IEDB-AR: immune epitope database-analysis resource in 2019

Proteome Based Approach Defines Candidates for Designing a Multitope Vaccine against the Nipah Virus

Nipah virus is one of the most harmful emerging viruses with deadly effects on both humans and animals. Because of the severe outbreaks, in 2018, the World Health Organization focused on the urgent need for the development of effective solutions against the virus. However, up to date, there is no effective vaccine against the Nipah virus in the market. In the current study, the complete proteome of the Nipah virus (nine proteins) was analyzed for the antigenicity score and the virulence role of each protein, where we came up with fusion glycoprotein (F), glycoprotein (G), protein (V), and protein (W) as the candidates for epitope prediction. Following that, the multitope vaccine was designed based on top-ranking CTL, HTL, and BCL epitopes from the selected proteins. We used suitable linkers, adjuvant, and PADRE peptides to finalize the constructed vaccine, which was analyzed for its physicochemical features, antigenicity, toxicity, allergenicity, and solubility. The designed vaccine passed these assessments through computational analysis and, as a final step, we ran a docking analysis between the designed vaccine and TLR-³ and validated the docked complex through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for Nipah virus. Here, we describe the computational approach for design and analysis of this vaccine.

An immunogenetic view of COVID-19

Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.

Predicted B Cell Epitopes Highlight the Potential for COVID-19 to Drive Self-Reactive Immunity

COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), whilst commonly characterised as a respiratory disease, is reported to have extrapulmonary manifestations in multiple organs. Extrapulmonary involvement in COVID-19 includes autoimmune-like diseases such as Guillain-Barré syndrome and Kawasaki disease, as well as the presence of various autoantibodies including those associated with autoimmune diseases such a systemic lupus erythematosus (e.g. ANA, anti-La). Multiple strains of SARS-CoV-2 have emerged globally, some of which are found to be associated with increased transmissibility and severe disease. We performed an unbiased comprehensive mapping of the potential for cross-reactivity with self-antigens across multiple SARS-CoV-2 proteins and compared identified immunogenic regions across multiples strains. Using the Immune Epitope Database (IEDB) B cell epitope prediction tool, regions predicted as antibody epitopes with high prediction scores were selected. Epitope sequences were then blasted to eight other global strains to identify mutations within these regions. Of the 15 sequences compared, eight had a mutation in at least one other global strain. Predicted epitopes were then compared to human proteins using the NCBI blast tool. In contrast to studies focusing on short sequences of peptide identity, we have taken an immunological approach to selection criteria for further analysis and have identified 136 alignments of 6–23 amino acids (aa) in 129 human proteins that are immunologically likely to be cross-reactive with SARS-CoV-2. Additionally, to identify regions with significant potential to interfere with host cell function-or promote immunopathology, we identified epitope regions more likely to be accessible to pathogenic autoantibodies in the host, selected using a novel combination of sequence similarity, and modelling protein and alignment localization with a focus on extracellular regions. Our analysis identified 11 new predicted B-cell epitopes in host proteins, potentially capable of explaining key aspects of COVID-19 extrapulmonary pathology, and which were missed in other in silico studies which used direct identity rather than immunologically related functional criteria.

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

Currently, no approved vaccine is available against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes severe respiratory disease. The spike glycoprotein is typically considered a suitable target for MERS-CoV vaccine candidates. A computational strategy can be used to design an antigenic vaccine against a pathogen. Therefore, we used immunoinformatics and computational approaches to design a multi-epitope vaccine that targets the spike glycoprotein of MERS-CoV. After using numerous immunoinformatics tools and applying several immune filters, a poly-epitope vaccine was constructed comprising cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon-gamma (IFN-γ)-inducing epitopes. In addition, various physicochemical, allergenic, and antigenic profiles were evaluated to confirm the immunogenicity and safety of the vaccine. Molecular interactions, binding affinities, and the thermodynamic stability of the vaccine were examined through molecular docking and dynamic simulation approaches, during which we identified a stable and strong interaction with Toll-like receptors (TLRs). In silico immune simulations were performed to assess the immune-response triggering capabilities of the vaccine. This computational analysis suggested that the proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat viral infections; however, experimental evaluations remain necessary to verify the exact safety and immunogenicity profile of this vaccine.

Know thy immune self and non-self: Proteomics informs on the expanse of self and non-self, and how and where they arise

T cells play an important role in the adaptive immune response to a variety of infections and cancers. Initiation of a T cell mediated immune response requires antigen recognition in a process termed MHC (major histocompatibility complex) restri ction. A T cell antigen is a composite structure made up of a peptide fragment bound within the antigen‐binding groove of an MHC‐encoded class I or class II molecule. Insight into the precise composition and biology of self and non‐self immunopeptidomes is essential to harness T cell mediated immunity to prevent, treat, or cure infectious diseases and cancers. T cell antigen discovery is an arduous task! The pioneering work in the early 1990s has made large‐scale T cell antigen discovery possible. Thus, advancements in mass spectrometry coupled with proteomics and genomics technologies make possible T cell antigen discovery with ease, accuracy, and sensitivity. Yet we have only begun to understand the breadth and the depth of self and non‐self immunopeptidomes because the molecular biology of the cell continues to surprise us with new secrets directly related to the source, and the processing and presentation of MHC ligands. Focused on MHC class I molecules, this review, therefore, provides a brief historic account of T cell antigen discovery and, against a backdrop of key advances in molecular cell biologic processes, elaborates on how proteogenomics approaches have revolutionised the field.

