Identifiying human MHC supertypes using bioinformatic methods

Uncommon P1 Anchor-featured Viral T Cell Epitope Preference within HLA-A*2601 and HLA-A*0101 Individuals

The individual HLA-related susceptibility to emerging viral diseases such as COVID-19 underscores the importance of understanding how HLA polymorphism influences peptide presentation and T cell recognition. Similar to HLA-A*0101, which is one of the earliest identified HLA alleles among the human population, HLA-A*2601 possesses a similar characteristic for the binding peptide and acts as a prevalent allomorph in HLA-I. In this study, we found that, compared with HLA-A*0101, HLA-A*2601 individuals exhibit distinctive features for the T cell responses to SARS-CoV-2 and influenza virus after infection and/or vaccination. The heterogeneous T cell responses can be attributed to the distinct preference of HLA-A*2601 and HLA-A*0101 to T cell epitope motifs with negative-charged residues at the P1 and P3 positions, respectively. Furthermore, we determined the crystal structures of the HLA-A*2601 complexed to four peptides derived from SARS-CoV-2 and human papillomavirus, with one structure of HLA-A*0101 for comparison. The shallow pocket C of HLA-A*2601 results in the promiscuous presentation of peptides with "switchable" bulged conformations because of the secondary anchor in the median portion. Notably, the hydrogen bond network formed between the negative-charged P1 anchors and the HLA-A*2601-specific residues lead to a "closed" conformation and solid placement for the P1 secondary anchor accommodation in pocket A. This insight sheds light on the intricate relationship between HLA I allelic allomorphs, peptide binding, and the immune response and provides valuable implications for understanding disease susceptibility and potential vaccine design.

Prognostic impact of HLA supertype mismatch in single-unit cord blood transplantation

The "human leukocyte antigen (HLA) supertype" is a functional classification of HLA alleles, which was defined by structural features and peptide specificities, and has been reportedly associated with the clinical outcomes of viral infections and autoimmune diseases. Although the disparity in each HLA locus was reported to have no clinical significance in single-unit cord blood transplantation (sCBT), the clinical significance of the HLA supertype in sCBT remains unknown. Therefore, we retrospectively analyzed clinical data of 1603 patients who received sCBT in eight institutes in Japan between 2000 and 2017. Each HLA allele was categorized into 19 supertypes, and the prognostic effect of disparities was then assessed. An HLA-B supertype mismatch was identified as a poor prognostic factor (PFS: hazard ratio [HR] = 1.23, p = 0.00044) and was associated with a higher cumulative incidence (CI) of relapse (HR = 1.24, p = 0.013). However, an HLA-B supertype mismatch was not associated with the CI of acute and chronic graft-versus-host-disease. The multivariate analysis for relapse and PFS showed the significance of an HLA-B supertype mismatch independent of allelic mismatches, and other previously reported prognostic factors. HLA-B supertype-matched grafts should be selected in sCBT.

Immunogenetics, sylvatic plague and its vectors: insights from the pathogen reservoir Mastomys natalensis in Tanzania

Yersinia pestis is a historically important vector-borne pathogen causing plague in humans and other mammals. Contemporary zoonotic infections with Y. pestis still occur in sub-Saharan Africa, including Tanzania and Madagascar, but receive relatively little attention. Thus, the role of wildlife reservoirs in maintaining sylvatic plague and spillover risks to humans is largely unknown. The multimammate rodent Mastomys natalensis is the most abundant and widespread rodent in peri-domestic areas in Tanzania, where it plays a major role as a Y. pestis reservoir in endemic foci. Yet, how M. natalensis' immunogenetics contributes to the maintenance of plague has not been investigated to date. Here, we surveyed wild M. natalensis for Y. pestis vectors, i.e., fleas, and tested for the presence of antibodies against Y. pestis using enzyme-linked immunosorbent assays (ELISA) in areas known to be endemic or without previous records of Y. pestis in Tanzania. We characterized the allelic and functional (i.e., supertype) diversity of the major histocompatibility complex (MHC class II) of M. natalensis and investigated links to Y. pestis vectors and infections. We detected antibodies against Y. pestis in rodents inhabiting both endemic areas and areas considered non-endemic. Of the 111 nucleotide MHC alleles, only DRB*016 was associated with an increased infestation with the flea Xenopsylla. Surprisingly, we found no link between MHC alleles or supertypes and antibodies of Y. pestis. Our findings hint, however, at local adaptations towards Y. pestis vectors, an observation that more exhaustive sampling could unwind in the future.

A biobank of pediatric patient-derived-xenograft models in cancer precision medicine trial MAPPYACTS for relapsed and refractory tumors

Pediatric patients with recurrent and refractory cancers are in most need for new treatments. This study developed patient-derived-xenograft (PDX) models within the European MAPPYACTS cancer precision medicine trial (NCT02613962). To date, 131 PDX models were established following heterotopical and/or orthotopical implantation in immunocompromised mice: 76 sarcomas, 25 other solid tumors, 12 central nervous system tumors, 15 acute leukemias, and 3 lymphomas. PDX establishment rate was 43%. Histology, whole exome and RNA sequencing revealed a high concordance with the primary patient's tumor profile, human leukocyte-antigen characteristics and specific metabolic pathway signatures. A detailed patient molecular characterization, including specific mutations prioritized in the clinical molecular tumor boards are provided. Ninety models were shared with the IMI2 ITCC Pediatric Preclinical Proof-of-concept Platform (IMI2 ITCC-P4) for further exploitation. This PDX biobank of unique recurrent childhood cancers provides an essential support for basic and translational research and treatments development in advanced pediatric malignancies.

HLA-Clus: HLA class I clustering based on 3D structure

BACKGROUND

In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria.

RESULTS

The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved.

CONCLUSIONS

The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository ( https://github.com/yshen25/HLA-Clus ).

