TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

Neural network models for sequence-based TCR and HLA association prediction.

T cells rely on their T cell receptors (TCRs) to discern foreign antigens presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual contain a record of this individual's past immune activities, such as immune response to infections or vaccines. Mining the TCR data may recover useful information or biomarkers for immune related diseases or conditions. Some TCRs are observed only in the individuals with certain HLA alleles, and thus characterizing TCRs requires a thorough understanding of TCR-HLA associations. The extensive diversity of HLA alleles and the rareness of some HLA alleles present a formidable challenge for this task. Existing methods either treat HLA as a categorical variable or represent an HLA by its alphanumeric name, and have limited ability to generalize to the HLAs that are not seen in the training process. To address this challenge, we propose a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We demonstrate that DePTH is capable of making reasonable predictions for TCR-HLA associations, even when neither the HLA nor the TCR have been included in the training dataset. Furthermore, we establish that DePTH can be used to quantify the functional similarities among HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatments.

Computational identification of a multi-peptide vaccine candidate in E2 glycoprotein against diverse Hepatitis C virus genotypes.

Hepatitis C Virus (HCV) is estimated to affect nearly 180 million people worldwide, culminating in ∼0.7 million yearly casualties. However, a safe vaccine against HCV is not yet available. This study endeavored to identify a multi-genotypic, multi-epitopic, safe, and globally competent HCV vaccine candidate. We employed a consensus epitope prediction strategy to identify multi-epitopic peptides in all known envelope glycoprotein (E2) sequences, belonging to diverse HCV genotypes. The obtained peptides were screened for toxicity, allergenicity, autoimmunity and antigenicity, resulting in two favorable peptides viz., P2 (VYCFTPSPVVVG) and P3 (YRLWHYPCTV). Evolutionary conservation analysis indicated that P2 and P3 are highly conserved, supporting their use as part of a designed multi-genotypic vaccine. Population coverage analysis revealed that P2 and P3 are likely to be presented by >89% Human Leukocyte Antigen (HLA) molecules from six geographical regions. Indeed, molecular docking predicted the physical binding of P2 and P3 to various representative HLAs. We designed a vaccine construct using these peptides and assessed its binding to toll-like receptor 4 (TLR-4) by molecular docking and simulation. Subsequent analysis by energy-based and machine learning tools predicted high binding affinity and pinpointed the key binding residues (i.e. hotspots) in P2 and P3. Also, a favorable immunogenic profile of the construct was predicted by immune simulations. We encourage the scientific community to validate our vaccine construct in vitro and in vivo.Communicated by Ramaswamy H. Sarma.

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction.

Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.

Computational design of immunogenic peptide constructs comprising multiple human leukocyte antigen restricted dengue virus envelope epitopes.

Dengue virus (DENV) is endemic in 100 countries with the ability to impact nearly 50% of world population. DENV envelope (E) protein is responsible for viral attachment to host cells and has been target of various countermeasure development efforts. The current study focuses on a consensus computational approach to identify cross-reactive, immunogenic DENV-2 E peptides displaying promiscuity with a wide array of human leukocyte antigen (HLA) molecules. Four conserved peptides (FP-1, FP-2, FP-3 and FP-4) containing multiple CD8+ and CD4+ T cell epitopes were identified by employment of various immunoinformatics tools. FP-1, FP-2, FP-3 and FP-4 were estimated to bind with 227, 1787, 1008 and 834 HLA alleles, respectively. Root mean square deviation (RMSD) values obtained by molecular docking (CABS-Dock) with 20 HLA alleles (10 each of HLA classes I and II) resulted into comparable RMSD values of identified epitopes with native peptides, which represents the natural presentation of epitopes to HLA molecules. These peptides were also found to be part of previous experimentally validated immunogenic peptides. Further, a dengue immunogenic peptide construct was generated by linking the four peptides, an adjuvant and a 6× histidine tag. The construct showed strong binding and stability with Toll-like receptor. Collectively, these results provide strong evidence in the support of the immunogenic potential of the dengue immunogenic peptide construct.

