Naturally processed HLA class II peptides reveal highly conserved immunogenic flanking region sequence preferences that reflect antigen processing rather than peptide-MHC interactions

Relationship between potential aggregation-prone regions and HLA-DR-binding T-cell immune epitopes: implications for rational design of novel and follow-on therapeutic antibodies

Aggregation and unwanted immunogenicity are hurdles to avoid in successful commercial development of antibody-based therapeutics. In this article, the relationship between aggregation-prone regions (APRs), capable of forming cross-β motifs/amyloid fibrils, and major histocompatibility complex class II-restricted human leukocyte antigen (HLA)-DR-binding T-cell immune epitopes (TcIEs) is analyzed using amino acid sequences of 25 therapeutic antibodies, 55 TcIEs recognized by T-regulatory cells (tregitopes), 1000 randomly generated 15-residue-long peptides, 2257 human self-TcIEs (autoantigens), and 11 peptides in HLA-peptide cocrystal structures. Sequence analyses from these diverse sources consistently show a high level of correlation between APRs and TcIEs: approximately one-third of TcIEs contain APRs, but the majority of APRs occur within TcIE regions (TcIERs). Tregitopes also contain APRs. Most APR-containing TcIERs can bind multiple HLA-DR alleles, suggesting that aggregation-driven adverse immune responses could impact a broad segment of patient population. This article has identified common molecular sequence-structure loci that potentially contribute toward both manufacturability and safety profiles of the therapeutic antibodies, thereby laying a foundation for simultaneous optimization of these attributes in novel and follow-on candidates. Incidence of APRs within TcIERs is not special to biotherapeutics, self-TcIEs from human proteins, involved in various diseases, also contain predicted APRs and experimentally proven amyloid-fibril-forming peptide sequence portions.

Modification of the carboxy-terminal flanking region of a universal influenza epitope alters CD4⁺ T-cell repertoire selection

Human CD4(+) αβ T cells are activated via T-cell receptor recognition of peptide epitopes presented by major histocompatibility complex (MHC) class II (MHC-II). The open ends of the MHC-II binding groove allow peptide epitopes to extend beyond a central nonamer core region at both the amino- and carboxy-terminus. We have previously found that these non-bound C-terminal residues can alter T cell activation in an MHC allele-transcending fashion, although the mechanism for this effect remained unclear. Here we show that modification of the C-terminal peptide-flanking region of an influenza hemagglutinin (HA(305-320)) epitope can alter T-cell receptor binding affinity, T-cell activation and repertoire selection of influenza-specific CD4(+) T cells expanded from peripheral blood. These data provide the first demonstration that changes in the C-terminus of the peptide-flanking region can substantially alter T-cell receptor binding affinity, and indicate a mechanism through which peptide flanking residues could influence repertoire selection.

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points.

NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign.

Dana-Farber repository for machine learning in immunology

The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.

Interaction of allergens, major histocompatibility complex molecules, and T cell receptors: a 'ménage à trois' that opens new avenues for therapeutic intervention in type I allergy

T cells are major players in the initiation and perpetuation of the allergic immune response. In this review, we summarize the current knowledge on allergen recognition by T lymphocytes and address the components of the trimeric recognition complex: T cell receptors, major histocompatibility complex molecules, and allergen-derived peptides. Furthermore, possible implications of this scientific background for future therapeutic developments are discussed.

Predicting peptide binding to Major Histocompatibility Complex molecules

The Major Histocompatibility Complex (MHC) constitutes an important part of the human immune system. During infection, pathogenic proteins are processed into peptide fragments by the antigen processing machinery. These peptides bind to MHC molecules and the MHC-peptide complex is then transported to the cell membrane where it elicits an immune response via T-cell binding. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. One of the most challenging aspects of this area of research is understanding the specificity and sensitivity of the binding process. An empirical approach to the problem is unfeasible as there are over 512 billion potential binding peptides for each MHC molecule. Computational approaches offer the promise of predicting peptide binding, thus dramatically reducing the number of peptides proceeding to experimental verification. Various bioinformatic approaches have been developed to predict whether or not a particular peptide will bind to a particular MHC allele. Currently, peptide binding prediction methods can be categorised into three major groups: motif- and scoring matrix-based methods, artificial intelligence- (AI-) based methods, and structure-based methods. The first two are sequence-based approaches and are generally based on common sequence motifs in peptides known to bind to MHC molecules. The structure-based approach concerns the structural features and the distribution of energy between the binding peptide and the MHC molecule. Although knowledge of the molecular structure of the MHC molecules is expected to lead to better predictions of peptide binding, the development of structure-based methods has been relatively slow compared to sequence-based methods. Comparisons of various methods showed that the best sequence-based methods significantly outperform structure-based methods. This may be improved by producing more structures and binding data desperately needed by many alleles, especially class II molecules. On the other hand, the large number of verification methods and indicators used by structure-based studies hinders critical evaluation of the methods. Adopting commonly used assessment procedures can demonstrate the relative performance of structure-based methods in a straightforward comparison with other methods. This review provides an overview of current methods for predicting peptide binding to the MHC, with a focus on structure-based methods, and explores the potential for future development in this area.

