Specific and general HLA-DR binding motifs: comparison of algorithms

In silico design of a T-cell epitope vaccine candidate for parasitic helminth infection

Trichuris trichiura is a parasite that infects 500 million people worldwide, leading to colitis, growth retardation and Trichuris dysentery syndrome. There are no licensed vaccines available to prevent Trichuris infection and current treatments are of limited efficacy. Trichuris infections are linked to poverty, reducing children's educational performance and the economic productivity of adults. We employed a systematic, multi-stage process to identify a candidate vaccine against trichuriasis based on the incorporation of selected T-cell epitopes into virus-like particles. We conducted a systematic review to identify the most appropriate in silico prediction tools to predict histocompatibility complex class II (MHC-II) molecule T-cell epitopes. These tools were used to identify candidate MHC-II epitopes from predicted ORFs in the Trichuris genome, selected using inclusion and exclusion criteria. Selected epitopes were incorporated into Hepatitis B core antigen virus-like particles (VLPs). Bone marrow-derived dendritic cells and bone marrow-derived macrophages responded in vitro to VLPs irrespective of whether the VLP also included T-cell epitopes. The VLPs were internalized and co-localized in the antigen presenting cell lysosomes. Upon challenge infection, mice vaccinated with the VLPs+T-cell epitopes showed a significantly reduced worm burden, and mounted Trichuris-specific IgM and IgG2c antibody responses. The protection of mice by VLPs+T-cell epitopes was characterised by the production of mesenteric lymph node (MLN)-derived Th2 cytokines and goblet cell hyperplasia. Collectively our data establishes that a combination of in silico genome-based CD4+ T-cell epitope prediction, combined with VLP delivery, offers a promising pipeline for the development of an effective, safe and affordable helminth vaccine.

A novel method to measure HLA-DM-susceptibility of peptides bound to MHC class II molecules based on peptide binding competition assay and differential IC(50) determination

HLA-DM (DM) functions as a peptide editor that mediates the exchange of peptides loaded onto MHCII molecules by accelerating peptide dissociation and association kinetics. The relative DM-susceptibility of peptides bound to MHCII molecules correlates with antigen presentation and immunodominance hierarchy, and measurement of DM-susceptibility has been a key effort in this field. Current assays of DM-susceptibility, based on differential peptide dissociation rates measured for individually labeled peptides over a long time base, are difficult and cumbersome. Here, we present a novel method to measure DM-susceptibility based on peptide binding competition assays performed in the presence and absence of DM, reported as a delta-IC(50) (change in 50% inhibition concentration) value. We simulated binding competition reactions of peptides with various intrinsic and DM-catalyzed kinetic parameters and found that under a wide range of conditions the delta-IC(50) value is highly correlated with DM-susceptibility as measured in off-rate assay. We confirmed experimentally that DM-susceptibility measured by delta-IC(50) is comparable to that measured by traditional off-rate assay for peptides with known DM-susceptibility hierarchy. The major advantage of this method is that it allows simple, fast and high throughput measurement of DM-susceptibility for a large set of unlabeled peptides in studies of the mechanism of DM action and for identification of CD4+ T cell epitopes.

Scrutinizing MHC-I binding peptides and their limits of variation

Designed peptides that bind to major histocompatibility protein I (MHC-I) allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2K^b is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2K^b in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012).

Future success in vaccine development will critically depend on identifying potent epitopes with reduced side effects. Among such candidate molecules, immunogenic peptides binding to major histocompatibility protein I (MHC-I) represent a preferred class of biomolecules for vaccine design. Computational models assist in the selection of the best candidate peptides by providing a mathematical rationale for antigen recognition by MHC-I. Here we present a machine-learning model that was trained on recognizing features of known MHC-I binding and non-binding peptide sequences with sustained accuracy. We were able to biochemically validate the computational predictions in a direct binding assay measuring complex formation between synthesized candidate peptides and MHC-I. Strong correspondence between the predictions and the experimentally determined binding potential corroborate the machine-learning model as viable for future antigen design. Thus, our study provides a concept for rapidly finding innovative MHC-I binding peptides with limited experimental effort.

