Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome

Promising Strategies to Reduce the SARS-CoV-2 Amyloid Deposition in the Brain and Prevent COVID-19-Exacerbated Dementia and Alzheimer's Disease

The COVID-19 pandemic, caused by infection with the SARS-CoV-2 virus, is associated with cognitive impairment and Alzheimer's disease (AD) progression. Once it enters the brain, the SARS-CoV-2 virus stimulates accumulation of amyloids in the brain that are highly toxic to neural cells. These amyloids may trigger neurological symptoms in COVID-19. The meningeal lymphatic vessels (MLVs) play an important role in removal of toxins and mediate viral drainage from the brain. MLVs are considered a promising target to prevent COVID-19-exacerbated dementia. However, there are limited methods for augmentation of MLV function. This review highlights new discoveries in the field of COVID-19-mediated amyloid accumulation in the brain associated with the neurological symptoms and the development of promising strategies to stimulate clearance of amyloids from the brain through lymphatic and other pathways. These strategies are based on innovative methods of treating brain dysfunction induced by COVID-19 infection, including the use of photobiomodulation, plasmalogens, and medicinal herbs, which offer hope for addressing the challenges posed by the SARS-CoV-2 virus.

COVID-19 Variants and Vaccine Development

Coronavirus disease 2019 (COVID-19), the global pandemic caused by severe acute respiratory syndrome 2 virus (SARS-CoV-2) infection, has caused millions of infections and fatalities worldwide. Extensive SARS-CoV-2 research has been conducted to develop therapeutic drugs and prophylactic vaccines, and even though some drugs have been approved to treat SARS-CoV-2 infection, treatment efficacy remains limited. Therefore, preventive vaccination has been implemented on a global scale and represents the primary approach to combat the COVID-19 pandemic. Approved vaccines vary in composition, although vaccine design has been based on either the key viral structural (spike) protein or viral components carrying this protein. Therefore, mutations of the virus, particularly mutations in the S protein, severely compromise the effectiveness of current vaccines and the ability to control COVID-19 infection. This review begins by describing the SARS-CoV-2 viral composition, the mechanism of infection, the role of angiotensin-converting enzyme 2, the host defence responses against infection and the most common vaccine designs. Next, this review summarizes the common mutations of SARS-CoV-2 and how these mutations change viral properties, confer immune escape and influence vaccine efficacy. Finally, this review discusses global strategies that have been employed to mitigate the decreases in vaccine efficacy encountered against new variants.

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen. However PBVs have received less attention despite holding huge untapped potential for boosting vaccine safety and immunogenicity. To identify these TCEs for designing PBV, wet-lab experiments are difficult, expensive, and time-consuming. Machine learning (ML) techniques can accurately predict TCEs, saving time and cost for speedy vaccine development. This work proposes novel hybrid ML techniques based on the physicochemical properties of peptides to predict SARS-CoV-2 TCEs. The proposed hybrid ML technique was evaluated using various ML model evaluation metrics and demonstrated promising results. The hybrid technique of decision tree classifier with chi-squared feature weighting technique and forward search optimal feature searching algorithm has been identified as the best model with an accuracy of 98.19%. Furthermore, K-fold cross-validation (KFCV) was performed to ensure that the model is reliable and the results indicate that the hybrid random forest model performs consistently well in terms of accuracy with respect to other hybrid approaches. The predicted TCEs are highly likely to serve as promising vaccine targets, subject to evaluations both in-vivo and in-vitro. This development could potentially save countless lives globally, prevent future epidemic-scale outbreaks, and reduce the risk of mutation escape.

An Immunoinformatics-Based Study of Mycobacterium tuberculosis Region of Difference-2 Uncharacterized Protein (Rv1987) as a Potential Subunit Vaccine Candidate for Preliminary Ex Vivo Analysis

Mycobacterium tuberculosis (Mtb) is the pathogen that causes tuberculosis and develops resistance to many of the existing drugs. The sole licensed TB vaccine, BCG, is unable to provide a comprehensive defense. So, it is crucial to maintain the immunological response to eliminate tuberculosis. Our previous in silico study reported five uncharacterized proteins as potential vaccine antigens. In this article, we considered the uncharacterized Mtb H37Rv regions of difference (RD-2) Rv1987 protein as a promising vaccine candidate. The vaccine quality of the protein was analyzed using reverse vaccinology and immunoinformatics-based quality-checking parameters followed by an ex vivo preliminary investigation. In silico analysis of Rv1987 protein predicted it as surface localized, secretory, single helix, antigenic, non-allergenic, and non-homologous to the host protein. Immunoinformatics analysis of Rv1987 by CD4 + and CD8 + T-cells via MHC-I and MHC-II binding affinity and presence of B-cell epitope predicted its immunogenicity. The docked complex analysis of the 3D model structure of the protein with immune cell receptor TLR-4 revealed the protein's capability for potential interaction. Furthermore, the target protein-encoded gene Rv1987 was cloned, over-expressed, purified, and analyzed by mass spectrometry (MS) to report the target peptides. The qRT-PCR gene expression analysis shows that it is capable of activating macrophages and significantly increasing the production of a number of key cytokines (TNF-α, IL-1β, and IL-10). Our in-silico analysis and ex vivo preliminary investigations revealed the immunogenic potential of the target protein. These findings suggest that the Rv1987 be undertaken as a potent subunit vaccine antigen and that further animal model immuno-modulation studies would boost the novel TB vaccine discovery and/or BCG vaccine supplement pipeline.

A comprehensive review on immunogen and immune-response proteins of SARS-CoV-2 and their applications in prevention, diagnosis, and treatment of COVID-19

Exposure to severe acute respiratory syndrome-corona virus-2 (SARS-CoV-2) prompts humoral immune responses in the human body. As the auxiliary diagnosis of a current infection, the existence of viral proteins can be checked from specific antibodies (Abs) induced by immunogenic viral proteins. For people with a weakened immune system, Ab treatment can help neutralize viral antigens to resist and treat the disease. On the other hand, highly immunogenic viral proteins can serve as effective markers for detecting prior infections. Additionally, the identification of viral particles or the presence of antibodies may help establish an immune defense against the virus. These immunogenic proteins rather than SARS-CoV-2 can be given to uninfected people as a vaccination to improve their coping ability against COVID-19 through the generation of memory plasma cells. In this work, we review immunogenic and immune-response proteins derived from SARS-CoV-2 with regard to their classification, origin, and diverse applications (e.g., prevention (vaccine development), diagnostic testing, and treatment (via neutralizing Abs)). Finally, advanced immunization strategies against COVID-19 are discussed along with the contemporary circumstances and future challenges.

