In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST-BST-XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model's reliability and an average accuracy of 97.21% was recorded for the ChST-BST-XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development.

Targeting aging and age-related diseases with vaccines

Aging is a major risk factor for numerous chronic diseases. Vaccination offers a promising strategy to combat these age-related diseases by targeting specific antigens and inducing immune responses. Here, we provide a comprehensive overview of recent advances in vaccine-based interventions targeting these diseases, including Alzheimer's disease, type II diabetes, hypertension, abdominal aortic aneurysm, atherosclerosis, osteoarthritis, fibrosis and cancer, summarizing current approaches for identifying disease-associated antigens and inducing immune responses against these targets. Further, we reflect on the recent development of vaccines targeting senescent cells, as a strategy for more broadly targeting underlying causes of aging and associated pathologies. In addition to highlighting recent progress in these areas, we discuss important next steps to advance the therapeutic potential of these vaccines, including improving and robustly demonstrating efficacy in human clinical trials, as well as rigorously evaluating the safety and long-term effects of these vaccine strategies.

CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses

Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.

Cross-Reactivity Assessment of Vaccine-Derived SARS-CoV-2 T Cell Responses against BA.2.86 and JN.1

The SARS-CoV-2 Omicron sub-variants BA.2.86 and JN.1 contain multiple mutations in the spike protein that were not present in previous variants of concern and Omicron sub-variants. Preliminary research suggests that these variants reduce the neutralizing capability of antibodies induced by vaccines, which is particularly significant for JN.1. This raises concern as many widely deployed COVID-19 vaccines are based on the spike protein of the ancestral Wuhan strain of SARS-CoV-2. While T cell responses have been shown to be robust against previous SARS-CoV-2 variants, less is known about the impact of mutations in BA.2.86 and JN.1 on T cell responses. We evaluate the effect of mutations specific to BA.2.86 and JN.1 on experimentally determined T cell epitopes derived from the spike protein of the ancestral Wuhan strain and the spike protein of the XBB.1.5 strain that has been recommended as a booster vaccine. Our data suggest that BA.2.86 and JN.1 affect numerous T cell epitopes in spike compared to previous variants; however, the widespread loss of T cell recognition against these variants is unlikely.

Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development

BACKGROUND

The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches?

METHOD

Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers.

RESULTS

Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs).

CONCLUSIONS

Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.

Rational design of a multivalent vaccine targeting arthropod-borne viruses using reverse vaccinology strategies

Viruses transmitted by arthropods, such as Dengue, Zika, and Chikungunya, represent substantial worldwide health threats, particularly in countries like India. The lack of approved vaccines and effective antiviral therapies calls for developing innovative strategies to tackle these arboviruses. In this study, we employed immunoinformatics methodologies, incorporating reverse vaccinology, to design a multivalent vaccine targeting the predominant arboviruses. Epitopes of B and T cells were recognized within the non-structural proteins of Dengue, Zika, and Chikungunya viruses. The predicted epitopes were enhanced with adjuvants β-defensin and RS-09 to boost the vaccine's immunogenicity. Sixteen distinct vaccine candidates were constructed, each incorporating epitopes from all three viruses. FUVAC-11 emerged as the most promising vaccine candidate through molecular docking and molecular dynamics simulations, demonstrating favorable binding interactions and stability. Its effectiveness was further evaluated using computational immunological studies confirming strong immune responses. The in silico cloning performed using the pET-28a(+) plasmid facilitates the future experimental implementation of this vaccine candidate, paving the way for potential advancements in combating these significant arboviral threats. However, further in vitro and in vivo studies are warranted to confirm the results obtained in this computational study, which highlights the effectiveness of immunoinformatics and reverse vaccinology in creating vaccines against major Arboviruses, offering a promising model for developing vaccines for other vector-borne diseases and enhancing global health security.

Identification of potential SLA-I-specific T-cell epitopes within the structural proteins of porcine deltacoronavirus

Porcine deltacoronavirus (PDCoV) is an emerging swine enteropathogenic coronavirus that mainly threatens newborn piglets and poses a potential broad cross-species transmission risk. The antigenic epitopes of PDCoV are currently unidentified, and no information about T cell epitopes is available. Here, T-cell epitopes of PDCoV structural proteins were predicted using computational methods. 17 epitope peptides were synthesized and then screened using ELIspot, intracellular cytokine staining (ICS), and RT-qPCR detection of IFN-γ mRNA to evaluate their ability to elicit interferon-gamma (IFN-γ) responses in peripheral blood mononuclear cells (PBMCs) from PDCoV-challenged pigs. Five peptides (M1, M2, M3, N6, and S4) elicited high levels of IFN-γ and were investigated further as potential T-cell epitope candidates. All five peptides were cytotoxic T lymphocyte (CTL) epitopes, and two peptides (M3, N6) were recognized simultaneously by CD8 + and CD4 + T cells. A multi-epitope peptide combining the five epitopes (designated "5T") was synthesized and its immune response and protection efficacy was evaluated in a piglet model. ELISpot assay results indicated that 5T induces robust epitope-specific cellular immune responses. Four epitopes (M1, M2, N6, S4) elicited IFN-γ responses in 5T-vaccinated piglets. No obvious protection efficacy was detected in piglets vaccinated with 5T alone. Our results provide valuable information concerning PDCoV-related antigenic epitopes and will be useful in the design of epitope-based vaccines.

Coarse-grained molecular dynamics-guided immunoinformatics to explain the binder and non-binder classification of Cytotoxic T-cell epitope for SARS-CoV-2 peptide-based vaccine discovery

Epitope-based peptide vaccine can elicit T-cell immunity against SARS-CoV-2 to clear the infection. However, finding the best epitope from the whole antigen is challenging. A peptide screening using immunoinformatics usually starts from MHC-binding peptide, immunogenicity, cross-reactivity with the human proteome, to toxicity analysis. This pipeline classified the peptides into three categories, i.e., strong-, weak-, and non-binder, without incorporating the structural aspect. For this reason, the molecular detail that discriminates the binders from non-binder is interesting to be investigated. In this study, five CTL epitopes against HLA-A*02:01 were identified from the coarse-grained molecular dynamics-guided immunoinformatics screening. The strong binder showed distinctive activities from the non-binder in terms of structural and energetic properties. Furthermore, the second residue from the nonameric peptide was most important in the interaction with HLA-A*02:01. By understanding the nature of MHC-peptide interaction, we hoped to improve the chance of finding the best epitope for a peptide vaccine candidate.