Computational Resources for MHC Ligand Identification

Advances in the high-throughput determination of functional modulators of major histocompatibility complex (MHC) and improved computational predictions of MHC ligands have rendered the rational design of immunomodulatory peptides feasible. Proteome-derived peptides and 'reverse vaccinology' by computational means will play a driving role in future vaccine design. Here we review the molecular mechanisms of the MHC mediated immune response, present the computational approaches that have emerged in this area of biotechnology, and provide an overview of publicly available computational resources for predicting and designing new peptidic MHC ligands.

HLA-DM constrains epitope selection in the human CD4 T cell response to vaccinia virus by favoring the presentation of peptides with longer HLA-DM-mediated half-lives

HLA-DM (DM) is a nonclassical MHC class II (MHC II) protein that acts as a peptide editor to mediate the exchange of peptides loaded onto MHC II during Ag presentation. Although the ability of DM to promote peptide exchange in vitro and in vivo is well established, the role of DM in epitope selection is still unclear, especially in human response to infectious disease. In this study, we addressed this question in the context of the human CD4 T cell response to vaccinia virus. We measured the IC(50), intrinsic dissociation t(1/2), and DM-mediated dissociation t(1/2) for a large set of peptides derived from the major core protein A10L and other known vaccinia epitopes bound to HLA-DR1 and compared these properties to the presence and magnitude of peptide-specific CD4(+) T cell responses. We found that MHC II-peptide complex kinetic stability in the presence of DM distinguishes T cell epitopes from nonrecognized peptides in A10L peptides and also in a set of predicted tight binders from the entire vaccinia genome. Taken together, these analyses demonstrate that DM-mediated dissociation t(1/2) is a strong and independent factor governing peptide immunogenicity by favoring the presentation of peptides with greater kinetic stability in the presence of DM.

NEURAL MODELS FOR PREDICTING VIRAL VACCINE TARGETS

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve — AROC > 0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC > 0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS–CoV) membrane protein were predicted by using MULTIPRED.

GM-CSF production allows the identification of immunoprevalent antigens recognized by human CD4+ T cells following smallpox vaccination

The threat of bioterrorism with smallpox and the broad use of vaccinia vectors for other vaccines have led to the resurgence in the study of vaccinia immunological memory. The importance of the role of CD4+ T cells in the control of vaccinia infection is well known. However, more CD8+ than CD4+ T cell epitopes recognized by human subjects immunized with vaccinia virus have been reported. This could be, in part, due to the fact that most of the studies that have identified human CD4+ specific protein-derived fragments or peptides have used IFN-γ production to evaluate vaccinia specific T cell responses. Based on these findings, we reasoned that analyzing a large panel of cytokines would permit us to generate a more complete analysis of the CD4 T cell responses. The results presented provide clear evidence that TNF-α is an excellent readout of vaccinia specificity and that other cytokines such as GM-CSF can be used to evaluate the reactivity of CD4+ T cells in response to vaccinia antigens. Furthermore, using these cytokines as readout of vaccinia specificity, we present the identification of novel peptides from immunoprevalent vaccinia proteins recognized by CD4+ T cells derived from smallpox vaccinated human subjects. In conclusion, we describe a “T cell–driven” methodology that can be implemented to determine the specificity of the T cell response upon vaccination or infection. Together, the single pathogen in vitro stimulation, the selection of CD4+ T cells specific to the pathogen by limiting dilution, the evaluation of pathogen specificity by detecting multiple cytokines, and the screening of the clones with synthetic combinatorial libraries, constitutes a novel and valuable approach for the elucidation of human CD4+ T cell specificity in response to large pathogens.

