An immunoinformatics approach to study the epitopes of SARS-CoV-2 helicase, Nsp13.

Introduction and objective: Vaccines are administered worldwide to control on-going coronavirus disease-19 (COVID-19) pandemic caused by SARS-CoV-2. Vaccine efficacy is largely contributed by the epitopes present on the viral proteins and their alteration might help emerging variants to escape host immune surveillance. Therefore, this study was designed to study SARS-CoV-2 Nsp13 protein, its epitopes and evolution. Methods: Clustal Omega was used to identify mutations in Nsp13 protein. Secondary structure and disorder score was predicted by CFSSP and PONDR-VSL2 webservers. Protein stability was predicted by DynaMut webserver. B cell epitopes were predicted by IEDB DiscoTope 2.0 tools and their 3D structures were represented by discovery studio. Antigenicity and allergenicity of epitopes were predicted by Vaxijen2.0 and AllergenFPv.1.0. Physiochemical properties of epitopes were predicted by Toxinpred, HLP webserver tool. Results: Our data revealed 182 mutations in Nsp13 among Indian SARS-CoV-2 isolates, which were characterised by secondary structure and per-residue disorderness, stability and dynamicity predictions. To correlate the functional impact of these mutations, we characterised the most prominent B cell and T cell epitopes contributed by Nsp13. Our data revealed twenty-one epitopes, which exhibited antigenicity, stability and interactions with MHC class-I and class-II molecules. Subsequently, the physiochemical properties of these epitopes were analysed. Furthermore, eighteen mutations reside in these Nsp13 epitopes. Conclusions: We report appearance of eighteen mutations in the predicted twenty-one epitopes of Nsp13. Among these, at least seven epitopes closely matches with the functionally validated epitopes. Altogether, our study shows the pattern of evolution of Nsp13 epitopes and their probable implications.

Introducción y objetivo: Las vacunas se administran a nivel mundial para controlar la pandemia en curso de la enfermedad por coronavirus de 2019 (COVID-19) causada por SARS-CoV-2. A la eficacia de la vacuna contribuyen ampliamente los epítopes presentes en las proteínas virales, y su alteración puede contribuir a que las variantes emergentes se escapen de la vigilancia inmunológica del huésped. Por tanto, este estudio fue diseñado para estudiar la proteína Nsp13 de SARS-CoV-2, sus epítopes y su evolución. Métodos: Se utilizó Clustal Omega para identificar las mutaciones de la proteína Nsp13. La estructura secundaria y la tasa de desorden se predijeron mediante los servidores web CFSSP y PONDR-VSL2. La estabilidad de la proteína fue predicha mediante el servidor web DynaMut. Los epítopes de las células B fueron predichos mediante las herramientas DiscoTope 2.0 de IEDB, y sus estructuras en 3D fueron representadas mediante Discovery Studio.La antigenicidad y alergenicidad de los epítopes fueron predichas mediante Vaxijen2.0 y AlergenFPv.1.0. Las propiedades fisioquímicas de los epítopes fueron predichas mediante Toxinpred, la herramienta del servidor web HLP. Resultados: Nuestros datos revelaron 182 mutaciones en Nsp13 entre los aislados indios de SARS-CoV-2, que fueron caracterizadas mediante las predicciones de la estructura secundaria y la capacidad de desorden por residuo, la estabilidad y la dinamicidad. Para correlacionar el impacto funcional de estas mutaciones, caracterizamos los epítopes más prominentes de las células B y las células T a los que contribuyó Nsp13. Nuestros datos revelaron veintiún epítopes, que exhibieron antigenicidad, estabilidad e interacciones con las moléculas MHC de clase I y clase II. Seguidamente se analizaron las propiedades fisioquímicas de estos epítopes. Además, en estos epítopes de Nsp13 residen ocho mutaciones. Conclusiones: Reportamos el aspecto de ocho mutaciones en los veintiún epítopes de Nsp13 predichos. Entre estos, al menos siete epítopes concuerdan estrechamente con los epítopes funcionalmente validados. En su conjunto, nuestro estudio refleja el patrón evolutivo de los epítopes de Nsp13 y sus implicaciones probables.