Class I HLA Allele Predicted Restricted Antigenic Coverages for Fap2 Protein of Fusobacterium Nucleatum Are Associated with Colorectal Cancer Incidence

OBJECTIVE

This study investigates the association between HLA-A and -B allele diversity, Fusobacterium nucleatum Fap2 protein-derived antigenic coverage, and colorectal cancer (CRC) epidemiology across diverse populations.

METHODS

We examined 75 HLA-I alleles and explored 698 potential HLA-A and B-restricted Fap2-derived antigens, assessing how 21 countries may respond to these peptides based on their HLA-I distribution frequencies. Additionally, we correlated in-silico predicted Fap2 population coverage with CRC epidemiology. CRC incidence and mortality data were obtained from the Global Cancer Observatory, and HLA-A and HLA-B allele frequencies from the Allele Frequency Net Database. Binding predictions for Fap2 antigens were performed using netMHCpan4, with stringent selection criteria applied to identify relevant peptides. Population coverage was calculated using the IEDB population coverage tool, and data analysis conducted using the R programming language.

RESULTS

Clustering of HLA-A and -B allele frequencies partially differentiated countries with lower CRC incidence from others. Distinct patterns of Fap2 protein coverage were observed among different populations. interestingly, we found a significant inverse correlation between CRC incidence (p = 0.0037, R = -0.6) and predicted Fap2 antigen coverage, as well as CRC mortality (p = 0.013, R = -0.53). Furthermore, we identified a specific set of Fap2-derived peptides that bind to HLA supertypes, providing a global coverage of 99.04%.

CONCLUSION

Our population-based study is the first to demonstrate that higher Fap2 coverage is associated with lower CRC incidence, underscoring the potential significance of Fap2-specific CD8+ T cell responses in CRC tumorigenesis.

Epitopes and Mimotopes Identification Using Phage Display for Vaccine Development against Infectious Pathogens

Traditional vaccines use inactivated or weakened forms of pathogens which could have side effects and inadequate immune responses. To overcome these challenges, phage display has emerged as a valuable tool for identifying specific epitopes that could be used in vaccines. This review emphasizes the direct connection between epitope identification and vaccine development, filling a crucial gap in the field. This technique allows vaccines to be engineered to effectively stimulate the immune system by presenting carefully selected epitopes. Phage display involves screening libraries of random peptides or gene/genome fragments using serum samples from infected, convalescent, or vaccinated individuals. This method has been used to identify epitopes from various pathogens including SARS-CoV-2, Mycobacterium tuberculosis, hepatitis viruses, H5N1, HIV-1, Human T-lymphotropic virus 1, Plasmodium falciparum, Trypanosoma cruzi, and Dirofilaria repens. Bacteriophages offer advantages such as being immunogenic carriers, low production costs, and customization options, making them a promising alternative to traditional vaccines. The purpose of this study has been to highlight an approach that encompasses the entire process from epitope identification to vaccine production using a single technique, without requiring additional manipulation. Unlike conventional methods, phage display demonstrates exceptional efficiency and speed, which could provide significant advantages in critical scenarios such as pandemics.

An mRNA-based T-cell-inducing antigen strengthens COVID-19 vaccine against SARS-CoV-2 variants

Herd immunity achieved through mass vaccination is an effective approach to prevent contagious diseases. Nonetheless, emerging SARS-CoV-2 variants with frequent mutations largely evaded humoral immunity induced by Spike-based COVID-19 vaccines. Herein, we develop a lipid nanoparticle (LNP)-formulated mRNA-based T-cell-inducing antigen, which targeted three SARS-CoV-2 proteome regions that enriched human HLA-I epitopes (HLA-EPs). Immunization of HLA-EPs induces potent cellular responses to prevent SARS-CoV-2 infection in humanized HLA-A*02:01/DR1 and HLA-A*11:01/DR1 transgenic mice. Of note, the sequences of HLA-EPs are highly conserved among SARS-CoV-2 variants of concern. In humanized HLA-transgenic mice and female rhesus macaques, dual immunization with the LNP-formulated mRNAs encoding HLA-EPs and the receptor-binding domain of the SARS-CoV-2 B.1.351 variant (RBD_beta) is more efficacious in preventing infection of SARS-CoV-2 Beta and Omicron BA.1 variants than single immunization of LNP-RBD_beta. This study demonstrates the necessity to strengthen the vaccine effectiveness by comprehensively stimulating both humoral and cellular responses, thereby offering insight for optimizing the design of COVID-19 vaccines.

Idiotope-Driven T-Cell/B-Cell Collaboration-Based T-Cell Epitope Prediction Using B-Cell Receptor Repertoire Sequences in Infectious Diseases

T-cell recognition of antigen epitopes is a crucial step for the induction of adaptive immune responses, and the identification of such T-cell epitopes is, therefore, important for understanding diverse immune responses and controlling T-cell immunity. A number of bioinformatic tools exist that predict T-cell epitopes; however, many of these methods highly rely on evaluating conventional peptide presentation by major histocompatibility complex (MHC) molecules, but they ignore epitope sequences recognized by T-cell receptor (TCR). Immunogenic determinant idiotopes are present on the variable regions of immunoglobulin molecules expressed on and secreted by B-cells. In idiotope-driven T-cell/B-cell collaboration, B-cells present the idiotopes on MHC molecules for recognition by idiotope-specific T-cells. According to the idiotype network theory formulated by Niels Jerne, such idiotopes found on anti-idiotypic antibodies exhibit molecular mimicry of antigens. Here, by combining these concepts and defining the patterns of TCR-recognized epitope motifs (TREMs), we developed a T-cell epitope prediction method that identifies T-cell epitopes derived from antigen proteins by analyzing B-cell receptor (BCR) sequences. This method allowed us to identify T-cell epitopes that contain the same TREM patterns between BCR and viral antigen sequences in two different infectious diseases caused by dengue virus and SARS-CoV-2 infection. The identified epitopes were among the T-cell epitopes detected in previous studies, and T-cell stimulatory immunogenicity was confirmed. Thus, our data support this method as a powerful tool for the discovery of T-cell epitopes from BCR sequences.