An introduction to epitope prediction methods and software

In this paper, current prediction methods and algorithms for both T- and B cell epitopes are reviewed, and a comprehensive summary of epitope prediction software and databases currently available online is also provided. This review can offer researchers in this field a sense of direction and insights for future work. However, our main purpose is to introduce clinical and basic biomedical researchers who are not familiar with these biological analysis tools and databases to these online resources and to provide guidance on how to use them effectively.

Building a meta-predictor for MHC class II-binding peptides

Prediction of class II major histocompatibility complex (MHC)-peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this chapter, the procedure of building such a meta-predictor based on Naïve Bayesian approach is introduced. The system is designed in such a way that results obtained from any number of individual predictors can be easily incorporated. This meta-predictor is expected to give users more confidence in the prediction.

A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach

The identification of MHC class II restricted peptide epitopes is an important goal in immunological research. A number of computational tools have been developed for this purpose, but there is a lack of large-scale systematic evaluation of their performance. Herein, we used a comprehensive dataset consisting of more than 10,000 previously unpublished MHC-peptide binding affinities, 29 peptide/MHC crystal structures, and 664 peptides experimentally tested for CD4+ T cell responses to systematically evaluate the performances of publicly available MHC class II binding prediction tools. While in selected instances the best tools were associated with AUC values up to 0.86, in general, class II predictions did not perform as well as historically noted for class I predictions. It appears that the ability of MHC class II molecules to bind variable length peptides, which requires the correct assignment of peptide binding cores, is a critical factor limiting the performance of existing prediction tools. To improve performance, we implemented a consensus prediction approach that combines methods with top performances. We show that this consensus approach achieved best overall performance. Finally, we make the large datasets used publicly available as a benchmark to facilitate further development of MHC class II binding peptide prediction methods.

A critical step in developing immune response against pathogens is the recognition of antigenic peptides presented by MHC class II molecules. Since experiments for MHC class II binding peptide identification are expensive and time consuming, computational tools have been developed as fast alternatives but with inferior performance. Here, we carried out a large-scale systematic evaluation of existing prediction tools with the aim of establishing a benchmark for performance comparison and to identify directions that can further improve prediction performance. We provide an unbiased ranking of the performance of publicly available MHC class II prediction tools and demonstrate that the MHC class II prediction tools did not perform as well as the MHC class I tools. In addition, we show that the size of training data and the correct identification of the binding core are the two factors limiting the performance of existing tools. Finally, we make available to the immunology community a large dataset to facilitate the evaluation and development of MHC class II binding prediction tools.

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.

A probabilistic meta-predictor for the MHC class II binding peptides

Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.

Human CD4+ T cell epitopes from vaccinia virus induced by vaccination or infection

Despite the importance of vaccinia virus in basic and applied immunology, our knowledge of the human immune response directed against this virus is very limited. CD4⁺ T cell responses are an important component of immunity induced by current vaccinia-based vaccines, and likely will be required for new subunit vaccine approaches, but to date vaccinia-specific CD4⁺ T cell responses have been poorly characterized, and CD4⁺ T cell epitopes have been reported only recently. Classical approaches used to identify T cell epitopes are not practical for large genomes like vaccinia. We developed and validated a highly efficient computational approach that combines prediction of class II MHC-peptide binding activity with prediction of antigen processing and presentation. Using this approach and screening only 36 peptides, we identified 25 epitopes recognized by T cells from vaccinia-immune individuals. Although the predictions were made for HLA-DR1, eight of the peptides were recognized by donors of multiple haplotypes. T cell responses were observed in samples of peripheral blood obtained many years after primary vaccination, and were amplified after booster immunization. Peptides recognized by multiple donors are highly conserved across the poxvirus family, including variola, the causative agent of smallpox, and may be useful in development of a new generation of smallpox vaccines and in the analysis of the immune response elicited to vaccinia virus. Moreover, the epitope identification approach developed here should find application to other large-genome pathogens.

