Genetic Analysis and Epitope Prediction of SARS-CoV-2 Genome in Bahia, Brazil: An In Silico Analysis of First and Second Wave Genomics Diversity

COVID-19 is an infectious disease caused by SARS-CoV-2. This virus presents high levels of mutation and transmissibility, which contributed to the emergence of the pandemic. Our study aimed to analyze, in silico, the genomic diversity of SARS-CoV-2 strains in Bahia State by comparing patterns in variability of strains circulating in Brazil with the first isolated strain NC_045512 (reference sequence). Genomes were collected using GISAID, and subsequently aligned and compared using structural and functional genomic annotation. A total of 744 genomes were selected, and 20,773 mutations were found, most of which were of the SNP type. Most of the samples presented low mutational impact, and of the samples, the P.1 (360) lineage possessed the highest prevalence. The most prevalent epitopes were associated with the ORF1ab protein, and in addition to P.1, twenty-one other lineages were also detected during the study period, notably B.1.1.33 (78). The phylogenetic tree revealed that SARS-CoV-2 variants isolated from Bahia were clustered closely together. It is expected that the data collected will help provide a better epidemiological understanding of the COVID-19 pandemic (especially in Bahia), as well as helping to develop more effective vaccines that allow less immunogenic escape. [ FROM AUTHOR] Copyright of COVID is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Evaluation of expression and titration of recombinant nucleocapsid protein as an immunogenic candidate against SARS-CoV2

Introduction: Covid-19 epidemic results from an infection caused by SARS-CoV2. Evolution-based analyses on the nucleotide sequences show that SARS-CoV2 is a member of the genus Beta-coronaviruses and its genome consists of a single-stranded RNA, encoding 16 proteins. Among the structural proteins, the nucleocapsid is the most abundant protein in virus structure, highly immunogenic, with sequence conservatory. Due to a large number of mutations in the spike protein, the aim of this study was to investigate bioinformatics, expression of nucleocapsid protein and evaluate its immunogenicity as an immunogenic candidate. Materials and Methods: B and T cell epitopes of nucleocapsid protein were examined in the IEDB database. The PET28a-N plasmid was transferred to E. coli BL21(DE3) expression host, and IPTG induced recombinant protein expression. The protein was purified using Ni-NTA column affinity chromatography, and the Western blotting method was utilized to confirm it. Finally, mice were immunized with three routes of purified protein. Statistical analysis of the control group injection and test results was carried out by t-test from SPSS software. Results: The optimized gene had a Codon adaptation index (CAI) of 0/97 Percentage of codons having high- frequency distribution was improved to 85%. Expression of recombinant protein in E. coli led to the production of BoNT/B-HCC with a molecular weight of 45 kDa. The total yield of purified protein was 43 mg/L. Immunization of mice induced serum antibody response. Statistical analysis showed that the antibody titer ratio was significantly different compared to the control sample and the antibody titer was acceptable up to a dilution of 1.256000. Conclusion: According to the present study results, the protein can be used as an immunogenic candidate for developing vaccines against SARS-CoV2 in future research.

Reverse vaccinology-based prediction of a multi-epitope SARS-CoV-2 vaccine and its tailoring to new coronavirus variants.

The genome feature of SARS-CoV-2 leads the virus to mutate and creates new variants of concern. Tackling viral mutations is also an important challenge for the development of a new vaccine. Accordingly, in the present study, we undertook to identify B- and T-cell epitopes with immunogenic potential for eliciting responses to SARS-CoV-2, using computational approaches and its tailoring to coronavirus variants. A total of 47 novel epitopes were identified as immunogenic triggering immune responses and no toxic after investigation with in silico tools. Furthermore, we found these peptide vaccine candidates showed a significant binding affinity for MHC I and MHC II alleles in molecular docking investigations. We consider them to be promising targets for developing peptide-based vaccines against SARS-CoV-2. Subsequently, we designed two efficient multi-epitopes vaccines against the SARS-CoV-2, the first one based on potent MHC class I and class II T-cell epitopes of S (FPNITNLCPF-NYNYLYRLFR-MFVFLVLLPLVSSQC), M (MWLSYFIASF-GLMWLSYFIASFRLF), E (LTALRLCAY-LLFLAFVVFLLVTLA), and N (SPRWYFYYL-AQFAPSASAFFGMSR). The second candidate is the result of the tailoring of the first designed vaccine according to three classes of SARS-CoV-2 variants. Molecular docking showed that the protein-protein binding interactions between the vaccines construct and TLR2-TLR4 immune receptors are stable complexes. These findings confirmed that the final multi-epitope vaccine could be easily adapted to new viral variants. Our study offers a shortlist of promising epitopes that can accelerate the development of an effective and safe vaccine against the virus and its adaptation to new variants.Communicated by Ramaswamy H. Sarma.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

