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

Applications for T-cell epitope queries and tools in the Immune Epitope Database and Analysis Resource

The Immune Epitope Database and Analysis Resource (IEDB, http://www.iedb.org) hosts a continuously growing set of immune epitope data curated from the literature, as well as data submitted directly by experimental scientists. In addition, the IEDB hosts a collection of prediction tools for both MHC class I and II restricted T-cell epitopes that are regularly updated. In this review, we provide an overview of T-cell epitope data and prediction tools provided by the IEDB. We then illustrate effective use of these resources to support experimental studies. We focus on two applications, namely identification of conserved epitopes in novel strains of a previously studied pathogen, and prediction of novel T-cell epitopes to facilitate vaccine design. We address common questions and concerns faced by users, and identify patterns of usage that have proven successful.

Leishmaniasis

Leishmaniasis is a disease that ranges in severity from skin lesions to serious disfigurement and fatal systemic infection. WHO has classified the disease as emerging and uncontrolled and estimates that the infection results in two million new cases a year. There are 12 million people currently infected worldwide, and leishmaniasis threatens 350 million people in 88 countries. Vaccination remains the best hope for control of all forms of the disease, and the development of a safe, effective and affordable antileishmanial vaccine is a critical global public-health priority. However, to date, no such vaccine is available despite substantial efforts by many laboratories. Main obstacle in vaccine design is the transition from the laboratory to the field and extrapolation of data from animal models to humans. This review discusses recent findings in the antileishmania vaccine field and current difficulties hampering vaccine implementation.

HLA-DP2 binding prediction by molecular dynamics simulations

Major histocompatibility complex (MHC) II proteins bind peptide fragments derived from pathogen antigens and present them at the cell surface for recognition by T cells. MHC proteins are divided into Class I and Class II. Human MHC Class II alleles are grouped into three loci: HLA-DP, HLA-DQ, and HLA-DR. They are involved in many autoimmune diseases. In contrast to HLA-DR and HLA-DQ proteins, the X-ray structure of the HLA-DP2 protein has been solved quite recently. In this study, we have used structure-based molecular dynamics simulation to derive a tool for rapid and accurate virtual screening for the prediction of HLA-DP2-peptide binding. A combinatorial library of 247 peptides was built using the "single amino acid substitution" approach and docked into the HLA-DP2 binding site. The complexes were simulated for 1 ns and the short range interaction energies (Lennard-Jones and Coulumb) were used as binding scores after normalization. The normalized values were collected into quantitative matrices (QMs) and their predictive abilities were validated on a large external test set. The validation shows that the best performing QM consisted of Lennard-Jones energies normalized over all positions for anchor residues only plus cross terms between anchor-residues.

Efficacy of HLA-DRB1∗03:01 and H2E transgenic mouse strains to correlate pathogenic thyroglobulin epitopes for autoimmune thyroiditis

Thyroglobulin (Tg), a homodimer of 660 kD comprising 2748 amino acids, is the largest autoantigen known. The prevalence of autoimmune thyroid disease, including Hashimoto's thyroiditis and Graves' disease, has provided the impetus for identifying pathogenic T cell epitopes from human Tg over two decades. With no known dominant epitopes, the search has long been a challenge for investigators. After identifying HLA-DRB1∗03:01 (HLA-DR3) and H2E(b) as susceptibility alleles for Tg-induced experimental autoimmune thyroiditis in transgenic mouse strains, we searched for naturally processed T cell epitopes with MHC class II-binding motif anchors and tested the selected peptides for pathogenicity in these mice. The thyroiditogenicity of one peptide, hTg2079, was confirmed in DR3 transgenic mice and corroborated in clinical studies. In H2E(b)-expressing transgenic mice, we identified three T cell epitopes from mouse Tg, mTg179, mTg409 and mTg2342, based on homology to epitopes hTg179, hTg410 and hTg2344, respectively, which we and others have found stimulatory or pathogenic in both DR3- and H2E-expressing mice. The high homology among these peptides with shared presentation by DR3, H2E(b) and H2E(k) molecules led us to examine the binding pocket residues of these class II molecules. Their similar binding characteristics help explain the pathogenic capacity of these T cell epitopes. Our approach of using appropriate human and murine MHC class II transgenic mice, combined with the synthesis and testing of potential pathogenic Tg peptides predicted from computational models of MHC-binding motifs, should continue to provide insights into human autoimmune thyroid disease.