Synthetic multiepitope neoantigen DNA vaccine for personalized cancer immunotherapy

Neoantigen-based personalized vaccination has emerged as a viable method for tumor immunotherapy. Here we set up a DNA-based neoantigen vaccine platform with comprehensive identification of individual somatic mutations using whole-exome sequencing (WES) and RNA-seq, bioinformatic prediction of neo-epitopes, dendritic cell (DC)-based efficacy prevalidation of vaccine candidates, optimization of the DNA vaccine and its nanocarrier and adjuvant, and preparation of a liposome-encapsulated multiepitope DNA vaccine. The DNA vaccine was efficiently uptaken by DCs and induced effective immune response against mouse melanoma cells, leading to significant inhibition of melanoma tumor growth and reduction of lung metastasis in a mouse model. Numerous intratumoral infiltrated CD8+ T-cells with specific in vitro killing ability towards melanoma cells were identified. Our study offers evidence that a multiepitope neoantigen DNA vaccine in a nanocarrier can be exploited for personalized tumor immunotherapy and as a reliable prevalidation approach for rapid enrichment of effective neoantigens.

Allele-Specific Thresholds of Eluted Ligands for T-Cell Epitope Prediction

A common strategy for predicting candidate human leukocyte antigen class I T-cell epitopes is to use an affinity-based threshold of 500 nM. Although a 500 nM threshold across alleles effectively identifies most epitopes across alleles, findings showing that major histocompatibility complex repertoire sizes vary by allele indicate that using thresholds specific to individual alleles may improve epitope identification. In this work, we compare different strategies utilizing common and allele-specific thresholds to identify an optimal approach for T-cell epitope prediction. First, we confirmed previous observations that different human leukocyte antigen class I alleles correspond with varying repertoire sizes. Here, we define general and allele-specific thresholds that capture 80% of eluted ligands and a different set of thresholds associated with capturing 9-mer T-cell epitopes at 80% sensitivity. Our analysis revealed that allele-specific threshold performance was roughly equivalent to that of a common threshold when considering a relatively large number of alleles. However, when predicting epitopes for only a few alleles, the use of allele-specific thresholds would be preferable. Finally, we present here for public use a set of allele-specific thresholds that may be used to identify T-cell epitopes at 80% sensitivity.

•

Confirmed findings that different HLA class I alleles have varying repertoire sizes.

•

Defined common and allele-specific thresholds that capture 80% of eluted ligands.

•

Defined common and allele-specific thresholds that capture 80% of T-cell epitopes.

•

Allele-specific thresholds perform more consistently when analyzing a few alleles.

Impact of SARS-CoV-2 variants on the total CD4+ and CD8+ T cell reactivity in infected or vaccinated individuals

The emergence of SARS-CoV-2 variants with evidence of antibody escape highlight the importance of addressing whether the total CD4+ and CD8+ T cell recognition is also affected. Here, we compare SARS-CoV-2-specific CD4+ and CD8+ T cells against the B.1.1.7, B.1.351, P.1, and CAL.20C lineages in COVID-19 convalescents and in recipients of the Moderna (mRNA-1273) or Pfizer/BioNTech (BNT162b2) COVID-19 vaccines. The total reactivity against SARS-CoV-2 variants is similar in terms of magnitude and frequency of response, with decreases in the 10%-22% range observed in some assay/VOC combinations. A total of 7% and 3% of previously identified CD4+ and CD8+ T cell epitopes, respectively, are affected by mutations in the various VOCs. Thus, the SARS-CoV-2 variants analyzed here do not significantly disrupt the total SARS-CoV-2 T cell reactivity; however, the decreases observed highlight the importance for active monitoring of T cell reactivity in the context of SARS-CoV-2 evolution.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Low Genetic Diversity of Hepatitis B Virus Surface Gene amongst Australian Blood Donors

Variants in the small surface gene of hepatitis B virus (HBV), which codes for viral surface antigen (HBsAg), can affect the efficacy of HBsAg screening assays and can be associated with occult HBV infection (OBI). This study aimed to characterise the molecular diversity of the HBV small surface gene from HBV-reactive Australian blood donors. HBV isolates from 16 HBsAg-positive Australian blood donors' plasma were sequenced and genotyped by phylogenies of viral coding genes and/or whole genomes. An analysis of the genetic diversity of eight HBV small surface genes from our 16 samples was conducted and compared with HBV sequences from NCBI of 164 international (non-Australian) blood donors. Genotypes A-D were identified in our samples. The region of HBV small surface gene that contained the sequence encoding the 'a' determinant had a greater genetic diversity than the remaining part of the gene. No escape mutants or OBI-related variants were observed in our samples. Variant call analysis revealed two samples with a nucleotide deletion leading to truncation of polymerase and/or large/middle surface amino acid sequences. Overall, we found that HBV small surface gene sequences from Australian donors demonstrated a lower level of genetic diversity than those from non-Australian donor population included in the study.

Integrated Core Proteomics, Subtractive Proteomics, and Immunoinformatics Investigation to Unveil a Potential Multi-Epitope Vaccine against Schistosomiasis