HLA Class I Supertype Classification Based on Structural Similarity

HLA class I proteins, a critical component in adaptive immunity, bind and present intracellular Ags to CD8+ T cells. The extreme polymorphism of HLA genes and associated peptide binding specificities leads to challenges in various endeavors, including neoantigen vaccine development, disease association studies, and HLA typing. Supertype classification, defined by clustering functionally similar HLA alleles, has proven helpful in reducing the complexity of distinguishing alleles. However, determining supertypes via experiments is impractical, and current in silico classification methods exhibit limitations in stability and functional relevance. In this study, by incorporating three-dimensional structures we present a method for classifying HLA class I molecules with improved breadth, accuracy, stability, and flexibility. Critical for these advances is our finding that structural similarity highly correlates with peptide binding specificity. The new classification should be broadly useful in peptide-based vaccine development and HLA-disease association studies.

Pan-Cancer HLA Gene-Mediated Tumor Immunogenicity and Immune Evasion

Human leukocyte antigen (HLA) expression contributes to the activation of antitumor immunity through interactions with T-cell receptors. Pan-cancer HLA-mediated immunogenicity and immunoediting mechanisms have not been systematically studied previously. In a retrospective analysis of 33 tumor types from the Cancer Genome Atlas (TCGA), we characterized the differential expression of HLA class I and class II genes across various oncogenic pathways and immune subtypes. While HLA I genes were upregulated in all immunogenically hot tumors, HLA II genes were upregulated in an inflammatory immune subtype associated with best prognosis and were systematically downregulated in specific oncogenic pathways. A subset of immunogenically hot tumors which upregulated HLA class I but not class II genes exploited HLA-mediated escape strategies. Furthermore, with a machine learning model, we demonstrated that HLA gene expression data can be used to predict the immune subtypes of patients receiving immune checkpoint blockade and stratify patient survival. Interestingly, tumors with the highest immune infiltration did not have the best prognosis but showed significantly higher immune exhaustion.

IMPLICATIONS

Taken together, we highlight the prognostic potential of HLA genes in immunotherapies and suggest that higher tumor immunogenicity mediated by HLA expression may sometimes lead to tumor escape under strong selective pressure.

Accounting for population structure in genetic studies of cystic fibrosis

CFTR F508del (c.1521_1523delCTT, p.Phe508delPhe) is the most common pathogenic allele underlying cystic fibrosis (CF), and its frequency varies in a geographic cline across Europe. We hypothesized that genetic variation associated with this cline is overrepresented in a large cohort (N > 5,000) of persons with CF who underwent whole-genome sequencing and that this pattern could result in spurious associations between variants correlated with both the F508del genotype and CF-related outcomes. Using principal-component (PC) analyses, we showed that variation in the CFTR region disproportionately contributes to a PC explaining a relatively high proportion of genetic variance. Variation near CFTR was correlated with population structure among persons with CF, and this correlation was driven by a subset of the sample inferred to have European ancestry. We performed genome-wide association studies comparing persons with CF with one versus two copies of the F508del allele; this allowed us to identify genetic variation associated with the F508del allele and to determine that standard PC-adjustment strategies eliminated the significant association signals. Our results suggest that PC adjustment can adequately prevent spurious associations between genetic variants and CF-related traits and are therefore effective tools to control for population structure even when population structure is confounded with disease severity and a common pathogenic variant.

Dominant epitopes presented by prevalent HLA alleles permit wide use of banked CMVpp65 T-cells in adoptive therapy

We established and characterized a bank of 138 CMVpp65 peptide-specific T-cell lines (CMVpp65CTLs) from healthy marrow transplant donors who consented to their use for treatment of individuals other than their transplant recipient. CMVpp65CTL lines included 131 containing predominantly CD8+ T-cells and 7 CD4+ T-cell. CD8+ CMVpp65CTLs were specific for 1-3 epitopes each presented by one of only 34 of the 148 class I alleles in the bank. Similarly, the 7 predominantly CD4+ CMVpp65CTL lines were each specific for epitopes presented by 14 of 40 HLA DR alleles in the bank. Although the number of HLA alleles presenting CMV epitopes is low, their prevalence is high, permitting selection of CMVpp65CTLs restricted by an HLA allele shared by transplant recipient and HCT donor for >90% of an ethnogeographically diverse population of HCT recipients. Within individuals, responses to CMVpp65 peptides presented by different HLA alleles are hierarchical. Furthermore, within groups, epitopes presented by HLA B*07:02 and HLA A*02:01 consistently elicit immunodominant CMVpp65 CTLs, irrespective of other HLA alleles inherited. All dominant CMVpp65CTLs exhibited HLA-restricted cytotoxicity against epitope loaded targets, and usually cleared CMV infections. However, immunodominant CMVpp65 CTL responding to epitopes presented by certain HLA B*35 alleles were ineffective in lysing CMV infected cells in vitro or controlling CMV infections post adoptive therapy. Analysis of the hierarchy of T-cell responses to CMVpp65, the HLA alleles presenting immunodominant CMVpp65 epitopes, and the responses they induce, may lead to detailed algorithms for optimal choice of 3rd party CMVpp65CTLs for effective adoptive therapy.

An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles-mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.

Searching Epitope-Based Vaccines Using Bioinformatics Studies

Epitope-based vaccines is one of the most recent methodologies applied in bioinformatics studies. This strategy consists of identifying regions of the protein (peptides or epitopes) which show antigen properties capable of stimulating the immune system against proteins from virus, bacteria, fungi, etc. This chapter describes a general procedure to identify epitopes to be used as epitope vaccine using bioinformatics methods including primary protein sequence analyses, epitope predictor, docking, and molecular dynamics simulations for the selection of T- and B-cell epitopes.

