Immunoinformatics Identification of B- and T-Cell Epitopes in the RNA-Dependent RNA Polymerase of SARS-CoV-2

Evaluation of the Safety and Immunogenicity of a Multiple Epitope Polypeptide from Canine Distemper Virus (CDV) in Mice

BACKGROUND

Morbillivirus canis is the etiological agent of a highly contagious disease that affects diverse domestic and wild animals. Vaccination is considered the most suitable strategy for controlling CDV dissemination, transmission, and distemper disease. However, the emergence of new CDV strains has led to the need to update the current vaccine strategies employed to prevent CDV infection in domestic and wild animals. Currently, there is a lack of effective alternatives for wild animals. Diverse computational tools, especially peptide-based therapies, enable the development of new universal vaccines.

OBJECTIVE

The aim of this study was to evaluate the safety and humoral and cellular immune response of a new generation of vaccines based on CDV peptides as single-peptide mixtures or multiepitope CDV polypeptides in mice.

METHODS

Twenty-four BALB/c mice were subjected to a three-dose regimen for 28 days. Seroconversion was evaluated via ELISA, and cellular immune responses were evaluated via flow cytometry through activation-induced markers (AIMs).

RESULTS

Compared with the placebo, the peptide mixture and multiepitope CDV polypeptide were safe, and seroconversion was statistically significant in the multiepitope CDV polypeptide and commercial vaccine (CV) groups. The numbers of antigen-specific CD4+CD134+ and IFN-γ+ T cells, CD8+ T cells and TNF-α- and IL-6-producing cells were greater in the mice immunized with the multiepitope CDV polypeptide than in the control mice.

CONCLUSION

This combined approach represents a potential step forward in developing new immunization candidates or enhancing current commercial vaccines to control CDV disease in domestic dogs and wild animals.

Universal peptide-based potential vaccine design against canine distemper virus (CDV) using a vaccinomic approach

Canine distemper virus (CDV) affects many domestic and wild animals. Variations among CDV genome linages could lead to vaccination failure. To date, there are several vaccine alternatives, such as a modified live virus and a recombinant vaccine; however, most of these alternatives are based on the ancestral strain Onderstepoort, which has not been circulating for years. Vaccine failures and the need to update vaccines have been widely discussed, and the development of new vaccine candidates is necessary to reduce circulation and mortality. Current vaccination alternatives cannot be used in wildlife animals due to the lack of safety data for most of the species, in addition to the insufficient immune response against circulating strains worldwide in domestic species. Computational tools, including peptide-based therapies, have become essential for developing new-generation vaccines for diverse models. In this work, a peptide-based vaccine candidate with a peptide library derived from CDV H and F protein consensus sequences was constructed employing computational tools. The molecular docking and dynamics of the selected peptides with canine MHC-I and MHC-II and with TLR-2 and TLR-4 were evaluated. In silico safety was assayed through determination of antigenicity, allergenicity, toxicity potential, and homologous canine peptides. Additionally, in vitro safety was also evaluated through cytotoxicity in cell lines and canine peripheral blood mononuclear cells (cPBMCs) and through a hemolysis potential assay using canine red blood cells. A multiepitope CDV polypeptide was constructed, synthetized, and evaluated in silico and in vitro by employing the most promising peptides for comparison with single CDV immunogenic peptides. Our findings suggest that predicting immunogenic CDV peptides derived from most antigenic CDV proteins could aid in the development of new vaccine candidates, such as multiple single CDV peptides and multiepitope CDV polypeptides, that are safe in vitro and optimized in silico. In vivo studies are being conducted to validate potential vaccines that may be effective in preventing CDV infection in domestic and wild animals.