A Note on the Potential BCG Vaccination - COVID-19 Molecular Link

Objective: Our goal was to elucidate a potential molecular link between the past and current tuberculosis vaccine Bacillus Calmette-Guerin (BCG;a live attenuated strain of Mycobacterium bovis) immunization policies and COVID-19. Method(s): Our sequence homology analyses have demonstrated that there is an intriguing level of sequence homology between a few of the BCG and Sars-CoV-2 proteins. Result(s): The data suggest that the BCG-specific memory B-cells that are preserved in BCG-vaccinated patients cross-recognize SARS-CoV-2 and that this cross-recognition may affect the virus proliferation and COVID-19 severity. Conclusion(s): Our results can stimulate the sharply focused follow-up experimental studies.Copyright © 2020 Bentham Science Publishers.

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.

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

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

Epitopes Determination for OMICRON: The COVID-19 New Variant

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved in the last couple of years. This has created major havoc and concern because it has affected millions of people around the world. The new variants of covid-19 are classified into two types, VOI (variant of interest) and VOC (variant of concern). The major variants of concern (VOCs) have shared mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike proteins of the novel coronavirus located mostly on the S1 unit result in a higher transmissibility rate and affect the viral virulence and clinical outcome. The spike protein and other non-structural protein mutations in VOCs may lead to escape the approved vaccinations. Here the VOC mutations i.e., OMICRON VARIANT have been discussed in detail, and the therapeutic strategies to enhance the host immune responses have been proposed. Additionally, a computational approach is proposed to design the drug and vaccine for the variant. The protein structure for the OMICRON variant has been predicted through bioinformatics tools and several databases have been used to identify suitable natural inhibitors. The OMICRON variant was analyzed to identify suitable vaccine candidates by identifying B-Cell epitopes. To design a drug, REPAGLINIDE and ENT-NATEGLINIDE were identified as natural inhibitors based on docking score. To design a vaccine the B-cell epitope i.e., CLIGAEYVNNSYECD was found to the highest antigenicity score. The present study identifies natural inhibitors and potential antigenic Epitopes which may be used as effective drug and vaccine candidates to suppress the novel coronavirus. Copyright 2023 by the authors. Licensee Zoological Society of Pakistan.

An immunoinformatic approach towards development of a potent and effective multi-epitope vaccine against monkeypox virus (MPXV).

Monkeypox is a viral zoonotic disease, often transmitted to humans from animals. While the whole world is haggling with the COVID-19 pandemic, the emergence of the monkeypox virus (MPXV) arose as a new challenge to mankind. Till date, numerous cases related to the MPXV have been reported in several countries across the globe, but, its momentary distribution in the current time has left everyone in fright with increasing mortality and limited clinically approved treatments. Therefore, it is of immense importance to develop a potent and highly effective vaccine capable of inducing desired immunogenic responses against the highly contagious MPXV. Herein, using various immunoinformatic and computational biology tools, we made an attempt to develop a multi-epitope vaccine construct against the MPXV which is antigenic, non-allergen and non-toxic in nature and capable of exhibiting immunogenic behavior. The sequence of vaccine construct was designed using the proposed 4 MHC-I, 3 MHC-II and 4 B-cell epitopes linked with suitable adjuvant and linkers. The modeled structure of the vaccine construct was used to assess its interaction with the Toll-like Receptor 4 (TLR4) using ClusPro and HADDOCK. All-atoms molecular dynamics simulation of the MPXV vaccine construct-TLR4 complex followed by a high level of gene expression of the construct within the bacterial system affirmed its stability along with induction of immunogenic response within the host cell. Altogether, our immunoinformatic approach aid in the development of a stable chimeric vaccine construct against MPXV and needs further experimental validation for its immunological relevance and usefulness as a vaccine candidate.Communicated by Ramaswamy H. Sarma.

SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

Identification of virus-specific B-cell epitopes by convalescent plasma from COVID-19 patients.

Identification of immunologic epitopes against SARS-CoV-2 is crucial for the discovery of diagnostic, therapeutic, and preventive targets. In this study, we used a pan-coronavirus peptide microarray to screen for potential B-cell epitopes and validated the results with peptide-based ELISA. Specifically, we identified three linear B-cell epitopes on the SARS-CoV-2 proteome, which were recognized by convalescent plasma from COVID-19 patients. Interestingly, two epitopes (S 809-823 and R1ab 909-923) strongly reacted to convalescent plasma collected at the early phase (< 90 days) of COVID-19 symptom onset, whereas one epitope (M 5-19) reacted to convalescent plasma collected > 90 days after COVID-19 symptom onset. Neutralization assays using antibody depletion with the identified spike (S) peptides revealed that three S epitopes (S 557-571, S 789-803, and S 809-823) elicited neutralizing antibodies in COVID-19 patients. However, the levels of virus-specific antibody targeting S 789-803 only positively correlated with the neutralizing rates at the early phase (<60 days) after disease onset, and the antibody titers diminished quickly with no correlation to the neutralizing activity beyond two months after recovery from COVID-19. Importantly, stimulation of peripheral blood mononuclear cells from COVID-19-recovered patients with these SARS-CoV-2 S peptides resulted in poor virus-specific B cell activation, proliferation, differentiation into memory B cells, and production of immunoglobulin G (IgG) antibodies, despite the B-cells being functionally competent as demonstrated by their response to non-specific stimulation. Taken together, these findings indicate that these newly identified SARS-CoV-2-specific B-cell epitopes can elicit neutralizing antibodies, with titers and/or neutralizing activities declining significantly within 2-3 months in the convalescent plasma of COVID-19 patients.

A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features.

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

B-Cell-Epitope-Based Fluorescent Quantum Dot Biosensors for SARS-CoV-2 Enable Highly Sensitive COVID-19 Antibody Detection.

A new antibody diagnostic assay with more rapid and robust properties is demanded to quantitatively evaluate anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunity in a large population. Here, we developed a nanometer-scale fluorescent biosensor system consisting of CdSe-ZnS quantum dots (QDs) coupled with the highly sensitive B-cell epitopes of SARS-CoV-2 that could remarkably identify the corresponding antibody with a detection limit of 100 pM. Intriguingly, we found that fluorescence quenching of QDs was stimulated more obviously when coupled with peptides than the corresponding proteins, indicating that the energy transfer between QDs and peptides was more effective. Compared to the traditional enzyme-linked immunosorbent assay (ELISA), the B-cell-epitope-based QD-biosensor could robustly distinguish coronavirus disease 2019 (COVID-19) antibody-positive patients from uninfected individuals with a higher sensitivity (92.3-98.1% positive rates by QD-biosensor vs. 78.3-83.1% positive rates by ELISAs in 207 COVID-19 patients' sera) in a more rapid (5 min) and labor-saving manner. Taken together, the 'QD-peptides' biosensor provided a novel real-time, quantitative, and high-throughput method for clinical diagnosis and home-use tests.

Identification and Characterization of Novel Mutants of Nsp13 Protein among Indian SARS-CoV-2 Isolates