Schistosomiasis is a parasitic infection that causes considerable morbidity and mortality in the world. Infections of parasitic blood flukes, known as schistosomes, cause the disease. No vaccine is available yet and thus there is a need to design an effective vaccine against schistosomiasis. Schistosoma japonicum, Schistosoma mansoni, and Schistosoma haematobium are the main pathogenic species that infect humans. In this research, core proteomics was combined with a subtractive proteomics pipeline to identify suitable antigenic proteins for the construction of a multi-epitope vaccine (MEV) against human-infecting Schistosoma species. The pipeline revealed two antigenic proteins-calcium binding and mycosubtilin synthase subunit C-as promising vaccine targets. T and B cell epitopes from the targeted proteins were predicted using multiple bioinformatics and immunoinformatics databases. Seven cytotoxic T cell lymphocytes (CTL), three helper T cell lymphocytes (HTL), and four linear B cell lymphocytes (LBL) epitopes were fused with a suitable adjuvant and linkers to design a 217 amino-acid-long MEV. The vaccine was coupled with a TLR-4 agonist (RS-09; Sequence: APPHALS) adjuvant to enhance the immune responses. The designed MEV was stable, highly antigenic, and non-allergenic to human use. Molecular docking, molecular dynamics (MD) simulations, and molecular mechanics/generalized Born surface area (MMGBSA) analysis were performed to study the binding affinity and molecular interactions of the MEV with human immune receptors (TLR2 and TLR4) and MHC molecules (MHC I and MHC II). The MEV expression capability was tested in an Escherichia coli (strain-K12) plasmid vector pET-28a(+). Findings of these computer assays proved the MEV as highly promising in establishing protective immunity against the pathogens; nevertheless, additional validation by in vivo and in vitro experiments is required to discuss its real immune-protective efficacy.

Can molecular mimicry explain the cytokine storm of SARS-CoV-2?: An in silico approach

PARP14 and PARP9 play a key role in macrophage immune regulation. SARS‐CoV‐2 is an emerging viral disease that triggers hyper‐inflammation known as a cytokine storm. In this study, using in silico tools, we hypothesize about the immunological phenomena of molecular mimicry between SARS‐CoV‐2 Nsp3 and the human PARP14 and PARP9. The results showed an epitope of SARS‐CoV‐2 Nsp3 protein that contains consensus sequences for both human PARP14 and PARP9 that are antigens for MHC Classes 1 and 2, which can potentially induce an immune response against human PARP14 and PARP9; while its depletion causes a hyper‐inflammatory state in SARS‐CoV‐2 patients.

Molecular mimicry could produce an inflammatory state in SARS‐CoV‐2 patients.

Human PARP14 and PARP9 are the proteins involves in this phenomena.

Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions

After the announcement of a new coronavirus in China in December 2019, which was then called SARS-CoV-2, this virus changed to a global concern and it was then declared as a pandemic by WHO. Human leukocyte antigen (HLA) alleles, which are one of the most polymorphic genes, play a pivotal role in both resistance and vulnerability of the body against viruses and other infections as well as chronic diseases. The association between HLA alleles and preexisting medical conditions such as cardiovascular diseases and diabetes mellitus is reported in various studies. In this review, we focused on the bioinformatic HLA studies to summarize the HLA alleles which responded to SARS-CoV-2 peptides and have been used to design vaccines. We also reviewed HLA alleles that are associated with comorbidities and might be related to the high mortality rate among COVID-19 patients. Since both genes and patients' medical conditions play a key role in both severity of the disease and the mortality rate in COVID-19 patients, a better understanding of the connection between HLA alleles and SARS-CoV-2 can provide a wider perspective on the behavior of the virus. Such understanding can help scientists, especially in terms of protecting healthcare workers and designing effective vaccines.

Pre-existing T Cell Memory against Zika Virus

The mosquito-borne Zika virus (ZIKV) has spread rapidly into regions where dengue virus (DENV) is endemic, and flavivirus cross-reactive T cell responses have been observed repeatedly in animal models and in humans. Preexisting cellular immunity to DENV is thought to contribute to protection in subsequent ZIKV infection, but the epitope targets of cross-reactive T cell responses have not been comprehensively identified. Using human blood samples from the regions of Nicaragua and Sri Lanka where DENV is endemic that were collected before the global spread of ZIKV in 2016, we employed an in vitro expansion strategy to map ZIKV T cell epitopes in ZIKV-unexposed, DENV-seropositive donors. We identified 93 epitopes across the ZIKV proteome, and we observed patterns of immunodominance that were dependent on antigen size and sequence identity to DENV. We confirmed the immunogenicity of these epitopes through a computational HLA binding analysis, and we showed that cross-reactive T cells specifically recognize ZIKV peptides homologous to DENV sequences. We also found that these CD4 responses were derived from the memory T cell compartment. These data have implications for understanding the dynamics of flavivirus-specific T cell immunity in areas of endemicity.

IMPORTANCE Multiple flaviviruses, including Zika virus (ZIKV) and the four serotypes of dengue virus (DENV), are prevalent in the same large tropical and equatorial areas, which are inhabited by hundreds of millions of people. The interplay of DENV and ZIKV infection is especially relevant, as these two viruses are endemic in largely overlapping regions, have significant sequence similarity, and share the same arthropod vector. Here, we define the targets of preexisting immunity to ZIKV in unexposed subjects in areas where dengue is endemic. We demonstrate that preexisting immunity to DENV could shape ZIKV-specific responses, and DENV-ZIKV cross-reactive T cell populations can be expanded by stimulation with ZIKV peptides. The issue of potential ZIKV and DENV cross-reactivity is of relevance for understanding patterns of natural immunity, as well as for the development of diagnostic tests and vaccines.

