Experimental trials of predicted CD4+ and CD8+ T-cell epitopes of respiratory syncytial virus.

Background: Respiratory syncytial virus (RSV) is the most common cause of viral lower respiratory tract infections (LRTIs) in young children around the world and an important cause of LRTI in the elderly. The available treatments and FDA-approved vaccines for RSV only lessen the severity of the infection and are recommended for infants and elderly people. Methods: We focused on developing a broad-spectrum vaccine that activates the immune system to directly combat RSV. The objective of this study is to identify CD4+ and CD8+ T-cell epitopes using an immunoinformatics approach to develop RSV vaccines. The efficacy of these peptides was validated through in-vitro and in-vivo studies involving healthy and diseased animal models. Results: For each major histocompatibility complex (MHC) class-I and II, we found three epitopes of RSV proteins including F, G, and SH with an antigenic score of >0.5 and a projected SVM score of <5. Experimental validation of these peptides on female BALB/c mice was conducted before and after infection with the RSV A2 line 19f. We found that the 3RVMHCI (CD8+) epitope of the F protein showed significant results of white blood cells (19.72 × 103 cells/µl), neutrophils (6.01 × 103 cells/µl), lymphocytes (12.98 × 103 cells/µl), IgG antibodies (36.9 µg/ml), IFN-γ (86.96 ng/L), and granzyme B (691.35 pg/ml) compared to control at the second booster dose of 10 µg. Similarly, 4RVMHCII (CD4+) of the F protein substantially induced white blood cells (27.08 × 103 cells/µl), neutrophils (6.58 × 103 cells/µl), lymphocytes (16.64 × 103 cells/µl), IgG antibodies (46.13 µg/ml), IFN-γ (96.45 ng/L), and granzyme B (675.09 pg/ml). In-vitro studies showed that 4RVMHCII produced a significant level of antibodies in sera on day 45 comparable to mice infected with the virus. 4RVMHCII also induced high IFN-γ and IL-2 secretions on the fourth day of the challenge compared to the preinfectional stage. Conclusion: In conclusion, epitopes of the F protein showed considerable immune response and are suitable for further validation.

Rational immunosilencing of a promiscuous T-cell epitope in the capsid of an adeno-associated virus.

Owing to the immunogenicity of adeno-associated viruses (AAVs), gene therapies using AAVs face considerable obstacles. Here, by leveraging ex vivo T-cell assays, the prediction of epitope binding to major histocompatibility complex class-II alleles, sequence-conservation analysis in AAV phylogeny and site-directed mutagenesis, we show that the replacement of amino acid residues in a promiscuous and most immunodominant T-cell epitope in the AAV9 capsid with AAV5 sequences abrogates the immune responses of peripheral blood mononuclear cells to the chimaeric vector while preserving its functions, potency, cellular specificity, transduction efficacy and biodistribution. This rational approach to the immunosilencing of capsid epitopes promiscuously binding to T cells may be applied to other AAV vectors and epitope regions.

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.

MOTIVATION: The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. RESULTS: In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. AVAILABILITY AND IMPLEMENTATION: The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings.

MOTIVATION: T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. RESULTS: We have developed a new machine learning model that utilizes information about the TCR from both α and ß chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. AVAILABILITY AND IMPLEMENTATION: https://github.com/DaniTheOrange/EPIC-TRACE.

Determining epitope specificity of T-cell receptors with transformers.