Human leukocyte antigen eplet mismatching is associated with increased risk of graft loss and rejection after pediatric heart transplant.

BACKGROUND: While mismatching between donor and recipient human leukocyte antigen (HLA) alleles has been associated with increased graft loss in pediatric heart recipients, it is actually the surface amino acid structures, termed eplets, which determine the antigenicity of each HLA molecule. We hypothesized that HLA eplet mismatch analysis is a better predictor of adverse outcomes after pediatric heart transplant than conventional allele mismatch comparison. METHODS: A retrospective review of the Pediatric Heart Transplant Society database identified pediatric heart recipients (<18 years at listing) with complete donor and recipient HLA typing (A, B, and DR). Imputed high-resolution HLA genotypes were entered into HLAMatchmaker software which then calculated the number of eplet mismatches between each donor-recipient pair. Multivariable Cox regression analysis was used to examine associations between allele or eplet mismatching and adverse outcomes. RESULTS: Compared to those with <20 HLA class I eplet mismatches, recipients with 20 or more HLA class I eplet mismatches had an increased risk of graft loss (HR 1.46 [1.01-2.12], p = .049). HLA class I eplet mismatching was also associated with rejection (>20 mismatches: HR 1.30 [1.03-1.65], p = .030), while HLA class II eplet mismatching was associated with specified antibody-mediated rejection (10-20 mismatches: HR 1.57 [1.06-2.34], p = .025; >20 mismatches: HR 3.14 [1.72-5.71], p < .001). Neither HLA class I nor class II allele mismatching was significantly associated with graft loss or rejection. CONCLUSION: Eplet mismatch analysis was more predictive of adverse post-transplant outcomes (including graft loss and rejection) than allele mismatch comparison. Further study, including prospective high-resolution HLA typing, is warranted.

Building Personalized Cancer Therapeutics through Multi-Omics Assays and Bacteriophage-Eukaryotic Cell Interactions.

Bacteriophage-eukaryotic cell interaction provides the biological foundation of Phage Display technology, which has been widely adopted in studies involving protein-protein and protein-peptide interactions, and it provides a direct link between the proteins and the DNA encoding them. Phage display has also facilitated the development of new therapeutic agents targeting personalized cancer mutations. Proteins encoded by mutant genes in cancers can be processed and presented on the tumor cell surface by human leukocyte antigen (HLA) molecules, and such mutant peptides are called Neoantigens. Neoantigens are naturally existing tumor markers presented on the cell surface. In clinical settings, the T-cell recognition of neoantigens is the foundation of cancer immunotherapeutics. This year, we utilized phage display to successfully develop the 1st antibody-based neoantigen targeting approach for next-generation personalized cancer therapeutics. In this article, we discussed the strategies for identifying neoantigens, followed by using phage display to create personalized cancer therapeutics-a complete pipeline for personalized cancer treatment.

Integrin Activation Enables Sensitive Detection of Functional CD4+ and CD8+ T Cells: Application to Characterize SARS-CoV-2 Immunity.

We have previously shown that conformational change in the ß2-integrin is a very early activation marker that can be detected with fluorescent multimers of its ligand intercellular adhesion molecule (ICAM)-1 for rapid assessment of antigen-specific CD8+ T cells. In this study, we describe a modified protocol of this assay for sensitive detection of functional antigen-specific CD4+ T cells using a monoclonal antibody (clone m24 Ab) specific for the open, high-affinity conformation of the ß2-integrin. The kinetics of ß2-integrin activation was different on CD4+ and CD8+ T cells (several hours vs. few minutes, respectively); however, m24 Ab readily stained both cell types 4-6 h after antigen stimulation. With this protocol, we were able to monitor ex vivo effector and memory CD4+ and CD8+ T cells specific for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cytomegalovirus (CMV), Epstein-Barr virus (EBV), and hepatitis B virus (HBV) in whole blood or cryopreserved peripheral blood mononuclear cells (PBMCs) of infected or vaccinated individuals. By costaining ß2-integrin with m24 and CD154 Abs, we assessed extremely low frequencies of polyfunctional CD4+ T cell responses. The novel assay used in this study allows very sensitive and simultaneous screening of both CD4+ and CD8+ T cell reactivities, with versatile applicability in clinical and vaccination studies.