Salmonella-mediated oral delivery of multiple-target vaccine constructs with conserved and variable regions of SARS-CoV-2 protect against the Delta and Omicron variants in hamster

Since the emergence of the pandemic COVID19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of vaccines has been the prime strategy to control the disease transmission. Most of the developed vaccines target the spike protein, however, the emerging variants have alterations, particularly at the same region which may pose resistance to neutralizing antibodies. In this study, we explored the variable and conserved regions of SARS-CoV-2 as a potential inclusion in a multiple-target vaccine with the exploitation of Salmonella-based vector for oral mRNA vaccine against Delta and Omicron variants. Increased IgG and IgA levels imply the induction of humoral response and the CD4⁺, CD8⁺ and IFN-γ⁺ sub-population level exhibits cell-mediated immune responses. The degree of CD44⁺ cells indicates the induction of memory cells corresponding to long-term immune responses. Furthermore, we assessed the protective efficacy of the vaccines against the Delta and Omicron variants in the hamster model. The vaccine constructs induced neutralizing antibodies and protected the viral-challenged hamsters with significant decrease in lung viral load and reduced histopathological lesions. These results reinforce the use of the conserved and variable regions as potential antigen targets of SARS-CoV-2 as well as the exploitation of bacteria-mediated delivery for oral mRNA vaccine development.

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.

Analysis of SARS-CoV-2 mutations in the main viral protease (NSP5) and its implications on the vaccine designing strategies

SARS-CoV-2 (Severe Acute Respiratory Syndrome), an etiolating agent of novel COVID-19 (coronavirus 2019) pandemic, rapidly spread worldwide, creating an unprecedented public health crisis globally. NSP5, the main viral protease, is a highly conserved protein, encoded by the genome of SARS-CoV-2 and plays an important role in the viral replication cycle. In the present study, we detected a total of 33 mutations from 675 sequences submitted from India in the month of March 2020 to April 2021. Out of 33 mutations, we selected 8 frequent mutations (K236R, N142L, K90R, A7V, L75F, C22N, H246Y and I43V) for further analysis. Subsequently, protein models were constructed, revealing significant alterations in the 3-D structure of NSP5 protein when compared to the wild type protein sequence which also altered the secondary structure of NSP5 protein. Further, we identified 9 B-cell, 10 T-cell and 6 MHC-I promising epitopes using predictive tools of immunoinformatics, out of these epitopes some were non-allergenic as well as highly immunogenic. Results of our study, however, revealed that 10 B-cell epitopes reside in the mutated region of NSP5. Additionally, hydrophobicity, physiochemical properties, toxicity and stability of NSP5 protein were estimated to demonstrate the specificity of the multiepitope candidates. Taken together, variations arising as a consequence of multiple mutations may cause alterations in the structure and function of NSP5 which generate crucial insights to better understand structural aspects of SARS-CoV-2. Our study also revealed, NSP5, a main protease, can be a potentially good target for the design and development of vaccine candidate against SARS-CoV-2.

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.

Analysis of SARS-CoV-2 mutations in the main viral protease (NSP5) and its implications on the vaccine designing strategies

SARS-CoV-2 (Severe Acute Respiratory Syndrome), an etiolating agent of novel COVID-19 (coronavirus 2019) pandemic, rapidly spread worldwide, creating an unprecedented public health crisis globally. NSP5, the main viral protease, is a highly conserved protein, encoded by the genome of SARS-CoV-2 and plays an important role in the viral replication cycle. In the present study, we detected a total of 33 mutations from 675 sequences submitted from India in the month of March 2020 to April 2021. Out of 33 mutations, we selected 8 frequent mutations (K236R, N142L, K90R, A7V, L75F, C22N, H246Y and I43V) for further analysis. Subsequently, protein models were constructed, revealing significant alterations in the 3-D structure of NSP5 protein when compared to the wild type protein sequence which also altered the secondary structure of NSP5 protein. Further, we identified 9 B-cell, 10 T-cell and 6 MHC-I promising epitopes using predictive tools of immunoinformatics, out of these epitopes some were non-allergenic as well as highly immunogenic. Results of our study, however, revealed that 10 B-cell epitopes reside in the mutated region of NSP5. Additionally, hydrophobicity, physiochemical properties, toxicity and stability of NSP5 protein were estimated to demonstrate the specificity of the multiepitope candidates. Taken together, variations arising as a consequence of multiple mutations may cause alterations in the structure and function of NSP5 which generate crucial insights to better understand structural aspects of SARS-CoV-2. Our study also revealed, NSP5, a main protease, can be a potentially good target for the design and development of vaccine candidate against SARS-CoV-2.