Background: SARS-CoV-2, the causative agent of COVID-19, has mutated rapidly, enabling it to adapt and evade the immune system of the host. Emerging SARS-CoV-2 variants with crucial mutations pose a global challenge in the context of therapeutic drugs and vaccines developing globally. There are currently no specific therapeutics or vaccines available to combat SARS-CoV-2 devastation. Concerning this, the current study aimed to identify and characterize the mutations found in the Nsp13 of SARS-CoV-2 in Indian isolates. Methods: In the present study, the Clustal omega tool was used for mutational analysis. The impact of mutations on protein stability, flexibility, and function was predicted using the DynaMut and PROVEAN tools. Furthermore, B-cell epitopes contributed by Nsp13 were identified using various predictive immunoinformatic tools. Results: Non-structural protein Nsp13 sequences from Indian isolates were analyzed by comparing them with the firstly reported Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) protein sequence in Wuhan, China. Out of 825 Nsp13 protein sequences, a total of 38 mutations were observed among Indian isolates. Our data showed that mutations in Nsp13 at various positions (H164Y, A237T, T214I, C309Y, S236I, P419S, V305E, G54S, H290Y, P53S, A308Y, and A308Y) have a significant impact on the protein's stability and flexibility. Moreover, the impact of Nsp13 mutations on protein function was predicted based on the PROVEAN score that indicated 15 mutants as neutral and 23 mutants as deleterious effects. Immunological parameters of Nsp13, such as antigenicity, allergenicity, and toxicity, were evaluated to predict the potential B-cell epitopes. The predicted peptide sequences were correlated with the observed mutants. Our predicted data showed that there are seven high-rank linear epitopes as well as 18 discontinuous B-cell epitopes based on immunoinformatic tools. Moreover, it was observed that out of the total 38 identified mutations among Indian SARS-CoV-2 Nsp13 protein, four mutant residues at positions 142 (E142), 245 (H245), 247 (V247), and 419 (P419) were localised in the predicted B cell epitopic region. Conclusion: Altogether, the results of the present in silico study might help to understand the impact of the identified mutations in Nsp13 protein on its stability, flexibility, and function. © 2022 Kumari et al.

Moving beyond Titers.

Vaccination to prevent and even eliminate disease is amongst the greatest achievements of modern medicine. Opportunities remain in vaccine development to improve protection across the whole population. A next step in vaccine development is the detailed molecular characterization of individual humoral immune responses against a pathogen, especially the rapidly evolving pathogens. New technologies such as sequencing the immune repertoire in response to disease, immunogenomics/vaccinomics, particularly the individual HLA variants, and high-throughput epitope characterization offer new insights into disease protection. Here, we highlight the emerging technologies that could be used to identify variation within the human population, facilitate vaccine discovery, improve vaccine safety and efficacy, and identify mechanisms of generating immunological memory. In today's vaccine-hesitant climate, these techniques used individually or especially together have the potential to improve vaccine effectiveness and safety and thus vaccine uptake rates. We highlight the importance of using these techniques in combination to understand the humoral immune response as a whole after vaccination to move beyond neutralizing titers as the standard for immunogenicity and vaccine efficacy, especially in clinical trials.

Conservation and Evolution of Antigenic Determinants of SARS-CoV-2: An Insight for Immune Escape and Vaccine Design.

Coronavirus disease 2019 (COVID-19) is the most devastating pandemic of the century, which is still far from over. The remarkable success of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines is the working hope, but the evolving variants are the huge concern that can turn the tide. Potential immune escape mutations (PIEMs) in the past and circulating variants were not studied at large scale (all available data). Hence, the conservation of antigenic determinants (epitopes) was analyzed in all available sequences of SARS-CoV-2 according to time (months), proteins, hosts, and variants. Numerous highly conserved B- and T-cell epitopes were identified in 24 proteins of SARS-CoV-2. A decrease in the conservation of epitopes with time was observed in almost all proteins, which was more rapid in neutralizing epitopes. Delta variant still has the highest PIEM in the circulating strains, which pose threat to the effectiveness of current vaccines. The inclusion of identified, highly conserved, and important epitopes in subunit vaccines can increase vaccine effectiveness against evolving variants. Trends in the conservation of epitopes in different proteins, hosts, and variants with time may also help to inspire the counter measure against the current pandemic.

In silico Design and Characterization of Multi-epitopes Vaccine for SARS-CoV2 from Its Spike Protein.