Visceral leishmaniasis: immunology and prospects for a vaccine

Human visceral leishmaniasis (HVL) is the most severe clinical form of a spectrum of neglected tropical diseases caused by protozoan parasites of the genus Leishmania. Caused mainly by L. donovani and L. infantum/chagasi, HVL accounts for more than 50 000 deaths every year. Drug therapy is available but costly, and resistance against several drug classes has evolved. Here, we review our current understanding of the immunology of HVL and approaches to and the status of vaccine development against this disease.

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/.

Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach

Background

MHC class II proteins bind oligopeptide fragments derived from proteolysis of pathogen antigens, presenting them at the cell surface for recognition by CD4+ T cells. Human MHC class II alleles are grouped into three loci: HLA-DP, HLA-DQ and HLA-DR. In contrast to HLA-DR and HLA-DQ, HLA-DP proteins have not been studied extensively, as they have been viewed as less important in immune responses than DRs and DQs. However, it is now known that HLA-DP alleles are associated with many autoimmune diseases. Quite recently, the X-ray structure of the HLA-DP2 molecule (DPA*0103, DPB1*0201) in complex with a self-peptide derived from the HLA-DR α-chain has been determined. In the present study, we applied a validated molecular docking protocol to a library of 247 modelled peptide-DP2 complexes, seeking to assess the contribution made by each of the 20 naturally occurred amino acids at each of the nine binding core peptide positions and the four flanking residues (two on both sides).

Results

The free binding energies (FBEs) derived from the docking experiments were normalized on a position-dependent (npp) and on an overall basis (nap), and two docking score-based quantitative matrices (DS-QMs) were derived: QMnpp and QMnap. They reveal the amino acid preferences at each of the 13 positions considered in the study. Apart from the leading role of anchor positions p1 and p6, the binding to HLA-DP2 depends on the preferences at p2. No effect of the flanking residues was found on the peptide binding predictions to DP2, although all four of them show strong preferences for particular amino acids. The predictive ability of the DS-QMs was tested using a set of 457 known binders to HLA-DP2, originating from 24 proteins. The sensitivities of the predictions at five different thresholds (5%, 10%, 15%, 20% and 25%) were calculated and compared to the predictions made by the NetMHCII and IEDB servers. Analysis of the DS-QMs indicated an improvement in performance. Additionally, DS-QMs identified the binding cores of several known DP2 binders.

Conclusions

The molecular docking protocol, as applied to a combinatorial library of peptides, models the peptide-HLA-DP2 protein interaction effectively, generating reliable predictions in a quantitative assessment. The method is structure-based and does not require extensive experimental sequence-based data. Thus, it is universal and can be applied to model any peptide - protein interaction.

Predicting MHC-II binding affinity using multiple instance regression

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark data sets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir.

PeptX: using genetic algorithms to optimize peptides for MHC binding

Background

The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions?

Results

Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.

Conclusion

We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.

Emerging vaccine informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

Whole-genome immunoinformatic analysis of F. tularensis: predicted CTL epitopes clustered in hotspots are prone to elicit a T-cell response

The cellular arm of the immune response plays a central role in the defense against intracellular pathogens, such as F. tularensis. To date, whole genome immunoinformatic analyses were limited either to relatively small genomes (e.g. viral) or to preselected subsets of proteins in complex pathogens. Here we present, for the first time, an unbiased bacterial global immunoinformatic screen of the 1740 proteins of F. tularensis subs. holarctica (LVS), aiming at identification of immunogenic peptides eliciting a CTL response. The very large number of predicted MHC class I binders (about 100,000, IC₅₀ of 1000 nM or less) required the design of a strategy for further down selection of CTL candidates. The approach developed focused on mapping clusters rich in overlapping predicted epitopes, and ranking these “hotspot” regions according to the density of putative binding epitopes. Limited by the experimental load, we selected to screen a library of 1240 putative MHC binders derived from 104 top-ranking highly dense clusters. Peptides were tested for their ability to stimulate IFNγ secretion from splenocytes isolated from LVS vaccinated C57BL/6 mice. The majority of the clusters contained one or more CTL responder peptides and altogether 127 novel epitopes were identified, of which 82 are non-redundant. Accordingly, the level of success in identification of positive CTL responders was 17–25 fold higher than that found for a randomly selected library of 500 predicted MHC binders (IC₅₀ of 500 nM or less). Most proteins (ca. 2/3) harboring the highly dense hotspots are membrane-associated. The approach for enrichment of true positive CTL epitopes described in this study, which allowed for over 50% increase in the dataset of known T-cell epitopes of F. tularensis, could be applied in immunoinformatic analyses of many other complex pathogen genomes.