A multifaceted approach for identification, validation, and immunogenicity of naturally processed and in silico-predicted highly conserved SARS-CoV-2 peptides

SARS-CoV-2 remains a major global public health concern. Antibody waning and immune escape variant emergence necessitate the development of next generation vaccines that induce cross-reactive durable immune responses. T cell responses to SARS-CoV-2 demonstrate higher conservation, antigenic breadth, and longevity than antibody responses. Therefore, we sought to identify pathogen-derived T cell epitopes for a potential peptide-based vaccine. We pursued an approach leveraging: 1) liquid chromatography and tandem mass spectrometry (LC-MS/MS)-based identification of peptides from ancestral SARS-CoV-2-infected cell lines, 2) epitope prediction algorithms, and 3) overlapping peptide libraries. From this strategy, we identified 380 unique SARS-CoV-2-derived peptide sequences, including 53 antigenic HLA class I and class II peptides from multiple structural and non-structural/accessory viral proteins. These peptide sequences were highly conserved across variants of concern/interest (VoC/VoIs), and are estimated to achieve coverage of >96% of the world population. Our findings validate this discovery pipeline for peptide identification and immunogenicity testing, and are a crucial step toward the development of a next-generation multi-epitope SARS-CoV-2 peptide vaccine, and a novel vaccine platform methodology.

Identification of conserved immunogenic peptides of SARS-CoV-2 nucleocapsid protein

The emergence of the new SARS-CoV-2 variants has led to major concern regarding the efficacy of approved vaccines. Nucleocapsid is a conserved structural protein essential for replication of the virus. This study focuses on identifying conserved epitopes on the nucleocapsid (N) protein of SARS-CoV-2. Using 510 unique amino acid sequences of SARS-CoV-2 N protein, two peptides (193 and 215 aa) with 90% conservancy were selected for T cell epitope prediction. Three immunogenic peptides containing multiple T cell epitopes were identified which were devoid of autoimmune and allergic immune response. These peptides were also conserved (100%) in recent Omicron variants reported in Jan-August 2023. HLA analysis reveals that these peptides are predicted as binding to large number of HLA alleles and 71-90% population coverage in six continents. Identified peptides displayed good binding score with both HLA class I and HLA class II molecules in the docking study. Also, a vaccine construct docked with TLR-4 receptor displays strong interaction with 20 hydrogen bonds and molecular simulation analysis reveals that docked complex are stable. Additionally, the immunogenicity of these N protein peptides was confirmed using SARS-CoV-2 convalescent serum samples. We conclude that the identified N protein peptides contain highly conserved and antigenic epitopes which could be used as a target for the future vaccine development against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Inferring linear-B cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm

Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting them from protein sequences is of primary importance. These epitopes consist of amino acids arranged in continuous or discontinuous patterns. Vaccines employ attenuated viruses and purified antigens. LBCE stimulate humoral immunity in the body, where B and T cells target circulating infections. To predict LBCE, the underlying protein sequences undergo a process of feature extraction, feature selection, and classification. Various system models have been proposed for this purpose, but their classification accuracy is only moderate. In order to enhance the accuracy of LBCE classification, this paper presents a novel 2-step metaheuristic variant-feature selection method that combines a linear support vector classifier (LSVC) with a Modified Genetic Algorithm (MGA). The feature selection model employs mono-peptide, dipeptide, and tripeptide features, focusing on the most diverse ones. These selected features are fed into a machine learning (ML)-based parallel ensemble classifier. The ensemble classifier combines correctly classified instances from various classifiers, including k-Nearest Neighbor (kNN), random forest (RF), logistic regression (LR), and support vector machine (SVM). The ensemble classifier came up with an impressively high accuracy of 99.3% as a result of its work. This accuracy is superior to the most recent models that are considered to be state-of-the-art for linear B-cell classification. As a direct consequence of this, the entire system model can now be utilised effectively in real-time clinical settings.

Serodominant SARS-CoV-2 Nucleocapsid Peptides Map to Unstructured Protein Regions

The SARS-CoV-2 nucleocapsid (N) protein is highly immunogenic, and anti-N antibodies are commonly used as markers for prior infection. While several studies have examined or predicted the antigenic regions of N, these have lacked consensus and structural context. Using COVID-19 patient sera to probe an overlapping peptide array, we identified six public and four private epitope regions across N, some of which are unique to this study. We further report the first deposited X-ray structure of the stable dimerization domain at 2.05 Å as similar to all other reported structures. Structural mapping revealed that most epitopes are derived from surface-exposed loops on the stable domains or from the unstructured linker regions. An antibody response to an epitope in the stable RNA binding domain was found more frequently in sera from patients requiring intensive care. Since emerging amino acid variations in N map to immunogenic peptides, N protein variation could impact detection of seroconversion for variants of concern. IMPORTANCE As SARS-CoV-2 continues to evolve, a structural and genetic understanding of key viral epitopes will be essential to the development of next-generation diagnostics and vaccines. This study uses structural biology and epitope mapping to define the antigenic regions of the viral nucleocapsid protein in sera from a cohort of COVID-19 patients with diverse clinical outcomes. These results are interpreted in the context of prior structural and epitope mapping studies as well as in the context of emergent viral variants. This report serves as a resource for synthesizing the current state of the field toward improving strategies for future diagnostic and therapeutic design.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Multidimensional futuristic approaches to address the pandemics beyond COVID-19

Globally, the impact of the coronavirus disease 2019 (COVID-19) pandemic has been enormous and unrelenting with ∼6.9 million deaths and ∼765 million infections. This review mainly focuses on the recent advances and potentially novel molecular tools for viral diagnostics and therapeutics with far-reaching implications in managing the future pandemics. In addition to briefly highlighting the existing and recent methods of viral diagnostics, we propose a couple of potentially novel non-PCR-based methods for rapid, cost-effective, and single-step detection of nucleic acids of viruses using RNA mimics of green fluorescent protein (GFP) and nuclease-based approaches. We also highlight key innovations in miniaturized Lab-on-Chip (LoC) devices, which in combination with cyber-physical systems, could serve as ideal futuristic platforms for viral diagnosis and disease management. We also discuss underexplored and underutilized antiviral strategies, including ribozyme-mediated RNA-cleaving tools for targeting viral RNA, and recent advances in plant-based platforms for rapid, low-cost, and large-scale production and oral delivery of antiviral agents/vaccines. Lastly, we propose repurposing of the existing vaccines for newer applications with a major emphasis on Bacillus Calmette-Guérin (BCG)-based vaccine engineering.

