Computer-Aided vaccine design for selected positive-sense single stranded RNA viruses

Spontaneous mutations and lack of replication fidelity in positive-sense single stranded RNA viruses (+ssRNA virus) result in emergence of genetic variants with diverse viral morphogenesis and surface proteins that affect its antigenicity. This high mutability in +ssRNA viruses has induced antiviral drug resistance and ability to overcome vaccines that subsequently resulted in rapid viral evolution and high mortality rate in human and livestock. Computer aided vaccine design and immunoinformatics play a crucial role in expediting the vaccine production protocols, antibody production and identifying suitable immunogenic regions or epitopes from the genome sequences of the pathogens. T cell and B cell epitopes can be identified in pathogens by immunoinformatics algorithms and methods that enhance the analysis of protective immunity, vaccine safety, immunity modelling and vaccine efficacy. This rapid and cost-effective computational vaccine design promotes development of potential vaccine that could induce immune response in host against rapidly mutating pathogens like +ssRNA viruses. Epitope-based vaccine is a striking concept that has been widely employed in recent years to construct vaccines targeting rapidly mutating +ssRNA viruses. Therefore, the present review provides an overview about the current progress and methodology in computer-aided vaccine design for the most notable +ssRNA viruses namely Hepatitis C virus, Dengue virus, Chikungunya virus and Coronaviruses. This review also highlights the applications of various immunoinformatics tools for vaccine design and for modelling immune response against +ssRNA viruses. © 2023, National Institute of Science Communication and Policy Research. All rights reserved.

An Integrated In Silico Approach to Develop Epitope-Based Peptide Vaccine Against SARS-CoV-2

Background: SARS-CoV-2 has been a topic of discussion ever since the beginning of 2020. Every country is trying all possible steps to combat the disease ranging from shutting the complete economy of the country to the repurposing of drugs and vaccine development. The rapid data analysis and widespread tools have made bioinformatics capable of giving new insights to deal with the current scenario more efficiently through an emerging field, vaccinomics. Objective(s): The present in silico study was attempted to identify peptide fragments from spike surface glycoprotein of SARS-CoV-2 that can be efficiently used for the development of an epi-tope-based vaccine designing approach. Method(s): The epitopes of B and T-cell are predicted using integrated computational tools. VaxiJen server, NetCTL, and IEDB tools were used to study, analyze, and predict potent T-cell epitopes, their subsequent MHC-I interactions, and B-cell epitopes. The 3D structure prediction of peptides and MHC-I alleles (HLA-C*03:03) was further made using AutoDock4.0. Result(s): Based on result interpretation, the peptide sequence from 1138-1145 amino acid and sequence WTAGAAAYY and YDPLQPEL were obtained as potential B-cell and T-cell epitopes, re-spectively. Conclusion(s): The peptide sequence WTAGAAAYY and the amino acid sequence from 1138-1145 of the spike protein of SARS-CoV-2 can be used as a probable B-cell epitope candidate. Also, the amino acid sequence YDPLQPEL can be used as a potent T-cell epitope. This in silico study will help us identify novel epitope-based peptide vaccine targets in the spike protein of SARS-CoV-2. Further, the in vitro and in vivo study needed to validate the findings.Copyright © 2022 Bentham Science Publishers.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design.

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Immunoinformatics Study: Multi-Epitope Based Vaccine Design from SARS-CoV-2 Spike Glycoprotein.