[Synthetic peptide vaccines]

This review considers the stages of the development of synthetic peptide vaccines against infectious agents, novel approaches and technologies employed in this process, including bioinformatics, genomics, proteomics, large-scale peptide synthesis, high-throughput screening methods, the use of transgenic animals for modelling human infections. An important role for the development and selection of efficient adjuvants for peptide immunogens is noted. Examples of synthetic peptide vaccine developments against three infectious diseases (malaria, hepatitis C, and foot-and-mouth disease) are given.

Computational prediction of MHC II-antigen binding supports divergent allele advantage and explains trans-species polymorphism

The major histocompatibility complex (MHC), coding for antigen presenting molecules of the adaptive immune system, represents one of the most polymorphic regions in the vertebrate genome. The exceptional polymorphism, which is potentially maintained by balancing selection under host-parasite coevolution, comprises excessive sequence divergence among alleles as well as ancient allelic lineages that predate species divergence (trans-species polymorphism). Here, the mechanisms that are proposed to maintain such sequence divergence and ancient lineages are investigated. Established computational antigen-binding prediction algorithms, which are based on empirical databases, are employed to determine the overlap in bound antigens among individual MHC class IIB alleles. The results show that genetically more divergent allele pairs experience less overlap and thus present a broader range of potential antigens. These findings support the divergent allele advantage hypothesis and furthermore suggest an evolutionary advantage explaining the maintenance of divergent allelic lineages, that is, trans-species polymorphism. In addressing a quantitative rather than qualitative aspect of MHC alleles, these insights highlight a new direction for future research on MHC evolution.

HLA-DR-presented peptide repertoires derived from human monocyte-derived dendritic cells pulsed with blood coagulation factor VIII

Activation of T-helper cells is dependent upon the appropriate presentation of antigen-derived peptides on MHC class II molecules expressed on antigen presenting cells. In the current study we explored the repertoire of peptides presented on MHC class II molecules on human monocyte derived dendritic cells (moDCs) from four HLA-typed healthy donors. MHC class II-bound peptides could be routinely recovered from small cultures containing 5 × 10(6) cells. A fraction of the identified peptides were derived from proteins localized in the plasma membrane, endosomes, and lysosomes, but the majority of peptides that were presented on MHC class II originate from other organelles. Subsequently, we studied the antigen-specific peptide repertoire after endocytosis of a soluble antigen. Blood coagulation factor VIII (FVIII) was chosen as the antigen since our current knowledge on MHC class II presented peptides derived from this immunogenic therapeutic protein is limited. Analysis of the total repertoire of MHC class II-associated peptides revealed that per individual sample 20-50 FVIII-derived peptides were presented on FVIII-pulsed moDCs. Repertoires of FVIII-derived peptides eluted from moDCs derived from a panel of four HLA typed donors revealed that some MHC class II-presented FVIII peptides were presented by multiple donors, whereas the presentation of other FVIII peptides was donor-specific. In total 32 different core peptides were presented on FVIII-pulsed moDCs from four HLA-typed donors. Together our findings provide an unbiased approach to identify peptides that are presented by MHC class II on antigen-loaded moDCs from individual donors.

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.

Computational design of peptide ligands

Peptides possess several attractive features when compared to small molecule and protein therapeutics, such as high structural compatibility with target proteins, the ability to disrupt protein-protein interfaces, and small size. Efficient design of high-affinity peptide ligands via rational methods has been a major obstacle to the development of this potential drug class. However, structural insights into the architecture of protein-peptide interfaces have recently culminated in several computational approaches for the rational design of peptides that target proteins. These methods provide a valuable alternative to experimental high-resolution structures of target protein-peptide complexes, bringing closer the dream of in silico designed peptides for therapeutic applications.