Genetic engineering of trimers of hypoallergenic fragments of the major birch pollen allergen, Bet v 1, for allergy vaccination

An immunotherapy trial performed in allergic patients with hypoallergenic recombinant fragments, comprising aa 1-74 and 75-160 of the major birch pollen allergen, Bet v 1, has indicated that the induction of allergen-specific IgG responses may be an important mechanism of this treatment. To investigate whether the immunogenicity of the rBet v 1 fragments can be increased, recombinant trimers of the fragments were produced. For this purpose, DNA trimers of rBet v 1 aa 1-74 as well as of rBet v 1 aa 75-160 were subcloned into expression plasmid pET 17b, expressed in Escherichia coli and purified. The fragments as well as the fragment trimers showed a reduced IgE-binding capacity and allergenic activity compared to rBet v 1 wildtype when tested in allergic patients. Both rBet v 1 aa 75-160 monomer and trimer induced high titers of allergen-specific IgG1 Abs in mice. Interestingly, rBet v 1 aa 1-74 trimer induced a much higher IgG(1) response to rBet v 1 than rBet v 1 aa 1-74 monomer. Consequently, IgG Abs induced with the rBet v 1 aa 1-74 trimer inhibited birch pollen allergic patients' IgE-binding 10-fold more efficiently than IgG Abs induced with the monomer. Our data show that the immunogenicity of allergy vaccines can be increased by oligomerization.

State of the art and challenges in sequence based T-cell epitope prediction

Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the field has evolved significantly. Methods have now been developed that produce highly accurate binding predictions for many alleles and integrate both proteasomal cleavage and transport events. Moreover have so-called pan-specific methods been developed, which allow for prediction of peptide binding to MHC alleles characterized by limited or no peptide binding data. Most of the developed methods are publicly available, and have proven to be very useful as a shortcut in epitope discovery. Here, we will go through some of the history of sequence-based predictions of helper as well as cytotoxic T cell epitopes. We will focus on some of the most accurate methods and their basic background.

Quantitative prediction of MHC-II binding affinity using particle swarm optimization

OBJECTIVE

Helper T-cell epitopes (Th epitopes) are the basic units which activate helper T-cell's immune response, and they are helpful for understanding the immune mechanism and developing vaccines. Peptide and major histocompatibility complex class II (MHC-II) binding is an important prerequisite event for helper T-cell immune response, and the binding peptides are usually recognized as Th epitopes, therefore we can identify Th epitopes by predicting MHC-II binding peptides. Recently, instead of differentiating the peptides as binder or non-binder, researchers are more interested in predicting binding affinities between MHC-II molecules and peptides.

METHODOLOGY

Motivated by the collective search strategy of the particle swarm optimization algorithm (PSO), a method was developed to make the direct prediction of peptide binding affinity. In our paper, PSO was utilized to search for the optimal position-specific scoring matrices (PSSM) from the experimentally derived allele-related peptides, and then the prediction models were constructed based on the matrices. Moreover, we evaluated several factors influencing the binding affinity, including peptide length and flanking residue length, and incorporated them into our models.

RESULTS

The performance of our models was evaluated on three MHC-II alleles from AntiJen database and 14 MHC-II alleles from IEDB database. When compared to the existing popular quantitative methods such as MHCPred, SVRMHC, ARB and SMM-align, our method can give out better performance in terms of correlation coefficient (r) and area under ROC curve (AUC). In addition, the results demonstrated that the performance of models was further improved by incorporating the global length information, achieving average AUC value of 0.7534 and average r value of 0.4707.