Designing of a Multi-epitope Vaccine against the Structural Proteins of Marburg Virus Exploiting the Immunoinformatics Approach

Marburg virus disease (MVD) caused by the Marburg virus (MARV) generally appears with flu-like symptoms and leads to severe hemorrhagic fever. It spreads via direct contact with infected individuals or animals. Despite being considered to be less threatening in terms of appearances and the number of infected patients, the high fatality rate of this pathogenic virus is a major concern. Until now, no vaccine has been developed to combat this deadly virus. Therefore, vaccination for this virus is necessary to reduce its mortality. Our current investigation focuses on the design and formulation of a multi-epitope vaccine based on the structural proteins of MARV employing immunoinformatics approaches. The screening of potential T-cell and B-cell epitopes from the seven structural proteins of MARV was carried out through specific selection parameters. Afterward, we compiled the shortlisted epitopes by attaching them to an appropriate adjuvant and linkers. Population coverage analysis, conservancy analysis, and MHC cluster analysis of the shortlisted epitopes were satisfactory. Importantly, physicochemical characteristics, human homology assessment, and structure validation of the vaccine construct delineated convenient outcomes. We implemented disulfide bond engineering to stabilize the tertiary or quaternary interactions. Furthermore, stability and physical movements of the vaccine protein were explored using normal-mode analysis. The immune simulation study of the vaccine complexes also exhibited significant results. Additionally, the protein-protein docking and molecular dynamics simulation of the final construct exhibited a higher affinity toward toll-like receptor-4 (TLR4). From simulation trajectories, multiple descriptors, namely, root mean square deviations (rmsd), radius of gyration (Rg), root mean square fluctuations (RMSF), solvent-accessible surface area (SASA), and hydrogen bonds, have been taken into account to demonstrate the inflexible and rigid nature of receptor molecules and the constructed vaccine. Inclusively, our findings suggested the vaccine constructs' ability to regulate promising immune responses against MARV pathogenesis.

Emerging genetic diversity of SARS-CoV-2 RNA dependent RNA polymerase (RdRp) alters its B-cell epitopes

The RNA dependent RNA polymerase (RdRp) plays crucial role in virus life cycle by replicating the viral genome. The SARS-CoV-2 is an RNA virus that rapidly spread worldwide and acquired mutations. This study was carried out to identify mutations in RdRp as the SARS-CoV-2 spread in India. We compared 50217 RdRp sequences reported from India with the first reported RdRp sequence from Wuhan, China to identify 223 mutations acquired among Indian isolates. Our protein modelling study revealed that several mutants can potentially alter stability and flexibility of RdRp. We predicted the potential B cell epitopes contributed by RdRp and identified thirty-six linear continuous and twenty-five discontinuous epitopes. Among 223 RdRp mutants, 44% of them localises in the B cell epitopes region. Altogether, this study highlights the need to identify and characterize the variations in RdRp to understand the impact of these mutations on SARS-CoV-2.

Identification and characterization of mutations in the SARS-CoV-2 RNA-dependent RNA polymerase as a promising antiviral therapeutic target

The causative agent of COVID-19 is a novel betacoronavirus or severe acute respiratory syndrome coronavirus (SARS-CoV-2), which has emerged as a pandemic of global concern. Considering its rapid transmission, WHO has declared public health emergency on 11th March 2020 worldwide. SARS-CoV-2 is a genetically diverse positive sense RNA virus that typically exhibit high rates of mutation than DNA viruses. Higher rates of mutation bring higher genomic variability which may lead to viral evolution and enabling viruses to evade the pre-existing immunity of host and quickly acquire drug resistance properties. The objective of our study was to compare the SARS-CoV-2 RdRp sequences of Indian SARS-CoV-2 isolates with those of Wuhan type virus. A total of 384 point mutations were detected from 488 sequence of the RdRp protein of Indian SARS-CoV-2 genome, out of which seven were used for subsequent study. Furthermore, prediction of secondary structure, protein modeling and its dynamics were performed which revealed that seven mutations (R118C, T148I, Y149C, E802A, Q822H, V880I and D893Y) significantly altered the stability and flexibility of RdRp protein. Present study was therefore, undertaken to analyze the variations occurring in RdRp due to multiple mutations leading to the alterations in the structure and function of RNA-dependent RNA polymerase which is essential for the replication /transcription of this virus and hence can be utilized as a promising therapeutic target to curb SARS-CoV-2 infections.