The pockets guide to HLA class I molecules

Human leukocyte antigens (HLA) are cell-surface proteins that present peptides to T cells. These peptides are bound within the peptide binding cleft of HLA, and together as a complex, are recognised by T cells using their specialised T cell receptors. Within the cleft, the peptide residue side chains bind into distinct pockets. These pockets ultimately determine the specificity of peptide binding. As HLAs are the most polymorphic molecules in humans, amino acid variants in each binding pocket influences the peptide repertoire that can be presented on the cell surface. Here, we review each of the 6 HLA binding pockets of HLA class I (HLA-I) molecules. The binding specificity of pockets B and F are strong determinants of peptide binding and have been used to classify HLA into supertypes, a useful tool to predict peptide binding to a given HLA. Over the years, peptide binding prediction has also become more reliable by using binding affinity and mass spectrometry data. Crystal structures of peptide-bound HLA molecules provide a means to interrogate the interactions between binding pockets and peptide residue side chains. We find that most of the bound peptides from these structures conform to binding motifs determined from prediction software and examine outliers to learn how these HLAs are stabilised from a structural perspective.

NMD inhibition by 5-azacytidine augments presentation of immunogenic frameshift-derived neoepitopes

Frameshifted protein sequences elicit tumor-specific T cell-mediated immune responses in microsatellite-unstable (MSI) cancers if presented by HLA class I molecules. However, their expression and presentation are limited by nonsense-mediated RNA decay (NMD). We employed an unbiased immunopeptidomics workflow to analyze MSI HCT-116 cells and identified >10,000 HLA class I-presented peptides including five frameshift-derived InDel neoepitopes. Notably, pharmacological NMD inhibition with 5-azacytidine stabilizes frameshift-bearing transcripts and increases the HLA class I-mediated presentation of InDel neoepitopes. The frameshift mutation underlying one of the identified InDel neoepitopes is highly recurrent in MSI colorectal cancer cell lines and primary patient samples, and immunization with the corresponding neoepitope induces strong CD8+ T cell responses in an HLA-A∗02:01 transgenic mouse model. Our data show directly that pharmacological NMD inhibition augments HLA class I-mediated presentation of immunogenic frameshift-derived InDel neoepitopes thus highlighting the clinical potential of NMD inhibition in anti-cancer immunotherapy strategies.

Depletion of MHC supertype during domestication can compromise immunocompetence

The major histocompatibility complex (MHC) plays an important role in infectious disease resistance. The presence of certain MHC alleles and functionally similar groups of MHC alleles (i.e., supertypes) has been associated with resistance to particular parasite species. Farmed and domesticated fish stocks are often depleted in their MHC alleles and supertype diversity, possibly as a consequence of artificial selection for desirable traits, inbreeding (loss of heterozygosity), genetic drift (loss of allelic diversity) and/or reduced parasite biodiversity. Here we quantify the effects of depletion of MHC class II genotype and supertype variation on resistance to the parasite Gyrodactylus turnbulli in guppies (Poecilia reticulata). Compared to the descendants of wild‐caught guppies, ornamental fish had a significantly reduced MHC variation (i.e., the numbers of MHC alleles and supertypes per individual, and per population). In addition, ornamental fish were significantly more susceptible to G. turnbulli infections, accumulating peak intensity 10 times higher than that of their wildtype counterparts. Four out of 13 supertypes were associated with a significantly reduced parasite load, and the presence of some supertypes had a dramatic effect on the intensity of infection. Remarkably, the ornamental and wildtype fish differed in the supertypes that were associated with parasite resistance. Analysis with a genetic algorithm showed that resistance‐conferring supertypes of the wildtype and ornamental fish shared two unique amino acids in the peptide‐binding region of the MHC that were not found in any other alleles. These data show that the supertype demarcation captures some, but not all, of the variation in the immune function of the alleles. This study highlights the importance of managing functional MHC diversity in livestock, and suggests there might be some immunological redundancy among MHC supertypes.

HLA-A29 and Birdshot Uveitis: Further Down the Rabbit Hole

HLA class I alleles constitute established risk factors for non-infectious uveitis and preemptive genotyping of HLA class I alleles is standard practice in the diagnostic work-up. The HLA-A29 serotype is indispensable to Birdshot Uveitis (BU) and renders this enigmatic eye condition a unique model to better understand how the antigen processing and presentation machinery contributes to non-infectious uveitis or chronic inflammatory conditions in general. This review will discuss salient points regarding the protein structure of HLA-A29 and how key amino acid positions impact the peptide binding preference and interaction with T cells. We discuss to what extent the risk genes ERAP1 and ERAP2 uniquely affect HLA-A29 and how the discovery of a HLA-A29-specific submotif may impact autoantigen discovery. We further provide a compelling argument to solve the long-standing question why BU only affects HLA-A29-positive individuals from Western-European ancestry by exploiting data from the 1000 Genomes Project. We combine novel insights from structural and immunopeptidomic studies and discuss the functional implications of genetic associations across the HLA class I antigen presentation pathway to refine the etiological basis of Birdshot Uveitis.

Expected immune recognition of COVID-19 virus by memory from earlier infections with common coronaviruses in a large part of the world population

SARS-CoV-2 is the coronavirus agent of the COVID-19 pandemic causing high mortalities. In contrast, the widely spread human coronaviruses OC43, HKU1, 229E, and NL63 tend to cause only mild symptoms. The present study shows, by in silico analysis, that these common human viruses are expected to induce immune memory against SARS-CoV-2 by sharing protein fragments (antigen epitopes) for presentation to the immune system by MHC class I. A list of such epitopes is provided. The number of these epitopes and the prevalence of the common coronaviruses suggest that a large part of the world population has some degree of specific immunity against SARS-CoV-2 already, even without having been infected by that virus. For inducing protection, booster vaccinations enhancing existing immunity are less demanding than primary vaccinations against new antigens. Therefore, for the discussion on vaccination strategies against COVID-19, the available immune memory against related viruses should be part of the consideration.