CONCLUSIONS

Quantitative prediction of MHC-II binding affinity can be modeled as an optimization problem. Our PSO based method can find the optimal PSSM, which will then be used for identifying the binding cores and scoring the binding affinities of the peptides. The experiment results show that our method is promising for the prediction of MHC-II binding affinity.

MHC class II epitope predictive algorithms

SUMMARY

Major histocompatibility complex class II (MHC-II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been developed to predict peptide-MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC-II. Despite recent progress in method development, the predictive performance for MHC-II remains significantly lower than what can be obtained for MHC-I. One reason for this is that the MHC-II molecule is open at both ends allowing binding of peptides extending out of the groove. The binding core of MHC-II-bound peptides is therefore not known a priori and the binding motif is hence not readily discernible. Recent progress has been obtained by including the flanking residues in the predictions. All attempts to make ab initio predictions based on protein structure have failed to reach predictive performances similar to those that can be obtained by data-driven methods. Thousands of different MHC-II alleles exist in humans. Recently developed pan-specific methods have been able to make reasonably accurate predictions for alleles that were not included in the training data. These methods can be used to define supertypes (clusters) of MHC-II alleles where alleles within each supertype have similar binding specificities. Furthermore, the pan-specific methods have been used to make a graphical atlas such as the MHCMotifviewer, which allows for visual comparison of specificities of different alleles.

Limitations of Ab initio predictions of peptide binding to MHC class II molecules

Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

BACKGROUND

The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event.

RESULTS

Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods.

CONCLUSION

The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

Towards in silico design of epitope-based vaccines

BACKGROUND

Epitope-based vaccines (EVs) make use of immunogenic peptides (epitopes) to trigger an immune response. Due to their manifold advantages, EVs have recently been attracting growing interest. The success of an EV is determined by the choice of epitopes used as a basis. However, the experimental discovery of candidate epitopes is expensive in terms of time and money. Furthermore, for the final choice of epitopes various immunological requirements have to be considered.

METHODS

Numerous in silico approaches exist that can guide the design of EVs. In particular, computational methods for MHC binding prediction have already become standard tools in immunology. Apart from binding prediction and prediction of antigen processing, methods for epitope design and selection have been suggested. We review these in silico approaches for epitope discovery and selection along with their strengths and weaknesses. Finally, we discuss some of the obvious problems in the design of EVs.

CONCLUSION

State-of-the-art in silico approaches to MHC binding prediction yield high accuracies. However, a more thorough understanding of the underlying biological processes and significant amounts of experimental data will be required for the validation and improvement of in silico approaches to the remaining aspects of EV design.

Major histocompatibility complex class II molecule-human immunodeficiency virus peptide analysis using a microarray chip

Identification of major histocompatibility complex (MHC) class II binding peptides is a crucial step in rational vaccine design and immune monitoring. We designed a novel MHC class II molecule-peptide microarray binding assay and evaluated 346 peptides from already identified human immunodeficiency virus (HIV) epitopes and an additional set (n = 206) of 20-mer peptides, overlapping by 15 amino acid residues, from HIV type 1B (HIV-1B) gp160 and Nef as a paradigm. Peptides were attached via the N-terminal part to a linker that covalently binds to the epoxy glass slide. The 552 peptides were printed in triplicate on a single peptide microarray chip and tested for stable formation of MHC class II molecule-peptide complexes using recombinant soluble DRB1*0101(DR1), DRB1*1501(DR2), and DRB1*0401(DR4) molecules. Cluster analysis revealed unique patterns of peptide binding to all three, two, or a single MHC class II molecule. MHC class II binding peptides reside within previously described immunogenic regions of HIV gp160 and Nef, yet we could also identify new MHC class II binding peptides from gp160 and Nef. Peptide microarray chips allow the comprehensive and simultaneous screening of a high number of candidate peptide epitopes for MHC class II binding, guided by subsequent quality data extraction and binding pattern cluster analysis.