SUMMARY: T-cell receptors (TCRs) on T cells recognize and bind to epitopes presented by the major histocompatibility complex in case of an infection or cancer. However, the high diversity of TCRs, as well as their unique and complex binding mechanisms underlying epitope recognition, make it difficult to predict the binding between TCRs and epitopes. Here, we present the utility of transformers, a deep learning strategy that incorporates an attention mechanism that learns the informative features, and show that these models pre-trained on a large set of protein sequences outperform current strategies. We compared three pre-trained auto-encoder transformer models (ProtBERT, ProtAlbert, and ProtElectra) and one pre-trained auto-regressive transformer model (ProtXLNet) to predict the binding specificity of TCRs to 25 epitopes from the VDJdb database (human and murine). Two additional modifications were performed to incorporate gene usage of the TCRs in the four transformer models. Of all 12 transformer implementations (four models with three different modifications), a modified version of the ProtXLNet model could predict TCR-epitope pairs with the highest accuracy (weighted F1 score 0.55 simultaneously considering all 25 epitopes). The modification included additional features representing the gene names for the TCRs. We also showed that the basic implementation of transformers outperformed the previously available methods, i.e. TCRGP, TCRdist, and DeepTCR, developed for the same biological problem, especially for the hard-to-classify labels. We show that the proficiency of transformers in attention learning can be made operational in a complex biological setting like TCR binding prediction. Further ingenuity in utilizing the full potential of transformers, either through attention head visualization or introducing additional features, can extend T-cell research avenues. AVAILABILITY AND IMPLEMENTATION: Data and code are available on https://github.com/InduKhatri/tcrformer.

Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope.

Neoantigen vaccines are one of the most effective immunotherapies for personalized tumour treatment. The current immunogen design of neoantigen vaccines is usually based on whole-genome sequencing (WGS) and bioinformatics prediction that focuses on the prediction of binding affinity between peptide and MHC molecules, ignoring other peptide-presenting related steps. This may result in a gap between high prediction accuracy and relatively low clinical effectiveness. In this study, we designed an integrated in-silico pipeline, Neo-intline, which started from the SNPs and indels of the tumour samples to simulate the presentation process of peptides in-vivo through an integrated calculation model. Validation on the benchmark dataset of TESLA and clinically validated neoantigens illustrated that neo-intline could outperform current state-of-the-art tools on both sample level and melanoma level. Furthermore, by taking the mouse melanoma model as an example, we verified the effectiveness of 20 neoantigens, including 10 MHC-I and 10 MHC-II peptides. The in-vitro and in-vivo experiments showed that both peptides predicted by Neo-intline could recruit corresponding CD4+ T cells and CD8+ T cells to induce a T-cell-mediated cellular immune response. Moreover, although the therapeutic effect of neoantigen vaccines alone is not sufficient, combinations with other specific therapies, such as broad-spectrum immune-enhanced adjuvants of granulocyte-macrophage colony-stimulating factor (GM-CSF) and polyinosinic-polycytidylic acid (poly(I:C)), or immune checkpoint inhibitors, such as PD-1/PD-L1 antibodies, can illustrate significant anticancer effects on melanoma. Neo-intline can be used as a benchmark process for the design and screening of immunogenic targets for neoantigen vaccines.

Structural Framework for Analysis of CD4+ T-Cell Epitope Dominance in Viral Fusion Proteins.

Antigen conformation shapes CD4+ T-cell specificity through mechanisms of antigen processing, and the consequences for immunity may rival those from conformational effects on antibody specificity. CD4+ T cells initiate and control immunity to pathogens and cancer and are at least partly responsible for immunopathology associated with infection, autoimmunity, and allergy. The primary trigger for CD4+ T-cell maturation is the presentation of an epitope peptide in the MHC class II antigen-presenting protein (MHCII), most commonly on an activated dendritic cell, and then the T-cell responses are recalled by subsequent presentations of the epitope peptide by the same or other antigen-presenting cells. Peptide presentation depends on the proteolytic fragmentation of the antigen in an endosomal/lysosomal compartment and concomitant loading of the fragments into the MHCII, a multistep mechanism called antigen processing and presentation. Although the role of peptide affinity for MHCII has been well studied, the role of proteolytic fragmentation has received less attention. In this Perspective, we will briefly summarize evidence that antigen resistance to unfolding and proteolytic fragmentation shapes the specificity of the CD4+ T-cell response to selected viral envelope proteins, identify several remarkable examples in which the immunodominant CD4+ epitopes most likely depend on the interaction of processing machinery with antigen conformation, and outline how knowledge of antigen conformation can inform future efforts to design vaccines.

