Designing a Multi-epitope Vaccine against the SARS-CoV-2 Variant Based on an Immunoinformatics Approach.

BACKGROUND: SARS-CoV-2 is a life-threatening virus in the world. Scientific evidence indicates that this pathogen will emerge again in the future. Although the current vaccines have a pivotal role in the control of this pathogen, the emergence of new variants has a negative impact on their effectiveness. OBJECTIVE: Therefore, it is urgent to consider the protective and safe vaccine against all sub-coronavirus species and variants based on the conserved region of the virus. Multi-epitope peptide vaccine (MEV), comprised of immune-dominant epitopes, is designed by immunoinformatic tools and it is a promising strategy against infectious diseases. METHODS: Spike glycoprotein and nucleocapsid proteins from all coronavirus species and variants were aligned and the conserved region was selected. Antigenicity, toxicity, and allergenicity of epitopes were checked by a proper server. To robust the immunity of the multi-epitope vaccine, cholera toxin b (CTB) and three HTL epitopes of tetanus toxin fragment C (TTFrC) were linked at the N-terminal and C-terminal of the construct, respectively. Selected epitopes with MHC molecules and the designed vaccines with Toll-like receptors (TLR-2 and TLR-4) were docked and analyzed. The immunological and physicochemical properties of the designed vaccine were evaluated. The immune responses to the designed vaccine were simulated. Furthermore, molecular dynamic simulations were performed to study the stability and interaction of the MEV-TLRs complexes during simulation time by NAMD (Nanoscale molecular dynamic) software. Finally, the codon of the designed vaccine was optimized according to Saccharomyces boulardii. RESULTS: The conserved regions of spike glycoprotein and nucleocapsid protein were gathered. Then, safe and antigenic epitopes were selected. The population coverage of the designed vaccine was 74.83%. The instability index indicated that the designed multi-epitope was stable (38.61). The binding affinity of the designed vaccine to TLR2 and TLR4 was -11.4 and -11.1, respectively. The designed vaccine could induce humoral and cellular immunity. CONCLUSION: In silico analysis showed that the designed vaccine is a protective multi-epitope vaccine against SARS-CoV-2 variants.

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.

Immunoinformatics Approaches in Designing Vaccines Against COVID-19.

Since the onset of the COVID-19 pandemic, a number of approaches have been adopted by the scientific communities for developing efficient vaccine candidate against SARS-CoV-2. Conventional approaches of developing a vaccine require a long time and a series of trials and errors which indeed limit the feasibility of such approaches for developing a dependable vaccine in an emergency situation like the COVID-19 pandemic. Hitherto, most of the available vaccines have been developed against a particular antigen of SARS-CoV, spike protein in most of the cases, and intriguingly, these vaccines are not effective against all the pathogenic coronaviruses. In this context, immunoinformatics-based reverse vaccinology approaches enable a robust design of efficacious peptide-based vaccines against all the infectious strains of coronaviruses within a short frame of time. In this chapter, we enumerate the methodological trajectory of developing a universal anti-SARS-CoV-2 vaccine, namely, "AbhiSCoVac," through advanced computational biology-based immunoinformatics approach and its in-silico validation using molecular dynamics simulations.

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.

Development of a New Epitope Immunogenic Structure Based on the Coronavirus Membrane Glycoprotein M Using Immunoinformatics Tools

Backgrounds: Although conventional therapies have played an essential role in the treatment of many diseases, emerging diseases require new treatment methods with less complications. Therefore, it is important to develop an effective vaccine for infections caused by the coronavirus to prevent mortality and create immunity the community. Material(s) and Method(s): In this research bioinformatics tools were used to design a vaccine against the M membrane protein of SARS-CoV-2. A total of 27 epitopes confined to B cells and MHC I and II alleles were structurally constructed in M protein for immune stimulation and antibody recognition which were used in the construction of a chimeric peptide vaccine. Finding(s): The vaccine was predicted to be a stable, antigenic, and non-allergenic compound. TRL5/vaccine complex analysis and docking simulation indicated a sufficiently stable binding with appropriated receptor activation. The immune response simulation following hypothetical immunization indicated the potential of this vaccine to stimulate the production of active and memory B cells, CD8 + T and, CD4 + T cells, and effective immunological responses induced by Th2 and Th1. Conclusion(s): The analysis of in-silico processes showed that the vaccine structure induced high antigenicity and good cellular immunity in the host body and stimulates various immune receptors such as TLR5, MHC I, and MHC II. Vaccine function was also associated with an increase in IgM and IgG antibodies and a set of Th1 and Th2 cytokines. But the final confirmation of the effectiveness of the designed vaccine requires clinical processes.Copyright © 2022, TMU Press.

Designing multi-epitope mRNA construct as a universal influenza vaccine candidate for future epidemic/pandemic preparedness.