COVID 19 is a disease caused by a novel coronavirus, SARS-CoV2 originated in China most probably of Bat origin. Multiepitopes vaccine would be useful in eliminating SARS-CoV2 infections as asymptomatic patients are in large numbers. In response to this, we utilized bioinformatic tools to develop an efficient vaccine candidate against SARS-CoV2. The designed vaccine has effective BCR and TCR epitopes screened from the sequence of S-protein of SARS-CoV2. Predicted BCR and TCR epitopes found antigenic, non-toxic and probably non-allergen. Modeled and the refined tertiary structure predicted as valid for further use. Protein-Protein interaction prediction of TLR2/4 and designed vaccine indicates promising binding. The designed multiepitope vaccine has induced cell-mediated and humoral immunity along with increased interferon-gamma response. Macrophages and dendritic cells were also found to increase upon the vaccine exposure. In silico codon optimization and cloning in expression vector indicates that the vaccine can be efficiently expressed in E. coli. In conclusion, the predicted vaccine is a good antigen, probable no allergen, and has the potential to induce cellular and humoral immunity.

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.

Preclinical evaluation of a synthetic peptide vaccine against SARS-CoV-2 inducing multiepitopic and cross-reactive humoral neutralizing and cellular CD4 and CD8 responses.

Identification of relevant epitopes is crucial for the development of subunit peptide vaccines inducing neutralizing and cellular immunity against SARS-CoV-2. Our aim was the characterization of epitopes in the receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein to generate a peptide vaccine. Epitope mapping using a panel of 10 amino acid overlapped 15-mer peptides covering region 401-515 from RBD did not identify linear epitopes when tested with sera from infected individuals or from RBD-immunized mice. However, immunization of mice with these 15-mer peptides identified four peptides located at region 446-480 that induced antibodies recognizing the peptides and RBD/S1 proteins. Immunization with peptide 446-480 from S protein formulated with Freund's adjuvant or with CpG oligodeoxinucleotide/Alum induced polyepitopic antibody responses in BALB/c and C56BL/6J mice, recognizing RBD (titres of 3 × 104-3 × 105, depending on the adjuvant) and displaying neutralizing capacity (80-95% inhibition capacity; p < 0.05) against SARS-CoV-2. Murine CD4 and CD8T-cell epitopes were identified in region 446-480 and vaccination experiments using HLA transgenic mice suggested the presence of multiple human T-cell epitopes. Antibodies induced by peptide 446-480 showed broad recognition of S proteins and S-derived peptides belonging to SARS-CoV-2 variants of concern. Importantly, vaccination with peptide 446-480 or with a cyclic version of peptide 446-488 containing a disulphide bridge between cysteines 480 and 488, protected humanized K18-hACE2 mice from a lethal dose of SARS-CoV-2 (62.5 and 75% of protection; p < 0.01 and p < 0.001, respectively). This region could be the basis for a peptide vaccine or other vaccine platforms against Covid-19.

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

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) kills thousands of people worldwide every day, thus necessitating rapid development of countermeasures. Immunoinformatics analyses carried out here in search of immunodominant regions in recently identified SARS-CoV-2 unannotated open reading frames (uORFs) have identified eight linear B-cell, one conformational B-cell, 10 CD4+ T-cell, and 12 CD8+ T-cell promising epitopes. Among them, ORF9b B-cell and T-cell epitopes are the most promising followed by M.ext and ORF3c epitopes. ORF9b40-48 (CD8+ T-cell epitope) is found to be highly immunogenic and antigenic with the highest allele coverage. Furthermore, it has overlap with four potent CD4+ T-cell epitopes. Structure-based B-cell epitope prediction has identified ORF9b61-68 to be immunodominant, which partially overlaps with one of the linear B-cell epitopes (ORF9b65-69). ORF3c CD4+ T-cell epitopes (ORF3c2-16, ORF3c3-17, and ORF3c4-18) and linear B-cell epitope (ORF3c14-22) have also been identified as the candidate epitopes. Similarly, M.ext and 7a.iORF1 (overlap with M and ORF7a) proteins have promising immunogenic regions. By considering the level of antigen expression, four ORF9b and five M.ext epitopes are finally shortlisted as potent epitopes. Mutation analysis has further revealed that the shortlisted potent uORF epitopes are resistant to recurrent mutations. Additionally, four N-protein (expressed by canonical ORF) epitopes are found to be potent. Thus, SARS-CoV-2 uORF B-cell and T-cell epitopes identified here along with canonical ORF epitopes may aid in the design of a promising epitope-based polyvalent vaccine (when connected through appropriate linkers) against SARS-CoV-2. Such a vaccine can act as a bulwark against SARS-CoV-2, especially in the scenario of emergence of variants with recurring mutations in the spike protein.

