseases. The presence of human class I MHC allele HLA-B*2705 is one of the strong hypothesis that would lead spondyloarthropathies. In this paper, Support Vector Regression is used in order to predict binding affinity of peptides with the aid of experimentally determined peptide-MHC binding affinities of 222 peptides to HLA-B*2705 to get more insight into this problematic disease. The results yield a high correlation coefficient as much as 0.65 and the SVR-based predictive models can be considered as a useful tool in order to predict the binding affinities for newly discovered peptides.

A Dataset of Experimental HLA-B*2705 Peptide Binding Affinities

T-cell epitopes form the basis of many vaccines, diagnostics, and reagents. Current methods for the in silico identification of T-cell epitopes rely, in the main, on the accurate quantitative prediction of peptide-Major Histocompatibility Complex (pMHC) affinity using data-driven computational approaches. Here, we describe a dataset of experimentally determined pMHC binding affinities for the problematic human class I allele HLA-B*2705. Using an in-house, FACS-based, MHC stabilization assay, we measured binding of 223 peptides. This dataset includes both nonbinding and binding peptides, with measured affinities (expressed as −log10 of the half-maximal binding level) ranging from 1.2 to 7.4. This dataset should provide a useful independent benchmark for new and existing methods for predicting peptide binding to HLA-B*2705.

A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

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Combining epitope prediction methods with molecular docking techniques to identify MHC class I restricted T-cell epitopes.

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Based on available experimental data, the prediction accuracy is up to 90%.

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Providing a valuable step forward for the design of better vaccines.

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Better understanding the activation of T-cell epitopes by MHC binding peptides.

In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.

DIRECT PREDICTION OF T-CELL EPITOPES USING SUPPORT VECTOR MACHINES WITH NOVEL SEQUENCE ENCODING SCHEMES

New peptide encoding schemes are proposed to use with support vector machines for the direct recognition of T cell epitopes. The methods enable the presentation of information on (1) amino acid positions in peptides, (2) neighboring side chain interactions, and (3) the similarity between amino acids through a BLOSUM matrix. A procedure of feature selection is also introduced to strengthen the prediction. The computational results demonstrate competitive performance over previous techniques.

Identification of epitopes in Indian human papilloma virus 16 E6: a bioinformatics approach

HPV-16 is reported as the cause of cervical and other related carcinomas. The early expressed protein E6 in cancer cells is found to be the target for immune therapeutic methods. The sequence of HPV-16 E6 (Accession No: ABK32509) from NCBI databank has been taken for this study. Hydrophilicity, flexibility, accessibility, turns, exposed surface, polarity and antigenic propensity scales were used for the B cell epitope prediction. MHC Class I and Class II alleles for the accession were predicted by the MHCPred 2.0 Program. The epitope sequences were also found out. Computer-based prediction program results show, A0203 and DRB0101 lower IC50 than other alleles. The best peptide binding affinity was 21HLCTELQTT30 of A0203 allele. In DRB0101 allele the peptide found was 39YCKQQLLRR48. Different structural features of the protein have also been predicted including glycosylation, kinase C phosphorylation, casein kinase II phosphorylation and N-myristylation sites. These computational prediction programs show four glycosylation, five kinase C phosphorylation, two casein kinase II phosphorylation, zero N-myristylation sites and seven disulphide sites. Development and approval of new vaccines are the keys for control of cancer. Epitopes and other structural features of protein prediction could be the best source of information and can help in molecular and medical studies of viral infection and development of HPV associated cancer drugs.

Computational analysis of cysteine proteases (Clan CA, Family Cl) of Leishmania major to find potential epitopic regions

Leishmania is associated with a broad spectrum of diseases, ranging from simple cutaneous to invasive visceral leishmaniasis. Here, the sequences of ten cysteine proteases of types A, B and C of Leishmania major were obtained from GeneDB database. Prediction of MHC class I epitopes of these cysteine proteases was performed by NetCTL program version 1.2. In addition, by using BcePred server, different structural properties of the proteins were predicted to find out their potential B cell epitopes. According to this computational analysis, nine regions were predicted as B cell epitopes. The results provide useful information for designing peptide-based vaccines.

Qualitative and quantitative analysis of peptide microarray binding experiments using SVM-PEPARRAY

A main objective of analyzing peptide array-based binding experiments is to uncover the relationship between a peptide sequence and the binding outcome. Limited by the peptide array technologies available for applications, few attempts have been made to construct qualitative or quantitative models that depict the peptide sequence:binding strength relationships in peptide microarray-based binding studies. There has been a long history of similar modeling efforts based on low-throughput binding data in the areas of T-cell epitope screening and kinase substrate mapping, however. The keen needs in peptide array applications and the success of the modeling efforts in related fields have prompted us to develop SVM-PEPARRAY, a Web-based program capable of constructing qualitative and quantitative models based on peptide microarray binding datasets using support vector machine (SVM) modeling methods. We expect that such modeling analysis will allow researchers to quickly extract sequence-based biological information from improved peptide array binding results and provide more precise and accurate information about the biological systems investigated.

Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors

Major histocompatibility complex (MHC) molecules bind short peptides resulting from intracellular processing of foreign and self proteins, and present them on the cell surface for recognition by T-cell receptors. We propose a new robust approach to quantitatively model the binding affinities of MHC molecules by quantitative structure-activity relationships (QSAR) that use the physical-chemical amino acid descriptors E1-E5. These QSAR models are robust, sequence-based, and can be used as a fast and reliable filter to predict the MHC binding affinity for large protein databases.

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

BACKGROUND

T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots.

RESULTS

Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population.

CONCLUSION

Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design.

Processing sites are different in the generation of HLA-A2.1-restricted, T cell reactive tumor antigen epitopes and viral epitopes

In order to improve the processing efficiency of T cell tumor antigen epitopes, this bioinformatic study compares proteolytic sites in the generation of 47 experimentally identified HLA-A2.1-restricted immunodominant tumor antigen epitopes to those of 52 documented HLA-A2.1-restricted immunodominant viral antigen epitopes. Our results show that the amino acid frequencies in the C-terminal cleavage sites of the tumor antigen epitopes, as well as several positions within the 10 amino acid (aa) flanking regions, are significantly different from those of the viral antigen epitopes. In the 9 amino acid epitope region, frequencies differed somewhat in the secondary-anchored amino acid residues on E3 (the third aa of the epitope), E4, E6, E7 and E8; however, frequencies in the primary-anchored positions, on E2 and E9, for binding in the HLA-A2.1 groove, remained almost identical. The most frequently occurring amino acid pairs in both N-terminal and C-terminal cleavage sites in the generation of tumor antigen epitopes were different from those of the viral antigen epitopes. Our findings demonstrate for the first time that these two groups of epitopes may be cleaved by distinct sets of proteasomes and peptidases or similar enzymes with lower efficiencies for tumor epitopes. In the future, in order to more effectively generate tumor antigen epitopes, targeted activation of the immunoproteasomes and peptidases that mediate the cleavage of viral epitopes could be achieved, thus enhancing our potential for antigen-specific tumor immunotherapy.

Toward a multiscale model of antigen presentation in immunity

A functioning immune system and the process of antigen presentation in particular encompass events that occur at multiple length and time scales. Despite a wealth of information in the biological literature regarding each of these scales, no single representation synthesizing this information into a model of the overall immune response as it depends on antigen presentation is available. In this article, we outline an approach for integrating information over relevant biological and temporal scales to generate such a representation for major histocompatibility complex class II-mediated antigen presentation. In addition, we begin to address how such models can be used to answer questions about mechanisms of infection and new strategies for treatment and vaccines.

Optimally-connected hidden markov models for predicting MHC-binding peptides

Hidden Markov models (HMMs) are one of various methods that have been applied to prediction of major histo-compatibility complex (MHC) binding peptide. In terms of model topology, a fully-connected HMM (fcHMM) has the greatest potential to predict binders, at the cost of intensive computation. While a profile HMM (pHMM) performs dramatically fewer computations, it potentially merges overlapping patterns into one which results in some patterns being missed. In a profile HMM a state corresponds to a position on a peptide while in an fcHMM a state has no specific biological meaning. This work proposes optimally-connected HMMs (ocHMMs), which do not merge overlapping patterns and yet, by performing topological reductions, a model's connectivity is greatly reduced from an fcHMM. The parameters of ocHMMs are initialized using a novel amino acid grouping approach called "multiple property grouping." Each group represents a state in an ocHMM. The proposed ocHMMs are compared to a pHMM implementation using HMMER, based on performance tests on two MHC alleles HLA (Human Leukocyte Antigen)-A*0201 and HLA-B*3501. The results show that the heuristic approaches can be adjusted to make an ocHMM achieve higher predictive accuracy than HMMER. Hence, such obtained ocHMMs are worthy of trial for predicting MHC-binding peptides.

QSAR method for prediction of protein-peptide binding affinity: application to MHC class I molecule HLA-A*0201

The support vector machine (SVM), which is a novel algorithm from the machine learning community, was used to develop quantitative structure-activity relationship (QSAR) models for predicting the binding affinity of 152 nonapeptides, which can bind to class I MHC HLA-A*201 molecule. Each peptide was represented by a large pool of descriptors including constitutional, topological descriptors and physical-chemical properties. The heuristic method (HM) was then used to search the descriptor space for selecting the proper ones responsible for binding affinity. The four descriptors were obtained to build linear models based on HM and nonlinear models based on SVM method. The best results are found using SVM: root mean-square (RMS) errors for training, test and whole data set were 0.383, 0.385 and 0.384, respectively. This paper allow the prediction of the binding affinity of new, untested peptides and, through the analysis of contribution of each parameter of different residue at specific position of peptidic ligands, to understand nature of the forces governing binding behavior and suggest new ideas for further synthesis of high-affinity peptides.