CTLs' repertoire shaping in the thymus: a Monte Carlo simulation

MOTIVATION

The human immune system evolved a multi-layered control mechanism to eliminate self-reactive cells. Of these so-called tolerance induction mechanisms, lymphocytes T education in the thymus gland represents the very first one. This complicated process is not fully understood and quantitative models able to help in this endeavor are lacking. Here, we present a stochastic computational model of the thymus which combines data-driven prediction methods and a novel method based on protein-protein potential measurements for assessing molecular binding among cell receptors, major histocompatibility complex (MHC) molecules, and self-peptides.

RESULTS

Of all possible specificities of immature T cells entering the thymus, only a small fraction is actually selected for maturation. Monte Carlo simulations of thymocytes selection in the thymus are performed varying the size of the self and a parameter determining the number of encounter with antigen-presenting cells (APCs). We score the fraction of self-reacting thymocytes leaving the thymus as mature naive T cells and show that self-reactivity is only marginally dependent on the number of self-molecules presented by APCs, while it is strongly affected by a parameter proportional to the time spent in the thymus. We study how this measure changes when we vary the number of MHC alleles and found an optimal number not too different from what we have in reality. The main result of this study is more methodological than biological as we show that immunoinformatics data and methods can be used in systemic level simulation of immune processes.

B cell epitope mapping and characterization of naturally acquired antibodies to the Plasmodium vivax merozoite surface protein-3α (PvMSP-3α) in malaria exposed individuals from Brazilian Amazon

The Plasmodium vivax Merozoite Surface Protein-3α (PvMSP-3α) is considered as a potential vaccine candidate. However, the detailed investigations of the type of immune responses induced in naturally exposed populations are necessary. Therefore, we aim to characterize the naturally induced antibody to PvMSP-3α in 282 individuals with different levels of exposure to malaria infections residents in Brazilian Amazon. PvMSP3 specific antibodies (IgA, IgG and IgG subclass) to five recombinant proteins and the epitope mapping by Spot-synthesis technique to full-protein sequence of amino acids (15aa sequence with overlapping sequence of 9aa) were performed. Our results indicates that PvMSP3 is highly immunogenic in naturally exposed populations, where 78% of studied individuals present IgG immune response against the full-length recombinant protein (PVMSP3-FL) and IgG subclass profile was similar to all five recombinant proteins studied with a high predominance of IgG1 and IgG3. We also observe that IgG and subclass levels against PvMSP3 are associated with malaria exposure. The PvMSP3 epitope mapping by Spot-synthesis shows a natural recognition of at least 15 antigenic determinants, located mainly in the two blocks of repeats, confirming the high immunogenicity of this region. In conclusion, PvMSP-3α is immunogenic in naturally exposed individuals to malaria infections and that antibodies to PvMSP3 are induced to several B cell epitopes. The presence of PvMSP3 cytophilic antibodies (IgG1 and IgG3), suggests that this mechanism could also occur in P. vivax.

FLAVIdB: A data mining system for knowledge discovery in flaviviruses with direct applications in immunology and vaccinology

BACKGROUND

The flavivirus genus is unusually large, comprising more than 70 species, of which more than half are known human pathogens. It includes a set of clinically relevant infectious agents such as dengue, West Nile, yellow fever, and Japanese encephalitis viruses. Although these pathogens have been studied extensively, safe and efficient vaccines lack for the majority of the flaviviruses.

RESULTS

We have assembled a database that combines antigenic data of flaviviruses, specialized analysis tools, and workflows for automated complex analyses focusing on applications in immunology and vaccinology. FLAVIdB contains 12,858 entries of flavivirus antigen sequences, 184 verified T-cell epitopes, 201 verified B-cell epitopes, and 4 representative molecular structures of the dengue virus envelope protein. FLAVIdB was assembled by collection, annotation, and integration of data from GenBank, GenPept, UniProt, IEDB, and PDB. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). Further annotation of selected functionally relevant features was performed by organizing information extracted from the literature. The database was incorporated into a web-accessible data mining system, combining specialized data analysis tools for integrated analysis of relevant data categories (protein sequences, macromolecular structures, and immune epitopes). The data mining system includes tools for variability and conservation analysis, T-cell epitope prediction, and characterization of neutralizing components of B-cell epitopes. FLAVIdB is accessible at cvc.dfci.harvard.edu/flavi/

CONCLUSION

FLAVIdB represents a new generation of databases in which data and tools are integrated into a data mining infrastructures specifically designed to aid rational vaccine design by discovery of vaccine targets.