Nano-Enabled COVID-19 Vaccines: Meeting the Challenges of Durable Antibody Plus Cellular Immunity and Immune Escape

At the time of preparing this Perspective, large-scale vaccination for COVID-19 is in progress, aiming to bring the pandemic under control through vaccine-induced herd immunity. Not only does this vaccination effort represent an unprecedented scientific and technological breakthrough, moving us from the rapid analysis of viral genomes to design, manufacture, clinical trial testing, and use authorization within the time frame of less than a year, but it also highlights rapid progress in the implementation of nanotechnology to assist vaccine development. These advances enable us to deliver nucleic acid and conformation-stabilized subunit vaccines to regional lymph nodes, with the ability to trigger effective humoral and cellular immunity that prevents viral infection or controls disease severity. In addition to a brief description of the design features of unique cationic lipid and virus-mimicking nanoparticles for accomplishing spike protein delivery and presentation by the cognate immune system, we also discuss the importance of adjuvancy and design features to promote cooperative B- and T-cell interactions in lymph node germinal centers, including the use of epitope-based vaccines. Although current vaccine efforts have demonstrated short-term efficacy and vaccine safety, key issues are now vaccine durability and adaptability against viral variants. We present a forward-looking perspective of how vaccine design can be adapted to improve durability of the immune response and vaccine adaptation to overcome immune escape by viral variants. Finally, we consider the impact of nano-enabled approaches in the development of COVID-19 vaccines for improved vaccine design against other infectious agents, including pathogens that may lead to future pandemics.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches

Staphylococcus aureus is a deadly human bacterial pathogen that causes a wide variety of clinical manifestations. Invasive S. aureus infections in hospitals and the community are one of the main causes of mortality and morbidity, as virulent and multi-drug-resistant strains have evolved. There is an unmet and urgent clinical need for immune-based non-antibiotic approaches to treat these infections as the growing antibiotic resistance poses a significant public health danger. Subtractive proteomics assisted reverse vaccinology-based immunoinformatics pipeline was used in this study to target the suitable antigenic proteins for the development of multi-epitope vaccine (MEV). Three essential virulent and antigenic proteins were identified including Glycosyltransferase, Elastin Binding Protein, and Staphylococcal secretory antigen. A variety of immunoinformatics tools have been used to forecast T-cell and B-cell epitopes from target proteins. Seven CTL, five HTL, and eight LBL epitopes, connected through suitable linkers and adjuvant, were employed to design 444 amino acids long MEV construct. The vaccine was paired with the TLR4 agonist 50S ribosomal protein L7/L12 adjuvant to enhance the immune response towards the vaccine. The predicted MEV structure was assessed to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. Molecular docking simulation of the MEV with the human TLR4 (toll-like receptor 4) and major histocompatibility complex molecules (MHCI and MHCII) was performed to validate the interactions with the receptors. Molecular dynamics (MD) simulation and MMGBSA binding free energy analyses were carried out for the stability evaluation and binding of the MEV docked complexes with TLR4, MHCI and MHCII. To achieve maximal vaccine protein expression with optimal post-translational modifications, MEV was reverse translated, its mRNA structure was analyzed, and finally in silico cloning was performed into E. coli expression host. These rigorous computational analyses supported the effectivity of proposed MEV in protection against infections associated with S. aureus. However, further experimental validations are required to fully evaluate the potential of proposed vaccine candidate.

Mapping and role of T cell response in SARS-CoV-2-infected mice

Virus-specific T cells play essential roles in protection against multiple virus infections, including SARS-CoV and MERS-CoV. While SARS-CoV-2-specific T cells have been identified in COVID-19 patients, their role in the protection of SARS-CoV-2-infected mice is not established. Here, using mice sensitized for infection with SARS-CoV-2 by transduction with an adenovirus expressing the human receptor (Ad5-hACE2), we identified SARS-CoV-2-specific T cell epitopes recognized by CD4+ and CD8+ T cells in BALB/c and C57BL/6 mice. Virus-specific T cells were polyfunctional and were able to lyse target cells in vivo. Further, type I interferon pathway was proved to be critical for generating optimal antiviral T cell responses after SARS-CoV-2 infection. T cell vaccination alone partially protected SARS-CoV-2-infected mice from severe disease. In addition, the results demonstrated cross-reactive T cell responses between SARS-CoV and SARS-CoV-2, but not MERS-CoV, in mice. Understanding the role of the T cell response will guide immunopathogenesis studies of COVID-19 and vaccine design and validation.

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.

The Future of Blood Testing Is the Immunome

It is increasingly clear that an extraordinarily diverse range of clinically important conditions—including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers—leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person’s B cells (antibodies) and T cells. We refer to these as the immunome. Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome.

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

Most Japanese individuals are genetically predisposed to recognize an immunogenic protein fragment shared between COVID-19 and common cold coronaviruses

In the spring of 2020, we and others hypothesized that T cells in COVID-19 patients may recognize identical protein fragments shared between the coronaviruses of the common cold and COVID-19 and thereby confer cross-virus immune memory. Here, we look at this issue by screening studies that, since that time, have experimentally addressed COVID-19 associated T cell specificities. Currently, the identical T cell epitope shared between COVID-19 and common cold coronaviruses most convincingly identified as immunogenic is the CD8 + T cell epitope VYIGDPAQL if presented by the MHC class I allele HLA-A*24:02. The HLA-A*24:02 allele is found in the majority of Japanese individuals and several indigenous populations in Asia, Oceania, and the Americas. In combination with histories of common cold infections, HLA-A*24:02 may affect their protection from COVID-19.

Negligible impact of SARS-CoV-2 variants on CD4 + and CD8 + T cell reactivity in COVID-19 exposed donors and vaccinees

The emergence of SARS-CoV-2 variants highlighted the need to better understand adaptive immune responses to this virus. It is important to address whether also CD4+ and CD8+ T cell responses are affected, because of the role they play in disease resolution and modulation of COVID-19 disease severity. Here we performed a comprehensive analysis of SARS-CoV-2-specific CD4+ and CD8+ T cell responses from COVID-19 convalescent subjects recognizing the ancestral strain, compared to variant lineages B.1.1.7, B.1.351, P.1, and CAL.20C as well as recipients of the Moderna (mRNA-1273) or Pfizer/BioNTech (BNT162b2) COVID-19 vaccines. Similarly, we demonstrate that the sequences of the vast majority of SARS-CoV-2 T cell epitopes are not affected by the mutations found in the variants analyzed. Overall, the results demonstrate that CD4+ and CD8+ T cell responses in convalescent COVID-19 subjects or COVID-19 mRNA vaccinees are not substantially affected by mutations found in the SARS-CoV-2 variants.