Influence of polymorphic variations of IFNL, HLA, and IL-6 genes in severe cases of COVID-19

The administration of vaccination doses to the global population has led to a decrease in the incidence of COVID-19. However, the clinical picture developed by infected individuals remains extremely concerning due to the great variability in the severity of cases even in vaccinated individuals. The clinical progression of the pathology is characterized by various influential factors such as sex, age group, comorbidities, and the genetics of the individual. The immune response to viral infections can be strongly influenced by the genetics of individuals; nucleotide variations called single-nucleotide polymorphisms (SNPs) in structures involved in the innate and adaptive immune response such as interferon (IFN)-λ, human leukocyte antigen (HLA), and interleukin (IL)-6 are frequently associated with pathological progression. In this study, we conducted a review of the main SNPs of these structures that are associated with severity in COVID-19. Searches were conducted on some platforms of the National Center for Biotechnology and Information (NCBI), and 102 studies were selected for full reading according to the inclusion criteria. IFNs showed a strong association with antiviral function, specifically, IFN-λ3 (IL-28B) demonstrated genetic variants commonly related to clinical progression in various pathologies. For COVID-19, rs12979860 and rs1298275 presented frequently described unfavorable genotypes for pathological conditions of hepatitis C and hepatocellular carcinoma. The high genetic variability of HLA was reported in the studies as a crucial factor relevant to the late immune response, mainly due to its ability to recognize antigens, with the HLA-B*46:01 SNP being associated with susceptibility to COVID-19. For IL-6, rs1554606 showed a strong relationship with the clinical progression of COVID-19. In addition, rs2069837 was identified with possible host protection relationships when linked to this infection.

Viral T-cell epitopes - Identification, characterization and clinical application

T-cell immunity, mediated by CD4⁺ and CD8⁺ T cells, represents a cornerstone in the control of viral infections. Virus-derived T-cell epitopes are represented by human leukocyte antigen (HLA)-presented viral peptides on the surface of virus-infected cells. They are the prerequisite for the recognition of infected cells by T cells. Knowledge of viral T-cell epitopes provides on the one hand a diagnostic tool to decipher protective T-cell immune responses in the human population and on the other hand various prophylactic and therapeutic options including vaccination approaches and the transfer of virus-specific T cells. Such approaches have already been proven to be effective against various viral infections, particularly in immunocompromised patients lacking sufficient humoral, antibody-based immune response. This review provides an overview on the state of the art as well as current studies regarding the identification and characterization of viral T-cell epitopes and approaches of clinical application. In the first chapter in silico prediction tools and direct, mass spectrometry-based identification of viral T-cell epitopes is compared. The second chapter provides an overview of commonly used assays for further characterization of T-cell responses and phenotypes. The final chapter presents an overview of clinical application of viral T-cell epitopes with a focus on human immunodeficiency virus (HIV), human cytomegalovirus (HCMV) and severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), being representatives of relevant viruses.

Functional studies of HLA and its role in SARS-CoV-2: Stimulating T cell response and vaccine development

In the biological immune process, the major histocompatibility complex (MHC) plays an indispensable role in the expression of HLA molecules in the human body when viral infection activates the T-cell response to remove the virus. Since the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 2019, how to address and prevent SARS-CoV-2 has become a common problem facing all mankind. The T-cell immune response activated by MHC peptides is a way to construct a defense line and reduce the transmission and harm of the virus. Presentation of SARS-CoV-2 antigen is associated with different types of HLA phenotypes, and different HLA phenotypes induce different immune responses. The prediction of SARS-CoV-2 mutation information and the design of vaccines based on HLAs can effectively activate autoimmunity and cope with virus mutations, which can provide some references for the prevention and treatment of SARS-CoV-2.

Molecular modelling on multiepitope-based vaccine against SARS-CoV-2 using immunoinformatics, molecular docking, and molecular dynamics simulation

The pandemic of COVID-19 caused by SARS-CoV-2 has made a worldwide health emergency. Despite the fact that current vaccines are readily available, several SARSCoV-2 variants affecting the existing vaccine are to be less effective due to the mutations in the structural proteins. Furthermore, the appearance of the new variants cannot be easily predicted in the future. Therefore, the attempts to construct new vaccines or to modify the current vaccines are still pivotal works for preventing the spread of the virus. In the present investigation, the computational analysis through immunoinformatics, molecular docking, and molecular dynamics (MD) simulation is employed to construct an effective vaccine against SARS-CoV2. The structural proteins of SARS-CoV2 are utilized to create a multiepitope-based vaccine (MEV). According to our findings presented by systematic procedures in the current investigation, the MEV construct may be able to trigger a strong immunological response against the virus. Therefore, the designed MEV could be a potential vaccine candidate against SARS-CoV-2, and also it is expected to be effective for other variants.

High-resolution analysis of individual spike peptide-specific CD4+ T-cell responses in vaccine recipients and COVID-19 patients

Objectives

Potential differences in the breadth, distribution and magnitude of CD4+ T-cell responses directed against the SARS-CoV-2 spike glycoprotein between vaccinees, COVID-19 patients and subjects who experienced both ways of immunisation have not been comprehensively compared on a peptide level.

Methods

Following virus-specific in vitro cultivation, we determined the T-cell responses directed against 253 individual overlapping 15-mer peptides covering the entire SARS-CoV-2 spike glycoprotein using IFN-γ ELISpot and intracellular cytokine staining. In vitro HLA binding was determined for selected peptides.

Results

We mapped 955 single peptide-specific CD4+ T-cell responses in a cohort of COVID-19 patients (n = 8), uninfected vaccinees (n = 16) and individuals who experienced both infection and vaccination (n = 11). Patients and vaccinees (two-time and three-time vaccinees alike) had a comparable number of CD4+ T-cell responses (median 26 vs. 29, P = 0.7289). Most of these specificities were conserved in B.1.1.529 and the BA.4 and BA.5 sublineages. The highest magnitude of these in vitro IFN-γ CD4+ T-cell responses was observed in COVID-19 patients (median 0.35%), and three-time vaccinees showed a higher magnitude than two-time vaccinees (median 0.091% vs. 0.175%, P < 0.0001). Twelve peptide specificities were each detected in at least 40% of subjects. In vitro HLA binding showed promiscuous presentation by DRB1 molecules for several peptides.

Conclusion

Both SARS-CoV-2 infection and vaccination prime broadly directed T-cell responses directed against the SARS-CoV-2 spike glycoprotein. This comprehensive high-resolution analysis of spike peptide specificities will be a useful resource for further investigation of spike-specific T-cell responses.