Synthetic peptide vaccines

This review considers the stages of the development of synthetic peptide vaccines against infectious agents, novel approaches and technologies employed in this process, including bioinformatics, genomics, proteomics, large-scale peptide synthesis, high-throughput screening methods, the use of transgenic animals for modelling human infections. An important role for the development and selection of efficient adjuvants for peptide immunogens is noted. Examples of synthetic peptide vaccine developments against three infectious diseases (malaria, hepatitis C, and foot-and-mouth disease) are given.

MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles

MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes. NetMHCpan and NetMHCIIpan are used as prediction engines. The 13 HLA Class I supertypes are A1, A2, A3, A24, B7, B8, B27, B44, B58, B62, C1, and C4. The 13 HLA Class II DR supertypes are DR1, DR3, DR4, DR6, DR7, DR8, DR9, DR11, DR12, DR13, DR14, DR15, and DR16. In total, MULTIPRED2 enables prediction of peptide binding to 1077 variants representing 26 HLA supertypes. MULTIPRED2 has visualization modules for mapping promiscuous T-cell epitopes as well as those regions of high target concentration – referred to as T-cell epitope hotspots. Novel graphic representations are employed to display the predicted binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/.

Bidirectional binding of invariant chain peptides to an MHC class II molecule

T-cell recognition of peptides bound to MHC class II (MHCII) molecules is a central event in cell-mediated adaptive immunity. The current paradigm holds that prebound class II-associated invariant chain peptides (CLIP) and all subsequent antigens maintain a canonical orientation in the MHCII binding groove. Here we provide evidence for MHCII-bound CLIP inversion. NMR spectroscopy demonstrates that the interconversion from the canonical to the inverse alignment is a dynamic process, and X-ray crystallography shows that conserved MHC residues form a hydrogen bond network with the peptide backbone in both orientations. The natural catalyst HLA-DM accelerates peptide reorientation and the exchange of either canonically or inversely bound CLIP against antigenic peptide. Thus, noncanonical MHC-CLIP displays the hallmarks of a structurally and functionally intact antigen-presenting complex.

Highly conserved cross-reactive CD4+ T-cell HA-epitopes of seasonal and the 2009 pandemic influenza viruses

Please cite this paper as: Duvvuri et al. (2010) Highly conserved cross‐reactive CD4+ T‐cell HA‐epitopes of seasonal and the 2009 pandemic influenza viruses. Influenza and Other Respiratory Viruses 4(5), 249–258.

Background The relatively mild nature of the 2009 influenza pandemic (nH1N1) highlights the overriding importance of pre‐existing immune memory. The absence of cross‐reactive antibodies to nH1N1 in most individuals suggests that such attenuation may be attributed to pre‐existing cellular immune responses to epitopes shared between nH1N1 virus and previously circulating strains of inter‐pandemic influenza A viruses.

Results We sought to identify potential CD4+ T cell epitopes and predict the level of cross‐reactivity of responding T cells. By performing large‐scale major histocompatibility complex II analyses on Hemagglutinin (HA) proteins, we investigated the degree of T‐cell cross‐reactivity between seasonal influenza A (sH1N1, H3N2) from 1968 to 2009 and nH1N1 strains. Each epitope was examined against all the protein sequences that correspond to sH1N1, H3N2, and nH1N1. T‐cell cross‐reactivity was estimated to be 52%, and maximum conservancy was found between sH1N1 and nH1N1 with a significant correlation (P < 0·05).

Conclusions  Given the importance of cellular responses in kinetics of influenza infection in humans, our findings underscore the role of T‐cell assays for understanding the inter‐pandemic variability in severity and for planning treatment methods for emerging influenza viruses.

Peptide binding predictions for HLA DR, DP and DQ molecules

BACKGROUND

MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally.

RESULTS

In this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated their performance.

CONCLUSION

We found that 1) prediction methodologies developed for HLA DR molecules perform equally well for DP or DQ molecules. 2) Prediction performances were significantly increased compared to previous reports due to the larger amounts of training data available. 3) The presence of homologous peptides between training and testing datasets should be avoided to give real-world estimates of prediction performance metrics, but the relative ranking of different predictors is largely unaffected by the presence of homologous peptides, and predictors intended for end-user applications should include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform the NN-align method, but further research into how to best combine MHC class II binding predictions is required.