Distinct antibody repertoires against endemic human coronaviruses in children and adults

Four endemic human coronaviruses (HCoVs) are commonly associated with acute respiratory infection in humans. B cell responses to these “common cold” viruses remain incompletely understood. Here we report a comprehensive analysis of CoV-specific antibody repertoires in 231 children and 1168 adults using phage immunoprecipitation sequencing. Seroprevalence of antibodies against endemic HCoVs ranged between approximately 4% and 27% depending on the species and cohort. We identified at least 136 novel linear B cell epitopes. Antibody repertoires against endemic HCoVs were qualitatively different between children and adults in that anti-HCoV IgG specificities more frequently found among children targeted functionally important and structurally conserved regions of the spike, nucleocapsid, and matrix proteins. Moreover, antibody specificities targeting the highly conserved fusion peptide region and S2′ cleavage site of the spike protein were broadly cross-reactive with peptides of epidemic human and nonhuman coronaviruses. In contrast, an acidic tandem repeat in the N-terminal region of the Nsp3 subdomain of the HCoV-HKU1 polyprotein was the predominant target of antibody responses in adult donors. Our findings shed light on the dominant species-specific and pan-CoV target sites of human antibody responses to coronavirus infection, thereby providing important insights for the development of prophylactic or therapeutic monoclonal antibodies and vaccine design.

Comprehensive Analysis of CD4+ T Cell Responses to CMV pp65 Antigen Restricted by Single HLA-DR, -DQ, and -DP Allotype Within an Individual

Within an individual, six different HLA class II heterodimers are expressed co-dominantly by two alleles of HLA-DR, -DQ, and -DP loci. However, it remained unclear which HLA allotypes were used in T cell responses to a given antigen. For the measurement of the CD4⁺ T cell responses restricted by a single HLA allotype, we established a panel of artificial antigen-presenting cells (aAPCs) expressing each single HLA allele of 20 HLA-DRB1, 16 HLA-DQ, and 13 HLA-DP alleles. CD4⁺ T cell responses to cytomegalovirus (CMV) pp65 restricted by single HLA class II allotype defined in 45 healthy donors. The average magnitude of CD4⁺ T cell responses by HLA-DR allotypes was higher than HLA-DQ and HLA-DP allotypes. CD4⁺ T cell responses by DRA*01:01/DRB1*04:06, DQA1*01:02/DQB1*06:02, DPA1*02:02/DPB1*05:01 were higher among the other alleles in each HLA-DR, -DQ, and -DP locus. Interestingly, the frequencies of HLA-DR alleles and the positivity of specific allotypes showed an inverse correlation. One allotype within individuals is dominantly used in CD4⁺ T cell response in 49% of donors, and two allotypes showed that in 7% of donors, and any positive response was detected in 44% of donors. Even if one individual had several dominant alleles, CD4⁺ T cell responses tended to be restricted by only one of them. Furthermore, CD8⁺ and CD4⁺ T cell responses by HLA class I and class II were correlated. Our results demonstrate that the CD4⁺ T cell preferentially use a few dominant HLA class II allotypes within individuals, similar to CD8⁺ T cell response to CMV pp65.

An Overview of Current Uses and Future Opportunities for Computer-Assisted Design of Vaccines for Neglected Tropical Diseases

Neglected tropical diseases are infectious diseases that impose high morbidity and mortality rates over 1.5 billion people worldwide. Originally restricted to tropical and subtropical regions, changing climate conditions have increased their potential to emerge elsewhere. Control of their impact suffers from shortages like poor epidemiological surveillance or irregular drug distribution, and some NTDs still lack of appropriate diagnostics and/or efficient therapeutics. For these, availability of vaccines to prevent new infections, or the worsening of those already established, would mean a major breakthrough. However, only dengue and rabies count with approved vaccines at present. Herein, we review the state-of-the-art of vaccination strategies for NTDs, setting the focus on third generation vaccines and the concept of reverse vaccinology. Its capability to address pathogens´ biological complexity, likely contributing to save developmental costs is discussed. The use of computational tools is a fundamental aid to analyze increasingly large datasets aimed at designing vaccine candidates with the highest, possibly, opportunities to succeed. Ultimately, we identify and analyze those studies that took an in silico approach to find vaccine candidates, and experimentally assessed their immunogenicity and/or protection capabilities.

Differential Longevity of Memory CD4 and CD8 T Cells in a Cohort of the Mothers With a History of ZIKV Infection and Their Children

Background: Zika virus (ZIKV) infection causes for mild and self-limiting disease in healthy adults. In newborns, it can occasionally lead to a spectrum of malformations, the congenital Zika syndrome (CZS). Thus, little is known if mothers and babies with a history of ZIKV infection were able to develop long-lasting T-cell immunity. To these issues, we measure the prevalence of ZIKV T-cell immunity in a cohort of mothers infected to the ZIKV during pregnancy in the 2016–2017 Zika outbreak, who gave birth to infants affected by neurological complications or asymptomatic ones.

Results: Twenty-one mothers and 18 children were tested for IFN-γ ELISpot and T-cell responses for flow cytometry assays in response to CD4 ZIKV and CD8 ZIKV megapools (CD4 ZIKV MP and CD8 ZIKV MP). IFN-γ ELISpot responses to ZIKV MPs showed an increased CD4 and CD8 T-cell responses in mothers compared to children. The degranulation activity and IFN-γ-producing CD4 T cells were detected in most mothers, and children, while in CD8 T-cells, low responses were detected in these study groups. The total Temra T cell subset is enriched for IFN-γ+ CD4 T cells after stimulation of CD4 ZIKV MP.