Affinity-matured HLA class II dimers for robust staining of antigen-specific CD4+ T cells.

Peptide-major histocompatibility complex (pMHC) multimers enable the detection of antigen-specific T cells in studies ranging from vaccine efficacy to cancer immunotherapy. However, this technology is unreliable when applied to pMHC class II for the detection of CD4+ T cells. Here, using a combination of molecular biological and immunological techniques, we cloned sequences encoding human leukocyte antigen (HLA)-DP, HLA-DQ and HLA-DR molecules with enhanced CD4 binding affinity (with a Kd of 8.9 ± 1.1 µM between CD4 and affinity-matured HLA-DP4) and produced affinity-matured class II dimers that stain antigen-specific T cells better than conventional multimers in both in vitro and ex vivo analyses. Using a comprehensive library of dimers for HLA-DP4, which is the most frequent HLA allele in many ancestry groups, we mapped 103 HLA-DP4-restricted epitopes derived from diverse tumor-associated antigens and cloned the cognate T-cell antigen receptor (TCR) genes from in vitro-stimulated CD4+ T cells. The availability of affinity-matured class II dimers across HLA-DP, HLA-DQ and HLA-DR alleles will aid in the investigation of human CD4+ T-cell responses.

Preclinical Evaluation of Invariant Natural Killer T Cells Modified with CD38 or BCMA Chimeric Antigen Receptors for Multiple Myeloma.

Due to the CD1d restricted recognition of altered glycolipids, Vα24-invariant natural killer T (iNKT) cells are excellent tools for cancer immunotherapy with a significantly reduced risk for graft-versus-host disease when applied as off-the shelf-therapeutics across Human Leukocyte Antigen (HLA) barriers. To maximally harness their therapeutic potential for multiple myeloma (MM) treatment, we here armed iNKT cells with chimeric antigen receptors (CAR) directed against the MM-associated antigen CD38 and the plasma cell specific B cell maturation antigen (BCMA). We demonstrate that both CD38- and BCMA-CAR iNKT cells effectively eliminated MM cells in a CAR-dependent manner, without losing their T cell receptor (TCR)-mediated cytotoxic activity. Importantly, iNKT cells expressing either BCMA-CARs or affinity-optimized CD38-CARs spared normal hematopoietic cells and displayed a Th1-like cytokine profile, indicating their therapeutic utility. While the costimulatory domain of CD38-CARs had no influence on the cytotoxic functions of iNKT cells, CARs containing the 4-1BB domain showed a better expansion capacity. Interestingly, when stimulated only via CD1d+ dendritic cells (DCs) loaded with α-galactosylceramide (α-GalCer), both CD38- and BCMA-CAR iNKT cells expanded well, without losing their CAR- or TCR-dependent cytotoxic activities. This suggests the possibility of developing an off-the-shelf therapy with CAR iNKT cells, which might even be boostable in vivo by administration α-GalCer pulsed DCs.

Matchmaker, matchmaker make me a match: Opportunities and challenges in optimizing compatibility of HLA eplets in transplantation.