The coronavirus disease 2019 outbreak has become a huge challenge in the human sector for the past two years. The coronavirus is capable of mutating at a higher rate than other viruses. Thus, an approach for creating an effective vaccine is still needed to induce antibodies against multiple variants with lower side effects. Currently, there is a lack of research on designing a multiepitope of the COVID-19 spike protein for the Indonesian population with comprehensive immunoinformatic analysis. Therefore, this study aimed to design a multiepitope-based vaccine for the Indonesian population using an immunoinformatic approach. This study was conducted using the SARS-CoV-2 spike glycoprotein sequences from Indonesia that were retrieved from the GISAID database. Three SARS-CoV-2 sequences, with IDs of EIJK-61453, UGM0002, and B.1.1.7 were selected. The CD8+ cytotoxic T-cell lymphocyte (CTL) epitope, CD4+ helper T lymphocyte (HTL) epitope, B-cell epitope, and IFN-γ production were predicted. After modeling the vaccines, molecular docking, molecular dynamics, in silico immune simulations, and plasmid vector design were performed. The designed vaccine is antigenic, non-allergenic, non-toxic, capable of inducing IFN-γ with a population reach of 86.29% in Indonesia, and has good stability during molecular dynamics and immune simulation. Hence, this vaccine model is recommended to be investigated for further study.

Peptide microarray analysis of in-silico predicted B-cell epitopes in SARS-CoV-2 sero-positive healthcare workers in Bulawayo, Zimbabwe.

Immunogenic peptides that mimic linear B-cell epitopes coupled with immunoassay validation may improve serological tests for emerging diseases. This study reports a general approach for profiling linear B-cell epitopes derived from SARS-CoV-2 using an in-silico method and peptide microarray immunoassay, using healthcare workers' SARS-CoV-2 sero-positive sera. SARS-CoV-2 was tested using rapid chromatographic immunoassays and real-time reverse-transcriptase polymerase chain reaction. Immunogenic peptides mimicking linear B-cell epitopes were predicted in-silico using ABCpred. Peptides with the lowest sequence identity with human protein and proteins from other human pathogens were selected using the NCBI Protein BLAST. IgG and IgM antibodies against the SARS-CoV-2 spike protein, membrane glycoprotein and nucleocapsid derived peptides were measured in sera using peptide microarray immunoassay. Fifty-three healthcare workers included in the study were RT-PCR negative for SARS-CoV-2. Using rapid chromatographic immunoassays, 10 were SARS-CoV-2 IgM sero-positive and 7 were SARS-CoV-2 IgG sero-positive. From a total of 10 SARS-CoV-2 peptides contained on the microarray, 3 (QTH34388.1-1-14, QTN64908.1-135-148, and QLL35955.1-22-35) showed reactivity against IgG. Three peptides (QSM17284.1-76-89, QTN64908.1-135-148 and QPK73947.1-8-21) also showed reactivity against IgM. Based on the results we predicted one peptide (QSM17284.1-76-89) that had an acceptable diagnostic performance. Peptide QSM17284.1-76-89 was able to detect IgM antibodies against SARS-CoV-2 with area under the curve (AUC) 0.781 when compared to commercial antibody tests. In conclusion in silico peptide prediction and peptide microarray technology may provide a platform for the development of serological tests for emerging infectious diseases such as COVID-19. However, we recommend using at least three in-silico peptide prediction tools to improve the sensitivity and specificity of B-cell epitope prediction, to predict peptides with excellent diagnostic performances.

Critical review of conformational B-cell epitope prediction methods.

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

Structural Analysis and Epitope Prediction of S2 Domain of SARS-CoV-2, Conservation Analysis Among Major Variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. There are four structural proteins of the virus: spike, envelope, membrane, and nucleocapsid proteins. Various vaccines were designed and are effectively being used against the spike protein of the virus. However, several vaccine-related complications have been reported worldwide. Assuming that the structural integrity of the whole protein might be contributing to these complications, this study was performed to design epitopes using the S2 domain of the spike protein, which could trigger a strong immune response. We have also predicted antigenic and allergenic properties of the selected epitopes. A total of 49 B cell epitopes passing antigenicity and other assessment filters were found using three methods. Among them, RDLICAQ had the highest antigenicity score (1.1443). However, only one cytotoxic T lymphocyte epitope, RSFIEDLLF, passed the essential filters with an antigenicity score of 0.5782 to show an appropriate immune response for T cells, while among 21 helper T cell lymphocyte epitopes that were filtered, FAMQMAYRFNGIGVT showed the highest (1.3688) antigenicity score. Conservation analysis revealed that the S2 domain is significantly conserved, thus making it an ideal candidate for vaccine development. We have also designed a vaccine construct based on the best suiting components found during the whole study. This construct and S2 domain solely can be future subjects of interest or might be included in a subunit cocktail formulation for attaining unabridged immunogenicity.