3-Layer-based analysis of peptide-MHC interaction: in silico prediction, peptide binding affinity and T cell activation in a relevant allergen-specific model

CD4+ T cells recognize peptides bound to major histocompatibility complex (MHC) class II molecules on the surface of antigen presenting cells by their T cell receptor (TCR). Using a well-characterized allergen-specific model we studied peptide/MHC (pMHC) interactions by combining computational methods with experimental analyses. A 12-mer and an 18-mer peptide, both containing the human leukocyte antigen (HLA)-DR1-restricted, immunodominant T cell epitope of Art v 1, the major mugwort pollen allergen, were compared. A Molecular Dynamics simulation for a real time of 20 ns using GROMACS was performed. To this aim, the peptides were modelled into the binding groove of HLA-DRB1*0101 using different amino acid substitution tools. Binding of synthetic peptides to purified HLA-DRB1*0101 molecules was analysed in competition assays. The potency of the peptides to activate Art v 1-specific T cells was assessed using oligo- and monoclonal Art v 1-specific T cell cultures expanded from mugwort allergic individuals. All approaches revealed that the 18-mer peptide possessed higher HLA DR affinity as compared to the 12-mer. Computer modelling indicated that a loop-like structure within the additional N-terminal peptide flanking region of the 18-mer contributed to the pMHC interaction. Our approach, to combine computational methods validated by experimental results, demonstrates that Molecular Dynamics simulation may be a useful tool for the prediction of pMHC interactions in the future with possible applications in T cell-based immunotherapy e.g. in Type I allergy.

Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research

BACKGROUND

Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW.

RESULTS

Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies - the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (AROC), 17 predictors showed good (AROC > 0.8), 41 showed marginal (AROC > 0.7), and 55 showed poor performance (AROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds.

CONCLUSION

The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.

A Bayesian regression approach to the prediction of MHC-II binding affinity

Peptide-major histocompatibility complex (MHC) binding is an important prerequisite event and has immediate consequences to immune response. Those peptides binding to MHC molecules can activate the T-cell immunity, and they are useful for understanding the immune mechanism and developing vaccines for diseases. Recently, researchers are interested in making prediction about binding affinity instead of differentiating the peptides as binder or non-binder. In this paper, we use sparse Bayesian regression algorithm proposed by Tipping [M.E. Tipping, Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. (2001)] to derive position-specific scoring matrices from allele-related peptides, and develop the models allowing for the prediction of MHC-II binding affinity. We explore the peptide length and peptide flanking residue length's impact on binding affinity, and incorporate these factors into our models to enhance prediction performance. When applied to the datasets from AntiJen database and IEDB database, our method produces better performances than several popular quantitative methods.

Epitope-Based Immunome-Derived Vaccines: A Strategy for Improved Design and Safety

Vaccine science has extended beyond genomics to proteomics and has come to also encompass ‘immunomics,’ the study of the universe of pathogen-derived or neoplasm-derived peptides that interface with B and T cells of the host immune system. It has been theorized that effective vaccines can be developed using the minimum essential subset of T cell and B cell epitopes that comprise the ‘immunome.’ Researchers are therefore using bioinformatics sequence analysis tools, epitope-mapping tools, microarrays, and high-throughput immunology assays to discover the minimal essential components of the immunome. When these minimal components, or epitopes, are packaged with adjuvants in an appropriate delivery vehicle, the complete package comprises an epitope-based immunome-derived vaccine. Such vaccines may have a significant advantage over conventional vaccines, as the careful selection of the components may diminish undesired side effects such as have been observed with whole pathogen and protein subunit vaccines. This chapter will review the pre-clinical and anticipated clinical development of computer-driven vaccine design and the validation of epitope-based immunome-derived vaccines in animal models; it will also include an overview of heterologous immunity and other emerging issues that will need to be addressed by vaccines of all types in the future.

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.

Identification of the HLA-DM/HLA-DR interface

Human leukocyte antigen (HLA)-DM is a critical participant in antigen presentation that catalyzes the dissociation of the Class II-associated Invariant chain-derived Peptide (CLIP) from the major histocompatibility complex (MHC) Class II molecules. There is competition amongst peptides for access to an MHC Class II groove and it has been hypothesised that DM functions as a 'peptide editor' that catalyzes the replacement of one peptide for another within the groove. It is established that the DM catalyst interacts directly with the MHC Class II but the precise location of the interface is unknown. Here, we combine previously described mutational data with molecular docking and energy minimisation simulations to identify a putative interaction site of >4000A2 which agrees with known point mutational data for both the DR and DM molecule. The docked structure is validated by comparison with experimental data and previously determined properties of protein-protein interfaces. A possible dissociation mechanism is suggested by the presence of an acidic cluster near the N terminus of the bound peptide.

Modeling the adaptive immune system: predictions and simulations

MOTIVATION

Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered.

SUMMARY

Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

Background

Peptides binding to Major Histocompatibility Complex (MHC) class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides.