Conclusion: Donors with a history of ZIKV infection demonstrated long-term CD4 T cell immunity to ZIKV CD4 MP. However, the same was not observed in CD8 T cells with the ZIKV CD8 MP. One possibility is that the cytotoxic and pro-inflammatory activities of CD8 T cells are markedly demonstrated in the early stages of infection, but less detected in the disease resolution phase, when the virus has already been eliminated. The responses of mothers' T cells to ZIKV MPs do not appear to be related to their children's clinical outcome. There was also no marked difference in the T cell responses to ZIKV MP between children affected or not with CZS. These data still need to be investigated, including the evaluation of the response of CD8 T cells to other ZIKV peptides.

Discordance Between the Predicted Versus the Actually Recognized CD8+ T Cell Epitopes of HCMV pp65 Antigen and Aleatory Epitope Dominance

CD8+ T cell immune monitoring aims at measuring the size and functions of antigen-specific CD8+ T cell populations, thereby providing insights into cell-mediated immunity operational in a test subject. The selection of peptides for ex vivo CD8+ T cell detection is critical because within a complex antigen exists a multitude of potential epitopes that can be presented by HLA class I molecules. Further complicating this task, there is HLA class I polygenism and polymorphism which predisposes CD8+ T cell responses towards individualized epitope recognition profiles. In this study, we compare the actual CD8+ T cell recognition of a well-characterized model antigen, human cytomegalovirus (HCMV) pp65 protein, with its anticipated epitope coverage. Due to the abundance of experimentally defined HLA-A*02:01-restricted pp65 epitopes, and because in silico epitope predictions are most advanced for HLA-A*02:01, we elected to focus on subjects expressing this allele. In each test subject, every possible CD8+ T cell epitope was systematically covered testing 553 individual peptides that walk the sequence of pp65 in steps of single amino acids. Highly individualized CD8+ T cell response profiles with aleatory epitope recognition patterns were observed. No correlation was found between epitopes' ranking on the prediction scale and their actual immune dominance. Collectively, these data suggest that accurate CD8+ T cell immune monitoring may necessitate reliance on agnostic mega peptide pools, or brute force mapping, rather than electing individual peptides as representative epitopes for tetramer and other multimer labeling of surface antigen receptors.

Modulating the quantity of HIV Env-specific CD4 T cell help promotes rare B cell responses in germinal centers

Immunodominance to nonneutralizing epitopes is a roadblock in designing vaccines against several diseases of high interest. One hypothetical possibility is that limited CD4 T cell help to B cells in a normal germinal center (GC) response results in selective recruitment of abundant, immunodominant B cells. This is a central issue in HIV envelope glycoprotein (Env) vaccine designs, because precursors to broadly neutralizing epitopes are rare. Here, we sought to elucidate whether modulating the quantity of T cell help can influence recruitment and competition of broadly neutralizing antibody precursor B cells at a physiological precursor frequency in response to Env trimer immunization. To do so, two new Env-specific CD4 transgenic (Tg) T cell receptor (TCR) mouse lines were generated, carrying TCR pairs derived from Env-protein immunization. Our results suggest that CD4 T cell help quantitatively regulates early recruitment of rare B cells to GCs.

Glycoproteogenomics: Setting the Course for Next-generation Cancer Neoantigen Discovery for Cancer Vaccines

Molecular-assisted precision oncology gained tremendous ground with high-throughput next-generation sequencing (NGS), supported by robust bioinformatics. The quest for genomics-based cancer medicine set the foundations for improved patient stratification, while unveiling a wide array of neoantigens for immunotherapy. Upfront pre-clinical and clinical studies have successfully used tumor-specific peptides in vaccines with minimal off-target effects. However, the low mutational burden presented by many lesions challenges the generalization of these solutions, requiring the diversification of neoantigen sources. Oncoproteogenomics utilizing customized databases for protein annotation by mass spectrometry (MS) is a powerful tool toward this end. Expanding the concept toward exploring proteoforms originated from post-translational modifications (PTMs) will be decisive to improve molecular subtyping and provide potentially targetable functional nodes with increased cancer specificity. Walking through the path of systems biology, we highlight that alterations in protein glycosylation at the cell surface not only have functional impact on cancer progression and dissemination but also originate unique molecular fingerprints for targeted therapeutics. Moreover, we discuss the outstanding challenges required to accommodate glycoproteomics in oncoproteogenomics platforms. We envisage that such rationale may flag a rather neglected research field, generating novel paradigms for precision oncology and immunotherapy.

Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases

T cells are involved in control of SARS-CoV-2 infection. To establish the patterns of immunodominance of different SARS-CoV-2 antigens and precisely measure virus-specific CD4⁺ and CD8⁺ T cells, we study epitope-specific T cell responses of 99 convalescent coronavirus disease 2019 (COVID-19) cases. The SARS-CoV-2 proteome is probed using 1,925 peptides spanning the entire genome, ensuring an unbiased coverage of human leukocyte antigen (HLA) alleles for class II responses. For HLA class I, we study an additional 5,600 predicted binding epitopes for 28 prominent HLA class I alleles, accounting for wide global coverage. We identify several hundred HLA-restricted SARS-CoV-2-derived epitopes. Distinct patterns of immunodominance are observed, which differ for CD4⁺ T cells, CD8⁺ T cells, and antibodies. The class I and class II epitopes are combined into epitope megapools to facilitate identification and quantification of SARS-CoV-2-specific CD4⁺ and CD8⁺ T cells.