Improved Antibody-Specific Epitope Prediction Using AlphaFold and AbAdapt.

Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we systematically assessed the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline. By incorporating more accurate antibody models, we observed improvement in docking, paratope prediction, and prediction of antibody-specific epitopes. We further applied AbAdapt-AF in an anti-receptor binding domain (RBD) antibody complex benchmark and found AbAdapt-AF outperformed three alternative docking methods. Also, AbAdapt-AF demonstrated higher epitope prediction accuracy than other tested epitope prediction tools in the anti-RBD antibody complex benchmark. We anticipate that AbAdapt-AF will facilitate prediction of antigen-antibody interactions in a wide range of applications.

Candidate Multi-Epitope Vaccine against Corona B.1.617 Lineage: In Silico Approach.

Various mutations have accumulated since the first genome sequence of SARS-CoV2 in 2020. Mutants of the virus carrying the D614G and P681R mutations in the spike protein are increasingly becoming dominant all over the world. The two mutations increase the viral infectivity and severity of the disease. This report describes an in silico design of SARS-CoV-2 multi-epitope carrying the spike D614G and P681R mutations. The designed vaccine harbors the D614G mutation that increases viral infectivity, fitness, and the P681R mutation that enhances the cleavage of S to S1 and S2 subunits. The designed multi-epitope vaccine showed an antigenic property with a value of 0.67 and the immunogenicity of the predicted vaccine was calculated and yielded 3.4. The vaccine construct is predicted to be non-allergenic, thermostable and has hydrophilic nature. The combination of the selected CTL and HTL epitopes in the vaccine resulted in 96.85% population coverage globally. Stable interactions of the vaccine with Toll-Like Receptor 4 were tested by docking studies. The multi-epitope vaccine can be a good candidate against highly infecting SARS-CoV-2 variants.

Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm.

Development of vaccines and therapeutic antibodies to deal with infectious and other diseases are the most perceptible scientific interventions that have had huge impact on public health including that in the current Covid-19 pandemic. From inactivation methodologies to reverse vaccinology, vaccine development strategies of 21st century have undergone several transformations and are moving towards rational design approaches. These developments are driven by data as the combinatorials involved in antigenic diversity of pathogens and immune repertoire of hosts are enormous. The computational prediction of epitopes is central to these developments and numerous B-cell epitope prediction methods developed over the years in the field of immunoinformatics have contributed enormously. Most of these methods predict epitopes that could potentially bind to an antibody regardless of its type and only a few account for antibody class specific epitope prediction. Recent studies have provided evidence of more than one class of antibodies being associated with a particular disease. Therefore, it is desirable to predict and prioritize 'peptidome' representing B-cell epitopes that can potentially bind to multiple classes of antibodies, as an open problem in immunoinformatics. To address this, AbCPE, a novel algorithm based on multi-label classification approach has been developed for prediction of antibody class(es) to which an epitope can potentially bind. The epitopes binding to one or more antibody classes (IgG, IgE, IgA and IgM) have been used as a knowledgebase to derive features for prediction. Multi-label algorithms, Binary Relevance and Label Powerset were applied along with Random Forest and AdaBoost. Classifier performance was assessed using evaluation measures like Hamming Loss, Precision, Recall and F1 score. The Binary Relevance model based on dipeptide composition, Random Forest and AdaBoost achieved the best results with Hamming Loss of 0.1121 and 0.1074 on training and test sets respectively. The results obtained by AbCPE are promising. To the best of our knowledge, this is the first multi-label method developed for prediction of antibody class(es) for sequential B-cell epitopes and is expected to bring a paradigm shift in the field of immunoinformatics and immunotherapeutic developments in synthetic biology. The AbCPE web server is available at http://bioinfo.unipune.ac.in/AbCPE/Home.html.