In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA) for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs.

Results

The proposed methods are intended for finding peptides binding to MHC class II I-A^g7 molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-A^g7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1) an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2) quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules.

Conclusion

We present two MOEA-based algorithms for finding motifs, one for self-discovery and the other for guided-discovery by experimentally determined motifs, and thereby predicting binding peptides to I-A^g7 molecule. Our experiments show that the proposed MOEA-based algorithms are better than earlier methods in predicting binding sites not only on I-A^g7 but also on most alleles of class II MHC benchmark datasets. This shows that our methods could be applicable to find binding motifs in a wide range of alleles.

Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores

Background

Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel.

Results

The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 A_ROC for the MHCBench data sets (up from 0.756), and an average of 0.96 A_ROC for multiple alleles of the MHCPEP database.

Conclusion

The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Recombinant HLA-DP2 binds beryllium and tolerizes beryllium-specific pathogenic CD4+ T cells

Chronic beryllium disease is a lung disorder caused by beryllium exposure in the workplace and is characterized by granulomatous inflammation and the accumulation of beryllium-specific, HLA-DP2-restricted CD4+ T lymphocytes in the lung that proliferate and secrete Th1-type cytokines. To characterize the interaction among HLA-DP2, beryllium, and CD4+ T cells, we constructed rHLA-DP2 and rHLA-DP4 molecules consisting of the alpha-1 and beta-1 domains of the HLA-DP molecules genetically linked into single polypeptide chains. Peptide binding to rHLA-DP2 and rHLA-DP4 was consistent with previously published peptide-binding motifs for these MHC class II molecules, with peptide binding dominated by aromatic residues in the P1 pocket. 9Be nuclear magnetic resonance spectroscopy showed that beryllium binds to the HLA-DP2-derived molecule, with no binding to the HLA-DP4 molecule that differs from DP2 by four amino acid residues. Using beryllium-specific CD4+ T cell lines derived from the lungs of chronic beryllium disease patients, beryllium presentation to those cells was independent of Ag processing because fixed APCs were capable of presenting BeSO4 and inducing T cell proliferation. Exposure of beryllium-specific CD4+ T cells to BeSO4 -pulsed, plate-bound rHLA-DP2 molecules induced IFN-gamma secretion. In addition, pretreatment of beryllium-specific CD4+ T cells with BeSO4-pulsed, plate-bound HLA-DP2 blocked proliferation and IL-2 secretion upon re-exposure to beryllium presented by APCs. Thus, the rHLA-DP2 molecules described herein provide a template for engineering variants that retain the ability to tolerize pathogenic CD4+ T cells, but do so in the absence of the beryllium Ag.

Relative immunogenicity of Fya and K antigens in a Caucasian population, based on HLA class II restriction analysis

BACKGROUND

It has long been known that relative immunogenicity is a characteristic of protein red blood cell (RBC) antigens, but the mechanisms remain unclear. The aim of this work was to elucidate the mechanisms underlying this relative immunogenicity.

STUDY DESIGN AND METHODS

Two RBC antigens were used as a model--the highly immunogenic K antigen (KEL1) and the less immunogenic Fya antigen (FY1)--and analyzed the distribution of DRB1* molecules in two groups of Caucasian individuals producing anti-Fya (n = 29) or anti-K (n = 30) alloantibodies. These experimental results were compared to the results generated by TEPITOPE, a DRB1* peptide-binding motif prediction algorithm.

RESULTS

It was found that within the anti-Fya group, the DRB1*04 phenotypic frequency was 100 percent, indicating that the DRB1*04 molecule is the restriction molecule. In the anti-K group, numerous DRB1* molecules were identified, demonstrating a high degree of histocompatibility promiscuity, corresponding to the predominant molecules in the Caucasian population. These findings were confirmed by TEPITOPE.

CONCLUSION

These results strongly suggest that protein RBC intrinsic immunogenicity depends on the distribution of DRB1* restriction molecules.

Contribution of peptides to multiple sclerosis research

Multiple sclerosis (MS) is an autoimmune disease associated with chronic inflammatory demyelination of the central nervous system in genetically susceptible individuals. Because of the disease complexity and heterogeneity, its pathogenesis remains unknown despite extensive research efforts, and specific effective treatments have not yet been developed. Peptide-based research has been important in attempts to unravel particular aspects of this complex disease, including the characterization of the different molecular mechanisms of MS, with the goal of providing useful products for immune-mediated therapies. In fact, in the past decade, peptide-based research has been predominant in research aimed to identify and/or develop target antigens as synthetic probes for specific biomarkers as well as innovative immunomodulating therapies. This review presents an overview of the contributions of peptide science to MS research and discusses future directions of peptide-based investigations.