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure

Background

Binding of peptides to Major Histocompatibility class II (MHC-II) molecules play a central role in governing responses of the adaptive immune system. MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Predicting which peptides bind to an MHC-II molecule is therefore of pivotal importance for understanding the immune response and its effect on host-pathogen interactions. The experimental cost associated with characterizing the binding motif of an MHC-II molecule is significant and large efforts have therefore been placed in developing accurate computer methods capable of predicting this binding event. Prediction of peptide binding to MHC-II is complicated by the open binding cleft of the MHC-II molecule, allowing binding of peptides extending out of the binding groove. Moreover, the genes encoding the MHC molecules are immensely diverse leading to a large set of different MHC molecules each potentially binding a unique set of peptides. Characterizing each MHC-II molecule using peptide-screening binding assays is hence not a viable option.

Results

Here, we present an MHC-II binding prediction algorithm aiming at dealing with these challenges. The method is a pan-specific version of the earlier published allele-specific NN-align algorithm and does not require any pre-alignment of the input data. This allows the method to benefit also from information from alleles covered by limited binding data. The method is evaluated on a large and diverse set of benchmark data, and is shown to significantly out-perform state-of-the-art MHC-II prediction methods. In particular, the method is found to boost the performance for alleles characterized by limited binding data where conventional allele-specific methods tend to achieve poor prediction accuracy.

Conclusions

The method thus shows great potential for efficient boosting the accuracy of MHC-II binding prediction, as accurate predictions can be obtained for novel alleles at highly reduced experimental costs. Pan-specific binding predictions can be obtained for all alleles with know protein sequence and the method can benefit by including data in the training from alleles even where only few binders are known. The method and benchmark data are available at http://www.cbs.dtu.dk/services/NetMHCIIpan-2.0

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.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

Reverse vaccinology: developing vaccines in the era of genomics

The sequence of microbial genomes made all potential antigens of each pathogen available for vaccine development. This increased by orders of magnitude potential vaccine targets in bacteria, parasites, and large viruses and revealed virtually all their CD4(+) and CD8(+) T cell epitopes. The genomic information was first used for the development of a vaccine against serogroup B meningococcus, and it is now being used for several other bacterial vaccines. In this review, we will first summarize the impact that genome sequencing has had on vaccine development, and then we will analyze how the genomic information can help further our understanding of immunity to infection or vaccination and lead to the design of better vaccines by diving into the world of T cell immunity.

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.

EpiTOP--a proteochemometric tool for MHC class II binding prediction

MOTIVATION

T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein.

RESULTS

Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89% of known epitopes within the top 20% of predicted binders, reducing laboratory labour, materials and time by 80%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time.

AVAILABILITY

EpiTOP is freely accessible at http://www.pharmfac.net/EpiTOP.

High throughput T epitope mapping and vaccine development

Mapping of antigenic peptide sequences from proteins of relevant pathogens recognized by T helper (Th) and by cytolytic T lymphocytes (CTL) is crucial for vaccine development. In fact, mapping of T-cell epitopes provides useful information for the design of peptide-based vaccines and of peptide libraries to monitor specific cellular immunity in protected individuals, patients and vaccinees. Nevertheless, epitope mapping is a challenging task. In fact, large panels of overlapping peptides need to be tested with lymphocytes to identify the sequences that induce a T-cell response. Since numerous peptide panels from antigenic proteins are to be screened, lymphocytes available from human subjects are a limiting factor. To overcome this limitation, high throughput (HTP) approaches based on miniaturization and automation of T-cell assays are needed. Here we consider the most recent applications of the HTP approach to T epitope mapping. The alternative or complementary use of in silico prediction and experimental epitope definition is discussed in the context of the recent literature. The currently used methods are described with special reference to the possibility of applying the HTP concept to make epitope mapping an easier procedure in terms of time, workload, reagents, cells and overall cost.

Major histocompatibility complex class I binding predictions as a tool in epitope discovery

SUMMARY

Over the last decade, in silico models of the major histocompatibility complex (MHC) class I pathway have developed significantly. Before, peptide binding could only be reliably modelled for a few major human or mouse histocompatibility molecules; now, high-accuracy predictions are available for any human leucocyte antigen (HLA) -A or -B molecule with known protein sequence. Furthermore, peptide binding to MHC molecules from several non-human primates, mouse strains and other mammals can now be predicted. In this review, a number of different prediction methods are briefly explained, highlighting the most useful and historically important. Selected case stories, where these 'reverse immunology' systems have been used in actual epitope discovery, are briefly reviewed. We conclude that this new generation of epitope discovery systems has become a highly efficient tool for epitope discovery, and recommend that the less accurate prediction systems of the past be abandoned, as these are obsolete.