Insight into molecular characteristics of SARS-CoV-2 spike protein following D614G point mutation, a molecular dynamics study

Undoubtedly, the SARS-CoV-2 has become a major concern for all societies due to its catastrophic effects on public health. In addition, mutations and changes in the structure of the virus make it difficult to design effective treatment. Moreover, the amino acid sequence of a protein is a major factor in the formation of the second and tertiary structure in a protein. Amino acid replacement can have noticeable effects on the folding of a protein, especially if an asymmetric change (substitution of polar residue with non-polar, charged with an uncharged, positive charge with a negative charge, or large residue with small residue) occurs. D614G as a spike mutant of SARS-CoV-2 previously identified as an associated risk factor with a high mortality rate of this virus. Using structural bioinformatics, our group determined that D614G mutation could cause extensive changes in SARS-CoV-2 behavior including the secondary structure, receptor binding pattern, 3D conformation, and stability of it.

Communicated by Ramaswamy H. Sarma

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.

Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering

The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC)-II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein's immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other pathogens.

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

Computational Analysis of African Swine Fever Virus Protein Space for the Design of an Epitope-Based Vaccine Ensemble

African swine fever virus is the etiological agent of African swine fever, a transmissible severe hemorrhagic disease that affects pigs, causing massive economic losses. There is neither a treatment nor a vaccine available, and the only method to control its spread is by extensive culling of pigs. So far, classical vaccine development approaches have not yielded sufficiently good results in terms of concomitant safety and efficacy. Nowadays, thanks to advances in genomic and proteomic techniques, a reverse vaccinology strategy can be explored to design alternative vaccine formulations. In this study, ASFV protein sequences were analyzed using an in-house pipeline based on publicly available immunoinformatic tools to identify epitopes of interest for a prospective vaccine ensemble. These included experimentally validated sequences from the Immune Epitope Database, as well as de novo predicted sequences. Experimentally validated and predicted epitopes were prioritized following a series of criteria that included evolutionary conservation, presence in the virulent and currently circulating variant Georgia 2007/1, and lack of identity to either the pig proteome or putative proteins from pig gut microbiota. Following this strategy, 29 B-cell, 14 CD4⁺ T-cell and 6 CD8⁺ T-cell epitopes were selected, which represent a starting point to investigating the protective capacity of ASFV epitope-based vaccines.

Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases

T cells are involved in control of SARS-CoV-2 infection. To establish the patterns of immunodominance of different SARS-CoV-2 antigens, and precisely measure virus-specific CD4 ⁺ and CD8 ⁺ T cells, we studied epitope-specific T cell responses of approximately 100 convalescent COVID-19 cases. The SARS-CoV-2 proteome was probed using 1,925 peptides spanning the entire genome, ensuring an unbiased coverage of HLA alleles for class II responses. For HLA class I, we studied an additional 5,600 predicted binding epitopes for 28 prominent HLA class I alleles, accounting for wide global coverage. We identified several hundred HLA-restricted SARS-CoV-2-derived epitopes. Distinct patterns of immunodominance were observed, which differed for CD4 ⁺ T cells, CD8 ⁺ T cells, and antibodies. The class I and class II epitopes were combined into new epitope megapools to facilitate identification and quantification of SARS-CoV-2-specific CD4 ⁺ and CD8 ⁺ T cells.

Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4+ T Cells in COVID-19

The contribution of CD4+ T cells to protective or pathogenic immune responses to SARS-CoV-2 infection remains unknown. Here, we present single-cell transcriptomic analysis of >100,000 viral antigen-reactive CD4+ T cells from 40 COVID-19 patients. In hospitalized patients compared to non-hospitalized patients, we found increased proportions of cytotoxic follicular helper cells and cytotoxic T helper (TH) cells (CD4-CTLs) responding to SARS-CoV-2 and reduced proportion of SARS-CoV-2-reactive regulatory T cells (TREG). Importantly, in hospitalized COVID-19 patients, a strong cytotoxic TFH response was observed early in the illness, which correlated negatively with antibody levels to SARS-CoV-2 spike protein. Polyfunctional TH1 and TH17 cell subsets were underrepresented in the repertoire of SARS-CoV-2-reactive CD4+ T cells compared to influenza-reactive CD4+ T cells. Together, our analyses provide insights into the gene expression patterns of SARS-CoV-2-reactive CD4+ T cells in distinct disease severities.

Identification and Characterization of CD4+ T Cell Epitopes after Shingrix Vaccination

Infections with varicella-zoster virus (VZV) are associated with a range of clinical manifestations. Primary infection with VZV causes chicken pox. The virus remains latent in neurons, and it can reactivate later in life, causing herpes zoster (HZ). Two different vaccines have been developed to prevent HZ; one is based on a live attenuated VZV strain (Zostavax), and the other is based on adjuvanted gE recombinant protein (Shingrix). While Zostavax efficacy wanes with age, Shingrix protection retains its efficacy in elderly subjects (individuals 80 years of age and older). In this context, it is of much interest to understand if there is a role for T cell immunity in the differential clinical outcome and if there is a correlate of protection between T cell immunity and Shingrix efficacy. In this study, we characterized the Shingrix-specific ex vivo CD4 T cell responses in the context of natural exposure and HZ vaccination using pools of predicted epitopes. We show that T cell reactivity following natural infection and Zostavax vaccination dominantly targets nonstructural (NS) proteins, while Shingrix vaccination redirects dominant reactivity to target gE. We mapped the gE-specific responses following Shingrix vaccination to 89 different gE epitopes, 34 of which accounted for 80% of the response. Using antigen presentation assays and single HLA molecule-transfected lines, we experimentally determined HLA restrictions for 94 different donor/peptide combinations. Finally, we used our results as a training set to assess strategies to predict restrictions based on measured or predicted HLA binding and the corresponding HLA types of the responding subjects.IMPORTANCE Understanding the T cell profile associated with the protection observed in elderly vaccinees following Shingrix vaccination is relevant to the general definition of correlates of vaccine efficacy. Our study enables these future studies by clarifying the patterns of immunodominance associated with Shingrix vaccination, as opposed to natural infection or Zostavax vaccination. Identification of epitopes recognized by Shingrix-induced CD4 T cells and their associated HLA restrictions enables the generation of tetrameric staining reagents and, more broadly, the capability to characterize the specificity, magnitude, and phenotype of VZV-specific T cells.