What do cancer-specific CD8+ T cells see? The contribution of immunopeptidomics.

Immunopeptidomics is the survey of all peptides displayed on a cell or tissue when bound to human leukocyte antigen (HLA) molecules using tandem mass spectrometry. When attempting to determine the targets of tumour-specific CD8+ T cells, a survey of the potential ligands in tumour tissues is invaluable, and, in comparison with in-silico predictions, provides greater certainty of the existence of individual epitopes, as immunopeptidomics-confirmed CD8+ T-cell epitopes are known to be immunogenic, and direct observation should avoid the risk of autoreactivity which could arise following immunisation with structural homologues. The canonical sources of CD8+ T-cell tumour specific epitopes, such as tumour associated antigens, may be well conserved between patients and tumour types, but are often only weakly immunogenic. Direct observation of tumour-specific neoantigens by immunopeptidomics is rare, although valuable. Thus, there has been increasing interest in the non-canonical origins of tumour-reactive CD8+ T-cell epitopes, such as those arising from proteasomal splicing events, translational/turnover defects and alternative open reading frame reads. Such epitopes can be identified in silico, although validation is more challenging. Non-self CD8+ T-cell epitopes such as viral epitopes may be useful in certain cancer types with known viral origins, however these have been relatively unexplored with immunopeptidomics to date, possibly due to the paucity of source viral proteins in tumour tissues. This review examines the latest evidence for canonical, non-canonical and non-human CD8+ T-cell epitopes identified by immunopeptidomics, and concludes that the relative contribution for each of these sources to anti-tumour CD8+ T-cell reactivity is currently uncertain.

Nanovaccine Displaying Immunodominant T Cell Epitopes of Fibroblast Activation Protein Is Effective Against Desmoplastic Tumors.

Cancer-associated fibroblasts (CAFs), which are dominant cell types in the tumor microenvironment (TME), support tumor growth by secreting cytokines and forming an extracellular matrix (ECM) that hampers the penetration of chemical and biological therapeutics within the tumor and thereby limits their therapeutic efficacy. Here, we report a cancer nanovaccine targeting fibroblast activation protein α (FAP)-expressing CAFs as a potential pan-tumor vaccine. We predicted immunodominant FAP-specific epitope peptides in silico and selected two candidate peptides after in vitro and in vivo screening for immunogenicity and antitumor efficacy. Next, we developed a nanoparticle-based vaccine that displays the two selected epitope peptides on the surface of lipid nanoparticles encapsulating CpG adjuvant (FAPPEP-SLNPs). Immunization with one of two FAPPEP-SLNP nanovaccines led to considerable growth inhibition of various tumors, including desmoplastic tumors, by depleting FAP+ CAFs and thereby reducing ECM production in the TME while causing little appreciable adverse effects. Furthermore, when combined with a chemotherapeutic drug, the FAPPEP-SLNP nanovaccine increased drug accumulation and resulted in a synergistic antitumor efficacy far better than that of each corresponding monotherapy. These findings suggest that our FAPPEP-SLNP nanovaccine has potential for use as an "off-the-shelf" pan-tumor vaccine applicable to a variety of tumors and may be a suitable platform for use in various combination therapies.

An in silico approach for prediction of B cell and T cell epitope candidates against Chikungunya virus.

Several outbreaks of Chikungunya virus (CHIKV) had been reported since 1952 when mankind had his first encounter against the virus in Tanzania. Although these reports designate the CHIKV to be rarely fatal, cases of outbreaks in the last decade accompanied by severe complications and death poses a challenge to the development of effective treatment methods. Several attempts to vaccine development against CHIKV still remains unsuccessful. In this study, we aimed at the prediction of B-cell and T cell epitopes against CHIKV by using immunoinformatics. This, in turn, can contribute to development of an epitope based vaccine against CHIKV. Both linear and discontinuous B-cell epitopes, as well as Cytotoxic T-lymphocyte epitopes, were predicted for the CHIKV Envelope (E1 and E2) glycoproteins and (NS2). The antigenic CTL epitopes with highest binding affinities with type-1 MHC were selected and the peptides were docked to them. Docking followed by molecular dynamics simulations were performed to assess the stability of the docked complexes.