PromPDD, a web-based tool for the prediction, deciphering and design of promiscuous peptides that bind to HLA class I molecules

Promiscuous peptides that can be presented by multiple human leukocyte antigens (HLAs) have great potential for the development of vaccines with wide population coverage. However, the current available methods for the prediction of peptides that bind to major histocompatibility complex (MHC) are mainly aimed at the rapid or mass screening of potential T cell epitopes from pathogen antigens or proteomics. The current approaches do not allow deciphering the contribution of the residue at each peptide position to the promiscuous binding ability of the peptide or obtaining guidelines for the design of promiscuous peptides. In this study, we re-evaluated and characterized four matrix-based prediction models that have been extensively used for the prediction of HLA-binding peptides and found that the prediction models generated based on the average relative binding (ARB) matrix shared a consistent and conservative threshold for all well-studied HLA class I alleles. Evaluations performed using datasets of HLA supertype-specific peptides with various cross-binding abilities and peptide mutant analogues indicated that the ARB-based binding matrices could be used to decipher and design promiscuous peptides that bind to multiple HLA molecules. A web-based tool called PromPDD was developed using ARB matrix-based models, and this tool enables the prediction, deciphering and design of promiscuous peptides that bind to multiple HLA molecules within or across HLA supertypes in a simpler and more direct manner. Furthermore, we expanded the application of PromPDD to HLA class I alleles with limited experimentally verified data by generating pan-specific matrices using a derived modular method, and 2641 HLA molecules encoded by HLA-A and HLA-B genes are available in PromPDD. PromPDD, which is freely available at http://www.immunoinformatics.net/PromPDD/, is the first tool for the deciphering and design of promiscuous peptides that bind to HLA class I molecules.

Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios

BACKGROUND

The adaptive immune response intrinsically depends on hypervariable human leukocyte antigen (HLA) genes. Concomitantly, correct HLA phenotyping is crucial for successful donor-patient matching in organ transplantation. The cost and technical limitations of current laboratory techniques, together with advances in next-generation sequencing (NGS) methodologies, have increased the need for precise computational typing methods.

RESULTS

We tested two widespread HLA typing methods using high quality full genome sequencing data from 150 individuals in 50 family trios from the Genome Denmark project. First, we computed descendant accuracies assessing the agreement in the inheritance of alleles from parents to offspring. Second, we compared the locus-specific homozygosity rates as well as the allele frequencies; and we compared those to the observed values in related populations. We provide guidelines for testing the accuracy of HLA typing methods by comparing family information, which is independent of the availability of curated alleles.

CONCLUSIONS

Although current computational methods for HLA typing generally provide satisfactory results, our benchmark - using data with ultra-high sequencing depth - demonstrates the incompleteness of current reference databases, and highlights the importance of providing genomic databases addressing current sequencing standards, a problem yet to be resolved before benefiting fully from personalised medicine approaches HLA phenotyping is essential.

Update on Tumor Neoantigens and Their Utility: Why It Is Good to Be Different

Antitumor rejection by the immune system is a complex process that is regulated by several factors. Among these factors are the quality and quantity of mutational events that occur in cancer cells. Perhaps one of the most important types of mutations that influence antitumor immunity is the neoantigen, that is, a non-self-antigen that arises as a result of somatic mutation. Recent work has demonstrated that neoantigens hold significant promise for developing new diagnostic and therapeutic modalities. Therapeutic targeting of neoantigens is important for achieving benefit following therapy with immune checkpoint blockade agents or for cancer vaccines targeting mutations. Here, we review our understanding of neoantigens and discuss new developments in the quest to use them in cancer immunotherapy.

Evolutionary genetics of immunological supertypes reveals two faces of the Red Queen

Red Queen host-parasite co-evolution can drive adaptations of immune genes by positive selection that erodes genetic variation (Red Queen arms race) or results in a balanced polymorphism (Red Queen dynamics) and long-term preservation of genetic variation (trans-species polymorphism). These two Red Queen processes are opposite extremes of the co-evolutionary spectrum. Here we show that both Red Queen processes can operate simultaneously by analysing the major histocompatibility complex (MHC) in guppies (Poecilia reticulata and P. obscura) and swamp guppies (Micropoecilia picta). Sub-functionalisation of MHC alleles into 'supertypes' explains how polymorphisms persist during rapid host-parasite co-evolution. Simulations show the maintenance of supertypes as balanced polymorphisms, consistent with Red Queen dynamics, whereas alleles within supertypes are subject to positive selection in a Red Queen arms race. Building on the divergent allele advantage hypothesis, we show that functional aspects of allelic diversity help to elucidate the evolution of polymorphic genes involved in Red Queen co-evolution.

Unveiling the Peptide Motifs of HLA-C and HLA-G from Naturally Presented Peptides and Generation of Binding Prediction Matrices

The classical HLA-C and the nonclassical HLA-E and HLA-G molecules play important roles both in the innate and adaptive immune system. Starting already during embryogenesis and continuing throughout our lives, these three Ags exert major functions in immune tolerance, defense against infections, and anticancer immune responses. Despite these important roles, identification and characterization of the peptides presented by these molecules has been lacking behind the more abundant HLA-A and HLA-B gene products. In this study, we elucidated the peptide specificities of these HLA molecules using a comprehensive analysis of naturally presented peptides. To that end, the 15 most frequently expressed HLA-C alleles as well as HLA-E*01:01 and HLA-G*01:01 were transfected into lymphoblastoid C1R cells expressing low endogenous HLA. Identification of naturally presented peptides was performed by immunoprecipitation of HLA and subsequent analysis of HLA-bound peptides by liquid chromatographic tandem mass spectrometry. Peptide motifs of HLA-C unveil anchors in position 2 or 3 with high variances between allotypes, and a less variable anchor at the C-terminal end. The previously reported small ligand repertoire of HLA-E was confirmed within our analysis, and we could show that HLA-G combines a large ligand repertoire with distinct features anchoring peptides at positions 3 and 9, supported by an auxiliary anchor in position 1 and preferred residues in positions 2 and 7. The wealth of HLA ligands resulted in prediction matrices for octa-, nona-, and decamers. Matrices were validated in terms of their binding prediction and compared with the latest NetMHC prediction algorithm NetMHCpan-3.0, which demonstrated their predictive power.

Scrutinizing human MHC polymorphism: Supertype analysis using Poisson-Boltzmann electrostatics and clustering

Peptide-binding MHC proteins are thought the most variable across the human population; the extreme MHC polymorphism observed is functionally important and results from constrained divergent evolution. MHCs have vital functions in immunology and homeostasis: cell surface MHC class I molecules report cell status to CD8+ T cells, NKT cells and NK cells, thus playing key roles in pathogen defence, as well as mediating smell recognition, mate choice, Adverse Drug Reactions, and transplantation rejection. MHC peptide specificity falls into several supertypes exhibiting commonality of binding. It seems likely that other supertypes exist relevant to other functions. Since comprehensive experimental characterization is intractable, structure-based bioinformatics is the only viable solution. We modelled functional MHC proteins by homology and used calculated Poisson-Boltzmann electrostatics projected from the top surface of the MHC as multi-dimensional descriptors, analysing them using state-of-the-art dimensionality reduction techniques and clustering algorithms. We were able to recover the 3 MHC loci as separate clusters and identify clear sub-groups within them, vindicating unequivocally our choice of both data representation and clustering strategy. We expect this approach to make a profound contribution to the study of MHC polymorphism and its functional consequences, and, by extension, other burgeoning structural systems, such as GPCRs.

Retention of functional variation despite extreme genomic erosion: MHC allelic repertoires in the Lynx genus

BACKGROUND

Demographic bottlenecks erode genetic diversity and may increase endangered species' extinction risk via decreased fitness and adaptive potential. The genetic status of species is generally assessed using neutral markers, whose dynamic can differ from that of functional variation due to selection. The MHC is a multigene family described as the most important genetic component of the mammalian immune system, with broad implications in ecology and evolution. The genus Lynx includes four species differing immensely in demographic history and population size, which provides a suitable model to study the genetic consequences of demographic declines: the Iberian lynx being an extremely bottlenecked species and the three remaining ones representing common and widely distributed species. We compared variation in the most variable exon of the MHCI and MHCII-DRB loci among the four species of the Lynx genus.

RESULTS

The Iberian lynx was characterised by lower number of MHC alleles than its sister species (the Eurasian lynx). However, it maintained most of the functional genetic variation at MHC loci present in the remaining and genetically healthier lynx species at all nucleotide, amino acid, and supertype levels.

CONCLUSIONS

Species-wide functional genetic diversity can be maintained even in the face of severe population bottlenecks, which caused devastating whole genome genetic erosion. This could be the consequence of divergent alleles being retained across paralogous loci, an outcome that, in the face of frequent gene conversion, may have been favoured by balancing selection.

Combination of In Silico Methods in the Search for Potential CD4(+) and CD8(+) T Cell Epitopes in the Proteome of Leishmania braziliensis

The leishmaniases are neglected tropical diseases widespread throughout the globe, which are caused by protozoans from the genus Leishmania and are transmitted by infected phlebotomine flies. The development of a safe and effective vaccine against these diseases has been seen as the best alternative to control and reduce the number of cases. To support vaccine development, this work has applied an in silico approach to search for high potential peptide epitopes able to bind to different major histocompatibility complex Class I and Class II (MHC I and MHC II) molecules from different human populations. First, the predicted proteome of Leishmania braziliensis was compared and analyzed by modern linear programs to find epitopes with the capacity to trigger an immune response. This approach resulted in thousands of epitopes derived from 8,000 proteins conserved among different Leishmania species. Epitopes from proteins similar to those found in host species were excluded, and epitopes from proteins conserved between different Leishmania species and belonging to surface proteins were preferentially selected. The resulting epitopes were then clustered, to avoid redundancies, resulting in a total of 230 individual epitopes for MHC I and 2,319 for MHC II. These were used for molecular modeling and docking with MHC structures retrieved from the Protein Data Bank. Molecular docking then ranked epitopes based on their predicted binding affinity to both MHC I and II. Peptides corresponding to the top 10 ranked epitopes were synthesized and evaluated in vitro for their capacity to stimulate peripheral blood mononuclear cells (PBMC) from post-treated cutaneous leishmaniasis patients, with PBMC from healthy donors used as control. From the 10 peptides tested, 50% showed to be immunogenic and capable to stimulate the proliferation of lymphocytes from recovered individuals.

HLAsupE: an integrated database of HLA supertype-specific epitopes to aid in the development of vaccines with broad coverage of the human population

Background

Promiscuous T-cell epitopes that can be presented by multiple human leukocyte antigens (HLAs) are prime targets for vaccine and immunotherapy development because they are effective in a high proportion of the human population. Although there are a number of epitope databases currently available online, the epitope data in these databases were annotated using specific MHC restrictions, and none of these databases was specifically designed for retrieving data on promiscuous epitopes.

Description

HLAsupE is an integrated database of HLA supertype-specific epitopes (promiscuous T-cell epitopes in the context of HLA supertypes). The source data for the T-cell activities and HLA-binding capacities of peptides with a specific HLA restriction were extracted from public epitope databases. After a manual curation, these allele-specific data were integrated into supertype-specific datasets based on the defined supertypes and corresponding alleles. Each supertype-specific peptide in HLAsupE is annotated in terms of its cross-reactivity to HLA molecules within the same supertype. Promiscuous peptides that can be presented by multiple HLA molecules across multiple HLA supertypes were also included in this database. Several web-based tools are provided to access and download the data.

Conclusions

HLAsupE is the first database of promiscuous T cell epitopes that is organized based on the HLA supertypes. The main advantage of this database is the ability to search for promiscuous T-cell epitopes based on the cross-reactivity to specific alleles or supertypes. HLAsupE will be a valuable resource for the development of epitope-based vaccines and immunotherapies with broad coverage of human population.