Despite the availability of prevention and treatment strategies and advancing immunization approaches, the influenza virus remains a global threat that continues to plague humanity with unpredictable pandemics. Due to the unusual genetic variability and segmented genome, the reassortment between different strains of influenza is facilitated and the viruses continuously evolve and adapt to the host cell's immunity. This underlies the seasonal vaccine mismatches that decrease the vaccine efficacy and increase the risk of outbreaks. Thus, the development of a universal vaccine covering all the influenza A and B strains would reduce the pervasiveness of the influenza virus. In the current study, a potentially universal influenza multi-epitope vaccine was designed based on the experimentally tested conserved T cell and B cell epitopes of hemagglutinin (HA), neuraminidase (NA), nucleoprotein (NP), and matrix-2 proton channel (M2) of the virus. The immune simulation and molecular docking of the vaccine construct with TLR2, TLR3, and TLR4 elicited the favorable immunogenicity of the vaccine and the formation of stable complexes, respectively. Ultimately, based on the immunoinformatics analysis, the universal mRNA multi-epitope vaccine designed in this study might have a protection potential against the various subtypes of influenza A and B.

Epitopes screening and vaccine molecular design of SADS-CoV based on immunoinformatics.

The regional outbreak of the Swine acute diarrhea syndrome coronavirus (SADS-CoV) has seriously threatened the swine industry. There is an urgent need to discover safe and effective vaccines to contain them quickly. The coronavirus spike protein mediates virus entry into host cells, one of the most important antigenic determinants and a potential vaccine target. Therefore, this study aims to conduct a predictive analysis of the epitope of S protein B cells and T cells (MHC class I and class II) by immunoinformatics methods by screening and identifying protective antigenic epitopes that induce major neutralized antibodies and activate immune responses to construct epitope vaccines. The study explored primary, secondary, and tertiary structures, disulfide bonds, protein docking, immune response simulation, and seamless cloning of epitope vaccines. The results show that the spike protein dominant epitope of the screening has a high conservativeness and coverage of IFN-γ, IL-4-positive Th epitope, and CTL epitope. The constructed epitope vaccine interacts stably with TLR-3 receptors, and the immune response simulation shows good immunogenicity, which could effectively activate humoral and cellular immunity. After codon optimization, it was highly likely to be efficiently and stably expressed in the Escherichia coli K12 expression system. Therefore, the constructed epitope vaccine will provide a new theoretical basis for the design of SADS-CoV antiviral drugs and related research on coronaviruses such as SARS-CoV-2.

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.

Genomics, metagenomics, and pan-genomics approaches in COVID-19

The coronavirus outbreak, which initially started in Wuhan China, has rapidly led to numerous morbidities and mortalities worldwide. Although potential antiviral and antiinflammatory medicines are available, several individuals are dying daily. In order to thoroughly tackle this deadly virus, the knowledge of genomics, metagenomics, and pan genomics is required, not only to devise new treatment regimens but also to improve the present approaches. Understanding the genomic organization, diversity and structural complexity of this virus can help to figure out several previously unanswered questions. SARS-CoV-2 corona virus comprises of four major structural proteins, namely, the spike surface glycoprotein, tiny envelope protein, matrix protein, and nucleocapsid protein along with accessory proteins that contribute to pathogenesis and persistence of the virus in one way or the other. This chapter covers genomics, metagenomics, and pan-genomics-based strategies that can facilitate to figure the possible mutational recombination and trace the phylogenetic background of the species. Sequencing has been performed and the hence derived viral sequences have been deposited into exclusive repositories to speed up research and explore targeted treatment options. Moreover, immunoinformatics approaches and reverse vaccinology have been applied to speed up the process of formulating reliable, safe, and specific therapeutic options, rapidly. © 2023 Elsevier Inc. All rights reserved.

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.

Proteome Exploration of human coronaviruses for Identifying Novel vaccine candidate: A Hierarchical Subtractive Genomics and Reverse Vaccinology Approach.

BACKGROUND: The SARSCoV-2 is responsible for infecting more than 271,000,000 people in 222 countries by December 10, of which 5,300,000 have died. COVID-19 was introduced by World Health Organization as a global concern and a pandemic disease due to the prevalence of disease. OBJECTIVES: Developing of preventive or therapeutics medication against novel-cov2019 is an urgent need, and has high priority among scientific societies, in this regard, the production of effective vaccines is one of the most significant and high-priority necessity. To date specific antiviral therapeutic and prophylactic vaccine for novel coronavirus (n-CoV2019) are not available. Because of costing and time-consuming of experimental strategies during vaccine design procedure, different immunoinformatics methods were developed. Recently Because of defect study on proteins of n-cov2019, its recommended to study other human coronaviruses. METHODS: At the beginning of vaccine design, the proteome study is essential. In this investigation, the whole human coronaviruses proteome was evaluated using proteome subtraction strategy. Out of 5945 human coronavirus proteins, five new antigenic proteins were selected by analyzing the hierarchical proteome subtraction and then their various physicochemical and immunological properties were also investigated bioinformatically. RESULTS: All five protein sequences are antigenic and non-allergenic proteins; moreover, spike protein group including Spike glycoprotein (E2) (Peplomer protein), spike fragment and spike glycoprotein fragment showed acceptable stability, which can be used to design new vaccines against human coronaviruses. CONCLUSION: These selected peptides and the other introduced protein in this study (HE, orf7a, SARS_X4 domain-containing protein and protein 8) can be employed as a suitable candidate for developing novel prophylactic or therapeutic vaccine against human coronaviruses.