Immunogenicity and protective efficacy of a multi-epitope recombinant toxin antigen of Pasteurella multocida against virulent challenge in mice

Pasteurella multocida (P. multocida) infection frequently results in porcine atrophic rhinitis and swine plague, leading to large economic losses for the swine industry worldwide. P. multocida toxin (PMT, 146 kDa) is a highly virulent key virulence factor that plays a vital role in causing lung and turbinate lesions. This study developed a multi-epitope recombinant antigen of PMT (rPMT) that showed excellent immunogenicity and protection in a mouse model. Using bioinformatics to analyse the dominant epitopes of PMT, we constructed and synthesized rPMT containing 10 B-cell epitopes, 8 peptides with multiple B-cell epitopes and 13 T-cell epitopes of PMT and a rpmt gene (1,974 bp) with multiple epitopes. The rPMT protein (97 kDa) was soluble and contained a GST tag protein. Immunization of mice with rPMT stimulated significantly elevated serum IgG titres and splenocyte proliferation, and serum IFN-γ and IL-12 were upregulated by 5-fold and 1.6-fold, respectively, but IL-4 was not. Furthermore, the rPMT immunization group exhibited alleviated lung tissue lesions and a significantly decreased degree of neutrophil infiltration compared with the control groups post-challenge. In the rPMT vaccination group, 57.1% (8/14) of the mice survived the challenge, similar to the bacterin HN06 group, while all the mice in the control groups succumbed to the challenge. Thus, rPMT could be a suitable candidate antigen for developing a subunit vaccine against toxigenic P. multocida infection.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

Characterization and analysis of linear epitopes corresponding to SARS-CoV-2 outbreak in Jilin Province, China

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants have caused hundreds of thousands of deaths and shown serious social influence worldwide. Jilin Province, China, experienced the first wave of the outbreak from December 2020 to February 2021. Here, we analyzed the genomic characteristics of the SARS-CoV-2 outbreak in Jilin province using a phylogeographic tree and found that clinical isolates belonged to the B.1 lineage, which was considered to be the ancestral lineage. Several dominant SARS-CoV-2 specific linear B cell epitopes that reacted with the convalescent sera were also analysed and identified using a peptide microarray composed of S, M, and E proteins. Moreover, the serum of convalescent patients infected with SARS-CoV-2 showed neutralizing activity against four widely spreading SARS-CoV-2 variants; however, significant differences were observed in neutralizing activities against different SARS-CoV-2 variants. These data provide important information on genomic characteristics, linear epitopes, and neutralizing activity of SARS-CoV-2 outbreak in Jilin Province, China, which may aid in understanding disease patterns and regional aspects of the pandemic.

Design and preclinical evaluation of a universal SARS-CoV-2 mRNA vaccine

Because of the rapid mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an effective vaccine against SARS-CoV-2 variants is needed to prevent coronavirus disease 2019 (COVID-19). T cells, in addition to neutralizing antibodies, are an important component of naturally acquired protective immunity, and a number of studies have shown that T cells induced by natural infection or vaccination contribute significantly to protection against several viral infections including SARS-CoV-2. However, it has never been tested whether a T cell-inducing vaccine can provide significant protection against SARS-CoV-2 infection in the absence of preexisting antibodies. In this study, we designed and evaluated lipid nanoparticle (LNP) formulated mRNA vaccines that induce only T cell responses or both T cell and neutralizing antibody responses by using two mRNAs. One mRNA encodes SARS-CoV-2 Omicron Spike protein in prefusion conformation for induction of neutralizing antibodies. The other mRNA encodes over one hundred T cell epitopes (multi-T cell epitope or MTE) derived from non-Spike but conserved regions of the SARS-CoV-2. We show immunization with MTE mRNA alone protected mice from lethal challenge with the SARS-CoV-2 Delta variant or a mouse-adapted virus MA30. Immunization with both mRNAs induced the best protection with the lowest viral titer in the lung. These results demonstrate that induction of T cell responses, in the absence of preexisting antibodies, is sufficient to confer protection against severe disease, and that a vaccine containing mRNAs encoding both the Spike and MTE could be further developed as a universal SARS-CoV-2 vaccine.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

Intramuscular injection of a mixture of COVID-19 peptide vaccine and tetanus vaccine in horse induced neutralizing antibodies against authentic virus of SARS-CoV-2 Delta variant

Peptide vaccine is not effective due to its low immunogenicity. To improve the efficacy of peptide vaccine against COVID-19, a novel method was developed by mixing a COVID-19 peptide vaccine with a tetanus vaccine. In this study, intramuscular injection of a mixture of COVID-19 peptide vaccine and tetanus vaccine twice, i.e., first dose on day 0 and second dose on day 21, induced neutralizing antibodies against authentic virus of SARS-CoV-2 Delta variant in a horse. Horse serum of day 35, i.e., two weeks after the second dose, neutralized authentic virus of SARS-CoV-2 Delta variant, equal to half effectiveness of human serum from vaccinees of Moderna COVID-19 vaccine. However, neither horse serum nor human serum neutralized Omicron variant authentic virus. No side effects were observed after each dose. This study indicates intramuscular injection of a mixture of COVID-19 peptide vaccine and tetanus vaccine may work in humans to improve peptide vaccine efficacy against SARS-CoV-2.

A common allele of HLA mediates asymptomatic SARS-CoV-2 infection

Despite some inconsistent reporting of symptoms, studies have demonstrated that at least 20% of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will remain asymptomatic. Although most global efforts have focused on understanding factors underlying severe illness in COVID-19 (coronavirus disease of 2019), the examination of asymptomatic infection provides a unique opportunity to consider early disease and immunologic features promoting rapid viral clearance. Owing to its critical role in the immune response, we postulated that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection. We enrolled 29,947 individuals registered in the National Marrow Donor Program for whom high-resolution HLA genotyping data were available in the UCSF Citizen Science smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n=1428) was comprised of unvaccinated, self-identified subjects who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci (HLA-A, -B, -C, -DRB1, -DQB1) with disease course and identified a strong association of HLA-B*15:01 with asymptomatic infection, and reproduced this association in two independent cohorts. Suggesting that this genetic association is due to pre-existing T-cell immunity, we show that T cells from pre-pandemic individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF, and 100% of the reactive cells displayed memory phenotype. Finally, we characterize the protein structure of HLA-B*15:01-peptide complexes, demonstrating that the NQKLIANQF peptide from SARS-CoV-2, and the highly homologous NQKLIANAF from seasonal coronaviruses OC43-CoV and HKU1-CoV, share similar ability to be stabilized and presented by HLA-B*15:01, providing the molecular basis for T-cell cross-reactivity and HLA-B*15:01-mediated pre-existing immunity.