Strategic construction of mRNA vaccine derived from conserved and experimentally validated epitopes of avian influenza type A virus: a reverse vaccinology approach.

Purpose: The development of vaccines that confer protection against multiple avian influenza A (AIA) virus strains is necessary to prevent the emergence of highly infectious strains that may result in more severe outbreaks. Thus, this study applied reverse vaccinology approach in strategically constructing messenger RNA (mRNA) vaccine construct against avian influenza A (mVAIA) to induce cross-protection while targeting diverse AIA virulence factors. Materials and Methods: Immunoinformatics tools and databases were utilized to identify conserved experimentally validated AIA epitopes. CD8+ epitopes were docked with dominant chicken major histocompatibility complexes (MHCs) to evaluate complex formation. Conserved epitopes were adjoined in the optimized mVAIA sequence for efficient expression in Gallus gallus. Signal sequence for targeted secretory expression was included. Physicochemical properties, antigenicity, toxicity, and potential cross-reactivity were assessed. The tertiary structure of its protein sequence was modeled and validated in silico to investigate the accessibility of adjoined B-cell epitope. Potential immune responses were also simulated in C-ImmSim. Results: Eighteen experimentally validated epitopes were found conserved (Shannon index <2.0) in the study. These include one B-cell (SLLTEVETPIRNEWGCR) and 17 CD8+ epitopes, adjoined in a single mRNA construct. The CD8+ epitopes docked favorably with MHC peptide-binding groove, which were further supported by the acceptable ΔGbind (-28.45 to -40.59 kJ/mol) and Kd (<1.00) values. The incorporated Sec/SPI (secretory/signal peptidase I) cleavage site was also recognized with a high probability (0.964814). Adjoined B-cell epitope was found within the disordered and accessible regions of the vaccine. Immune simulation results projected cytokine production, lymphocyte activation, and memory cell generation after the 1st dose of mVAIA. Conclusion: Results suggest that mVAIA possesses stability, safety, and immunogenicity. In vitro and in vivo confirmation in subsequent studies are anticipated.

Targets and cross-reactivity of human T cell recognition of common cold coronaviruses.

The coronavirus (CoV) family includes several viruses infecting humans, highlighting the importance of exploring pan-CoV vaccine strategies to provide broad adaptive immune protection. We analyze T cell reactivity against representative Alpha (NL63) and Beta (OC43) common cold CoVs (CCCs) in pre-pandemic samples. S, N, M, and nsp3 antigens are immunodominant, as shown for severe acute respiratory syndrome 2 (SARS2), while nsp2 and nsp12 are Alpha or Beta specific. We further identify 78 OC43- and 87 NL63-specific epitopes, and, for a subset of those, we assess the T cell capability to cross-recognize sequences from representative viruses belonging to AlphaCoV, sarbecoCoV, and Beta-non-sarbecoCoV groups. We find T cell cross-reactivity within the Alpha and Beta groups, in 89% of the instances associated with sequence conservation >67%. However, despite conservation, limited cross-reactivity is observed for sarbecoCoV, indicating that previous CoV exposure is a contributing factor in determining cross-reactivity. Overall, these results provide critical insights in developing future pan-CoV vaccines.