A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods

The emergence of machine learning-based in silico tools has enabled rapid and high-quality predictions in the biomedical field. In the COVID-19 pandemic, machine learning methods have been used in many topics such as predicting the death of patients, modeling the spread of infection, determining future effects, diagnosis with medical image analysis, and forecasting the vaccination rate. However, there is a gap in the literature regarding identifying epitopes that can be used in fast, useful, and effective vaccine design using machine learning methods and bioinformatics tools. Machine learning methods can give medical biotechnologists an advantage in designing a faster and more successful vaccine. The motivation of this study is to propose a successful hybrid machine learning method for SARS-CoV-2 epitope prediction and to identify nonallergen, nontoxic, antigen peptides that can be used in vaccine design from the predicted epitopes with bioinformatics tools. The identified epitopes will be effective not only in the design of the COVID-19 vaccine but also against viruses from the SARS family that may be encountered in the future. For this purpose, epitope prediction performances of random forest, support vector machine, logistic regression, bagging with decision tree, k-nearest neighbor and decision tree methods were examined. In the SARS-CoV and B-cell datasets used for education in the study, epitope estimation was performed again after the datasets were balanced with the synthetic minority oversampling technique (SMOTE) method since the epitope class samples were in the minority compared to the nonepitope class. The experimental results obtained were compared and the most successful predictions were obtained with the random forest (RF) method. The epitope prediction performance in balanced datasets was found to be higher than that in the original datasets (94.0% AUC and 94.4% PRC for the SMOTE-SARS-CoV dataset; 95.6% AUC and 95.3% PRC for the SMOTE-B-cell dataset). In this study, 252 peptides out of 20312 peptides were determined to be epitopes with the SMOTE-RF-SVM hybrid method proposed for SARS-CoV-2 epitope prediction. Determined epitopes were analyzed with AllerTOP 2.0, VaxiJen 2.0 and ToxinPred tools, and allergic, nonantigen, and toxic epitopes were eliminated. As a result, 11 possible nonallergic, high antigen and nontoxic epitope candidates were proposed that could be used in protein-based COVID-19 vaccine design ("VGGNYNY", "VNFNFNGLTG", "RQIAPGQTGKI", "QIAPGQTGKIA", "SYECDIPIGAGI", "STFKCYGVSPTKL", "GVVFLHVTYVPAQ", "KNHTSPDVDLGDI", "NHTSPDVDLGDIS", "AGAAAYYVGYLQPR", "KKSTNLVKNKCVNF"). It is predicted that the few epitopes determined by machine learning-based in silico methods will help biotechnologists design fast and accurate vaccines by reducing the number of trials in the laboratory environment.

CAVES: A Novel Tool for Comparative Analysis of Variant Epitope Sequences

In silico methods for immune epitope prediction have become essential for vaccine and therapeutic design, but manual intra-species comparison of putative epitopes remains challenging and subject to human error. Created initially for analyzing SARS-CoV-2 variants of concern, comparative analysis of variant epitope sequences (CAVES) is a novel tool designed to carry out rapid comparative analyses of epitopes amongst closely related pathogens, substantially reducing the required time and user workload. CAVES applies a two-level analysis approach. The Level-one (L1) analysis compares two epitope prediction files, and the Level-two (L2) analysis incorporates search results from the IEDB database of experimentally confirmed epitopes. Both L1 and L2 analyses sort epitopes into categories of exact matches, partial matches, or novel epitopes based on the degree to which they match with peptides from the compared file. Furthermore, CAVES uses positional sequence data to improve its accuracy and speed, taking only a fraction of the time required by manual analyses and minimizing human error. CAVES is widely applicable for evolutionary analyses and antigenic comparisons of any closely related pathogen species. CAVES is open-source software that runs through a graphical user interface on Windows operating systems, making it widely accessible regardless of coding expertise. The CAVES source code and test dataset presented here are publicly available on the CAVES GitHub page.

T Cell Epitope Discovery in the Context of Distinct and Unique Indigenous HLA Profiles

CD8+ T cells are a pivotal part of the immune response to viruses, playing a key role in disease outcome and providing long-lasting immunity to conserved pathogen epitopes. Understanding CD8+ T cell immunity in humans is complex due to CD8+ T cell restriction by highly polymorphic Human Leukocyte Antigen (HLA) proteins, requiring T cell epitopes to be defined for different HLA allotypes across different ethnicities. Here we evaluate strategies that have been developed to facilitate epitope identification and study immunogenic T cell responses. We describe an immunopeptidomics approach to sequence HLA-bound peptides presented on virus-infected cells by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Using antigen presenting cell lines that stably express the HLA alleles characteristic of Indigenous Australians, this approach has been successfully used to comprehensively identify influenza-specific CD8+ T cell epitopes restricted by HLA allotypes predominant in Indigenous Australians, including HLA-A*24:02 and HLA-A*11:01. This is an essential step in ensuring high vaccine coverage and efficacy in Indigenous populations globally, known to be at high risk from influenza disease and other respiratory infections.

CHARM: COVID-19 Health Action Response for Marines-Association of antigen-specific interferon-gamma and IL2 responses with asymptomatic and symptomatic infections after a positive qPCR SARS-CoV-2 test

SARS-CoV-2 T cell responses are associated with COVID-19 recovery, and Class I- and Class II-restricted epitopes have been identified in the spike (S), nucleocapsid (N) and membrane (M) proteins and others. This prospective COVID-19 Health Action Response for Marines (CHARM) study enabled assessment of T cell responses against S, N and M proteins in symptomatic and asymptomatic SARS-CoV-2 infected participants. At enrollment all participants were negative by qPCR; follow-up occurred biweekly and bimonthly for the next 6 weeks. Study participants who tested positive by qPCR SARS-CoV-2 test were enrolled in an immune response sub-study. FluoroSpot interferon-gamma (IFN-γ) and IL2 responses following qPCR-confirmed infection at enrollment (day 0), day 7 and 14 and more than 28 days later were measured using pools of 17mer peptides covering S, N, and M proteins, or CD4+CD8 peptide pools containing predicted epitopes from multiple SARS-CoV-2 antigens. Among 124 asymptomatic and 105 symptomatic participants, SARS-CoV-2 infection generated IFN-γ responses to the S, N and M proteins that persisted longer in asymptomatic cases. IFN-γ responses were significantly (p = 0.001) more frequent to the N pool (51.4%) than the M pool (18.9%) among asymptomatic but not symptomatic subjects. Asymptomatic IFN-γ responders to the CD4+CD8 pool responded more frequently to the S pool (55.6%) and N pool (57.1%), than the M pool (7.1%), but not symptomatic participants. The frequencies of IFN-γ responses to the S and N+M pools peaked 7 days after the positive qPCR test among asymptomatic (S pool: 22.2%; N+M pool: 28.7%) and symptomatic (S pool: 15.3%; N+M pool 21.9%) participants and dropped by >28 days. Magnitudes of post-infection IFN-γ and IL2 responses to the N+M pool were significantly correlated with IFN-γ and IL2 responses to the N and M pools. These data further support the central role of Th1-biased cell mediated immunity IFN-γ and IL2 responses, particularly to the N protein, in controlling COVID-19 symptoms, and justify T cell-based COVID-19 vaccines that include the N and S proteins.