Immune epitopes identification and designing of a multi-epitope vaccine against bovine leukemia virus: a molecular dynamics and immune simulation approaches

Background

Bovine leukemia virus (BLV) is an oncogenic delta-retrovirus causing bovine leucosis. Studies on BLV have shown the association with human breast cancer. However, the exact molecular mechanism is neither known nor their appropriate preventative measure to halt the disease initiation and progression. In this study, we designed a multi-epitope vaccine against BLV using a computational analyses.

Methods

Following a rigorous assessment, the vaccine was constructed using the T-cell epitopes from each BLV-derived protein with suitable adjuvant and linkers. Both physicochemistry and immunogenic potency as well as the safeness of the vaccine candidate were assessed. Population coverage was done to evaluate the vaccine probable efficiency in eliciting the immune response worldwide. After homology modeling, the three-dimensional structure was refined and validated to determine the quality of the designed vaccine. The vaccine protein was then subjected to molecular docking with Toll-like receptor 3 (TLR3) to evaluate the binding efficiency followed by dynamic simulation for stable interaction.

Results

Our vaccine construct has the potential immune response and good physicochemical properties. The vaccine is antigenic and immunogenic, and has no allergenic or toxic effect on the human body. This novel vaccine contains a significant interactions and binding affinity with the TLR3 receptor.

Conclusions

The proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat BLV infections. However, experimental evaluations are essential to validate the exact safety and immunogenic profiling of this vaccine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-022-03181-w.

How concerning is a SARS-CoV-2 variant of concern? Computational predictions and the variants labeling system

In this study, we evaluated the use of a predictive computational approach for SARS-CoV-2 genetic variations analysis in improving the current variant labeling system. First, we reviewed the basis of the system developed by the World Health Organization (WHO) for the labeling of SARS-CoV-2 genetic variants and the derivative adapted by the United States Centers for Disease Control and Prevention (CDC). Both labeling systems are based on the virus’ major attributes. However, we found that the labeling criteria of the SARS-CoV-2 variants derived from these attributes are not accurately defined and are used differently by the two agencies. Consequently, discrepancies exist between the labels given by WHO and the CDC to the same variants. Our observations suggest that giving the variant of concern (VOC) label to a new variant is premature and might not be appropriate. Therefore, we used a comparative computational approach to predict the effects of the mutations on the virus structure and functions of five VOCs. By linking these data to the criteria used by WHO/CDC for variant labeling, we ascertained that a predictive computational comparative approach of the genetic variations is a good way for rapid and more accurate labeling of SARS-CoV-2 variants. We propose to label all emergent variants, variant under monitoring or variant being monitored (VUM/VBM), and to carry out computational predictive studies with thorough comparison to existing variants, upon which more appropriate and informative labels can be attributed. Furthermore, harmonization of the variant labeling system would be globally beneficial to communicate about and fight the COVID-19 pandemic.

SARS-CoV-2-Specific Memory B Cell Responses Are Maintained After Recovery from Natural Infection and Postvaccination

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), has resulted in major worldwide disruption and loss of life over the last 2 years. Many research studies have shown waning serological SARS-CoV-2-specific IgG antibody titers over time, yet, it is unclear whether these changes are reflected in the potential functional reactivation of SARS-CoV-2 antigen-specific memory B cells (MBC) populations. This is especially true in the contexts of differing COVID-19 disease severity and after vaccination regimens. This study aimed to investigate these by polyclonal in vitro reactivation of MBC populations followed by analysis using SAR-CoV-2 antigen-specific B cell ELISpots and IgG antibody ELISAs. Natural disease-associated differences were investigated in 52 donors who have recovered from COVID-19 with varying disease severity, from asymptomatic to severe COVID-19 disease, accompanied by a longitudinal evaluation in a subset of donors. Overall, these data showed limited disease severity-associated differences between donor groups but did show that COVID-19 serologically positive donors had strong antigen-specific MBC-associated responses. MBC responses were better maintained 6 months after recovery from infection when compared to serological antigen-specific IgG antibody titers. A similar investigation after vaccination using 14 donors showed robust serological antigen-specific antibody responses against spike protein that waned over time. MBC-associated responses against spike protein were also observed but showed less waning over time, indicating maintenance of a protective response 6 months after vaccination. Further research is required to evaluate these putatively functional SARS-CoV-2-specific responses in the context of long-term protection mediated by vaccination against this pathogen.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

Detection of SARS-CoV-2 peptide-specific antibodies in Syrian hamster serum by ELISA

Golden Syrian hamsters are increasingly used as a permissive animal model for SARS-CoV-2 virus studies, but the lack of immunological assays and other immunological reagents for hamsters limits its full potential. Herein, we developed an ELISA method to detect antibodies specific to peptides and proteins derived from SARS-CoV-2 virus in immunized golden Syrian hamsters. Under optimized conditions, this assay quantitates antibodies specific for individual viral peptides, peptide pools, and proteins. Hence, this ELISA method allows investigators to quantitatively assess humoral immune responses at the peptide and protein levels and has potential application in the development of peptide-based vaccines to be tested in hamsters.

Early and strong antibody responses to SARS-CoV-2 predict disease severity in COVID-19 patients

Background

Antibody response to SARS-CoV-2 is a valuable biomarker for the assessment of the spread of the virus in a population and evaluation of the vaccine candidates. Recent data suggest that antibody levels also may have a prognostic significance in COVID-19. Most of the serological studies so far rely on testing antibodies against spike (S) or nucleocapsid (N) protein, however antibodies can be directed against other structural and nonstructural proteins of the virus, whereas their frequency, biological and clinical significance is unknown.

Methods

A novel antigen array comprising 30 SARS-CoV-2 antigens or their fragments was developed and used to examine IgG, IgA, IgE and IgM responses to SARS-CoV-2 in sera from 103 patients with COVID-19 including 34 patients for whom sequential samples were available, and 20 pre-pandemic healthy controls.

Results

Antibody responses to various antigens are highly correlated and the frequencies and peak levels of antibodies are higher in patients with severe/moderate disease than in those with mild disease. This finding supports the idea that antibodies against SARS-CoV-2 may exacerbate the severity of the disease via antibody-dependent enhancement. Moreover, early IgG and IgA responses to full length S protein may be used as an additional biomarker for the identification of patients who are at risk of developing severe disease. Importantly, this is the first study reporting that SARS-CoV-2 elicits IgE responses and their serum levels positively correlate with the severity of the disease thus suggesting a link between high levels of antibodies and mast cell activation.