MHC Class II Binding Prediction-A Little Help from a Friend

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonetheless use of computational models of class II binding can still offer viable help to the work of the immunologist and vaccinologist.

MetaMHC: a meta approach to predict peptides binding to MHC molecules

As antigenic peptides binding to major histocompatibility complex (MHC) molecules is the prerequisite of cellular immune responses, an accurate computational predictor will be of great benefit to biologists and immunologists for understanding the underlying mechanism of immune recognition as well as facilitating the process of epitope mapping and vaccine design. Although various computational approaches have been developed, recent experimental results on benchmark data sets show that the development of improved predictors is needed, especially for MHC Class II peptide binding. To make the most of current methods and achieve a higher predictive performance, we developed a new web server, MetaMHC, to integrate the outputs of leading predictors by several popular ensemble strategies. MetaMHC consists of two components: MetaMHCI and MetaMHCII for MHC Class I peptide and MHC Class II peptide binding predictions, respectively. Experimental results by both cross-validation and using an independent data set show that the ensemble approaches outperform individual predictors, being statistically significant. MetaMHC is freely available at http://www.biokdd.fudan.edu.cn/Service/MetaMHC.html.

Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system

We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein-protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.

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.

Using epitope predictions to evaluate efficacy and population coverage of the Mtb72f vaccine for tuberculosis

Background

The Mtb72f subunit vaccine for tuberculosis, currently in clinical trials, is hoped to provide improved protection compared to the current BCG vaccine. It is not clear, however, whether Mtb72f would be equally protective in the different human populations suffering from a high burden of tuberculosis. Previous work by Hebert and colleagues demonstrated that the PPE18 protein of Mtb72f had significant variability in a sample of clinical M. tuberculosis isolates. However, whether this variation might impact the efficacy of Mtb72f in the context of the microbial and host immune system interactions remained to be determined. The present study assesses Mtb72f's predicted efficacy in people with different DRB1 genotypes to predict whether the vaccine will protect against diverse clinical strains of M. tuberculosis in a diverse host population.

Results

We evaluated the binding of epitopes in the vaccine to different alleles of the human DRB1 Class II MHC protein using freely available epitope prediction programs and compared protein sequences from clinical isolates to the sequences included in the Mtb72f vaccine. This analysis predicted that the Mtb72f vaccine would be less effective for several DRB1 genotypes, due either to limited vaccine epitope binding to the DRB1 proteins or to binding primarily by unconserved PPE18 epitopes. Furthermore, we found that these less-protective DRB1 alleles are found at a very high frequency in several populations with a high burden of tuberculosis.

Conclusion

Although the Mtb72f vaccine candidate has shown promise in animal and clinical trials thus far, it may not be optimally effective in some genotypic backgrounds. Due to variation in both M. tuberculosis protein sequences and epitope-binding capabilities of different HLA alleles, certain human populations with a high burden of tuberculosis may not be optimally protected by the Mtb72f vaccine. The efficacy of the Mtb72f vaccine should be further examined in these particular populations to determine whether additional protective measures might be necessary for these regions.

Advances in the design and delivery of peptide subunit vaccines with a focus on toll-like receptor agonists

Considerable success has been made with many peptide antigen formulations, and peptide-based vaccines are emerging as the next generation of prophylactic and remedial immunotherapy. However, finding an optimal platform balancing all of the requirements for an effective, specific and safe immune response remains a major challenge for many infectious and chronic diseases. This review outlines how peptide immunogenicity is influenced by the way in which peptides are presented to the immune system, underscoring the need for multifunctional delivery systems that couple antigen and adjuvant into a single construct. Particular attention is given to the ability of Toll-like receptor agonists to act as adjuvants. A survey of recent approaches to developing peptide antigen delivery systems is given, many of which incorporate Toll-like receptor agonists into the design.