Identification of Potential SARS-CoV-2 CD8+ T Cell Escape Mutants

Memory SARS-CoV-2-specific CD8+ T cell responses induced upon infection or COVID-19 vaccination have been important for protecting against severe COVID-19 disease while being largely robust against variants of concern (VOCs) observed so far. However, T cell immunity may be weakened by genetic mutations in future SARS-CoV-2 variants that lead to widespread T cell escape. The capacity for SARS-CoV-2 mutations to escape memory T cell responses requires comprehensive experimental investigation, though this is prohibited by the large number of SARS-CoV-2 mutations that have been observed. To guide targeted experimental studies, here we provide a screened list of potential SARS-CoV-2 T cell escape mutants. These mutants are identified as candidates for T cell escape as they lie within CD8+ T cell epitopes that are commonly targeted in individuals and are predicted to abrogate HLA-peptide binding.

Anti-COVID-19 Nanomaterials: Directions to Improve Prevention, Diagnosis, and Treatment

Following the announcement of the outbreak of COVID-19 by the World Health Organization, unprecedented efforts were made by researchers around the world to combat the disease. So far, various methods have been developed to combat this "virus" nano enemy, in close collaboration with the clinical and scientific communities. Nanotechnology based on modifiable engineering materials and useful physicochemical properties has demonstrated several methods in the fight against SARS-CoV-2. Here, based on what has been clarified so far from the life cycle of SARS-CoV-2, through an interdisciplinary perspective based on computational science, engineering, pharmacology, medicine, biology, and virology, the role of nano-tools in the trio of prevention, diagnosis, and treatment is highlighted. The special properties of different nanomaterials have led to their widespread use in the development of personal protective equipment, anti-viral nano-coats, and disinfectants in the fight against SARS-CoV-2 out-body. The development of nano-based vaccines acts as a strong shield in-body. In addition, fast detection with high efficiency of SARS-CoV-2 by nanomaterial-based point-of-care devices is another nanotechnology capability. Finally, nanotechnology can play an effective role as an agents carrier, such as agents for blocking angiotensin-converting enzyme 2 (ACE2) receptors, gene editing agents, and therapeutic agents. As a general conclusion, it can be said that nanoparticles can be widely used in disinfection applications outside in vivo. However, in in vivo applications, although it has provided promising results, it still needs to be evaluated for possible unintended immunotoxicity. Reviews like these can be important documents for future unwanted pandemics.

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.

A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine

B-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Therefore, although successful predictions with in silico methods are very important in epitope prediction, there are limited studies in this area. The aim of this study is to propose a new approach for successfully predicting B-cell epitopes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, the SARS-CoV B-cell epitope prediction performances of different fuzzy learning classification models genetic cooperative competitive learning (GCCL), fuzzy genetics-based machine learning (GBML), Chi's method (CHI), Ishibuchi's method with weight factor (W), structural learning algorithm on vague environment (SLAVE) and the state-of-the-art ensemble fuzzy classification model were compared. The obtained results showed that the proposed ensemble approach has the lowest error in SARS-CoV B-cell epitope estimation compared to the base fuzzy learners (average error rates; ensemble fuzzy=8.33, GCCL=30.42, GBML=23.82, CHI=29.17, W=46.25, and SLAVE=20.42). SARS-CoV and SARS-CoV-2 have high genome similarities. Therefore, the most successful method determined for SARS-CoV B-cell epitope prediction was used in SARS-CoV-2 cell epitope prediction. Finally, the eventual B-cell epitope prediction results obtained for SARS-CoV-2 with the ensemble fuzzy classification model were compared with the epitope sequences predicted by the BepiPred server and immunoinformatics studies in the literature for the same protein sequences according to VaxiJen 2.0 scores. We hope that the developed epitope prediction method will help design effective vaccines and drugs against future outbreaks of the coronavirus family, especially SARS-CoV-2 and its possible mutations.

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.

Evidence of a SARS-CoV-2 double Spike mutation D614G/S939F potentially affecting immune response of infected subjects

Objectives

Despite extensive efforts to monitor the diffusion of COVID-19, the actual wave of infection is worldwide characterized by the presence of emerging SARS-CoV-2 variants. The present study aims to describe the presence of yet undiscovered SARS-CoV-2 variants in Italy.

Methods

Next Generation Sequencing was performed on 16 respiratory samples from occasionally employed within the Bangladeshi community present in Ostia and Fiumicino towns. Computational strategy was used to identify all potential epitopes for reference and mutated Spike proteins. A simulation of proteasome activity and the identification of possible cleavage sites along the protein guided to a combined score involving binding affinity, peptide stability and T-cell propensity.

Results

Retrospective sequencing analysis revealed a double Spike D614G/S939F mutation in COVID-19 positive subjects present in Ostia while D614G mutation was evidenced in those based in Fiumicino. Unlike D614G, S939F mutation affects immune response by the slight but significant modulation of T-cell propensity and the selective enrichment of potential binding epitopes for some HLA alleles.

Conclusion

Collectively, our findings mirror further the importance of deep sequencing of SARS-CoV-2 genome as a unique approach to monitor the appearance of specific mutations as for those herein reported for Spike protein. This might have implications on both the type of immune response triggered by the viral infection and the severity of the related illness.

Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS)

CD4⁺ T cells play a vital role in the immune response, and their function requires T cell receptor (TCR) recognition of peptide epitopes presented in complex with MHC class II (MHCII) molecules. Consequently, rapidly identifying peptides that bind MHCII is critical to understanding and treating infectious disease, cancer, autoimmunity, allergy, and transplant rejection. Computational methods provide a fast, ultrahigh-throughput approach to predict MHCII-binding peptides but lack the accuracy of experimental methods. In contrast, experimental methods offer accurate, quantitative results at the expense of speed. To address the gap between these two approaches, we developed a high-throughput, semiquantitative experimental screening strategy termed microsphere-assisted peptide screening (MAPS). Here, we use the Zika virus envelope protein as an example to demonstrate the rapid identification of MHCII-binding peptides from a single pathogenic protein using MAPS. This process involves several key steps including peptide library design, peptide exchange into MHCII, peptide-MHCII loading onto microspheres, flow cytometry screening, and data analysis to identify peptides that bind to one or more MHCII alleles.

HLA repertoire of 115 UAE nationals infected with SARS-CoV-2

The class I and class II Human Leucocyte Antigens (HLA) are an integral part of the host adaptive immune system against viral infections. The characterization of HLA allele frequency in the population can play an important role in determining whether HLA antigens contribute to viral susceptibility. In this regard, global efforts are currently underway to study possible correlations between HLA alleles with the occurrence and severity of SARS-CoV-2 infection. Specifically, this study examined the possible association between specific HLA alleles and susceptibility to SARS-CoV-2 in a population from the United Arab Emirates (UAE). The frequencies of HLA class I (HLA-A, -B, and -C) and HLA class II alleles (HLA-DRB1 and -DQB1); defined using Next Generation Sequencing (NGS); from 115 UAE nationals with mild, moderate, and severe SARS-CoV-2 infection are presented here. HLA alleles and supertypes were compared between hospitalized and non-hospitalized subjects. Statistical significance was observed between certain HLA alleles and supertypes and the severity of the infection. Specifically, alleles HLA-B*51:01 and HLA-A*26:01 showed a negative association (suggestive of protection), whilst genotypes HLA-A*03:01, HLA-DRB1*15:01, and supertype B44 showed a positive association (suggestive of predisposition) to COVID-19 severity. The results support the potential use of HLA testing to differentiate between patients who require specific clinical management strategies.

In Silico Design of a New Multi-Epitope Peptide-Based Vaccine Candidate Against Q Fever

Novel types of the vaccines with high immunogenicity and low risks, including epitope-based vaccines, are sought. Among zoonotic disease, Q fever caused by Coxiella burnetii is an important target due to numerous outbreaks and the pandemic potential. Here we present a synthetic multi-epitope vaccine against Coxiella burnetii. This vaccine was developed using immunoinformatics approach. Antigenic proteins were studied, and five T cell epitopes were selected. Antigenicity, allergenicity, and toxicity of the selected epitopes were evaluated using the VaxiJen 2.0, AllerTOP, and ToxinPred servers, respectively. Selected epitopes were joined in a peptide sequence, with the cholera toxin B subunit (CTXB) as an adjuvant. The affinity of the proposed vaccine to MHC I and II molecules was measured in a molecular docking study. Resultant vaccine has high antigenicity, stability, and a half-life compatible with utilization in vaccination programs. In conclusion, the validated epitope sequences may be used as a potential vaccine to ensure protection against Q fever agent.

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.

Mass spectrometric identification of immunogenic SARS-CoV-2 epitopes and cognate TCRs

Durable protection against COVID-19 infection may be achieved by generating robust T cell responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and emerging SARS-CoV-2 variants; for those infected, effective treatments are urgently needed. For these strategies to be successful, accurate identification of T cell epitopes is critical. In this study, we used major histocompatibility complex immune precipitation, acid elution, and tandem mass spectrometry to define the SARS-CoV-2 immunopeptidome for membrane glycoprotein (MGP) and the nonstructural protein. Furthermore, taking advantage of a highly robust endogenous T cell workflow, we verify the immunogenicity of these MS-defined peptides by in vitro generation of MGP and NSP13 peptide-specific T cells and confirm T cell recognition of MGP or NSP13 endogenously expressing cell lines.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections elicit both humoral and cellular immune responses. For the prevention and treatment of COVID-19, the disease caused by SARS-CoV-2, it has become increasingly apparent that T cell responses are equally if not more important than humoral responses in mediating recovery and immune protection. One major challenge in developing T cell–based therapies for infectious and malignant diseases has been the identification of immunogenic epitopes that can elicit a meaningful T cell response. Traditionally, this has been achieved using sophisticated in silico methods to predict putative epitopes deduced from binding affinities. Our studies find that, in contrast to current convention, “immunodominant” SARS-CoV-2 peptides defined by such in silico methods often fail to elicit T cell responses recognizing naturally presented SARS-CoV-2 epitopes. We postulated that immunogenic epitopes for SARS-CoV-2 are best defined empirically by directly analyzing peptides eluted from the naturally processed peptide–major histocompatibility complex (MHC) and then validating immunogenicity by determining whether such peptides can elicit T cells recognizing SARS-CoV-2 antigen-expressing cells. Using a tandem mass spectrometry approach, we identified epitopes derived from not only structural but also nonstructural genes in regions highly conserved among SARS-CoV-2 strains, including recently recognized variants. Finally, there are no reported T cell receptor–engineered T cell technology that can redirect T cell specificity to recognize and kill SARS-CoV-2 target cells. We report here several SARS-CoV-2 epitopes defined by mass spectrometric analysis of MHC-eluted peptides, provide empiric evidence for their immunogenicity, and demonstrate engineered TCR-redirected killing.

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.

Identification of Unique Peptides for SARS-CoV-2 Diagnostics and Vaccine Development by an In Silico Proteomics Approach

Ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus strains is posing new COVID-19 diagnosis and treatment challenges. To help efforts to meet these challenges we examined data acquired from proteomic analyses of human SARS-CoV-2-infected cell lines and samples from COVID-19 patients. Initially, 129 unique peptides were identified, which were rigorously evaluated for repeats, disorders, polymorphisms, antigenicity, immunogenicity, toxicity, allergens, sequence similarity to human proteins, and contributions from other potential cross-reacting pathogenic species or the human saliva microbiome. We also screened SARS-CoV-2-infected NBHE and A549 cell lines for presence of antigenic peptides, and identified paratope peptides from crystal structures of SARS-CoV-2 antigen-antibody complexes. We then selected four antigen peptides for docking with known viral unbound T-cell receptor (TCR), class I and II peptide major histocompatibility complex (pMHC), and identified paratope sequences. We also tested the paratope binding affinity of SARS-CoV T- and B-cell peptides that had been previously experimentally validated. The resultant antigenic peptides have high potential for generating SARS-CoV-2-specific antibodies, and the paratope peptides can be directly used to develop a COVID-19 diagnostics assay. The presented genomics and proteomics-based in-silico approaches have apparent utility for identifying new diagnostic peptides that could be used to fight SARS-CoV-2.