Although the routine use of vaccinia virus for vaccination against smallpox was stopped after eradication of this disease, there is a possibility for an accidental or intentional release of this virus. In response to this challenge, vaccination of at least emergency personnel has been suggested. However, adverse reactions induced by the smallpox vaccine have had a negative impact in the success of this program. For these reasons development of new smallpox vaccines is a public health priority. Identification of strong helper T cell epitopes is central to these efforts. However, identification of T cell epitopes in large genomes like vaccinia is difficult using current screening methods. In this work, we develop a new computational approach for prediction of T cell epitopes, validate it using epitopes already identified by classical methods, and apply it to the prediction of vaccinia epitopes. Twenty-five of 36 peptides containing predicted sequences were recognized by T cells from individuals exposed to vaccinia virus. These peptides are highly conserved across the orthopox virus family and may be useful in development of a new generation of smallpox vaccines and in the analysis of the immune response against vaccinia virus.

Immunogenicity screening in protein drug development

Most therapeutic proteins in clinical trials or on the market are, to a variable extent, immunogenic. Formation of antidrug antibodies poses a risk that should be assessed during drug development, as it possibly compromises drug safety and alters pharmacokinetics. The generation of these antibodies is critically dependent on the presence of T helper cell epitopes, which have classically been identified by in vitro methods using blood cells from human donors. As a novel development, in silico methods that identify T cell epitopes have been coming on line. These methods are relatively inexpensive and allow the mapping of epitopes from virtually all human leukocyte antigen molecules derived from a wide genetic background. In vitro assays remain important, but guided by in silico data they can focus on selected peptides and human leukocyte antigen haplotypes, thereby significantly reducing time and cost.

Engineered promiscuous T helper peptides for the induction of immune responses

Following recognition of antigens by T helper (Th) lymphocytes, T cell help is elicited to induce humoral and cellular immune responses. These antigens are presented as short peptides, T helper peptides (THP), bound to MHC class II molecules. Since both endogenous THP (from antigens of interest) or exogenous THP (not encompassed by the sequence of the antigen of interest) are able to elicit T cell help, we decided to engineer promiscuous exogenous THP capable of binding to several HLA-DR molecules, in order to cover an important proportion of the human population. Some of these exogenous THP were able to bind to all seven HLA-DR molecules tested and were immunogenic in vivo in HLA-DR4 transgenic mice. Among them, peptides p37, p62 and p45 elicited Th1 cytokine profiles in vivo, providing help for the induction of potent CTL responses. Finally, in vitro stimulation assays carried out using human cells, showed that these peptides could induce T cell responses using cells obtained from individuals with a broad spectrum of HLA-DR molecules. Thus, engineered exogenous THP may be a valuable tool for the induction of immune responses in a large proportion of human population.

Melan-A/MART-1-Specific CD4 T Cells in Melanoma Patients: Identification of New Epitopes and Ex Vivo Visualization of Specific T Cells by MHC Class II Tetramers

Over the past decade, many efforts have been made to identify MHC class II-restricted epitopes from different tumor-associated Ags. Melan-A/MART-1(26-35) parental or Melan-A/MART-1(26-35(A27L)) analog epitopes have been widely used in melanoma immunotherapy to induce and boost CTL responses, but only one Th epitope is currently known (Melan-A51-73, DRB1*0401 restricted). In this study, we describe two novel Melan-A/MART-1-derived sequences recognized by CD4 T cells from melanoma patients. These epitopes can be mimicked by peptides Melan-A27-40 presented by HLA-DRB1*0101 and HLA-DRB1*0102 and Melan-A25-36 presented by HLA-DQB1*0602 and HLA-DRB1*0301. CD4 T cell clones specific for these epitopes recognize Melan-A/MART-1+ tumor cells and Melan-A/MART-1-transduced EBV-B cells and recognition is reduced by inhibitors of the MHC class II presentation pathway. This suggests that the epitopes are naturally processed and presented by EBV-B cells and melanoma cells. Moreover, Melan-A-specific Abs could be detected in the serum of patients with measurable CD4 T cell responses specific for Melan-A/MART-1. Interestingly, even the short Melan-A/MART-1(26-35(A27L)) peptide was recognized by CD4 T cells from HLA-DQ6+ and HLA-DR3+ melanoma patients. Using Melan-A/MART-1(25-36)/DQ6 tetramers, we could detect Ag-specific CD4 T cells directly ex vivo in circulating lymphocytes of a melanoma patient. Together, these results provide the basis for monitoring of naturally occurring and vaccine-induced Melan-A/MART-1-specific CD4 T cell responses, allowing precise and ex vivo characterization of responding T cells.