Identification of Potential Immunogenic Epitopes Against SARS-CoV-2 Using In-Silico Method: An Immunoinformatics Study

Background: Severe acute respiratory syndrome (SARS-CoV-2), a zoonotic virus, is the pathogenic causal agent for the ongoing pandemic. Despite the lethality of the disease, there are no therapeutic agents available to combat the disease outbreak, and the vaccines currently accessible are insufficient to control the widespread, fast-mutating virus infection. Objective(s): This research study focuses on determining potential epitopes by examining the entire proteome of the SARS-CoV-2 virus using an in-silico approach. Method(s): To develop a vaccine for the deadly virus, researchers screened the whole proteome of the SARS-CoV-2 virus for potential epitopes in order to find a powerful peptide candidate that is both unique and fulfils the vaccine's objective. It is mandatory to identify the suitable B-cell and T-cell epitopes of the observed SARS-CoV-2 surface glycoprotein (QKN61229.1). These epitopes were subjected to various tests, including antigenicity, allergenicity, and other physicochemical proper-ties. The T-cell epitopes that met the criteria were subjected to population coverage analysis. It helped in better understanding epitope responses to the target population, computing peptide con-servancy, and clustering epitopes based on sequence match, MHC binding, and T-cell restriction sites. Lastly, the interactions between the T-cell receptor (TCR) and a peptide-MHC were studied to thoroughly understand MHC restriction to design a peptide-vaccine. Result(s): The findings revealed that four B-cell epitopes, two MHC-I epitopes, and four MHC-II epitopes qualified for all of the tests and so have antigen affinity. Conclusion(s): Based on the results obtained from this study, the estimated peptides are promising candidates for peptide-vaccine design and development. Copyright © 2022 Bentham Science Publishers.

The Rv3874-Rv3875 chimeric protein shows a promiscuous serodiagnostic potential for tuberculosis.

Tuberculosis (TB) stays a major cause of death globally after COVID-19 and HIV. An early diagnosis to control TB effectively, needs a fast reliable diagnostic method with high sensitivity. Serodiagnosis involving polyclonal antibodies detection against an antigen of Mycobacterium tuberculosis (Mtb) in serum samples can be instrumental. In our study, Rv3874 and Rv3875 antigens were cloned, expressed, and purified individually and as a chimeric construct in Escherichia coli BL21. Enzyme-Linked Immunosorbent Assay (ELISA) based findings revealed that the Rv3874-Rv3875 chimeric construct was two-fold more sensitive (59.7%) than the individual sensitivities of Rv3874 (28.4%) and Rv3875 (24.9%) for 201 serum TB positive samples. Furthermore, the fusion construct was a little more sensitive (60.4%) for male subjects than that for females (58.8%). Lastly, our preliminary findings, molecular insights of secondary structure, and statistical and in silico analysis of each construct also advocate that CEP can be considered a better immunodiagnostic tool in addition to previously reported EC skin test.

Predicting Antigenic Peptides from Rocio Virus NS1 Protein for Immunodiagnostic Testing Using Immunoinformatics and Molecular Dynamics Simulation.

The mosquito-borne disease caused by the Rocio virus is a neglected threat, and new immune inputs for serological testing are urgently required for diagnosis in low-resource settings and epidemiological surveillance. We used in silico approaches to identify a specific antigenic peptide (p_ROCV2) in the NS1 protein of the Rocio virus that was theoretically predicted to be stable and exposed on its surface, where it demonstrated key properties allowing it to interact with antibodies. These findings related to the molecular dynamics of this peptide provide important insights for advancing diagnostic platforms and investigating therapeutic alternatives.

A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.

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.

Review on Approved and Inprogress COVID-19 Vaccines.