Developing Acid-Responsive Glyco-Nanoplatform Based Vaccines for Enhanced Cytotoxic T-lymphocyte Responses Against Cancer and SARS-CoV-2

Cytotoxic T-lymphocytes (CTLs) are central for eliciting protective immunity against malignancies and infectious diseases. Here, for the first time, partially oxidized acetalated dextran nanoparticles (Ox-AcDEX NPs) with an average diameter of 100 nm are fabricated as a general platform for vaccine delivery. To develop effective anticancer vaccines, Ox-AcDEX NPs are conjugated with a representative CTL peptide epitope (CTLp) from human mucin-1 (MUC1) with the sequence of TSAPDTRPAP (referred to as Mp1) and an immune-enhancing adjuvant R837 (referred to as R) via imine bond formation affording AcDEX-(imine)-Mp1-R NPs. Administration of AcDEX-(imine)-Mp1-R NPs results in robust and long-lasting anti-MUC1 CTL immune responses, which provides mice with superior protection from the tumor. To verify its universality, this nanoplatform is also exploited to deliver epitopes from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to prevent coronavirus disease 2019 (COVID-19). By conjugating Ox-AcDEX NPs with the potential CTL epitope of SARS-CoV-2 (referred to as Sp) and R837, AcDEX-(imine)-Sp-R NPs are fabricated for anti-SARS-CoV-2 vaccine candidates. Several epitopes potentially contributing to the induction of potent and protective anti-SARS-CoV-2 CTL responses are examined and discussed. Collectively, these findings shed light on the universal use of Ox-AcDEX NPs to deliver both tumor-associated and virus-associated epitopes.

Therapeutic targets and interventional strategies in COVID-19: mechanisms and clinical studies

Owing to the limitations of the present efforts on drug discovery against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the lack of the understanding of the biological regulation mechanisms underlying COVID-19, alternative or novel therapeutic targets for COVID-19 treatment are still urgently required. SARS-CoV-2 infection and immunity dysfunction are the two main courses driving the pathogenesis of COVID-19. Both the virus and host factors are potential targets for antiviral therapy. Hence, in this study, the current therapeutic strategies of COVID-19 have been classified into "target virus" and "target host" categories. Repurposing drugs, emerging approaches, and promising potential targets are the implementations of the above two strategies. First, a comprehensive review of the highly acclaimed old drugs was performed according to evidence-based medicine to provide recommendations for clinicians. Additionally, their unavailability in the fight against COVID-19 was analyzed. Next, a profound analysis of the emerging approaches was conducted, particularly all licensed vaccines and monoclonal antibodies (mAbs) enrolled in clinical trials against primary SARS-CoV-2 and mutant strains. Furthermore, the pros and cons of the present licensed vaccines were compared from different perspectives. Finally, the most promising potential targets were reviewed, and the update of the progress of treatments has been summarized based on these reviews.

T-CoV: a comprehensive portal of HLA-peptide interactions affected by SARS-CoV-2 mutations

Rapidly appearing SARS-CoV-2 mutations can affect T cell epitopes, which can help the virus to evade either CD8 or CD4 T-cell responses. We developed T-cell COVID-19 Atlas (T-CoV, https://t-cov.hse.ru) – the comprehensive web portal, which allows one to analyze how SARS-CoV-2 mutations alter the presentation of viral peptides by HLA molecules. The data are presented for common virus variants and the most frequent HLA class I and class II alleles. Binding affinities of HLA molecules and viral peptides were assessed with accurate in silico methods. The obtained results highlight the importance of taking HLA alleles diversity into account: mutation-mediated alterations in HLA-peptide interactions were highly dependent on HLA alleles. For example, we found that the essential number of peptides tightly bound to HLA-B*07:02 in the reference Wuhan variant ceased to be tight binders for the Indian (Delta) and the UK (Alpha) variants. In summary, we believe that T-CoV will help researchers and clinicians to predict the susceptibility of individuals with different HLA genotypes to infection with variants of SARS-CoV-2 and/or forecast its severity.

Immunogenic SARS-CoV2 Epitopes Defined by Mass Spectrometry

SARS-CoV-2 infections elicit both humoral and cellular immune responses. For the prevention and treatment of COVID19, the disease caused by SARS-CoV-2, T cell responses are important in mediating recovery and immune-protection. The identification of immunogenic epitopes that can elicit a meaningful T cell response can be elusive. Traditionally, this has been achieved using sophisticated in silico methods to predict putative epitopes; however, our previous studies find that 'immunodominant' SARS-CoV-2 peptides defined by such in silico methods often fail to elicit T cell responses recognizing SARS-CoV-2. We postulated that immunogenic epitopes for SARS-CoV-2 are best defined by directly analyzing peptides eluted from the peptide-MHC complex and then validating immunogenicity empirically by determining if such peptides can elicit T cells recognizing SARS-CoV-2 antigen-expressing cells. Using a tandem mass spectrometry approach, we identified epitopes of SARS-CoV-2 derived not only from structural but also non-structural genes in regions highly conserved among SARS-CoV-2 strains including recently recognized variants. We report here, for the first time, several novel SARS-CoV-2 epitopes from membrane glycol-protein (MGP) and non-structure protein-13 (NSP13) defined by mass-spectrometric analysis of MHC-eluted peptides, provide empiric evidence for their immunogenicity to induce T cell response.

Significance Statement

Current state of the art uses putative epitope peptides based on in silico prediction algorithms to evaluate the T cell response among COVID-19 patients. However, none of these peptides have been tested for immunogenicity, i.e. the ability to elicit a T cell response capable of recognizing endogenously presented peptide. In this study, we used MHC immune-precipitation, acid elution and tandem mass spectrometry to define the SARS-CoV-2 immunopeptidome for membrane glycol-protein and the non-structural protein. Furthermore, taking advantage of a highly robust endogenous T cell (ETC) workflow, we verify the immunogenicity of these MS-defined peptides by in vitro generation of MGP and NSP13 peptide-specific T cells and confirm T cell recognition of MGP or NSP13 endogenously expressing cell lines.