Promiscuous T-cell epitopes of Plasmodium merozoite surface protein 9 (PvMSP9) induces IFN-gamma and IL-4 responses in individuals naturally exposed to malaria in the Brazilian Amazon

Plasmodium vivax merozoite surface protein (PvMSP9) stimulates both cellular and humoral immune responses in individuals who are naturally infected by this parasite species. To identify immunodominant human T-cell epitopes in PvMSP9, we used the MHC class II binding peptide prediction algorithm ProPred. Eleven synthetic peptides representing predicted putative promiscuous T-cell epitopes were tested in IFN-gamma and IL-4 ELISPOT assays using peripheral blood mononuclear cells (PBMC) derived from 142 individuals from Rondonia State, Brazil who had been naturally exposed to P. vivax infections. To determine whether the predicted epitopes are preferentially recognized in the context of multiple alleles, MHC Class II typing of the cohort was also performed. Five synthetic peptides elicited robust cellular responses, and the overall frequencies of IFN-gamma and IL-4 responders to at least one of the promiscuous peptides were 62% and 46%, respectively. The frequencies of IFN-gamma and IL-4 responders to each peptide were not associated with a particular HLA-DRB1 allelic group since most of the peptides induced a response in individuals of 12 out of 13 studied allelic groups. The prediction of promiscuous epitopes using ProPred led to the identification of immunodominant epitopes recognized by PBMC from a significant proportion of a genetically heterogeneous population exposed to malaria infections. The combination of several such T-cell epitopes in a vaccine construct may increase the frequency of responders and the overall efficacy of subunit vaccines in genetically distinct populations.

CD4+ T cell epitope discovery and rational vaccine design

T cell epitope-driven vaccine design employs bioinformatic algorithms to identify potential targets of vaccines against infectious diseases or cancer. Potential epitopes can be identified with major histocompatibility complex (MHC)-binding algorithms, and the ability to bind to MHC class I or class II indicates a predominantly CD4(+) or CD8(+) T cell response. Furthermore, an epitope-based vaccine can circumvent evolutionary events favoring immune escape present in native proteins from pathogens. It can also focus on only the most relevant epitopes (i.e. conserved and promiscuous) recognized by the majority of the target population. Mounting evidence points to the critical role of CD4(+) T cells in natural antigen encounter and active immunization. In this paper the need for CD4(+) T cell help in vaccine development, the selection of CD4(+) T cell epitopes for an epitope-based vaccine, and how the approach can be used to induce a protective effect are reviewed.

The MHC motif viewer: a visualization tool for MHC binding motifs

In vertebrates, the onset of cellular immune reactions is controlled by presentation of peptides in complex with major histocompatibility complex (MHC) molecules to T cell receptors. In humans, MHCs are called human leukocyte antigens (HLAs). Different MHC molecules present different subsets of peptides, and knowledge of their binding specificities is important for understanding differences in the immune response between individuals. Algorithms predicting which peptides bind a given MHC molecule have recently been developed with high prediction accuracy. The utility of these algorithms is hampered by the lack of tools for browsing and comparing specificity of these molecules. We have developed a Web server, MHC Motif Viewer, which allows the display of the binding motif for MHC class I proteins for human, chimpanzee, rhesus monkey, mouse, and swine, as well as HLA-DR protein sequences. The binding motif for each MHC molecule is predicted using state-of-the-art, pan-specific peptide-MHC binding-prediction methods, and is visualized as a sequence logo, in a format that allows for a comprehensive interpretation of binding motif anchor positions and amino acid preferences.

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

Background

The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC.

Results

We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides.

Conclusions

The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/.

HLA-DR: molecular insights and vaccine design

Vaccines are one of the most cost effective methods to control infectious diseases and at the same time one of the most complex products of the pharmaceutical industry. In contrast to other drugs, vaccines are used mainly in healthy individuals, often in children. For this reason, very high standards are set for their production. Subunit vaccines, especially peptide vaccines, can provide a safe and cost-effective alternative to vaccines produced from attenuated or inactivated pathogen preparations. Biochemical and structural studies of class II MHC-peptide complexes are beginning to provide a conceptual foundation for the rational design of subunit and peptide vaccines. In this review, we show how analysis of peptide-class II MHC complexes together with developing understanding of antigen processing pathways has opened the door to understanding the major rules that govern selection of T cell epitopes. We review progress towards computational prediction of such epitopes, and efforts to evaluate algorithms that incorporate various structural and/or biochemical aspects of the MHC-peptide interaction. Finally, using malaria as a model, we describe the development of a minimal subunit vaccine for the human malaria parasite Plasmodium falciparum.