Immunodominant regions prediction of nucleocapsid protein for SARS-CoV-2 early diagnosis: a bioinformatics and immunoinformatics study

COVID-19 caused by SARS-CoV-2 is sweeping the world and posing serious health problems. Rapid and accurate detection along with timely isolation is the key to control the epidemic. Nucleic acid test and antibody-detection have been applied in the diagnosis of COVID-19, while both have their limitations. Comparatively, direct detection of viral antigens in clinical specimens is highly valuable for the early diagnosis of SARS-CoV-2. The nucleocapsid (N) protein is one of the predominantly expressed proteins with high immunogenicity during the early stages of infection. Here, we applied multiple bioinformatics servers to forecast the potential immunodominant regions derived from the N protein of SARS-CoV-2. Since the high homology of N protein between SARS-CoV-2 and SARS-CoV, we attempted to leverage existing SARS-CoV immunological studies to develop SARS-CoV-2 diagnostic antibodies. Finally, N_229-269, N_349-399, and N_405-419 were predicted to be the potential immunodominant regions, which contain both predicted linear B-cell epitopes and murine MHC class II binding epitopes. These three regions exhibited good surface accessibility and hydrophilicity. All were forecasted to be non-allergen and non-toxic. The final construct was built based on the bioinformatics analysis, which could help to develop an antigen-capture system for the early diagnosis of SARS-CoV-2.

Epitope prediction and identification- adaptive T cell responses in humans

Epitopes, in the context of T cell recognition, are short peptides typically derived by antigen processing, and presented on the cell surface bound to MHC molecules (HLA molecules in humans) for TCR scrutiny. The identification of epitopes is a context-dependent process, with consideration given to, for example, the source pathogen and protein, the host organism, and state of the immune reaction (e.g., following natural infection, vaccination, etc.). In the following review, we consider the various approaches used to define T cell epitopes, including both bioinformatic and experimental approaches, and discuss the concepts of immunodominance and immunoprevalence. We also discuss HLA polymorphism and epitope restriction, and the resulting impact on the identification of, and potential population coverage afforded by, epitopes or epitope-based vaccines. Finally, some examples of the practical application of T cell epitope identification are provided, showing how epitopes have been valuable for deriving novel immunological insights in the context of the immune response to various pathogens and allergens.

Main Strategies for the Identification of Neoantigens

Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.

Enzyme annotation in UniProtKB using Rhea

Motivation

To provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ChEBI (Chemical Entities of Biological Interest) ontology.

Results

We replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that Rhea and ChEBI provide.

Availability and implementation

UniProtKB at https://www.uniprot.org; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/; Rhea at https://www.rhea-db.org.

MASS SPECTROMETRY IN VIROLOGICAL SCIENCES

Virology, as a branch of the life sciences, discovered mass spectrometry (MS) to be the pivotal tool around two decades ago. The technique unveiled the complex network of interactions between the living world of pro- and eukaryotes and viruses, which delivered "a piece of bad news wrapped in protein" as defined by Peter Medawar, Nobel Prize Laureate, in 1960. However, MS is constantly evolving, and novel approaches allow for a better understanding of interactions in this micro- and nanoworld. Currently, we can investigate the interplay between the virus and the cell by analyzing proteomes, interactomes, virus-cell interactions, and search for the compounds that build viral structures. In addition, by using MS, it is possible to look at the cell from the broader perspective and determine the role of viral infection on the scale of the organism, for example, monitoring the crosstalk between infected tissues and the immune system. In such a way, MS became one of the major tools for the modern virology, allowing us to see the infection in the context of the whole cell or the organism. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Rational Design of SARS-CoV-2 Spike Glycoproteins To Increase Immunogenicity By T Cell Epitope Engineering

The current COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and thousands of deaths globally. Extensive efforts and progress have been made to develop effective and safe vaccines against COVID-19. A primary target of these vaccines is the SARS-CoV-2 spike (S) protein, and many studies utilized structural vaccinology techniques to either stabilize the protein or fix the receptor-binding domain at certain states. In this study, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants without perturbing the surface conformation and B cell epitopes of the S protein. We then evaluated the mutated S protein candidates based on predicted MHC-II T cell promiscuous epitopes as well as the epitopes' similarity to human peptides. The presented strategy aims to improve the S protein's immunogenicity and antigenicity by inducing stronger CD4 T cell response while maintaining the protein's native structure and function. The top EvoDesign S protein candidate (Design-10705) recovered 31 out of 32 MHC-II T cell promiscuous epitopes in the native S protein, in which two epitopes were present in all seven human coronaviruses. This newly designed S protein also introduced nine new MHC-II T cell promiscuous epitopes and showed high structural similarity to its native conformation. The proposed structural vaccinology method provides an avenue to rationally design the antigen's structure with increased immunogenicity, which could be applied to the rational design of new COVID-19 vaccine candidates.