Bioinformatics Prediction of SARS-CoV-2 Epitopes as Vaccine Candidates for the Colombian Population

Coronavirus disease (COVID-19) pandemic caused by the coronavirus SARS-CoV-2 represents an enormous challenge to global public health, with thousands of infections and deaths in over 200 countries worldwide. The purpose of this study was to identify SARS-CoV-2 epitopes with potential to interact in silico with the alleles of the human leukocyte antigen class I (HLA I) and class II (HLA II) commonly found in the Colombian population to promote both CD4 and CD8 immune responses against this virus. The generation and evaluation of the peptides in terms of HLA I and HLA II binding, immune response, toxicity and allergenicity were performed by using computer-aided tools, such as NetMHCpan 4.1, NetMHCIIpan 4.0, VaxiJem, ToxinPred and AllerTop. Furthermore, the interaction between the predicted epitopes with HLA I and HLA II proteins frequently found in the Colombian population was studied through molecular docking simulations in AutoDock Vina and interaction analysis in LigPlot+. One of the promising peptides proposed in this study is the HLA I epitope YQPYRVVVL, which displayed an estimated coverage of over 82% and 96% for the Colombian and worldwide population, respectively. These findings could be useful for the design of new epitope-vaccines that include Colombia among their population target.

SARS-CoV-2 Vaccines Based on the Spike Glycoprotein and Implications of New Viral Variants

Coronavirus 19 Disease (COVID-19) originating in the province of Wuhan, China in 2019, is caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), whose infection in humans causes mild or severe clinical manifestations that mainly affect the respiratory system. So far, the COVID-19 has caused more than 2 million deaths worldwide. SARS-CoV-2 contains the Spike (S) glycoprotein on its surface, which is the main target for current vaccine development because antibodies directed against this protein can neutralize the infection. Companies and academic institutions have developed vaccines based on the S glycoprotein, as well as its antigenic domains and epitopes, which have been proven effective in generating neutralizing antibodies. However, the emergence of new SARS-CoV-2 variants could affect the effectiveness of vaccines. Here, we review the different types of vaccines designed and developed against SARS-CoV-2, placing emphasis on whether they are based on the complete S glycoprotein, its antigenic domains such as the receptor-binding domain (RBD) or short epitopes within the S glycoprotein. We also review and discuss the possible effectiveness of these vaccines against emerging SARS-CoV-2 variants.

In Silico Defined SARS-CoV2 Epitopes May Not Predict Immunogenicity to COVID19

SARS-CoV-2 infections elicit both humoral and cellular immune responses. For the prevention and treatment of COVID19, the disease caused by SARS-CoV-2, it has become increasingly apparent that T cell responses are equally, if not more important than humoral responses in mediating recovery and immune-protection. One of the major challenges in developing T cell-based therapies for infectious and malignant diseases has been the identification of immunogenic epitopes that can elicit a meaningful T cell response. Traditionally, this has been achieved using sophisticated in silico methods to predict putative epitopes deduced from binding affinities and consensus data. Our studies find that, in contrast to current dogma, 'immunodominant' SARS-CoV-2 peptides defined by such in silico methods often fail to elicit T cell responses recognizing naturally presented SARS-CoV-2 epitopes.

Landscape of epitopes targeted by T cells in 852 individuals recovered from COVID-19: Meta-analysis, immunoprevalence, and web platform

Knowledge of the epitopes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) targeted by T cells in recovered (convalescent) individuals is important for understanding T cell immunity against coronavirus disease 2019 (COVID-19). This information can aid development and assessment of COVID-19 vaccines and inform novel diagnostic technologies. Here, we provide a unified description and meta-analysis of SARS-CoV-2 T cell epitopes compiled from 18 studies of cohorts of individuals recovered from COVID-19 (852 individuals in total). Our analysis demonstrates the broad diversity of T cell epitopes that have been recorded for SARS-CoV-2. A large majority are seemingly unaffected by current variants of concern. We identify a set of 20 immunoprevalent epitopes that induced T cell responses in multiple cohorts and in a large fraction of tested individuals. The landscape of SARS-CoV-2 T cell epitopes we describe can help guide immunological studies, including those related to vaccines and diagnostics. A web-based platform has been developed to help complement these efforts.

Mapping the SARS-CoV-2 spike glycoprotein-derived peptidome presented by HLA class II on dendritic cells

Understanding and eliciting protective immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an urgent priority. To facilitate these objectives, we profile the repertoire of human leukocyte antigen class II (HLA-II)-bound peptides presented by HLA-DR diverse monocyte-derived dendritic cells pulsed with SARS-CoV-2 spike (S) protein. We identify 209 unique HLA-II-bound peptide sequences, many forming nested sets, which map to sites throughout S including glycosylated regions. Comparison of the glycosylation profile of the S protein to that of the HLA-II-bound S peptides reveals substantial trimming of glycan residues on the latter, likely induced during antigen processing. Our data also highlight the receptor-binding motif in S1 as a HLA-DR-binding peptide-rich region and identify S2-derived peptides with potential for targeting by cross-protective vaccine-elicited responses. Results from this study will aid analysis of CD4⁺ T cell responses in infected individuals and vaccine recipients and have application in next-generation vaccine design.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Bioinformatic prediction of immunodominant regions in spike protein for early diagnosis of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Background

To contain the pandemics caused by SARS-CoV-2, early detection approaches with high accuracy and accessibility are critical. Generating an antigen-capture based detection system would be an ideal strategy complementing the current methods based on nucleic acids and antibody detection. The spike protein is found on the outside of virus particles and appropriate for antigen detection.

Methods

In this study, we utilized bioinformatics approaches to explore the immunodominant fragments on spike protein of SARS-CoV-2.

Results

The S1 subunit of spike protein was identified with higher sequence specificity. Three immunodominant fragments, Spike_56-94, Spike_199-264, and Spike_577-612, located at the S1 subunit were finally selected via bioinformatics analysis. The glycosylation sites and high-frequency mutation sites on spike protein were circumvented in the antigen design. All the identified fragments present qualified antigenicity, hydrophilicity, and surface accessibility. A recombinant antigen with a length of 194 amino acids (aa) consisting of the selected immunodominant fragments as well as a universal Th epitope was finally constructed.

Conclusion

The recombinant peptide encoded by the construct contains multiple immunodominant epitopes, which is expected to stimulate a strong immune response in mice and generate qualified antibodies for SARS-CoV-2 detection.