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.

The relationship between immunodominance, DM editing, and the kinetic stability of MHC class II:peptide complexes

Immunodominance refers to the restricted antigen specificity of T cells detected in the immune response after immunization with complex antigens. Despite the presence of many potential peptide epitopes within these immunogens, the elicited T-cell response apparently focuses on a very limited number of peptides. Over the last two decades, a number of distinct explanations have been put forth to explain this very restricted specificity of T cells, many of which suggest that endosomal antigen processing restricts the array of peptides available to recruit CD4 T cells. In this review, we present evidence from our laboratory that suggest that immunodominance in CD4 T-cell responses is primarily due to an intrinsic property of the peptide:class II complexes. The intrinsic kinetic stability of peptide:class II complexes controls DM editing within the antigen-presenting cells and thus the initial epitope density on priming dendritic cells. Additionally, we hypothesize that peptides that possess high kinetic stability interactions with class II molecules display persistence at the cell surface over time and will more efficiently promote T-cell signaling and differentiation than competing, lower-stability peptides contained within the antigen. We discuss this model in the context of the existing data in the field of immunodominance.

Immune tolerance to hepatitis C virus acquired during engraftment of bone marrow transplant

The CD4+ T-cell response appears to be important for clearance of hepatitis C virus (HCV) in the majority of individuals. We have recently described a series of human leucocyte antigen (HLA)-DR11-restricted T-cell epitopes derived from HCV proteins which enables distinct populations of memory CD4+ T cells to be detected and counted in all nonviraemic HCV subjects. We examined the case of an HLA-DR11+ recipient of a haematopoietic stem-cell transplant who was concurrently infected with HCV from an HLA-DR11+ donor sibling. An acute HCV hepatitis developed and was treated with type I interferon. After successful viral clearance, the recipient demonstrated a selective lack of HCV epitope-specific CD4+ T cells and absence of serological responses compared with the treated donor. The recipient had no evidence of any nonspecific immunosuppression. The subsequent effects of concurrent infection during immune reconstitution are not known in adult humans, but data from murine models suggest this can lead to a skewing of the T-cell repertoire because of thymic selection. From the above observations, it is plausible that the introduction of foreign viral antigen into the thymus may lead to subsequent acquired central tolerance.

CD4+ T cell epitope affinity to MHC II influences the magnitude of CTL responses elicited by DNA epitope vaccines

Immunization with naked plasmid DNA elicits strong cell-mediated immune responses. In the present study, we examine strategies to enhance epitope-specific cytotoxic T lymphocyte (CTL) responses using DNA constructs, expressing a minimal class I epitope of the gp120 of HIV-IIIB. Here, we evaluate the effect of CD4+ T cell (T(H)) epitope affinity for the MHC II molecule on the immunogenicity of our DNA vaccines. Our data indicate that a low-affinity T(H) epitope decreased the magnitude of the CTL responses. In addition, we observed decreased numbers of epitope-specific T helper cells and CTLs, as well as diminished cytokine secretion and proliferative responses. Thus, the immunogenicity of a DNA epitope vaccine can be modulated by altering the affinity of the T(H) epitope.

A novel approach to identify antigens recognized by CD4 T cells using complement-opsonized bacteria expressing a cDNA library

In patients with hematological malignancies receiving HLA-matched stem cell transplantation, T cells specific for minor histocompatibility antigens play a major role in graft rejection, induction of graft-versus-host disease and beneficial graft-versus-leukemia reactivity. Several human minor histocompatibility antigens recognized by T cells have been identified, but only two are presented by HLA class II molecules. In search of an efficient approach to identify antigenic peptides processed through the HLA class II pathway, we constructed a cDNA library in bacteria that were induced to express proteins. Bacteria were opsonized with complement to enforce receptor-mediated uptake by Epstein-Barr virus immortalized B cells that were subsequently used as antigen-presenting cells. This approach was validated with an HLA class II-restricted antigen encoded by gene DBY. We were able to identify bacteria expressing DBY diluted into a 300-fold excess of bacteria expressing a nonrelevant gene. Screening of a bacterial library using a DBY-specific CD4 T cell clone resulted in the isolation of several DBY cDNAs. We propose this strategy for a rapid identification of HLA class II-restricted antigenic peptides recognized by CD4 T cells.