Precise Epitope Organization with Self-adjuvant Framework Nucleic Acid for Efficient COVID-19 Peptide Vaccine Construction.

Peptide vaccines have advantages in easy fabrication and high safety, but their effectiveness is hampered by the poor immunogenicity of the epitopes themselves. Herein, we constructed a series of framework nucleic acids (FNAs) with regulated rigidity and size to precisely organize epitopes in order to reveal the influence of epitope spacing and carrier rigidity on the efficiency of peptide vaccines. We found that assembling epitopes on rigid tetrahedral FNAs (tFNAs) with the appropriate size could efficiently enhance their immunogenicity. Further, by integrating epitopes from SARS-CoV-2 on preferred tFNAs, we constructed a COVID-19 peptide vaccine which could induce high titers of IgG against the receptor binding domain (RBD) of SARS-CoV-2 spike protein and increase the ratio of memory B and T cells in mice. Considering the good biocompatibility of tFNAs, our research provides a new idea for developing efficient peptide vaccines against viruses and possibly other diseases.

Immunogenic multi-epitope-based vaccine development to combat cyclosporiasis of immunocompromised patients applying computational biology method.

Cyclospora cayetanensis infections, also known as cyclosporiasis, persist to be the prevalent emerging protozoan parasite and an opportunist that causes digestive illness in immunocompromised individuals. In contrast, this causal agent can affect people of all ages, with children and foreigners being the most susceptible populations. For most immunocompetent patients, the disease is self-limiting; in extreme circumstances, this illness can manifest as severe or persistent diarrhea as well as colonize on secondary digestive organs leading to death. According to recent reports, worldwide 3.55% of people are infected by this pathogen, with Asia and Africa being more prevalent. For the treatment, trimethoprim-sulfamethoxazole is the only licensed drug and does not appear to work as well in some patient populations. Therefore, the much more effective strategy to avoid this illness is immunization through the vaccine. This present study uses immunoinformatics for identifying a computational multi-epitope-based peptide vaccine candidate for Cyclospora cayetanensis. Following the review of the literature, a highly efficient, secure, and vaccine complex based on multi-epitopes was designed by utilizing the identified proteins. These selected proteins were then used to predict non-toxic and antigenic HTL-epitopes, B-cell-epitopes, and CTL-epitopes. Ultimately, both a few linkers and an adjuvant were combined to create a vaccine candidate with superior immunological epitopes. Then, to establish the vaccine-TLR complex binding constancy, the TLR receptor and vaccine candidates were placed into the FireDock, PatchDock, and ClusPro servers for molecular docking and iMODS server for molecular-dynamic simulation. Finally, this selected vaccine construct was cloned into Escherichia coli strain-K12; thus, the constructed vaccines against Cyclospora cayetanensiscould improve the host immune response and can be produced experimentally.

Contemplating immunopeptidomes to better predict them.

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

Direct identification of HLA class I and class II-restricted T cell epitopes in pancreatic cancer tissues by mass spectrometry.

BACKGROUND: Identifying T cell epitopes on pancreatic ductal adenocarcinoma (PDAC) associated antigens or neoantigens has been a challenge. In this study, we attempted to identify PDAC T cell epitopes by mass spectrometry (MS). METHODS: We isolated HLA class I (HLA-I) and HLA class II (HLA-II)-restricted peptides, respectively, from tissues of human PDAC by using the pan-HLA-I or pan-HLA-II affinity purification column and identified T cell epitopes by peptidome analysis with MS. RESULTS: Through peptidome analysis, we identified T cell epitopes shared by multiple patients with different HLA types and those containing sequences of both anti-HLA-I and HLA-II antibodies-affinity purified peptides. The identified epitopes bound non-matched HLA molecules and induced T cell response in peripheral T cells from both HLA-type matched and non-matched patients. Peptides containing both HLA class I and class II epitopes were able to induce polyfunctional cytokine responses in peripheral T cells. CONCLUSIONS: T cell epitopes in PDAC can be discovered by the MS approach and can be designed into vaccine and TCR-T cell therapies for both HLA-type matched and non-matched patients.