Multiepitope Proteins for the Differential Detection of IgG Antibodies against RBD of the Spike Protein and Non-RBD Regions of SARS-CoV-2.

The COVID-19 pandemic has exposed the extent of global connectivity and collective vulnerability to emerging diseases. From its suspected origins in Wuhan, China, it spread to all corners of the world in a matter of months. The absence of high-performance, rapid diagnostic methods that could identify asymptomatic carriers contributed to its worldwide transmission. Serological tests offer numerous benefits compared to other assay platforms to screen large populations. First-generation assays contain targets that represent proteins from SARS-CoV-2. While they could be quickly produced, each actually has a mixture of specific and non-specific epitopes that vary in their reactivity for antibodies. To generate the next generation of the assay, epitopes were identified in three SARS-Cov-2 proteins (S, N, and Orf3a) by SPOT synthesis analysis. After their similarity to other pathogen sequences was analyzed, 11 epitopes outside of the receptor-binding domain (RBD) of the spike protein that showed high reactivity and uniqueness to the virus. These were incorporated into a ß-barrel protein core to create a highly chimeric protein. Another de novo protein was designed that contained only epitopes in the RBD. In-house ELISAs suggest that both multiepitope proteins can serve as targets for high-performance diagnostic tests. Our approach to bioengineer chimeric proteins is highly amenable to other pathogens and immunological uses.

Integrative immunoinformatics paradigm for predicting potential B-cell and T-cell epitopes as viable candidates for subunit vaccine design against COVID-19 virulence.

BACKGROUND: The increase in global mortality rates from SARS-COV2 (COVID-19) infection has been alarming thereby necessitating the continual search for viable therapeutic interventions. Due to minimal microbial components, subunit (peptide-based) vaccines have demonstrated improved efficacies in stimulating immunogenic responses by host B- and T-cells. METHODS: Integrative immunoinformatics algorithms were used to determine linear and discontinuous B-cell epitopes from the S-glycoprotein sequence. End-point selection of the most potential B-cell epitope was based on highly essential physicochemical attributes. NetCTL-I and NetMHC-II algorithms were used to predict probable MHC-I and II T-cell epitopes for globally frequent HLA-A∗O2:01, HLA-B∗35:01, HLA-B∗51:01 and HLA-DRB1∗15:02 molecules. Highly probable T-cell epitopes were selected based on their high propensities for C-terminal cleavage, transport protein (TAP) processing and MHC-I/II binding. RESULTS: Preferential epitope binding sites were further identified on the HLA molecules using a blind peptide-docking method. Phylogenetic analysis revealed close relativity between SARS-CoV-2 and SARS-CoV S-protein. LALHRSYLTPGDSSSGWTAGAA242→263 was the most probable B-cell epitope with optimal physicochemical attributes. MHC-I antigenic presentation pathway was highly favourable for YLQPRTFLL269-277 (HLA-A∗02:01), LPPAYTNSF24-32 (HLA-B∗35:01) and IPTNFTISV714-721 (HLA-B∗51:01). Also, LTDEMIAQYTSALLA865-881 exhibited the highest binding affinity to HLA-DR B1∗15:01 with core interactions mediated by IAQYTSALL870-878. COVID-19 YLQPRTFLL269-277 was preferentially bound to a previously undefined site on HLA-A∗02:01 suggestive of a novel site for MHC-I-mediated T-cell stimulation. CONCLUSION: This study implemented combinatorial immunoinformatics methods to model B- and T-cell epitopes with high potentials to trigger immunogenic responses to the S protein of SARS-CoV-2.

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

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