Identification of an I-Ed-restricted T-cell epitope of Escherichia coli outer membrane protein F

A predominant T-cell epitope of Escherichia coli outer membrane protein F (OmpF) that encompasses amino acids 295 to 314 was identified in H-2(d) mice. BALB/c-derived T-cell hybridomas generated against this region were CD3(+), CD4(+), CD8(-), and T-cell receptor alphabeta(+) and secreted TH-1-associated cytokines (interleukin-2 [IL-2] and gamma interferon), but not a TH-2-associated cytokine (IL-4), when restimulated with peptide 295-314. Class II(+) mouse lymphoma (A20) cells, but not class II(-) mouse mastocytoma (P815) cells, supported IL-2 secretion of hybridomas when substituted for syngeneic splenocytes as antigen-presenting cells (APCs). Antibodies specific for I-E(d) blocked IL-2 secretion by hybridomas, but I-A(d)-specific antiserum did not. When transfected L cells expressing I-A(d) (AalphaAbeta(d)), I-E(d) (EalphaEbeta(d)), or the hybrid molecule I-EalphaAbeta(d) were used as APCs, hybridomas recognized peptide only when presented by the I-E(d)-transfected cells. When peptide 295-314 truncated at either the C or the N terminus of the sequence was used, the minimal epitope was determined. Critical residues were determined by using alanine-substituted peptide analogues. T-cell hybridomas were only stimulated by peptides that encompassed amino acids 295 to 303 (9-mer), and the core sequence required a minimum of three additional amino acids at either the amino or the carboxy terminus to induce IL-2 secretion. Critical residues were determined to be phenylalanine at position 295, threonine at position 300, and tyrosines at positions 301 and 302. This study is the first to identify a minimal T-cell epitope and major histocompatibility complex restriction element of the OmpF protein and confirms previous observations that there is considerable degeneracy in the length of peptides that can bind I-E(d) and variability in the amino acid composition of the C and N termini of these peptides.

Fine specificity and cross-clade reactivity of HIV type 1 Gag-specific CD4+ T cells

Despite growing evidence that HIV-1-specific CD4(+) T helper (Th) cells may play a role in the control of viremia, discrete Th cell epitopes remain poorly defined. Furthermore, it is not known whether Th cell responses generated using vaccines based on clade B virus sequences will elicit immune responses that are effective in regions of the world where non-clade B viruses predominate. To address these issues we isolated CD4(+) T cell clones from individuals with vigorous HIV-1-specific Th cell responses and identified the minimum epitopes recognized. The minimum peptide length required for induction of CD4(+) T cell proliferation, IFN-gamma secretion, and cytolytic activity ranged from 9 to 16 amino acids in the five epitopes studied. Cross-clade recognition of the defined epitopes was examined for variant peptides from clades A, B, C, D, and AE. Over half the variant epitopes (17 of 32) exhibited impaired recognition, defined as less than 50% of the IFN-gamma secretion elicited by B clade consensus sequence. There was no evidence for antagonistic activity mediated by the variant peptides, and despite strong responses there was no escape of autologous virus from Th responses in the epitopes we studied. Abrogated recognition of variant CD4(+) T cell epitopes presents a potential obstacle to vaccine development.

Propagation of CD4+ T cells specific for HIV type 1 envelope gp120 from chronically HIV type 1-infected subjects

HIV-specific CD4+ T cell responses, in particular to the HIV envelope antigen gp120, are often undetectable in the peripheral blood of HIV-infected individuals. The failure to detect these cells poses a significant impediment to studying the T cell populations that are considered to be essential for controlling HIV infection and has led to speculation that these cells are entirely depleted during HIV infection. This study was designed to test whether gp120-specific CD4+ T cells exist in HIV-infected subjects and can be expanded from peripheral blood mononuclear cells by in vitro stimulation with the gp120 antigen, allowing better characterization of these cells. Although gp120-specific T cell responses were barely observed in patient cells ex vivo before antigenic stimulation, CD4+ T cells specific for gp120 were successfully propagated from the blood of each asymptomatic chronically HIV-infected subject studied. The dominant epitopes recognized by gp120-specific CD4+ T cells from these HIV-infected subjects were mapped to well-conserved sites in the C1 and C2 domains of gp120. Two CD4+ T cell lines recognizing these two regions were subsequently established. The CD4+ T cell lines proliferated and produced interferon gamma in response to the specific epitopes, and the responses were MHC class II restricted. These T cell lines also exhibited cross-reactivity with gp120 from T cell line-adapted HIV-1 strains IIIB and MN, as well as with gp120 from primary isolates SF33 (subtype B), CA1 (subtype A), and CA10 (subtype A/E). The data demonstrate that CD4+ T cells specific for gp120 are not entirely depleted from the peripheral blood of chronically HIV-infected subjects; these cells are present in low numbers but can be expanded after antigenic stimulation in vitro.