The development of donor-specific antibodies (DSAs) is a major complication in transplantation, which is associated with inferior graft survival, impaired quality of life, and increased healthcare costs. DSA develop upon recognition of nonself HLA by the recipient's immune system. HLA molecules contain epitopes, which are the surface regions of HLA molecules recognized by antibodies. HLAMatchmaker is an algorithm for assessing donor:recipient HLA compatibility at the level of structurally defined HLA targets called eplets. The consideration of eplets, rather than the whole HLA molecule, could offer some advantages when classifying the immune risk associated with particular donor:recipient pairs. Assessing compatibility at the level of HLA eplets could decrease misclassification of post-transplant immune risk by improving specificity, when antibodies are confirmed to be directed against donor eplets missing from the recipient's repertoire of eplets. Consideration of eplets may also increase the sensitivity of immune risk assessment, when identifying mismatched eplets that could give rise to new, not previously detected, donor-specific antibodies post-transplant. Eplet matching can serve as a rational strategy for immune risk mitigation. Herein, we review the evolution of HLA (in) compatibility assessment for organ allocation. We outline challenges in the implementation of eplet-based donor:recipient matching, including unavailability of allele-level donor genotypes for 11 HLA loci at the time of organ allocation and difficulty in assessing the hierarchy of immune risk associated with particular HLA eplet mismatches. Opportunities to address some of the current shortcomings of donor genotyping and HLAMatchmaker are also discussed. While there is a demonstrated benefit in the application of HLAMatchmaker for donor: recipient HLA (in)compatibility assessment, evolving long-read genotyping methods, compilation of large data sets with allele-level genotypes, and standardization of methods to verify eplets as determinants of immune-mediated injuries are required before HLA eplet matching is implemented in organ allocation to improve upon transplant outcomes.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach.

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

A Comprehensive Evaluation of the Antibody-Verified Status of Eplets Listed in the HLA Epitope Registry.

Matching strategies based on HLA eplets instead of HLA antigens in solid organ transplantation may not only increase the donor pool for highly sensitized patients, but also decrease the incidence of de novo donor-specific antibody formation. However, since not all eplets are equally capable of inducing an immune response, antibody verification is needed to confirm their ability to be bound by antibodies, such that only clinically relevant eplets are considered. The HLA Epitope Registry has documented all theoretically defined HLA eplets along with their antibody verification status and has been the foundation for many clinical studies investigating eplet mismatch in transplantation. The verification methods for eplets in the Registry range from polyclonal sera from multi- and uni-parous women to murine and human monoclonal antibodies (mAbs), and antibodies purified by adsorption and elution from sera of HLA immunized individuals. The classification of antibody verification based on different methods for validation is problematic, since not all approaches represent the same level of evidence. In this study, we introduce a classification system to evaluate the level of evidence for the antibody-verified status of all eplets in the HLA Epitope Registry. We demonstrate that for a considerable number of eplets, the antibody-verified status is solely based on polyclonal serum reactivity of multiparous women or on reactivity of murine mAbs. Furthermore, we noted that a substantial proportion of patient sera analyses and human mAb data presented in the HLA Epitope Registry Database has never been published in a peer-reviewed journal. Therefore, we tested several unpublished human HLA-specific mAbs by luminex single antigen beads assay to analyze their HLA reactivity for eplet antibody verification. Although the majority of analyzed mAbs indeed verified their assigned eplets, this was not the case for a number of eplets. This comprehensive overview of evidence for antibody verification of eplets in the HLA Epitope Registry is instrumental for future investigations towards eplet immunogenicity and clinical studies considering antibody-verified eplet mismatch in transplantation and warrants further standardization of antibody verification using high quality data.

Nasopharyngeal carcinoma MHC region deep sequencing identifies HLA and novel non-HLA TRIM31 and TRIM39 loci.

Despite pronounced associations of major histocompatibility complex (MHC) regions with nasopharyngeal carcinoma (NPC), causal variants underlying NPC pathogenesis remain elusive. Our large-scale comprehensive MHC region deep sequencing study of 5689 Hong Kong Chinese identifies eight independent NPC-associated signals and provides mechanistic insight for disrupted transcription factor binding, altering target gene transcription. Two novel protective variants, rs2517664 (Trs2517664 = 4.6%, P = 6.38 × 10-21) and rs117495548 (Grs117495548 = 3.0%, P = 4.53 × 10-13), map near TRIM31 and TRIM39/TRIM39-RPP21; multiple independent protective signals map near HLA-B including a previously unreported variant, rs2523589 (P = 1.77 × 10-36). The rare HLA-B*07:05 allele (OR < 0.015, P = 5.83 × 10-21) is absent in NPC, but present in controls. The most prevalent haplotype lacks seven independent protective alleles (OR = 1.56) and the one with additional Asian-specific susceptibility rs9391681 allele (OR = 2.66) significantly increased NPC risk. Importantly, this study provides new evidence implicating two non-human leukocyte antigen (HLA) genes, E3 ubiquitin ligases, TRIM31 and TRIM39, impacting innate immune responses, with NPC risk reduction, independent of classical HLA class I/II alleles.