The last generation of Coronavirus named COVID-19 is responsible for the recent worldwide outbreak. Concerning the widespread and quick predominance, there is a critical requirement for designing appropriate vaccines to surmount this grave problem. Correspondingly, in this revision, COVID-19 vaccines (which are being developed until March 29th, 2021) are classified into specific and non-specific categories. Specific vaccines comprise genetic-based vaccines (mRNA, DNA), vector-based, protein/recombinant protein vaccines, inactivated viruses, live-attenuated vaccines, and novel strategies including microneedle arrays (MNAs), and nanoparticles vaccines. Moreover, specific vaccines such as BCG, MRR, and a few other vaccines are considered Non-specific. What is more, according to the significance of Bioinformatic sciences in the cutting-edge vaccine design and rapid outbreak of COVID-19, herein, Bioinformatic principles including reverse vaccinology, epitopes prediction/selection and, their further applications in the design of vaccines are discussed. Last but not least, safety, challenges, advantages, and future prospects of COVID-19 vaccines are highlighted.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins.

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.

Identification and Analysis of Unstructured, Linear B-Cell Epitopes in SARS-CoV-2 Virion Proteins for Vaccine Development.

The efficacy of SARS-CoV-2 nucleic acid-based vaccines may be limited by proteolysis of the translated product due to anomalous protein folding. This may be the case for vaccines employing linear SARS-CoV-2 B-cell epitopes identified in previous studies since most of them participate in secondary structure formation. In contrast, we have employed a consensus of predictors for epitopic zones plus a structural filter for identifying 20 unstructured B-cell epitope-containing loops (uBCELs) in S, M, and N proteins. Phylogenetic comparison suggests epitope switching with respect to SARS-CoV in some of the identified uBCELs. Such events may be associated with the reported lack of serum cross-protection between the 2003 and 2019 pandemic strains. Incipient variability within a sample of 1639 SARS-CoV-2 isolates was also detected for 10 uBCELs which could cause vaccine failure. Intermediate stages of the putative epitope switch events were observed in bat coronaviruses in which additive mutational processes possibly facilitating evasion of the bat immune system appear to have taken place prior to transfer to humans. While there was some overlap between uBCELs and previously validated SARS-CoV B-cell epitopes, multiple uBCELs had not been identified in prior studies. Overall, these uBCELs may facilitate the development of biomedical products for SARS-CoV-2.

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

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

Origin, phylogeny, variability and epitope conservation of SARS-CoV-2 worldwide.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses innumerous challenges, like understanding what triggered the emergence of this new human virus, how this RNA virus is evolving or how the variability of viral genome may impact the primary structure of proteins that are targets for vaccine. We analyzed 19471 SARS-CoV-2 genomes available at the GISAID database from all over the world and 3335 genomes of other Coronoviridae family members available at GenBank, collecting SARS-CoV-2 high-quality genomes and distinct Coronoviridae family genomes. Additionally, we analyzed 199,984 spike glycoprotein sequences. Here, we identify a SARS-CoV-2 emerging cluster containing 13 closely related genomes isolated from bat and pangolin that showed evidence of recombination, which may have contributed to the emergence of SARS-CoV-2. The analyzed SARS-CoV-2 genomes presented 9632 single nucleotide variants (SNVs) corresponding to a variant density of 0.3 over the genome, and a clear geographic distribution. SNVs are unevenly distributed throughout the genome and hotspots for mutations were found for the spike gene and ORF 1ab. We describe a set of predicted spike protein epitopes whose variability is negligible. Additionally, all predicted epitopes for the structural E, M and N proteins are highly conserved. The amino acid changes present in the spike glycoprotein of variables of concern (VOCs) comprise between 3.4% and 20.7% of the predicted epitopes of this protein. These results favors the continuous efficacy of the available vaccines targeting the spike protein, and other structural proteins. Multiple epitopes vaccines should sustain vaccine efficacy since at least some of the epitopes present in variability regions of VOCs are conserved and thus recognizable by antibodies.