Conclusions

This is the first study assessing the prevalence and dynamics IgG, IgA, IgE and IgM responses to multiple SARS-CoV-2 antigens simultaneously. Results provide important insights into the pathogenesis of COVID-19 and have implications in planning and interpreting antibody-based epidemiological studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03382-y.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

DNA Vaccines Expressing the Envelope and Membrane Proteins Provide Partial Protection Against SARS-CoV-2 in Mice

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a public health emergency of international concern, and an effective vaccine is urgently needed to control the pandemic. Envelope (E) and membrane (M) proteins are highly conserved structural proteins among SARS-CoV-2 and SARS-CoV and have been proposed as potential targets for the development of cross-protective vaccines. Here, synthetic DNA vaccines encoding SARS-CoV-2 E/M proteins (called p-SARS-CoV-2-E/M) were developed, and mice were immunised with three doses via intramuscular injection and electroporation. Significant cellular immune responses were elicited, whereas no robust humoral immunity was detected. In addition, novel H-2d-restricted T-cell epitopes were identified. Notably, although no drop in lung tissue virus titre was detected in DNA-vaccinated mice post-challenge with SARS-CoV-2, immunisation with either p-SARS-CoV-2-E or p-SARS-CoV-2-M provided minor protection and co-immunisation with p-SARS-CoV-2-E+M increased protection. Therefore, E/M proteins should be considered as vaccine candidates as they may be valuable in the optimisation of vaccination strategies against COVID-19.

Strategies for fighting pandemic virus infections: Integration of virology and drug delivery

Respiratory viruses have sometimes resulted in worldwide pandemics, with the influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) being major participants. Long-term efforts have made it possible to control the influenza virus, but seasonal influenza continues to take many lives each year, and a pandemic influenza virus sometimes emerges. Although vaccines for coronavirus disease 2019 (COVID-19) have been developed, we are not yet able to coexist with the SARS-CoV-2. To overcome such viruses, it is necessary to obtain knowledge about international surveillance systems, virology, ecology and to determine that immune responses are effective. The information must then be transferred to drugs. Delivery systems would be expected to contribute to the rational development of drugs. In this review, virologist and drug delivery system (DDS) researchers discuss drug delivery strategies, especially the use of lipid-based nanocarriers, for fighting to respiratory virus infections.

A Novel Multiepitope Vaccine Against Bladder Cancer Based on CTL and HTL Epitopes for Induction of Strong Immune Using Immunoinformatics Approaches

Bladder cancer is well-known cancer in two forms of muscle-invasive and non-muscle-invasive bladder cancer which is responsible for annual deaths worldwide. Common therapies methods are somewhat successful; however, these methods have the limitations such as the side effects of chemotherapy which necessitate the requirement for new preventive methods against bladder cancer. Hence, we explain a novel designed multi-epitope vaccine against bladder cancer using the immunoinformatics tool. Three well-known BLCAP, PRAM, and BAGE4 antigens were evaluated due to most repetitive CTL and HTL epitopes binding. IFNγ and IL10 inducer potential of selected epitopes were investigated, as well as liner and conformational B-cell epitopes. Human beta-defensin 3 and PADRE sequence were added to construct as adjuvants, along with EAAAK, AAY, and GGGS linkers to fuse CTL and HTL epitopes. Results showed this construct encodes a soluble, non-toxic, and non-allergic protein with 70 kDa molecular weight. Modeled 3D structure of vaccine was docked whit Toll-Like Receptors (TLR) of 7/8. Docking, molecular dynamics simulation and MMBPSA analysis confirmed stability of vaccine-TLR complexes. The immunogenicity showed this construct could elicit humoral and cellular immune responses. In silico and immunoinformatics evaluations suggest that this construct is a recombinant candidate vaccine against bladder cancer.

Peptides for Vaccine Development

This review discusses peptide epitopes used as antigens in the development of vaccines in clinical trials as well as future vaccine candidates. It covers peptides used in potential immunotherapies for infectious diseases including SARS-CoV-2, influenza, hepatitis B and C, HIV, malaria, and others. In addition, peptides for cancer vaccines that target examples of overexpressed proteins are summarized, including human epidermal growth factor receptor 2 (HER-2), mucin 1 (MUC1), folate receptor, and others. The uses of peptides to target cancers caused by infective agents, for example, cervical cancer caused by human papilloma virus (HPV), are also discussed. This review also provides an overview of model peptide epitopes used to stimulate non-specific immune responses, and of self-adjuvanting peptides, as well as the influence of other adjuvants on peptide formulations. As highlighted in this review, several peptide immunotherapies are in advanced clinical trials as vaccines, and there is great potential for future therapies due the specificity of the response that can be achieved using peptide epitopes.

Deciphering the immunogenic T-cell epitopes from spike protein of SARS-CoV-2 concerning the diverse population of India

Scientists are rigorously looking for an efficient vaccine against the current pandemic due to the SARS-CoV-2 virus. The reverse vaccinology approach may provide us with significant therapeutic leads in this direction and further determination of T-cell/B-cell response to antigen. In the present study, we conducted a population coverage analysis referring to the diverse Indian population. From the Immune epitope database (IEDB), HLA- distribution analysis was performed to find the most promiscuous T-cell epitope out of In silico determined epitope of Spike protein from SARS-CoV-2. Epitopes were selected based on their binding affinity with the maximum number of HLA alleles belonging to the highest population coverage rate values for the chosen geographical area in India. 404 cleavage sites within the 1288 amino acids sequence of spike glycoprotein were determined by NetChop proteasomal cleavage prediction suggesting the presence of adequate sites in the protein sequence for cleaving into appropriate epitopes. For population coverage analysis, 179 selected epitopes present the projected population coverage up to 97.45% with 56.16 average hit and 15.07 pc90. 54 epitopes are found with the highest coverage among the Indian population and highly conserved within the given spike RBD domain sequence. Among all the predicted epitopes, 9-mer TRFASVYAW and RFDNPVLPF along with 12-mer LLAGTITSGWTF and VSQPFLMDLEGK epitopes are observed as the best due to their decent docking score and best binding affinity to corresponding HLA alleles during MD simulations. Outcomes from this study could be critical to design a vaccine against SARS-CoV-2 for a different set of populations within the country.Communicated by Ramaswamy H. Sarma.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

Adoptive T-cell therapies to overcome T cell-dependent immune dysregulations in COVID-19

Coronavirus disease 2019 (COVID-19) pandemic has been an important global interest that affected millions of people, and it requires a deep investigation of the disease immunology for developing further therapeutic applications. Adoptive T cell therapy promises to address T cell-dependent immune dysregulation in COVID-19 patients by the generation of specific T cell clones against virus-specific antigens. Additionally, targeting B cell-dependent protection through COVID-19 vaccines, which have been developed in the recent year, possessed sufficient prevention for spreading the virus, since the cases and deaths related to COVID-19 tend to decrease after the vaccination. However, adoptive cell therapies are now encouraging scientists to deal with pathological challenges like inadequate T cell-dependent immune response or lymphopenia, since they are the most frequent outcome of severe infection, especially in immunocompromized patients. In this review, the current knowledge of immunopathology of COVID-19 was aimed to be highlighted along with the T cell responses against SARS-CoV-2 to comprise a basis for therapeutics. Moreover, current therapeutics and treatment strategies for COVID-19 were discussed to evaluate possible agents. Furthermore, the use of adoptive T cell therapy representing an emerging therapeutic approach was purposed to be presented comprehensively against SARS-CoV-2 infection. Even though further studies are needed to fully understand T cell response against SARS-CoV-2 in order to develop therapies to provide long term and efficient protection, adoptive cell therapies now meet the demand for a large population of people who suffer immunocompromization, considering the previous usage of the technique for different infectious diseases.