Designing a multi-epitope peptide based vaccine against SARS-CoV-2.

COVID-19 pandemic has resulted in 16,114,449 cases with 646,641 deaths from the 217 countries, or territories as on July 27th 2020. Due to multifaceted issues and challenges in the implementation of the safety and preventive measures, inconsistent coordination between societies-governments and most importantly lack of specific vaccine to SARS-CoV-2, the spread of the virus that initially emerged at Wuhan is still uprising after taking a heavy toll on human life. In the present study, we mapped immunogenic epitopes present on the four structural proteins of SARS-CoV-2 and we designed a multi-epitope peptide based vaccine that, demonstrated a high immunogenic response with a vast application on world's human population. On codon optimization and in-silico cloning, we found that candidate vaccine showed high expression in E. coli and immune simulation resulted in inducing a high level of both B-cell and T-cell mediated immunity. The results predicted that exposure of vaccine by administrating three injections significantly subsidized the antigen growth in the system. The proposed candidate vaccine found promising by yielding desired results and hence, should be validated by practical experimentations for its functioning and efficacy to neutralize SARS-CoV-2.

Defining the structural basis for human leukocyte antigen reactivity in clinical transplantation.

The current state-of-the-art technology employed to assess anti-human leukocyte antigen antibodies (Anti-HLA Ab) for donor-recipient matching and patient risk stratification in renal transplantation is the single antigen bead (SAB) assay. However, there are limitations to the SAB assay as it is not quantitative and due to variations in techniques and reagents, there is no standardization across laboratories. In this study, a structurally-defined human monoclonal alloantibody was employed to provide a mechanistic explanation for how fundamental alloantibody biology influences the readout from the SAB assay. Performance of the clinical SAB assay was evaluated by altering Anti-HLA Ab concentration, subclass, and detection reagents. Tests were conducted in parallel by two internationally accredited laboratories using standardized protocols and reagents. We show that alloantibody concentration, subclass, laboratory-specific detection devices, subclass-specific detection reagents all contribute to a significant degree of variation in the readout. We report a significant prozone effect affecting HLA alleles that are bound strongly by the test alloantibody as opposed to those bound weakly and this phenomenon is independent of complement. These data highlight the importance for establishing international standards for SAB assay calibration and have significant implications for our understanding of discordance in previous studies that have analyzed its clinical relevance.

Design of novel multiepitope constructs-based peptide vaccine against the structural S, N and M proteins of human COVID-19 using immunoinformatics analysis.

The causative agent of severe acute respiratory syndrome (SARS) reported by the Chinese Center for Disease Control (China CDC) has been identified as a novel Betacoronavirus (SARS-CoV-2). A computational approach was adopted to identify multiepitope vaccine candidates against SARS-CoV-2 based on S, N and M proteins being able to elicit both humoral and cellular immune responses. In this study, the sequence of the virus was obtained from NCBI database and analyzed with in silico tools such as NetMHCpan, IEDB, BepiPred, NetCTL, Tap transport/proteasomal cleavage, Pa3P, GalexyPepDock, I-TASSER, Ellipro and ClusPro. To identify the most immunodominant regions, after analysis of population coverage and epitope conservancy, we proposed three different constructs based on linear B-cell, CTL and HTL epitopes. The 3D structure of constructs was assessed to find discontinuous B-cell epitopes. Among CTL predicted epitopes, S257-265, S603-611 and S360-368, and among HTL predicted epitopes, N167-181, S313-330 and S1110-1126 had better MHC binding rank. We found one putative CTL epitope, S360-368 related to receptor-binding domain (RBD) region for S protein. The predicted epitopes were non-allergen and showed a high quality of proteasomal cleavage and Tap transport efficiency and 100% conservancy within four different clades of SARS-CoV-2. For CTL and HTL epitopes, the highest population coverage of the world's population was calculated for S27-37 with 86.27% and for S196-231, S303-323, S313-330, S1009-1030 and N328-349 with 90.33%, respectively. We identified overall 10 discontinuous B-cell epitopes for three multiepitope constructs. All three constructs showed strong interactions with TLRs 2, 3 and 4 supporting the hypothesis of SARS-CoV-2 susceptibility to TLRs 2, 3 and 4 like other Coronaviridae families. These data demonstrated that the novel designed multiepitope constructs can contribute to develop SARS-CoV-2 peptide vaccine candidates. The in vivo studies are underway using several vaccination strategies.