Immunoinformatics Approach to Design Novel Subunit Vaccine against the Epstein-Barr Virus.

Epstein-Barr virus (EBV) is a lymphotropic virus responsible for numerous epithelial and lymphoid cell malignancies, including gastric carcinoma, Hodgkin's lymphoma, nasopharyngeal carcinoma, and Burkitt lymphoma. Hundreds of thousands of people worldwide get infected with this virus, and in most cases, this viral infection leads to cancer. Although researchers are trying to develop potential vaccines and drug therapeutics, there is still no effective vaccine to combat this virus. In this study, the immunoinformatics approach was utilized to develop a potential multiepitope subunit vaccine against the two most common subtypes of EBV, targeting three of their virulent envelope glycoproteins. Eleven cytotoxic T lymphocyte (CTL) epitopes, 11 helper T lymphocyte (HTL) epitopes, and 10 B-cell lymphocyte (BCL) epitopes were predicted to be antigenic, nonallergenic, nontoxic, and fully conserved among the two subtypes, and nonhuman homologs were used for constructing the vaccine after much analysis. Later, further validation experiments, including molecular docking with different immune receptors (e.g., Toll-like receptors [TLRs]), molecular dynamics simulation analyses (including root means square deviation [RMSD], root mean square fluctuation [RMSF], radius of gyration [Rg], principal-component analysis [PCA], dynamic cross-correlation [DCC], definition of the secondary structure of proteins [DSSP], and Molecular Mechanics Poisson-Boltzmann Surface Area [MM-PBSA]), and immune simulation analyses generated promising results, ensuring the safe and stable response of the vaccine with specific immune receptors after potential administration within the human body. The vaccine's high binding affinity with TLRs was revealed in the docking study, and a very stable interaction throughout the simulation proved the potential high efficacy of the proposed vaccine. Further, in silico cloning was also conducted to design an efficient mass production strategy for future bulk industrial vaccine production. IMPORTANCE Epstein-Barr virus (EBV) vaccines have been developing for over 30 years, but polyphyletic and therapeutic vaccines have failed to get licensed. Our vaccine surpasses the limitations of many such vaccines and remains very promising, which is crucial because the infection rate is higher than most viral infections, affecting a whopping 90% of the adult population. One of the major identifications covers a holistic analysis of populations worldwide, giving us crucial information about its effectiveness for everyone's unique immunological system. We targeted three glycoproteins that enhance the virulence of the virus to design an epitope-based polyvalent vaccine against two different strains of EBV, type 1 and 2. Our methodology in this study is nonconventional yet swift to show effective results while designing vaccines.

Enhanced In Vitro and In Vivo Potency of a T Cell Epitope in the Ebola Virus Glycoprotein Following Amino Acid Replacement at HLA-A*02:01 Binding Positions.

Studies on Ebola virus disease (EVD) survivors and clinical studies on Ebola virus (EBOV) vaccine candidates have pinpointed the importance of a strong antibody response in protection and survival from EBOV infection. However, little is known about the T cell responses to EBOV or EBOV vaccines. We used HLA-A*02:01 (HLA-A2) transgenic mice to study HLA-A2-specific T cell responses elicited following vaccination with EBOV glycoprotein (EBOV-GP) presented with three different systems: (i) recombinant protein (rEBOV-GP), (ii) vesicular stomatitis replication-competent recombinant virus (VSV-EBOV-GP), and (iii) modified vaccinia Ankara virus recombinant (MVA-EBOV-GP). T cells from immunized animals were analyzed using peptide pools representing the entire GP region and individual peptides. Regardless of the vaccine formulation, we identified a minimal 9mer epitope containing an HLA-A2 motif (FLDPATTS), which was confirmed through HLA-A2 binding affinity and immunization studies. Using binding prediction software, we identified substitutions surrounding position 9 (S9V, P10V, and Q11V) that predicted enhanced binding to the HLA-A2 molecule. This enhanced binding was confirmed through in vitro binding studies and enhanced potency was shown with in vivo immunization studies using the enhanced sequences and the wild-type sequence. Of note, in silico studies predicted the enhanced 9mer epitope carrying the S9V substitution as the best overall HLA-A2 epitope for the full-length EBOV-GP. These results suggest that EBOV-GP-S9V and EBOV-GP-P10V represent more potent in vivo immunogens. Identification and enhancement of EBOV-specific human HLA epitopes could lead to the development of tools and reagents to induce more robust T cell responses in human subjects. IMPORTANCE Vaccine efficacy and immunity to viral infection are often measured by neutralizing antibody titers. T cells are specialized subsets of immune cells with antiviral activity, but this response is variable and difficult to track. We showed that the HLA-A2-specific T cell response to the Ebola virus glycoprotein can be enhanced significantly by a single residue substitution designed to improve an epitope binding affinity to one of the most frequent MHC alleles in the human population. This strategy could be applied to improve T cell responses to Ebola vaccines designed to elicit antibodies and adapted to target MHC alleles of populations in regions where endemic infections, like Ebola virus disease, are still causing outbreaks with concerning pandemic potential.

ATM-TCR: TCR-Epitope Binding Affinity Prediction Using a Multi-Head Self-Attention Model.

TCR-epitope pair binding is the key component for T cell regulation. The ability to predict whether a given pair binds is fundamental to understanding the underlying biology of the binding mechanism as well as developing T-cell mediated immunotherapy approaches. The advent of large-scale public databases containing TCR-epitope binding pairs enabled the recent development of computational prediction methods for TCR-epitope binding. However, the number of epitopes reported along with binding TCRs is far too small, resulting in poor out-of-sample performance for unseen epitopes. In order to address this issue, we present our model ATM-TCR which uses a multi-head self-attention mechanism to capture biological contextual information and improve generalization performance. Additionally, we present a novel application of the attention map from our model to improve out-of-sample performance by demonstrating on recent SARS-CoV-2 data.

Binding Affinity Calculations of Gluten Peptides to HLA Risk Modifiers: DQ2.5 versus DQ7.5.

Free energy perturbation (FEP) calculations can predict relative binding affinities of an antigen and its point mutants to the same human leukocyte antigen (HLA) with high accuracy (e.g., within 1.0 kcal/mol to experiment); however, a more challenging task is to compare binding affinities of wholly different antigens binding to completely different HLAs using FEP. Researchers have used a variety of different FEP schemes to compute and compare absolute binding affinities, with varied success. Here, we propose and assess a unifying scheme to compute the relative binding affinities of different antigens binding to completely different HLAs using absolute binding affinity FEP calculations. We apply our affinity calculation technique to HLA-antigen-T-cell receptor (TCR) systems relevant to celiac disease (CeD) by investigating binding affinity differences between HLA-DQ2.5 (enhanced CeD risk) and HLA-DQ7.5 (CeD protective) in the binary (HLA-gliadin) and ternary (HLA-gliadin-TCR) binding complexes for three gliadin derived epitopes: glia-α1, glia-α2, and glia-ω1. Based on FEP calculations with our carefully designed thermodynamic cycles, we demonstrate that HLA-DQ2.5 has higher binding affinity than HLA-DQ7.5 for gliadin and enhanced binding affinity with a common TCR, agreeing with known results that the HLA-DQ2.5 serotype exhibits increased risk for CeD. Our findings reveal that our proposed absolute binding affinity FEP method is appropriate for predicting HLA binding for disparate antigens with different genotypes. We also discuss atomic-level details of HLA genotypes interacting with gluten peptides and TCRs in regard to the pathogenesis of CeD.

CD4+ T-Cell Epitope Prediction by Combined Analysis of Antigen Conformational Flexibility and Peptide-MHCII Binding Affinity.