Immunoinformatics Analysis of SARS-CoV-2 ORF1ab Polyproteins to Identify Promiscuous and Highly Conserved T-Cell Epitopes to Formulate Vaccine for Indonesia and the World Population

SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon's entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Immunoinformatics mapping of potential epitopes in SARS-CoV-2 structural proteins

All approved coronavirus disease 2019 (COVID-19) vaccines in current use are safe, effective, and reduce the risk of severe illness. Although data on the immunological presentation of patients with COVID-19 is limited, increasing experimental evidence supports the significant contribution of B and T cells towards the resolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Despite the availability of several COVID-19 vaccines with high efficacy, more effective vaccines are still needed to protect against the new variants of SARS-CoV-2. Employing a comprehensive immunoinformatic prediction algorithm and leveraging the genetic closeness with SARS-CoV, we have predicted potential immune epitopes in the structural proteins of SARS-CoV-2. The S and N proteins of SARS-CoV-2 and SARS-CoVs are main targets of antibody detection and have motivated us to design four multi-epitope vaccines which were based on our predicted B- and T-cell epitopes of SARS-CoV-2 structural proteins. The cardinal epitopes selected for the vaccine constructs are predicted to possess antigenic, non-allergenic, and cytokine-inducing properties. Additionally, some of the predicted epitopes have been experimentally validated in published papers. Furthermore, we used the C-ImmSim server to predict effective immune responses induced by the epitope-based vaccines. Taken together, the immune epitopes predicted in this study provide a platform for future experimental validations which may facilitate the development of effective vaccine candidates and epitope-based serological diagnostic assays.

SARS-CoV-2 infection as a trigger of humoral response against apolipoprotein A-1

BACKGROUND

Unravelling autoimmune targets triggered by SARS-CoV-2 infection may provide crucial insights into the physiopathology of the disease and foster the development of potential therapeutic candidate targets and prognostic tools. We aimed at determining (a) the association between anti-SARS-CoV-2 and anti-apoA-1 humoral response and (b) the degree of linear homology between SARS-CoV-2, apoA-1 and Toll-like receptor 2 (TLR2) epitopes.

DESIGN

Bioinformatics modelling coupled with mimic peptides engineering and competition experiments were used to assess epitopes sequence homologies. Anti-SARS-CoV-2 and anti-apoA-1 IgG as well as cytokines were assessed by immunoassays on a case-control (n = 101), an intensive care unit (ICU; n = 126) and a general population cohort (n = 663) with available samples in the pre and post-pandemic period.

RESULTS

Using bioinformatics modelling, linear sequence homologies between apoA-1, TLR2 and Spike epitopes were identified but without experimental evidence of cross-reactivity. Overall, anti-apoA-1 IgG levels were higher in COVID-19 patients or anti-SARS-CoV-2 seropositive individuals than in healthy donors or anti-SARS-CoV-2 seronegative individuals (P < .0001). Significant and similar associations were noted between anti-apoA-1, anti-SARS-CoV-2 IgG, cytokines and lipid profile. In ICU patients, anti-SARS-CoV-2 and anti-apoA-1 seroconversion rates displayed similar 7-day kinetics, reaching 82% for anti-apoA-1 seropositivity. In the general population, SARS-CoV-2-exposed individuals displayed higher anti-apoA-1 IgG seropositivity rates than nonexposed ones (34% vs 16.8%; P = .004).

CONCLUSION

COVID-19 induces a marked humoral response against the major protein of high-density lipoproteins. As a correlate of poorer prognosis in other clinical settings, such autoimmunity signatures may relate to long-term COVID-19 prognosis assessment and warrant further scrutiny in the current COVID-19 pandemic.

Mapping SARS-CoV-2 Antibody Epitopes in COVID-19 Patients with a Multi-Coronavirus Protein Microarray

The rapid worldwide spread of SARS-CoV-2 has accelerated research and development for controlling the COVID-19 pandemic. A multi-coronavirus protein microarray was created containing full-length proteins, overlapping protein fragments of various lengths, and peptide libraries from SARS-CoV-2 and four other human coronaviruses. Sera from confirmed COVID-19 patients as well as unexposed individuals were applied to multicoronavirus arrays to identify specific antibody reactivity. High-level IgG, IgM, and IgA reactivity to structural proteins S, M, and N of SARS-CoV-2, as well as accessory proteins such as ORF3a and ORF7a, were observed that were specific to COVID-19 patients. Antibody reactivity against overlapping 100-, 50-, and 30-amino acid fragments of SARS-CoV-2 proteins was used to identify antigenic regions. Numerous proteins of SARS-CoV, Middle East respiratory syndrome coronavirus (MERS-CoV), and the endemic human coronaviruses HCoV-NL63 and HCoV-OC43 were also more reactive with IgG, IgM, and IgA in COVID-19 patient sera than in unexposed control sera, providing further evidence of immunologic cross-reactivity between these viruses. Whereas unexposed individuals had minimal reactivity against SARS-CoV-2 proteins that poorly correlated with reactivity against HCoV-NL63 and HCoV-OC43 S2 and N proteins, COVID-19 patient sera had higher correlation between SARS-CoV-2 and HCoV responses, suggesting that de novo antibodies against SARS-CoV-2 cross-react with HCoV epitopes. Array responses were compared with validated spike protein-specific IgG enzyme-linked immunosorbent assays (ELISAs), showing agreement between orthologous methods. SARS-CoV-2 microneutralization titers were low in the COVID-19 patient sera but correlated with array responses against S and N proteins. The multi-coronavirus protein microarray is a useful tool for mapping antibody reactivity in COVID-19 patients. IMPORTANCE With novel mutant SARS-CoV-2 variants of concern on the rise, knowledge of immune specificities against SARS-CoV-2 proteins is increasingly important for understanding the impact of structural changes in antibody-reactive protein epitopes on naturally acquired and vaccine-induced immunity, as well as broader topics of cross-reactivity and viral evolution. A multi-coronavirus protein microarray used to map the binding of COVID-19 patient antibodies to SARS-CoV-2 proteins and protein fragments as well as to the proteins of four other coronaviruses that infect humans has shown specific regions of SARS-CoV-2 proteins that are highly reactive with patient antibodies and revealed cross-reactivity of these antibodies with other human coronaviruses. These data and the multi-coronavirus protein microarray tool will help guide further studies of the antibody response to COVID-19 and to vaccination against this worldwide pandemic.