Wave function analysis of MHC-peptide interactions

We have carried out an analysis of the wave function data for three MHC-peptide complexes: HLA-DRbeta1*0101-HA, HLA-DRbeta1*0401-HA and HLA-DRbeta1*0401-Col. We used quantum chemistry computer programs to generate wave function coefficients for these complexes, from which we obtained both molecular and atomic orbital data for both pocket and peptide amino acids within each pocket region. From these discriminated data, interaction molecular orbitals (IMOs) were identified as those with large and similar atomic orbital coefficient contributions from both pocket and peptide amino acids. The present results correlate well with our previous research where only electrostatic moments were used to explore molecular component interactions. Furthermore, we show a quantum chemical methodology to produce more fine-grained results concerning amino acid behavior in the MHC-peptide interaction.

Quantitative structure-activity relationships and the prediction of MHC supermotifs

The underlying assumption in quantitative structure-activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele. Supermotif definition is the initial in silico step of epitope-based vaccine design. The first QSAR method we review here--the additive method--is based on the assumption that the binding affinity of a peptide depends on contributions from both amino acids and the interactions between them. The second method is a 3D-QSAR method: comparative molecular similarity indices analysis (CoMSIA). Both methods were applied to 771 peptides binding to 9 HLA alleles. Five of the alleles (A*0201, A*0202, A*0203, A*0206 and A*6802) belong to the HLA-A2 superfamily and the other four (A*0301, A*1101, A*3101 and A*6801) to the HLA-A3 superfamily. For each superfamily, supermotifs defined by the two QSAR methods agree closely and are supported by many experimental data.

Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes.

Toward the quantitative prediction of T-cell epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201

It would be useful for vaccine development to develop a method of rapidly identifying peptide epitopes. In this paper, the empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models yielded a q2 = 0.5724 and a high rpred2 = 0.6955. The standard regression coefficients indicated that the hydrophobic interactions played an important role in peptide-MHC molecule binding and predicted the specific amino acid residue essential at a certain position of the peptide. The approach tested in the current paper is highly complementary to many of the methods described in references and possesses good predictability. It is a rapid and convenient method to detect high affinity peptide epitopes.

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.

Quantum chemical analysis explains hemagglutinin peptide–MHC Class II molecule HLA-DRβ1*0101 interactions

We present a new method to explore interactions between peptides and major histocompatibility complex (MHC) molecules using the resultant vector of the three principal multipole terms of the electrostatic field expansion. Being that molecular interactions are driven by electrostatic interactions, we applied quantum chemistry methods to better understand variations in the electrostatic field of the MHC Class II HLA-DRbeta1*0101-HA complex. Multipole terms were studied, finding strong alterations of the field in Pocket 1 of this MHC molecule, and weak variations in other pockets, with Pocket 1>>Pocket 4>Pocket 9 approximately Pocket 7>Pocket 6. Variations produced by "ideal" amino acids and by other occupying amino acids were compared. Two types of interactions were found in all pockets: a strong unspecific one (global interaction) and a weak specific interaction (differential interaction). Interactions in Pocket 1, the dominant pocket for this allele, are driven mainly by the quadrupole term, confirming the idea that aromatic rings are important in these interactions. Multipolar analysis is in agreement with experimental results, suggesting quantum chemistry methods as an adequate methodology to understand these interactions.

Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

Genome and genomics of schistosomes

Schistosomes infect over 200 million people and 600 million are at risk. Genomics and post-genomic studies of schistosomes will contribute greatly to developing new reagents for diagnostic purposes and new vaccines that are of interest to the biotechnology industry. In this review, the most recent advances in these fields as well as new projects and future perspectives will de described. A vast quantity of data is publicly available, including short cDNA and genomic sequences, complete large genomic fragments, and the mitochondrial genomes of three species of the genus Schistosoma. The physical structure of the genome is being studied by physically mapping large genomic fragments and characterizing the highly abundant repetitive DNA elements. Bioinformatic manipulations of the data have already been carried out, mostly dealing with the functional analysis of the genes described. Specific search tools have also been developed. Sequence variability has been used to better understand the phylogeny of the species and for population studies, and new polymorphic genomic markers are currently being developed. The information generated has been used for the development of post-genomic projects. A small microarray detected genes that were differentially expressed between male and female worms. The identification of two-dimensional spots by mass spectrometry has also been demonstrated.

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.

A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif

The 3D-QSAR CoMSIA technique was applied to a set of 458 peptides binding to the five most widespread HLA-A2-like alleles: A*0201, A*0202, A*0203, A*0206 and A*6802. Models comprising the main physicochemical properties (steric bulk, electron density, hydrophobicity and hydrogen-bond formation abilities) were obtained with acceptable predictivity (q2 ranged from 0.385 to 0.683). The use of coefficient contour maps allowed an A2-supermotif to be identified based on common favoured and disfavoured areas. The CoMSIA definition for the best HLA-A2 binder is as follows: hydrophobic aromatic amino acid at position 1; hydrophobic bulky side chains at positions 2, 6 and 9; non-hydrogen-bond-forming amino acids at position 3; small aliphatic hydrogen-bond donors at position 4; aliphatic amino acids at position 5; small aliphatic side chains at position 7; and small aliphatic hydrophilic and hydrogen-bond forming amino acids at position 8.