Precise Epitope Organization with Self‐adjuvant Framework Nucleic Acid for Efficient COVID‐19 Peptide Vaccine Construction

Peptide vaccines have advantages in easy fabrication and high safety, but their effectiveness is hampered by the poor immunogenicity of the epitopes themselves. Herein, we constructed a series of framework nucleic acids (FNAs) with regulated rigidity and size to precisely organize epitopes in order to reveal the influence of epitope spacing and carrier rigidity on the efficiency of peptide vaccines. We found that assembling epitopes on rigid tetrahedral FNAs (tFNAs) with the appropriate size could efficiently enhance their immunogenicity. Further, by integrating epitopes from SARS‐CoV‐2 on preferred tFNAs, we constructed a COVID‐19 peptide vaccine which could induce high titers of IgG against the receptor binding domain (RBD) of SARS‐CoV‐2 spike protein and increase the ratio of memory B and T cells in mice. Considering the good biocompatibility of tFNAs, our research provides a new idea for developing efficient peptide vaccines against viruses and possibly other diseases.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes' Response against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. © 2023 Tech Science Press. All rights reserved.

Preparation and identification of broad-spectrum monoclonal antibodies against N protein of avian infectious bronchitis virus

[Objective] To prepare broad-spectrum monoclonal antibody against N protein of avian infectious bronchitis virus (IBV), so as to lay a foundation for identifying conservative domain epitope of N protein and establish a universal IBV detection method. [Method] N protein of GX-YL5, a representative strain of IBV dominant serotype in Guangxi, was expressed in prokaryote. BALB/c mice were immunized with the purified protein. After the serum titer of the immunized mice reached 104 or more, the splenocytes were fused with SP2/0 myeloma cells. After screening by indirect ELISA, monoclonal antibody was prepared by ascites-induced method. Western blotting, IFA and indirect ELISA were used to identify the titer, subtype, reaction specificity and cross-reaction spectrum. And the prepared monoclonal antibody was used for immunohistochemical detection. And the prepared monoclonal antibody was used to detect the IBV in the trachea and kidney tissues of SPF chickens artificially infected with 4 representative IBV variants (GX-N130048, GX-N160421, GX-QZ171023 and GX-QZ170728). [Result] The prepared monoclonal antibody N2D5 had a titer greater than 217 and its subtype was IgG2b. The Western blotting and IFA results showed that the monoclonal antibody N2D5 only reacted with IBV, and were negative with Newcastle disease virus (NDV), infectious laryngotracheitis virus (ILTV), avian metapneumovirus (aMPV), infectious bursal disease virus (IBDV), avian leukosis virus (ALV) and Marek's disease virus (MDV). Monoclonal antibody N2D5 reacted with many genotypes in China and all 7 serotypes of IBV currently prevalent in Guangxi, including commonly used standard strains, vaccine strains and field strains. Immunohistochemistry showed that the virus signals could be detected in the trachea and kidney tissues of SPF chickens at different time after artificial infection of 3 representative IBV strains from chicken and 1 isolated strain from duck, which further proved its broad spectrum. [Conclusion] The monoclonal antibody N2D5 of IBV prepared based on hybridoma technology belongs to the IgG2b subtype. It has the characteristics of high specificity, wide response spectrum and strong binding ability with IBV. It can be used as a specific diagnostic antibody for clinical diagnosis of IBV and the study of virus distribution.

Pressured cytotoxic T cell epitope strength among SARS-CoV-2 variants correlates with COVID-19 severity.

Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we probe the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that the recognition of pressured epitopes from the parent strain Wuhan-Hu-1 correlates with COVID-19 severity. We also identify and rank list HLA-alleles and epitopes that offer protectivity against severe disease in infected individuals. Finally, we shortlist a set of 6 pressured and protective epitopes that represent regions in the viral proteome that are under high immune pressure across SARS-CoV-2 variants. Identification of such epitopes, defined by the distribution of HLA-genotypes among members of a population, could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens.

Harnessing immunoinformatics for developing a multiple-epitope peptide-based vaccination approach against SARS-CoV-2 spike protein.