Evaluation of a SARS-CoV-2 Capture IgM Antibody Assay in Convalescent Sera

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for a global pandemic with over 152 million cases and 3.19 million deaths reported by early May 2021. Understanding the serological response to SARS-CoV-2 is critical to determining the burden of infection and disease (coronavirus disease 2019 [COVID-19]) and transmission dynamics. We developed a capture IgM assay because it should have better sensitivity and specificity than the commonly used indirect assay. Here, we report the development and performance of a capture IgM enzyme-linked immunosorbent assay (ELISA) and a companion indirect IgG ELISA for the spike (S) and nucleocapsid (N) proteins and the receptor-binding domain (RBD) of S. We found that among the IgM ELISAs, the S ELISA was positive in 76% of 55 serum samples from SARS-CoV-2 PCR-positive patients, the RBD ELISA was positive in 55% of samples, and the N ELISA was positive in 15% of samples. The companion indirect IgG ELISAs were positive for S in 89% of the 55 serum samples, RBD in 78%, and N in 85%. While the specificities for IgM RBD, S, and N ELISAs and IgG S and RBD ELISAs were 97% to 100%, the specificity of the N IgG ELISA was lower (89%). RBD-specific IgM antibodies became undetectable by 3 to 6 months, and S IgM reached low levels at 6 months. The corresponding IgG S, RBD, and N antibodies persisted with some decreases in levels over this time period. These capture IgM ELISAs and the companion indirect IgG ELISAs should enhance serologic studies of SARS-CoV-2 infections. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has inflicted tremendous loss of lives, overwhelmed health care systems, and disrupted all aspects of life worldwide since its emergence in Wuhan, China, in December 2019. Detecting current and past infection by PCR or serology is important to understanding and controlling SARS-CoV-2. With increasing prevalence of past infection or vaccination, IgG antibodies are less helpful in diagnosing a current infection. IgM antibodies indicate a more recent infection and can supplement PCR diagnosis. We report an alternative method, capture IgM, to detect serum IgM antibodies, which should be more sensitive and specific than most currently used methods. We describe this capture IgM assay and a companion indirect IgG assay for the SARS-CoV-2 spike (S), nucleocapsid (N), and receptor-binding domain (RBD) proteins. These assays can add value to diagnostic and serologic studies of coronavirus disease 2019 (COVID-19).

Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets

An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.

Safety and potency of BIV1-CovIran inactivated vaccine candidate for SARS-CoV-2: A preclinical study

The development of effective and safe COVID‐19 vaccines is a major move forward in our global effort to control the SARS‐CoV‐2 pandemic. The aims of this study were (1) to develop an inactivated whole‐virus SARS‐CoV‐2 candidate vaccine named BIV1‐CovIran and (2) to determine the safety and potency of BIV1‐CovIran inactivated vaccine candidate against SARS‐CoV‐2. Infectious virus was isolated from nasopharyngeal swab specimen and propagated in Vero cells with clear cytopathic effects in a biosafety level‐3 facility using the World Health Organization’s laboratory biosafety guidance related to COVID‐19. After characterisation of viral seed stocks, the virus working seed was scaled‐up in Vero cells. After chemical inactivation and purification, it was formulated with alum adjuvant. Finally, different animal species were used to determine the toxicity and immunogenicity of the vaccine candidate. The study showed the safety profile in studied animals including guinea pig, rabbit, mice and monkeys. Immunisation at two different doses (3 or 5 μg per dose) elicited a high level of SARS‐CoV‐2 specific and neutralising antibodies in mice, rabbits and nonhuman primates. Rhesus macaques were immunised with the two‐dose schedule of 5 or 3 μg of the BIV1‐CovIran vaccine and showed highly efficient protection against 10⁴ TCID50 of SARS‐CoV‐2 intratracheal challenge compared with the control group. These results highlight the BIV1‐CovIran vaccine as a potential candidate to induce a strong and potent immune response that may be a promising and feasible vaccine to protect against SARS‐CoV‐2 infection.

SARS-CoV-2 specific memory T cell epitopes identified in COVID-19-recovered subjects

The COVID-19 pandemic caused by SARS-CoV-2 infection poses a serious threat to public health. An explicit investigation of COVID-19 immune responses, particularly the host immunity in recovered subjects, will lay a foundation for the rational design of therapeutics and/or vaccines against future coronaviral outbreaks. Here, we examined virus-specific T cell responses and identified T cell epitopes using peptides spanning SARS-CoV-2 structural proteins. These peptides were used to stimulate peripheral blood mononuclear cells (PBMCs) derived from COVID-19-recovered subjects, followed by an analysis of IFN-γ-secreting T cells by enzyme-linked immunosorbent spot (ELISpot). We also evaluated virus-specific CD4 or CD8 T cell activation by flow cytometry assay. By screening 52 matrix pools (comprised of 315 peptides) of the spike (S) glycoprotein and 21 matrix pools (comprised of 102 peptides) spanning the nucleocapsid (N) protein, we identified 28 peptides from S protein and 5 peptides from N protein as immunodominant epitopes. The immunogenicity of these epitopes was confirmed by a second ELISpot using single peptide stimulation in memory T cells, and they were mapped by HLA restrictions. Notably, SARS-CoV-2 specific T cell responses positively correlated with B cell IgG and neutralizing antibody responses to the receptor-binding domain (RBD) of the S protein. Our results demonstrate that defined levels of SARS-CoV-2 specific T cell responses are generated in some, but not all, COVID-19-recovered subjects, fostering hope for the protection of a proportion of COVID-19-exposed individuals against reinfection. These results also suggest that these virus-specific T cell responses may induce protective immunity in unexposed individuals upon vaccination, using vaccines generated based on the immune epitopes identified in this study. However, SARS-CoV-2 S and N peptides are not potently immunogenic, and none of the single peptides could universally induce robust T cell responses, suggesting the necessity of using a multi-epitope strategy for COVID-19 vaccine design.