Characterization of T-cell responses against immunodominant epitopes from hepatitis C virus E2 and NS4a proteins

Successful clearance of hepatitis C virus (HCV) infection has been associated with strong cellular immune responses against viral antigens. However, although the magnitude of these responses is clearly important for viral eradication, more studies are needed to unravel the fine specificity of the protective anti-HCV immunity in infected patients. This was the aim of the present study. Overlapping peptides spanning the sequence of HCV E2 and NS4a proteins were used to stimulate T cells from patients with chronic hepatitis C divided into three groups: naïve patients, patients who exhibited sustained response to interferon (IFN)-alpha therapy and patients who failed to respond to the treatment. Interleukin-2 production by stimulated cells was measured in each case. Patients with sustained response to therapy had stronger responses to E2 peptides than nonresponders, whereas naïve patients demonstrated intermediate reactivity. In the case of NS4a, responses against peptides where similar in all groups of patients. Analysis of the peptides recognized by T cells showed that responses were broad and heterogeneous, and some immunodominant epitopes, preferentially recognized by patients exhibiting sustained response to treatment, were found. These results confirm the role of cellular immune responses in viral clearance, and stress the importance of immunodominant regions within HCV antigens. These viral sequences may represent valuable immunogens for preparation of therapeutic or prophylactic vaccines.

A meta-predictor for MHC class II binding peptides based on Naïve Bayesian approach

Prediction of class II MHC-peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this paper, we introduce a procedure of building such a meta-predictor based on Naïve Bayesian approach. The system is designed in such a way that results obtained from any number of individual predictors can be easily incorporated. This meta-predictor is expected to give users more confidence in the prediction.

Prediction of MHC class II binding peptides based on an iterative learning model

BACKGROUND

Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides.

RESULTS

A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of prediction that is competitive compared to the advanced predictors (the Gibbs sampler and TEPITOPE). The average areas under the ROC curve obtained from one variant of our model are 0.753 and 0.715 for the original and homology reduced benchmark sets, respectively. The corresponding values are respectively 0.744 and 0.673 for the Gibbs sampler and 0.702 and 0.667 for TEPITOPE.

CONCLUSION

The iterative learning procedure appears to be effective in prediction of MHC class II binders. It offers an alternative approach to this important prediction problem.

Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications

Prediction of which peptides can bind major histocompatibility complex (MHC) molecules is commonly used to assist in the identification of T cell epitopes. However, because of the large numbers of different MHC molecules of interest, each associated with different predictive tools, tool generation and evaluation can be a very resource intensive task. A methodology commonly used to predict MHC binding affinity is the matrix or linear coefficients method. Herein, we described Average Relative Binding (ARB) matrix methods that directly predict IC(50) values allowing combination of searches involving different peptide sizes and alleles into a single global prediction. A computer program was developed to automate the generation and evaluation of ARB predictive tools. Using an in-house MHC binding database, we generated a total of 85 and 13 MHC class I and class II matrices, respectively. Results from the automated evaluation of tool efficiency are presented. We anticipate that this automation framework will be generally applicable to the generation and evaluation of large numbers of MHC predictive methods and tools, and will be of value to centralize and rationalize the process of evaluation of MHC predictions. MHC binding predictions based on ARB matrices were made available at http://epitope.liai.org:8080/matrix web server.

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.