The dynamics of T-lymphocyte responses during combination therapy for chronic hepatitis C virus infection

Hepatitis C virus (HCV) readily sets up a persistent infection and is a major cause of liver disease worldwide. Interferon alfa and ribavirin therapy lead to sustained clearance of virus in 31% to 64% of patients with type 1 and non-type 1 genotypes, respectively. It is not clear to what extent these drugs act directly to reduce HCV replication, or indirectly via host immune responses, and what evoked immune responses are associated with clinical outcome. We have examined prospectively 15 patients with chronic HCV infection before, during, and after combination therapy. Quantitative assays for HCV antigen-specific CD4+ and CD8+ T-cell responses, and flow cytometric assays for analysis of the phenotype of T cells, in addition to viral sequencing of core protein, were performed throughout the treatment and follow-up period over 18 months. We found enhancement of proliferative T-cell responses during therapy. Proliferative responses are strikingly heterogeneous in terms of specificity, kinetics, and magnitude. Proliferative responses are often not associated with interferon-gamma release. T-cell responses are rarely sustained irrespective of treatment outcome and this is not due to the evolution of new immune escape variants. T-cell responses tend to peak late in the course of treatment. In conclusion, combination therapy for HCV has a transient effect on host virus-specific T cells in the blood. Induction of sustained T-cell responses may require additional immune modulation later in therapy.

Evolution of epitope-specific memory CD4(+) T cells after clearance of hepatitis C virus

The generation of memory lymphocytes is one of the hallmarks of the specific immune response. The CD4(+) T cell response is of critical importance in maintaining long-term protective immunity after clearing many infections. However, accurate characterization of these memory CD4(+) T cells has relied mainly on mouse studies and is poorly understood in humans. We have detected and counted epitope-specific populations of CD4(+) memory cells in patients who have cleared hepatitis C virus. The kinetics of the recall response and the expression of the chemokine receptor CCR7 suggested the presence of distinct populations. A population of memory cells measured in an ex vivo IFN-gamma ELISPOT assay steadily declined after viral clearance. However, memory CD4(+) T cells only characterized after short-term culture with Ag and IL-2, and, recognizing the same epitopes, developed into a long-term stable population. Depletion of CCR7(+) cells from PBMCs markedly reduced the responses in the culture-positive population while having little effect on the ex vivo responses. The demonstration of these key memory subsets in man opens the way to defining their role in protective immune responses.

The majority of immunogenic epitopes generate CD4+ T cells that are dependent on MHC class II-bound peptide-flanking residues

Peptides bind to MHC class II molecules with a defined periodicity such that the peptide-flanking residues (PFRs) P-1 and P11, which lie outside the core binding sequence (P1-P9), are solvent exposed and accessible to the TCR. Using a novel MHC class II:peptide binding assay, we defined the binding register for nine immunogenic epitopes to formally identify the flanking residues. Seven of the nine epitopes, restricted by H-2A(k), H-2A(g7), or H-2E(k), were found to generate T cells that were completely dependent on either P-1 or P11, with dependency on P-1 favored over P11. Such PFR dependency appears to be influenced by the type of amino acid exposed, in that residues that can form salt bridges or hydrogen bonds are favored over small or hydrophobic residues. Peptides containing alanine substitutions at P-1 or P11 in place of PFRs that mediate dependency were considerably less immunogenic and mediated a substantially reduced in vitro recall response to the native protein, inferring that PFR recognition increases immunogenicity. Our data suggest that PFR recognition is a common event characteristic of all MHC class II-restricted T cell responses. This key feature, which is not shared by MHC class I-restricted responses, may underlie the broad functional diversity displayed by MHC class II-restricted T cells.