Spread of Mink SARS-CoV-2 Variants in Humans: A Model of Sarbecovirus Interspecies Evolution

The rapid spread of SARS-CoV-2 variants has quickly spanned doubts and the fear about their ability escape vaccine protection. Some of these variants initially identified in caged were also found in humans. The claim that these variants exhibited lower susceptibility to antibody neutralization led to the slaughter of 17 million minks in Denmark. SARS-CoV-2 prevalence tests led to the discovery of infected farmed minks worldwide. In this study, we revisit the issue of the circulation of SARS-CoV-2 variants in minks as a model of sarbecovirus interspecies evolution by: (1) comparing human and mink angiotensin I converting enzyme 2 (ACE2) and neuropilin 1 (NRP-1) receptors; (2) comparing SARS-CoV-2 sequences from humans and minks; (3) analyzing the impact of mutations on the 3D structure of the spike protein; and (4) predicting linear epitope targets for immune response. Mink-selected SARS-CoV-2 variants carrying the Y453F/D614G mutations display an increased affinity for human ACE2 and can escape neutralization by one monoclonal antibody. However, they are unlikely to lose most of the major epitopes predicted to be targets for neutralizing antibodies. We discuss the consequences of these results for the rational use of SARS-CoV-2 vaccines.

Revelation of Potent Epitopes Present in Unannotated ORF Antigens of SARS-CoV-2 for Epitope-Based Polyvalent Vaccine Design Using Immunoinformatics Approach

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) kills thousands of people worldwide every day, thus necessitating rapid development of countermeasures. Immunoinformatics analyses carried out here in search of immunodominant regions in recently identified SARS-CoV-2 unannotated open reading frames (uORFs) have identified eight linear B-cell, one conformational B-cell, 10 CD4+ T-cell, and 12 CD8+ T-cell promising epitopes. Among them, ORF9b B-cell and T-cell epitopes are the most promising followed by M.ext and ORF3c epitopes. ORF9b_40-48 (CD8+ T-cell epitope) is found to be highly immunogenic and antigenic with the highest allele coverage. Furthermore, it has overlap with four potent CD4+ T-cell epitopes. Structure-based B-cell epitope prediction has identified ORF9b_61-68 to be immunodominant, which partially overlaps with one of the linear B-cell epitopes (ORF9b_65-69). ORF3c CD4+ T-cell epitopes (ORF3c_2-16, ORF3c_3-17, and ORF3c_4-18) and linear B-cell epitope (ORF3c_14-22) have also been identified as the candidate epitopes. Similarly, M.ext and 7a.iORF1 (overlap with M and ORF7a) proteins have promising immunogenic regions. By considering the level of antigen expression, four ORF9b and five M.ext epitopes are finally shortlisted as potent epitopes. Mutation analysis has further revealed that the shortlisted potent uORF epitopes are resistant to recurrent mutations. Additionally, four N-protein (expressed by canonical ORF) epitopes are found to be potent. Thus, SARS-CoV-2 uORF B-cell and T-cell epitopes identified here along with canonical ORF epitopes may aid in the design of a promising epitope-based polyvalent vaccine (when connected through appropriate linkers) against SARS-CoV-2. Such a vaccine can act as a bulwark against SARS-CoV-2, especially in the scenario of emergence of variants with recurring mutations in the spike protein.

Immunogenicity and antigenicity based T-cell and B-cell epitopes identification from conserved regions of 10664 SARS-CoV-2 genomes

The surge of SARS-CoV-2 has created a wave of pandemic around the globe due to its high transmission rate. To contain this virus, researchers are working around the clock for a solution in the form of vaccine. Due to the impact of this pandemic, the economy and healthcare have immensely suffered around the globe. Thus, an efficient vaccine design is the need of the hour. Moreover, to have a generalised vaccine for heterogeneous human population, the virus genomes from different countries should be considered. Thus, in this work, we have performed genome-wide analysis of 10,664 SARS-CoV-2 genomes of 73 countries around the globe in order to identify the potential conserved regions for the development of peptide based synthetic vaccine viz. epitopes with high immunogenic and antigenic scores. In this regard, multiple sequence alignment technique viz. Clustal Omega is used to align the 10,664 SARS-CoV-2 virus genomes. Thereafter, entropy is computed for each genomic coordinate of the aligned genomes. The entropy values are then used to find the conserved regions. These conserved regions are refined based on the criteria that their lengths should be greater than or equal to 60 nt and their corresponding protein sequences are without any stop codons. Furthermore, Nucleotide BLAST is used to verify the specificity of the conserved regions. As a result, we have obtained 17 conserved regions that belong to NSP3, NSP4, NSP6, NSP8, RdRp, Helicase, endoRNAse, 2'-O-RMT, Spike glycoprotein, ORF3a protein, Membrane glycoprotein and Nucleocapsid protein. Finally, these conserved regions are used to identify the T-cell and B-cell epitopes with their corresponding immunogenic and antigenic scores. Based on these scores, the most immunogenic and antigenic epitopes are then selected for each of these 17 conserved regions. Hence, we have obtained 30 MHC-I and 24 MHC-II restricted T-cell epitopes with 14 and 13 unique HLA alleles and 21 B-cell epitopes for the 17 conserved regions. Moreover, for validating the relevance of these epitopes, the binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles. Also, the physico-chemical properties of the epitopes are reported along with Ramchandran plots and Z-Scores and the population coverage is shown as well. Overall, the analysis shows that the identified epitopes can be considered as potential candidates for vaccine design.

Lessons Learned from Cutting-Edge Immunoinformatics on Next-Generation COVID-19 Vaccine Research

Presently, immunoinformatics and bioinformatics approaches are contributing actively to COVID-19 vaccine research. The first immunoinformatics-based vaccine construct against SARS-CoV-2 was published in February 2020. Following this, immunoinformatics and bioinformatics approaches have created a new direction in COVID-19 vaccine research. Several researchers have designed the next-generation COVID-19 vaccines using these approaches. Presently, immunoinformatics has accelerated immunology research immensely in the area of COVID-19. Hence, we have tried to depict the current scenario of immunoinformatics and bioinformatics in COVID-19 vaccine research.

Antibodies Targeting Two Epitopes in SARS-CoV-2 Neutralize Pseudoviruses with the Spike Proteins from Different Variants

The COVID-19 pandemic was caused by SARS-CoV-2 infection. To prevent the spread of SARS-CoV-2, an effective vaccine is required. Two linear peptides from potential B-cell epitopes in the spike protein of SARS-CoV-2 (a.a. 440–460; a.a. 494–506) were synthesized and used to immunize rabbits. High-titer antibodies of IgG were produced, purified, and verified by Western blot analysis. Antibodies against these two epitopes could effectively neutralize SARS-CoV-2 pseudoviral particles with the spike proteins from not only the original strain (basal; wild-type), but also a strain with a single point mutation (D614G), and two other emerging variants (the Alpha and Beta variants) prevalent around the world, but not from SARS-CoV. In conclusion, antibodies against these two epitopes are protective. This information is important for the development of vaccines against SARS-CoV-2.