HLA-DRB1*1501 risk association in multiple sclerosis may not be related to presentation of myelin epitopes

Susceptibility to multiple sclerosis (MS) is associated genetically with human leucocyte antigen (HLA) class II alleles, including DRB1*1501, DRB5*0101, and DQB1*0602, and it is possible that these alleles contribute to MS through an enhanced ability to present encephalitogenic myelin peptides to pathogenic T cells. HLA-DRB1*1502, which contains glycine instead of valine at position 86 of the P1 peptide-binding pocket, is apparently not genetically associated with MS. To identify possible differences between these alleles in their antigen-presenting function, we determined if T-cell responses to known DRB1*1501-restricted myelin peptides might be diminished or absent in transgenic (Tg) DRB1*1502-expressing mice. We found that Tg DRB1*1502 mice had moderate to strong T-cell responses to several myelin peptides with favorable DRB1*1501 binding motifs, notably myelin oligodendrocyte glycoprotein (MOG)-35-55 (which was also encephalitogenic), proteolipid protein (PLP)-95-116, and MOG-194-208, as well as other PLP and MOG peptides. These peptides, with the exception of MOG-194-208, were also immunogenic in healthy human donors expressing either DRB1*1502 or DRB1*1501. In contrast, the DRB1*1502 mice had weak or absent responses to peptides with unfavorable DRB1*1501 binding motifs. Overall, none of the DRB1*1501-restricted myelin peptides tested selectively lacked immunogenicity in association with DRB1*1502. These results indicate that the difference in risk association with MS of DRB1*1501 versus DRB1*1502 is not due to a lack of antigen presentation by DRB1*1502, at least for this set of myelin peptides, and suggest that other mechanisms involving DRB1*1501 may account for increased susceptibility to MS.

Identification and characterization of a T-helper peptide from carcinoembryonic antigen

PURPOSE

The purpose of this research was to identify promiscuous T-helper cell determinants (THd) from carcinoembryonic antigen (CEA) to be used to prime T-cell help for cancer therapy. CEA was selected because this antigen is expressed in an important variety of carcinomas.

EXPERIMENTAL DESIGN

Potential promiscuous THd from CEA were predicted using available computer algorithms. Predicted peptides were synthesized and tested in binding experiments to different HLA-DR molecules. Binder peptides were then used to prime T-cell responses both in vitro and in vivo.

RESULTS

Twenty 15-mer peptides from CEA were predicted to bind to different HLA-DR molecules. The promiscuous character of these peptides was demonstrated in binding experiments. Fifteen of 20 peptides tested were able to bind to HLA-DR4, but only CEA (625-639) was shown to be presented after processing of recombinant CEA. CEA (625-639) was also found to be presented by HLA-DR53. Moreover, immunization of HLA-DR4 transgenic mice with CEA (625-639) in conjunction with class I epitope OVA (257-264), induced a CTL response specific of OVA (257-264).

CONCLUSIONS

CEA (625-639) might be a relevant promiscuous THd peptide for cancer therapy.

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.

Structural factors contributing to DM susceptibility of MHC class II/peptide complexes

Peptide loading of MHC class II (MHCII) molecules is assisted by HLA-DM, which releases invariant chain peptides from newly synthesized MHCII and edits the peptide repertoire. Determinants of susceptibility of peptide/MHCII complexes to DM remain controversial, however. Here we have measured peptide dissociation in the presence and the absence of DM for 36 different complexes of varying intrinsic stability. We found large variations in DM susceptibility for different complexes using either soluble or full-length HLA-DM. The DM effect was significantly less for unstable complexes than for stable ones, although this correlation was modest. Peptide sequence- and allele-dependent interactions along the entire length of the Ag binding groove influenced DM susceptibility. We also observed differences in DM susceptibility during peptide association. Thus, the peptide repertoire displayed to CD4(+) T cells is the result of a mechanistically complicated editing process and cannot be simply predicted from the intrinsic stability of the complexes in the absence of DM.

Prediction of promiscuous peptides that bind HLA class I molecules

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, MULTIPRED, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. MULTIPREDis both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. MULTIPREDreplaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that MULTIPRED can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-A11 binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes.