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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.

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan - 3 . 0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan - 3 . 0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0 .

Kyasanur forest disease virus (KFDV) causing tick-borne hemorrhagic fever which was earlier endemic to western Ghats, southern India, it is now encroaching into new geographic regions, but there is no approved medicine or effective vaccine against this deadly disease. In this study, we did in-silico design of multi-epitope subunit vaccine for KFDV. B-cell and T-cell epitopes were predicted from conserved regions of KFDV envelope protein and two vaccine candidates (VC1 and VC2) were constructed, those were found to be non-allergic and possess good antigenic properties, also gives cross-protection against Alkhurma hemorrhagic fever virus. The 3D structures of vaccine candidates were built and validated. Docking analysis of vaccine candidates with toll-like receptor-2 (TLR-2) by Cluspro and PatchDock revealed strong affinity between VC1 and TLR2. Ligplot tool was identified the intermolecular hydrogen bonds between vaccine candidates and TLR-2, iMOD server confirmed the stability of the docking complexes. JCAT sever ensured cloning efficiency of both vaccine constructs and in-silico cloning into pET30a (+) vector by SnapGene showed successful translation of epitope region. IMMSIM server was identified increased immunological responses. Finally, multi-epitope vaccine candidates were designed and validated their efficiency, it may pave the way for up-coming vaccine and diagnostic kit development.

The interaction between T cell receptor (TCR) and peptide (p)-Human Leukocyte Antigen (HLA) complexes is the critical first step in determining T cell responses. X-ray crystallographic studies of pHLA in TCR-bound and free states provide a structural perspective that can help understand T cell activation. These structures represent a static “snapshot”, yet the nature of pHLAs and their interactions with TCRs are highly dynamic. This has been demonstrated for HLA class I molecules with in silico techniques showing that some interactions, thought to stabilise pHLA-I, are only transient and prone to high flexibility. Here, we investigated the dynamics of HLA class II molecules by focusing on three allomorphs (HLA-DR1, -DR11 and -DR15) that are able to present the same epitope and activate CD4+ T cells. A single TCR (F24) has been shown to recognise all three HLA-DR molecules, albeit with different affinities. Using molecular dynamics and crystallographic ensemble refinement, we investigate the molecular basis of these different affinities and uncover hidden roles for HLA polymorphic residues. These polymorphisms were responsible for the widening of the antigen binding cleft and disruption of pHLA-TCR interactions, underpinning the hierarchy of F24 TCR binding affinity, and ultimately T cell activation. We expanded this approach to all available pHLA-DR structures and discovered that all HLA-DR molecules were inherently rigid. Together with in vitro protein stability and peptide affinity measurements, our results suggest that HLA-DR1 possesses inherently high protein stability, and low HLA-DM susceptibility.

The list of alleles in the HLA-DRB, HLA-DQA, and HLA-DQB gene loci has grown enormously since the last listing in this journal 8 years ago. Crystal structure determination of several human and mouse HLA class II alleles, representative of two gene loci in each species, enables a direct comparison of ortholog and paralog loci. A new numbering system is suggested, extending earlier suggestions by [Fremont et al. in Immunity 8:305-317, (1998)], which will bring in line all the structural features of various gene loci, regardless of animal species. This system allows for structural equivalence of residues from different gene loci. The listing also highlights all amino acid residues participating in the various functions of these molecules, from antigenic peptide binding to homodimer formation, CD4 binding, membrane anchoring, and cytoplasmic signal transduction, indicative of the variety of functions of these molecules. It is remarkable that despite the enormous number of unique alleles listed thus far (DQA = 22, DQB = 54, DRA = 2, and DRB = 409), there is invariance at many specific positions in man, but slightly less so in mouse or rat, despite their much lower number of alleles at each gene locus in the latter two species. Certain key polymorphisms (from substitutions to an eight-residue insertion in the cytoplasmic tail of certain DQB alleles) that have thus far gone unnoticed are highly suggestive of differences or diversities in function and thus call for further investigation into the properties of these specific alleles. This listing is amenable to supplementation by future additions of new alleles and the highlighting of new functions to be discovered, providing thus a unifying platform of reference in all animal species for the MHC class II allelic counterparts, aiding research in the field and furthering our understanding of the functions of these molecules.

Background CD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge. Results We developed the computational tool Predivac to predict CD4+ T-cell epitopes. Predivac can make predictions for 95% of all MHC class II protein variants (allotypes), a substantial advance over other available methods. Predivac bases its prediction on the concept of specificity-determining residues. The performance of the method was assessed both for high-affinity HLA class II peptide binding and CD4+ T-cell epitope prediction. In terms of epitope prediction, Predivac outperformed three available pan-specific approaches (delivering the highest specificity). A central finding was the high accuracy delivered by the method in the identification of immunodominant and promiscuous CD4+ T-cell epitopes, which play an essential role in epitope-based vaccine design. Conclusions The comprehensive HLA class II allele coverage along with the high specificity in identifying immunodominant CD4+ T-cell epitopes makes Predivac a valuable tool to aid epitope-based vaccine design in the context of a genetically heterogeneous human population.The tool is available at: http://predivac.biosci.uq.edu.au/ .

Sarcoidosis is an inflammatory disease of unknown etiology, most commonly affecting the lungs. Activated CD4+ T cells accumulate in the lungs of individuals with sarcoidosis and are considered to be of central importance for inflammation. We have previously shown that Scandinavian sarcoidosis patients expressing the HLA-DR allele DRB1*0301 are characterized by large accumulations in the lungs of CD4+ T cells expressing the TCR AV2S3 gene segment. This association afforded us a unique opportunity to identify a sarcoidosis-specific antigen recognized by AV2S3+ T cells. To identify candidates for the postulated sarcoidosis-specific antigen, lung cells from 16 HLA-DRB1*0301pos patients were obtained by bronchoalveolar lavage. HLA-DR molecules were affinity purified and bound peptides acid eluted. Subsequently, peptides were separated by reversed-phase HPLC and analyzed by liquid chromatography-mass spectrometry. We identified 78 amino acid sequences from self proteins presented in the lungs of sarcoidosis patients, some of which were well-known autoantigens such as vimentin and ATP synthase. For the first time, to our knowledge, we have identified HLA-bound peptides presented in vivo during an inflammatory condition. This approach can be extended to characterize HLA-bound peptides in various autoimmune settings.

Hashimoto's thyroiditis (HT) is associated with HLA, but the associated allele is still controversial. We hypothesized that specific HLA-DR pocket-sequence variants are associated with HT and that similar variants in the murine I-E locus (homologous to HLA-DR) predispose to experimental autoimmune thyroiditis (EAT), a classical mouse model of HT. Therefore, we sequenced the polymorphic exon 2 of the HLA-DR gene in 94 HT patients and 149 controls. In addition, we sequenced exon 2 of the I-E gene in 22 strains of mice, 12 susceptible to EAT and 10 resistant. Using logistic regression analysis, we identified a pocket amino acid signature, Tyr-26, Tyr-30, Gln-70, Lys-71, strongly associated with HT (P = 6.18 × 10⁵, OR = 3.73). Lys-71 showed the strongest association (P = 1.7 × 10⁻⁸, OR = 2.98). This association was seen across HLA-DR types. The 5-aa haplotype Tyr-26, Tyr-30, Gln-70, Lys-71, Arg-74 also was associated with HT (P = 3.66 × 10⁻⁴). In mice, the I-E pocket amino acids Val-28, Phe-86, and Asn-88 were strongly associated with EAT. Structural modeling studies demonstrated that pocket P4 was critical for the development of HT, and pockets P1 and P4 influenced susceptibility to EAT. Surprisingly, the structures of the HT- and EAT-susceptible pockets were different. We conclude that specific MHC II pocket amino acid signatures determine susceptibility to HT and EAT by causing structural changes in peptide-binding pockets that may influence peptide binding, selectivity, and presentation. Because the HT- and EAT-associated pockets are structurally different, it is likely that distinct antigenic peptides are associated with HT and EAT.

Background The etiology of type 1 diabetes (T1D) is largely unknown. Infections and microbial exposures are believed to play a role in the pathogenesis and in the development of islet autoimmunity in genetically susceptible individuals. Objective To assess the relationships between early childhood infections, islet autoimmunity, and progression to T1D in genetically predisposed children. Methods Children with human leukocyte antigen (HLA)‐conferred disease susceptibility (N=790; 51.5% males) from Finland (n = 386), Estonia (n = 322), and Russian Karelia (n = 82) were observed from birth up to the age of 3 years. Children attended clinical visits at the age of 3, 6, 12, 18, 24, and 36 months. Serum samples for analyzing T1D‐associated autoimmune markers were collected and health data recorded during the visits. Results Children developing islet autoimmunity (n = 46, 5.8%) had more infections during the first year of life (3.0 vs 3.0, mean rank 439.1 vs 336.2; P = .001) and their first infection occurred earlier (3.6 vs 5.0 months; P = .005) than children with no islet autoimmunity. By May 2016, 7 children (0.9%) had developed T1D (progressors). Compared with non‐diabetic children, T1D progressors were younger at first infection (2.2 vs 4.9 months; P = .004) and had more infections during the first 2 years of life (during each year 6.0 vs 3.0; P = .001 and P = .027, respectively). By 3 years of age, the T1D progressors had twice as many infections as the other children (17.5 vs 9.0; P = .006). Conclusions Early childhood infections may play an important role in the pathogenesis of T1D. Current findings may reflect either differences in microbial exposures or early immunological aberrations making diabetes‐prone children more susceptible to infections.

The multiple sclerosis (MS)-associated HLA major histocompatibility complex (MHC) class II alleles DRB1*1501, DRB5*0101 and DQB1*0602 are in strong linkage disequilibrium, making it difficult to determine which is the principal MS risk gene. Here we show that together the DRB1 and DRB5 loci may influence susceptibility to MS. We demonstrate that a T cell receptor (TCR) from an MS patient recognized both a DRB1*1501-restricted myelin basic protein (MBP) and DRB5*0101-restricted Epstein-Barr virus (EBV) peptide. Crystal structure determination of the DRB5*0101-EBV peptide complex revealed a marked degree of structural equivalence to the DRB1*1501–MBP peptide complex at the surface presented for TCR recognition. This provides structural evidence for molecular mimicry involving HLA molecules. The structural details suggest an explanation for the preponderance of MHC class II associations in HLA-associated diseases.