Human leukocyte antigen supertype matching after myeloablative hematopoietic cell transplantation with 7/8 matched unrelated donor allografts: a report from the Center for International Blood and Marrow Transplant Research

The diversity of the human leukocyte antigen (HLA) class I and II alleles can be simplified by consolidating them into fewer supertypes based on functional or predicted structural similarities in epitope-binding grooves of HLA molecules. We studied the impact of matched and mismatched HLA-A (265 versus 429), -B (230 versus 92), -C (365 versus 349), and -DRB1 (153 versus 51) supertypes on clinical outcomes of 1934 patients with acute leukemias or myelodysplasia/myeloproliferative disorders. All patients were reported to the Center for International Blood and Marrow Transplant Research following single-allele mismatched unrelated donor myeloablative conditioning hematopoietic cell transplantation. Single mismatched alleles were categorized into six HLA-A (A01, A01A03, A01A24, A02, A03, A24), six HLA-B (B07, B08, B27, B44, B58, B62), two HLA-C (C1, C2), and five HLA-DRB1 (DR1, DR3, DR4, DR5, DR9) supertypes. Supertype B mismatch was associated with increased risk of grade II-IV acute graft-versus-host disease (hazard ratio =1.78, P=0.0025) compared to supertype B match. Supertype B07-B44 mismatch was associated with a higher incidence of both grade II-IV (hazard ratio=3.11, P=0.002) and III-IV (hazard ratio=3.15, P=0.01) acute graft-versus-host disease. No significant associations were detected between supertype-matched versus -mismatched groups at other HLA loci. These data suggest that avoiding HLA-B supertype mismatches can mitigate the risk of grade II-IV acute graft-versus-host disease in 7/8-mismatched unrelated donor hematopoietic cell transplantation when multiple HLA-B supertype-matched donors are available. Future studies are needed to define the mechanisms by which supertype mismatching affects outcomes after alternative donor hematopoietic cell transplantation.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials

MOTIVATION

T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction.

RESULTS

We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles.

CONCLUSIONS

HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods.

AVAILABILITY AND IMPLEMENTATION

The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy

CONTACT

: nchandra@biochem.iisc.ernet.in

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reverse Vaccinology: The Pathway from Genomes and Epitope Predictions to Tailored Recombinant Vaccines

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the technology of reverse vaccinology. We focus here on bacterial infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.

Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Structure-based clustering of major histocompatibility complex (MHC) proteins for broad-based T-cell vaccine design

Structure-based clustering technique is useful for identifying superfamilies of major histocompatibility complex (MHC) proteins with similar binding specificities. The resolved MHC superfamilies play an important role in vaccine development, from discovering new targets for broad-based vaccines and therapeutics to optimizing the affinity and selectivity of hits. Here, we describe a protocol and provide a summary for grouping MHC proteins according to their structural interaction characteristics.

Clustering HLA class I superfamilies using structural interaction patterns

Human leukocyte antigen (HLA) class I molecules are critical components of the cell-mediated immune system that bind and present intracellular antigenic peptides to CD8(+) T cell receptors. To understand the interaction mechanism underlying human leukocyte antigen (HLA) class I specificity in detail, we studied the structural interaction characteristics of 16,393 nonameric peptides binding to 58 HLA-A and -B molecules. Our analysis showed for the first time that HLA-peptide intermolecular bonding patterns vary among different alleles and may be grouped in a superfamily dependent manner. Through the use of these HLA class I 'fingerprints', a high resolution HLA class I superfamily classification schema was developed. This classification is capable of separating HLA alleles into well resolved, non-overlapping clusters, which is consistent with known HLA superfamily definitions. Such structural interaction approach serves as an excellent alternative to the traditional methods of HLA superfamily definitions that use peptide binding motifs or receptor information, and will help identify appropriate antigens suitable for broad-based subunit vaccine design.

Designing immunogenic peptides

Peptides fulfill many roles in immunology, yet none are more important than their role as immunogenic epitopes driving the adaptive immune response, our ultimate bulwark against infectious disease. Peptide epitopes are mediated primarily by their interaction with major histocompatibility complexes (T-cell epitopes) and antibodies (B-cell epitopes). As pathogen genomes continue to be revealed, both experimental and computational epitope mapping are becoming crucial tools in vaccine discovery. Immunoinformatics offers many tools, techniques and approaches for in silico epitope characterization, which is capable of greatly accelerating epitope design.

Novel approaches in polyepitope T-cell vaccine development against HIV-1

RV144 clinical trial was modestly effective in preventing HIV infection. New alternative approaches are needed to design improved HIV-1 vaccines and their delivery strategies. One of these approaches is construction of synthetic polyepitope HIV-1 immunogen using protective T- and B-cell epitopes that can induce broadly neutralizing antibodies and responses of cytotoxic (CD8(+) CTL) and helpers (CD4(+) Th) T-lymphocytes. This approach seems to be promising for designing of new generation of vaccines against HIV-1, enables in theory to cope with HIV-1 antigenic variability, focuses immune responses on protective determinants and enables to exclude from the vaccine compound that can induce autoantibodies or antibodies enhancing HIV-1 infectivity. Herein, the authors will focus on construction and rational design of polyepitope T-cell HIV-1 immunogens and their delivery, including: advantages and disadvantages of existing T-cell epitope prediction methods; features of organization of polyepitope immunogens, which can generate high-level CD8(+) and CD4(+) T-lymphocyte responses; the strategies to optimize efficient processing, presentation and immunogenicity of polyepitope constructs; original software to design polyepitope immunogens; and delivery vectors as well as mucosal strategies of vaccination. This new knowledge may bring us a one step closer to developing an effective T-cell vaccine against HIV-1, other chronic viral infections and cancer.

Relation of HLA class I and II supertypes with spontaneous clearance of hepatitis C virus

Human leukocyte antigen (HLA) genotype has been associated with the probability of spontaneous clearance of hepatitis C virus (HCV). However, no prior studies have examined whether this relationship may be further characterized by grouping HLA alleles according to their supertypes, defined by their binding capacities. There is debate regarding the most appropriate method to define supertypes. Therefore, previously reported HLA supertypes (46 class I and 25 class II) were assessed for their relation with HCV clearance in a population of 758 HCV-seropositive women. Two HLA class II supertypes were significant in multivariable models that included: (i) supertypes with significant or borderline associations with HCV clearance after adjustment for multiple tests, and (ii) individual HLA alleles not part of these supertypes, but associated with HCV clearance in our prior study in this population. Specifically, supertype DRB3 (prevalence ratio (PR)=0.4; P=0.004) was associated with HCV persistence, whereas DR8 (PR=1.8; P=0.01) was associated with HCV clearance. Two individual alleles (B*57:01 and C*01:02) associated with HCV clearance in our prior study became nonsignificant in analysis that included supertypes, whereas B*57:03 (PR=1.9; P=0.008) and DRB1*07:01 (PR=1.7; P=0.005) retained their significance. These data provide epidemiologic support for the significance of HLA supertypes in relation to HCV clearance.

MHCcluster, a method for functional clustering of MHC molecules

The identification of peptides binding to major histocompatibility complexes (MHC) is a critical step in the understanding of T cell immune responses. The human MHC genomic region (HLA) is extremely polymorphic comprising several thousand alleles, many encoding a distinct molecule. The potentially unique specificities remain experimentally uncharacterized for the vast majority of HLA molecules. Likewise, for nonhuman species, only a minor fraction of the known MHC molecules have been characterized. Here, we describe a tool, MHCcluster, to functionally cluster MHC molecules based on their predicted binding specificity. The method has a flexible web interface that allows the user to include any MHC of interest in the analysis. The output consists of a static heat map and graphical tree-based visualizations of the functional relationship between MHC variants and a dynamic TreeViewer interface where both the functional relationship and the individual binding specificities of MHC molecules are visualized. We demonstrate that conventional sequence-based clustering will fail to identify the functional relationship between molecules, when applied to MHC system, and only through the use of the predicted binding specificity can a correct clustering be found. Clustering of prevalent HLA-A and HLA-B alleles using MHCcluster confirms the presence of 12 major specificity groups (supertypes) some however with highly divergent specificities. Importantly, some HLA molecules are shown not to fit any supertype classification. Also, we use MHCcluster to show that chimpanzee MHC class I molecules have a reduced functional diversity compared to that of HLA class I molecules. MHCcluster is available at www.cbs.dtu.dk/services/MHCcluster-2.0.

Structural basis for the differential classification of HLA-A*6802 and HLA-A*6801 into the A2 and A3 supertypes

High polymorphism is one of the most important features of human leukocyte antigen (HLA) alleles, which were initially classified by serotyping but have recently been re-grouped into supertypes according to their peptide presentation properties. Two relatively prevalent HLA alleles HLA-A*6801 and HLA-A*6802, are classified into the same serotype HLA-A68. However, based on their distinct peptide-binding characteristics, HLA-A*6801 is grouped into A3 supertype, whereas HLA-A*6802 belongs to A2 supertype, similar to HLA-A*0201. Thusfar, the structural basis of the different supertype definitions of these serotyping-identical HLA alleles remains largely unknown. Herein, we determined the structures of HLA-A*6801 and HLA-A*6802 presenting three typical A3 and A2 supertype-restricted peptides, respectively. The binding capabilities of these peptides to HLA-A*6801, HLA-A*6802, and HLA-A*0201 were analyzed. These data indicate that the similar conformations of the residues within the F pocket contribute to close-related peptide binding features of HLA-A*6802 and HLA-A*0201. However, the overall structure and the peptide conformation of HLA-A*6802 are more similar to HLA-A*6801 rather than HLA-A*0201 which illuminates the similar serotype grouping of HLA-A*6802 and HLA-A*6801. Our findings are helpful for understanding the divergent peptide presentation and virus-specific CTL responses impacted by MHC micropolymorphisms and also elucidate the molecular basis of HLA supertype definitions.

Using databases and data mining in vaccinology

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology.

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

Background

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

Results

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

Conclusions

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

The design and proof of concept for a CD8(+) T cell-based vaccine inducing cross-subtype protection against influenza A virus

In this study, we examined the reactivity of human peripheral blood mononuclear cells to a panel of influenza A virus (IAV) CD8(+) T-cell epitopes that are recognised by the major human leukocyte antigen (HLA) groups represented in the human population. We examined the level of recognition in a sample of the human population and the potential coverage that could be achieved if these were incorporated into a T-cell epitope-based vaccine. We then designed a candidate influenza vaccine that incorporated three of the examined HLA-A2-restricted influenza epitopes into Pam2Cys-based lipopeptides. These lipopeptides do not require the addition of an adjuvant and can be delivered directly to the respiratory mucosa enabling the generation of local memory cell populations that are crucial for clearance of influenza. Intranasal administration of a mixture of three lipopeptides to HLA-A2 transgenic HHD mice elicited multiple CD8(+) T-cell specificities in the spleen and lung that closely mimicked the response generated following natural infection with influenza. These CD8(+) T cells were associated with viral reduction following H3N1 influenza virus challenge for as long as 3 months after lipopeptide administration. In addition, lipopeptides containing IAV-targeting epitopes conferred substantial benefit against death following infection with a virulent H1N1 strain. Because CD8(+) T cell epitopes are often derived from highly conserved regions of influenza viruses, such vaccines need not be reformulated annually and unlike current antibody-inducing vaccines could provide cross-protective immunity against newly emerging pandemic viruses.

Predictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?