Biogenesis of HLA Ligand Presentation in Immune Cells Upon Activation Reveals Changes in Peptide Length Preference.

Induction of an effective tumor immunity is a complex process that includes the appropriate presentation of the tumor antigens, activation of specific T cells, and the elimination of malignant cells. Potent and efficient T cell activation is dependent on multiple factors, such as timely expression of co-stimulatory molecules, the differentiation state of professional antigen presenting cells (e.g., dendritic cells; DCs), the functionality of the antigen processing and presentation machinery (APPM), and the repertoire of HLA class I and II-bound peptides (termed immunopeptidome) presented to T cells. So far, how molecular perturbations underlying DCs maturation and differentiation affect the in vivo cross-presented HLA class I and II immunopeptidomes is largely unknown. Yet, this knowledge is crucial for further development of DC-based immunotherapy approaches. We applied a state-of-the-art sensitive MS-based immunopeptidomics approach to characterize the naturally presented HLA-I and -II immunopeptidomes eluted from autologous immune cells having distinct functional and biological states including CD14+ monocytes, immature DC (ImmDC) and mature DC (MaDC) monocyte-derived DCs and naive or activated T and B cells. We revealed a presentation of significantly longer HLA peptides upon activation that is HLA allotype specific. This was apparent in the self-peptidome upon cell activation and in the context of presentation of exogenously loaded antigens, suggesting that peptide length is an important feature with potential implications on the rational design of anti-cancer vaccines.

Generation of SARS-CoV-2 S1 Spike Glycoprotein Putative Antigenic Epitopes in Vitro by Intracellular Aminopeptidases.

Presentation of antigenic peptides by MHCI is central to cellular immune responses against viral pathogens. While adaptive immune responses versus SARS-CoV-2 can be of critical importance to both recovery and vaccine efficacy, how protein antigens from this pathogen are processed to generate antigenic peptides is largely unknown. Here, we analyzed the proteolytic processing of overlapping precursor peptides spanning the entire sequence of the S1 spike glycoprotein of SARS-CoV-2, by three key enzymes that generate antigenic peptides, aminopeptidases ERAP1, ERAP2, and IRAP. All enzymes generated shorter peptides with sequences suitable for binding onto HLA alleles, but with distinct specificity fingerprints. ERAP1 was the most efficient in generating peptides 8-11 residues long, the optimal length for HLA binding, while IRAP was the least efficient. The combination of ERAP1 with ERAP2 greatly limited the variability of peptide sequences produced. Less than 7% of computationally predicted epitopes were found to be produced experimentally, suggesting that aminopeptidase processing may constitute a significant filter to epitope presentation. These experimentally generated putative epitopes could be prioritized for SARS-CoV-2 immunogenicity studies and vaccine design. We furthermore propose that this in vitro trimming approach could constitute a general filtering method to enhance the prediction robustness for viral antigenic epitopes.

Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes.

BACKGROUND: The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2. METHODS: We leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8+ T cell responses. RESULTS: We first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8+ T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity. CONCLUSIONS: Using our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4+ and CD8+ T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8+ T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.