COVID-19 has spread to over 200 countries with variable severity and mortality rates. Computational analysis is a valuable tool for developing B-cell and T-cell epitope-based vaccines. In this study, by harnessing immunoinformatics tools, we designed a multiple-epitope vaccine to protect against COVID-19. The candidate epitopes were designed from highly conserved regions of the SARS-CoV-2 spike (S) glycoprotein. The consensus amino acids sequence of ten SARS-CoV-2 variants including Gamma, Beta, Epsilon, Delta, Alpha, Kappa, Iota, Lambda, Mu, and Omicron was involved. Applying the multiple sequence alignment plugin and the antigenic prediction tools of Geneious prime 2021, ten predicted variants were identified and consensus S-protein sequences were used to predict the antigenic part. According to ElliPro analysis of S-protein B-cell prediction, we explored 22 continuous linear epitopes with high scores ranging from 0.879 to 0.522. First, we reported five promising epitopes: BE1 1115-1192, BE2 481-563, BE3 287-313, BE4 62-75, and BE5 112-131 with antigenicity scores of 0.879, 0.86, 0.813, 0.779, and 0.765, respectively, while only nine discontinuous epitopes scored between 0.971 and 0.511. Next, we identified 194 Major Histocompatibility Complex (MHC) - I and 156 MHC - II epitopes with antigenic characteristics. These spike-specific peptide-epitopes with characteristically high immunogenic and antigenic scores have the potential as a SARS-CoV-2 multiple-epitope peptide-based vaccination strategy. Nevertheless, further experimental investigations are needed to test for the vaccine efficacy and efficiency.

A Porcine Epidemic Diarrhea Virus Isolated from a Sow Farm Vaccinated with CV777 Strain in Yinchuan, China: Characterization, Antigenicity, and Pathogenicity

Porcine epidemic diarrhea virus (PEDV) is a porcine enteric coronavirus globally, causing serious economic losses to the global pig industry since 2010. Here, a PEDV CH/Yinchuan/2021 strain was isolated in a CV777-vaccinated sow farm which experienced a large-scale PEDV invasion in Yinchuan, China, in 2021. Our results demonstrated that the CH/Yinchuan/2021 isolate could efficiently propagate in Vero cells, and its proliferation ability was weaker than that of CV777 at 10 passages (P10). Phylogenetic analysis of the S gene revealed that CH/Yinchuan/2021 was clustered into subgroup GIIa, forming an independent branch with 2020-2021 isolates in China. Moreover, GII was obviously allocated into four clades, showing regional and temporal differences in PEDV global isolates. Notably, CH/Yinchuan/2021 was analyzed as a recombinant originated from an American isolate and a Chinese isolate, with a big recombinant region spanning ORF1a and S1. Importantly, we found that CH/Yinchuan/2021 harbored multiple mutations relative to CV777 in neutralizing epitopes (S10, S1A, COE, and SS6). Homology modelling showed that these amino acid differences in S protein occur on the surface of its structure, especially the insertion and deletion of multiple consecutive residues at the S10 epitope. In addition, cross-neutralization analysis confirmed that the differences in the S protein of CH/Yinchuan/2021 changed its antigenicity compared with the CV777 strain, resulting in a different neutralization profile. Animal pathogenicity test showed that CH/Yinchuan/2021 caused PEDV-typified symptoms and 100% mortality in 3-day-old piglets. These data will provide valuable information to understand the epidemiology, molecular characteristics, evolution, and antigenicity of PEDV circulating in China.

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.

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

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

TNFepitope: A webserver for the prediction of TNF-α inducing epitopes.

Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine that is crucial in controlling the signaling pathways within the immune cells. Recent studies reported that higher expression levels of TNF-α are associated with the progression of several diseases, including cancers, cytokine release syndrome in COVID-19, and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF-α progression in various disease conditions. In the pilot study, we proposed a host-specific in-silico tool for predicting, designing, and scanning TNF-α inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF-α inducing/non-inducing epitopes from human and mouse hosts. Firstly, we developed alignment-free (machine learning based models using composition-based features of peptides) methods for predicting TNF-α inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, an alignment-based (using BLAST) method has been used for predicting TNF-α inducing epitopes. Finally, a hybrid method (combination of alignment-free and alignment-based method) has been developed for predicting epitopes. Hybrid approach achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified potential TNF-α inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2, and human insulin. The best models developed in this study has been incorporated in the webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope).

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.

Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition

The identification of the mechanisms by which T-cell receptors (TCRs) interact with human antigens provides a crucial opportunity to develop new vaccines, diagnostics and immunotherapies. However, the accurate prediction and recognition of TCR–antigen pairing represents a substantial computational challenge in immunology. Existing tools only learn the binding patterns of antigens from many known TCR binding repertoires and fail to recognize antigens that have never been presented to the immune system or for which only a few TCR binding repertoires are known. However, the binding specificity for neoantigens or exogenous peptides is crucial for immune studies and immunotherapy. Therefore, we developed Pan-Peptide Meta Learning (PanPep), a general and robust framework to recognize TCR–antigen binding, by combining the concepts of meta-learning and the neural Turing machine. The neural Turing machine adds external memory to avoid forgetting previously learned tasks, which is used here to accurately predict TCR binding specificity with any peptide, particularly unseen ones. We applied PanPep to various challenging clinical tasks, including (1) qualitatively measuring the clonal expansion of T cells;(2) efficiently sorting responsive T cells in tumour neoantigen therapy;and (3) accurately identifying immune-responsive TCRs in a large cohort from a COVID-19 study. Our comprehensive tests show that PanPep outperforms existing tools. PanPep also offers interpretability, revealing the nature of peptide and TCR interactions in 3D crystal structures. We believe PanPep can be a useful tool to decipher TCR–antigen interactions and that it has broad clinical applications. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

Synthesis of killer T cell epitopes for peptide-based vaccine of SARS-CoV-2. (Special Issue: Chemistry and global challenges (Part B).)

COVID-19 is a current global pandemic, which has prompted many countries to develop ways to deal with it. Peptides have many medicinal and diagnostic benefits, so recently, many researchers have been developing peptide-based vaccines against COVID-19. In peptide-based vaccines, peptides act as specific antigens that will provide a faster immune response because they do not go through the process of cutting proteins in the Major Histocompatibility complex (MHC) antigen-presenting cells (APC) and can be directly presented outside the cells so that they can be recognized by the host killer T cells (CTL). Vaccine development can be accelerated with the help of immunoinformatic to predict specific epitopes to induce the CTL. We have predicted the CTL epitope through the immunoinformatic method. This study aims to synthesize candidate CTL epitopes as a candidate for the SARS-CoV-2 vaccine using the SPPS method with the Fmoc/t-Bu strategy. In this study, two CTL epitopes were synthesized through a conventional solid-phase peptide synthesis (SPPS) method, and another CTL epitope was synthesized using a semi-automated peptide synthesizer. The SPPS method is faster because the purification is only carried out at the final stage, while the Fmoc/t-Bu strategy was applied because it provides a mild reaction condition. Both synthetic approaches were compared. The semi-automated peptide synthesizer made the synthesis faster and more efficient due to the use of an inert gas (N2) during the synthesis. The synthetic peptides were characterized by TOF-ESI-MS. The three peptides showed ion peaks at m/z 1137.5509 (M+H)+, 1064.3468 (M+H)+, and 916.5859 (M+H)+, indicating correct molecular ion peaks for EILDITPCSF, IPIGAGICASY, and FIAGLIAIV, respectively.

Proof-of-Concept Study of Liposomes with a Set of SARS-CoV-2 Viral Peptidic T-Cell Epitopes as a Vaccine

Abstract: Potential nonameric epitopes of CD8+ T lymphocytes were selected from the composition of structural, accessory, and nonstructural proteins of the SARS-CoV-2 virus (13 peptides) and a 15-mer epitope of CD4+ T lymphocytes, from the S-protein, based on the analysis of publications on genome-wide immunoinformatic analysis of T-cell epitopes of the virus (Wuhan strain), as well as a number of clinical studies of immunodominant epitopes among patients recovering from COVID-19 disease. The peptides were synthesized and five compositions of 6–7 peptides were included in liposomes from egg phosphatidylcholine and cholesterol (~200 nm size) obtained by extrusion. After double subcutaneous immunization of conventional mice, activation of cellular immunity was assessed by the level of cytokine synthesis by splenocytes in vitro in response to stimulation with relevant peptide compositions. Liposomal formulation exhibiting the best result in terms of the formation of specific cellular immunity in response to vaccination was selected for further experiments. Evaluation of the protective efficacy of this formulation in an infectious mouse model showed a positive trend in the frequency of occurrence of hyaline-like membranes in the lumen of the alveoli, as well as a somewhat lower severity of microcirculatory disorders. The latter circumstance can potentially help reduce the severity of the disease and prevent its adverse outcomes. A method to produce liposome preparations with peptide compositions for long-term storage is under development. © 2022, Pleiades Publishing, Ltd.

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.