Prediction methods as well as experimental methods for T-cell epitope discovery have developed significantly in recent years. High-throughput experimental methods have made it possible to perform full-length protein scans for epitopes restricted to a limited number of MHC alleles. The high costs and limitations regarding the number of proteins and MHC alleles that are feasibly handled by such experimental methods have made in silico prediction models of high interest. MHC binding prediction methods are today of a very high quality and can predict MHC binding peptides with high accuracy. This is possible for a large range of MHC alleles and relevant length of binding peptides. The predictions can easily be performed for complete proteomes of any size. Prediction methods are still, however, dependent on good experimental methods for validation, and should merely be used as a guide for rational epitope discovery. We expect prediction methods as well as experimental validation methods to continue to develop and that we will soon see clinical trials of products whose development has been guided by prediction methods.

Molecules and mating: positive selection and reproductive behaviour in primates

Sexual reproduction is generally thought to be more costly than asexual reproduction. However, it does have the advantage of accelerating rates of adaptation through processes such as recombination and positive selection. Comparative studies of the human and nonhuman primate genomes have demonstrated that positive selection has played an important role in the evolutionary history of humans and other primates. To date, many dozens of genes, thought to be affected by positive selection, have been identified. In this chapter, we will focus on genes that are associated with mating behaviours and reproductive processes, concentrating on genes that are most likely to enhance reproductive success and that also show evidence of positive selection. The genes encode phenotypic features that potentially influence mate choice decisions or impact the evolution and function of genes involved in the perception and regulation of, and the response to, phenotypic signals. We will also consider genes that influence precopulatory behavioural traits in humans and nonhuman primates, such as social bonding and aggression. The evolution of post-copulatory strategies such as sperm competition and selective abortion may also evolve in the presence of intense competition and these adaptations will also be considered. Although behaviour may not be solely determined by genes, the evidence suggests that the genes discussed in this chapter have some influence on human and nonhuman primate behaviour and that positive selection on these genes results in some degree of population differentiation and diversity.

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.

Disparate associations of HLA class I markers with HIV-1 acquisition and control of viremia in an African population

BACKGROUND

Acquisition of human immunodeficiency virus type 1 (HIV-1) infection is mediated by a combination of characteristics of the infectious and the susceptible member of a transmission pair, including human behavioral and genetic factors, as well as viral fitness and tropism. Here we report on the impact of established and potential new HLA class I determinants of heterosexual HIV-1 acquisition in the HIV-1-exposed seronegative (HESN) partners of serodiscordant Zambian couples.

METHODOLOGY/PRINCIPAL FINDINGS

We assessed the relationships of behavioral and clinically documented risk factors, index partner viral load, and host genetic markers to HIV-1 transmission among 568 cohabiting couples followed for at least nine months. We genotyped subjects for three classical HLA class I genes known to influence immune control of HIV-1 infection. From 1995 to December 2006, 240 HESNs seroconverted and 328 remained seronegative. In Cox proportional hazards models, HLA-A*68:02 and the B*42-C*17 haplotype in HESN partners were significantly and independently associated with faster HIV-1 acquisition (relative hazards = 1.57 and 1.55; p = 0.007 and 0.013, respectively) after controlling for other previously established contributing factors in the index partner (viral load and specific class I alleles), in the HESN partner (age, gender), or in the couple (behavioral and clinical risk score). Few if any previously implicated class I markers were associated here with the rate of acquiring infection.

CONCLUSIONS/SIGNIFICANCE

A few HLA class I markers showed modest effects on acquisition of HIV-1 subtype C infection in HESN partners of discordant Zambian couples. However, the striking disparity between those few markers and the more numerous, different markers found to determine HIV-1 disease course makes it highly unlikely that, whatever the influence of class I variation on the rate of infection, the mechanism mediating that phenomenon is identical to that involved in disease control.

HLA class I allele promiscuity revisited

The peptide repertoire presented on human leukocyte antigen (HLA) class I molecules is largely determined by the structure of the peptide binding groove. It is expected that the molecules having similar grooves (i.e., belonging to the same supertype) might present similar/overlapping peptides. However, the extent of promiscuity among HLA class I ligands remains controversial: while in many studies T cell responses are detected against epitopes presented by alternative molecules across HLA class I supertypes and loci, peptide elution studies report minute overlaps between the peptide repertoires of even related HLA molecules. To get more insight into the promiscuous peptide binding by HLA molecules, we analyzed the HLA peptide binding data from the large epitope repository, Immune Epitope Database (IEDB), and further performed in silico analysis to estimate the promiscuity at the population level. Both analyses suggest that an unexpectedly large fraction of HLA ligands (>50%) bind two or more HLA molecules, often across supertype or even loci. These results suggest that different HLA class I molecules can nevertheless present largely overlapping peptide sets, and that “functional” HLA polymorphism on individual and population level is probably much lower than previously anticipated.

Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes

Previous studies have attempted to define human leukocyte antigen (HLA) class II supertypes, analogous to the case for class I, on the basis of shared peptide-binding motifs or structure. In the present study, we determined the binding capacity of a large panel of non-redundant peptides for a set of 27 common HLA DR, DQ, and DP molecules. The measured binding data were then used to define class II supertypes on the basis of shared binding repertoires. Seven different supertypes (main DR, DR4, DRB3, main DQ, DQ7, main DP, and DP2) were defined. The molecules associated with the respective supertypes fell largely along lines defined by MHC locus and reflect, in broad terms, commonalities in reported peptide-binding motifs. Repertoire overlaps between molecules within the same class II supertype were found to be similar in magnitude to what has been observed for HLA class I supertypes. Surprisingly, however, the degree to which repertoires between molecules in the different class II supertypes also overlapped was found to be five to tenfold higher than repertoire overlaps noted between molecules in different class I supertypes. These results highlight a high degree of repertoire overlap amongst all HLA class II molecules, perhaps reflecting binding in multiple registers, and more pronounced dependence on backbone interactions rather than peptide anchor residues. This fundamental difference between HLA class I and class II would not have been predicted on the basis of analysis of either binding motifs or the sequence/predicted structures of the HLA molecules.

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/.