Antigen processing in the class II MHC pathway depends on conventional proteolytic enzymes, potentially acting on antigens in native-like conformational states. CD4+ epitope dominance arises from a competition among antigen folding, proteolysis, and MHCII binding. Protease-sensitive sites, linear antibody epitopes, and CD4+ T-cell epitopes were mapped in plague vaccine candidate F1-V to evaluate the various contributions to CD4+ epitope dominance. Using X-ray crystal structures, antigen processing likelihood (APL) predicts CD4+ epitopes with significant accuracy for F1-V without considering peptide-MHCII binding affinity. We also show that APL achieves excellent performance over two benchmark antigen sets. The profiles of conformational flexibility derived from the X-ray crystal structures of the F1-V proteins, Caf1 and LcrV, were similar to the biochemical profiles of linear antibody epitope reactivity and protease sensitivity, suggesting that the role of structure in proteolysis was captured by the analysis of the crystal structures. The patterns of CD4+ T-cell epitope dominance in C57BL/6, CBA, and BALB/c mice were compared to epitope predictions based on APL, MHCII binding, or both. For a sample of 13 diverse antigens, the accuracy of epitope prediction by the combination of APL and I-Ab-MHCII-peptide affinity reached 36%. When MHCII allele specificity was also diverse, such as in human immunity, prediction of dominant epitopes by APL alone reached 42% when using a stringent scoring threshold. Because dominant CD4+ epitopes tend to occur in conformationally stable antigen domains, crystal structures typically are available for analysis by APL, and thus, the requirement for a crystal structure is not a severe limitation.

Immunodominant MHC-II (Major Histocompatibility Complex II) Restricted Epitopes in Human Apolipoprotein B.

BACKGROUND: CD (cluster of differentiation) 4+ T-cell responses to APOB (apolipoprotein B) are well characterized in atherosclerotic mice and detectable in humans. CD4+ T cells recognize antigenic peptides displayed on highly polymorphic HLA (human leukocyte antigen)-II. Immunogenicity of individual APOB peptides is largely unknown in humans. Only 1 HLA-II-restricted epitope was validated using the DRB1*07:01-APOB3036-3050 tetramer. We hypothesized that human APOB may contain discrete immunodominant CD4+ T-cell epitopes that trigger atherosclerosis-related autoimmune responses in donors with diverse HLA alleles. METHODS: We selected 20 APOB-derived peptides (APOB20) from an in silico screen and experimentally validated binding to the most commonly occurring human HLA-II alleles. We optimized a restimulation-based workflow to evaluate antigenicity of multiple candidate peptides in HLA-typed donors. This included activation-induced marker assay, intracellular cytokine staining, IFNγ (interferon gamma) enzyme-linked immunospot and cytometric bead array. High-throughput sequencing revealed TCR (T-cell receptor) clonalities of APOB-reactive CD4+ T cells. RESULTS: Using stringent positive, negative, and crossover stimulation controls, we confirmed specificity of expansion-based protocols to detect CD4+ T cytokine responses to the APOB20 pool. Ex vivo assessment of AIM+CD4+ T cells revealed a statistically significant autoimmune response to APOB20 but not to a ubiquitously expressed negative control protein, actin. Resolution of CD4+ T responses to the level of individual peptides using IFNγ enzyme-linked immunospot led to the discovery of 6 immunodominant epitopes (APOB6) that triggered robust CD4+ T activation in most donors. APOB6-specific responding CD4+ T cells were enriched in unique expanded TCR clonotypes and preferentially expressed memory markers. Cytometric bead array analysis detected APOB6-induced secretion of both proinflammatory and regulatory cytokines. In clinical samples from patients with angiographically verified coronary artery disease, APOB6 stimulation induced higher activation and memory phenotypes and augmented secretion of proinflammatory cytokines TNF (tumor necrosis factor) and IFNγ, compared with patients with low coronary artery disease. CONCLUSIONS: Using 3 cohorts, each with ≈20 donors, we discovered and validated 6 immunodominant, HLA-II-restricted APOB epitopes. The immune response to these APOB epitopes correlated with coronary artery disease severity.