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.

An Immunoinformatics Approach to Design Novel and Potent Multi-Epitope-Based Vaccine to Target Lumpy Skin Disease

The lumpy skin disease (LSD) virus of the Poxviridae family is a serious threat that mostly affects cattle and causes significant economic loss. LSD has the potential to spread widely and its rapidly across borders. Despite the availability of information, there is still no competitive vaccine available for LSD. Therefore, the current study was conducted to develop an epitope-based LSD vaccine that is efficient, secure, and biocompatible and stimulates both innate and adaptive immune responses using immunoinformatics techniques. Initially, putative virion core proteins were manipulated;B-cell and T-cell epitopes have been predicted and connected with the help of adjuvants and linkers. Numerous bioinformatics methods, including antigenicity testing, transmembrane topology screening, allergenicity assessment, conservancy analysis, and toxicity evaluation, were employed to find superior epitopes. Based on promising vaccine candidates and immunogenic potential, the vaccine design was selected. Strong interactions between TLR4 and TLR9 and the anticipated vaccine design were revealed by molecular docking. Finally, based on the high docking score, computer simulations were performed in order to assess the stability, efficacy, and compactness of the constructed vaccine. The simulation outcomes showed that the polypeptide vaccine design was remarkably stable, with high expression, stability, immunogenic qualities, and considerable solubility. Additionally, computer-based research shows that the constructed vaccine provides adequate population coverage, making it a promising candidate for use in the design of vaccines against other viruses within the Poxviridae family and potentially other virus families as well. These outcomes suggest that the epitope-based vaccine developed in this study will be a significant candidate against LSD to control and prevent LSDV-related disorders if further investigated experimentally.

Immunoinformatics Study of SARS-CoV-2 Nucleocapsid Phosphoprotein Identifies Promising Epitopes with Mutational Implications.

The SARS-CoV-2 is rapidly evolving and new mutations are being reported from different parts of the world. In this study, we investigated the variations occurring in the nucleocapsid phosphoprotein (N-protein) of SARS-CoV-2 from India. We used several in silico prediction tools to characterise N-protein including IEDB webserver for B cell epitope prediction, Vaxijen 2.0 and AllergenFP v.1.0 for antigenicity and allergenicity prediction of epitopes, CLUSTAL Omega for mutation identification and PONDR webserver for disorder prediction, PROVEAN score for protein function and iMutantsuite for protein stability prediction. Our results show that 81 mutations have occurred in this protein among Indian SARS-CoV-2 isolates. Subsequently, we characterized the N-protein epitopes to identify seven most promising peptides. We mapped these mutations with seven N-protein epitopes to identify the loss of antigenicity in two of them, suggesting that the mutations occurring in the SARS-CoV-2 genome contribute to the alteration in the properties of epitopes. Altogether, our data strongly indicates that N-protein is gaining several mutations in its B cell epitope regions that might alter protein function.

MERS virus spike protein HTL-epitopes selection and multi-epitope vaccine design using computational biology.

MERS-CoV, a zoonotic virus, poses a serious threat to public health globally. Thus, it is imperative to develop an effective vaccination strategy for protection against MERS-CoV. Immunoinformatics and computational biology tools provide a faster and more cost-effective strategy to design potential vaccine candidates. In this work, the spike proteins from different strains of MERS-CoV were selected to predict HTL-epitopes that show affinity for T-helper MHC-class II HTL allelic determinant (HLA-DRB1:0101). The antigenicity and conservation of these epitopes among the selected spike protein variants in different MERS-CoV strains were analyzed. The analysis identified five epitopes with high antigenicity: QSIFYRLNGVGITQQ, DTIKYYSIIPHSIRS, PEPITSLNTKYVAPQ, INGRLTTLNAFVAQQ and GDMYVYSAGHATGTT. Then, a multi-epitope vaccine candidate was designed using linkers and adjuvant molecules. Finally, the vaccine construct was subjected to molecular docking with TLR5 (Toll-like receptor-5). The proposed vaccine construct had strong binding energy of -32.3 kcal/mol when interacting with TLR5.Molecular dynamics simulation analysis showed that the complex of the vaccine construct and TLR5 is stable. Analysis using in silico immune simulation also showed that the prospective multi-epitope vaccine design had the potential to elicit a response within 70 days, with the immune system producing cytokines and immunoglobulins. Finally, codon adaptation and in silico cloning analysis showed that the candidate vaccine could be expressed in the Escherichia coli K12 strain. Here we also designed support vaccine construct MEV-2 by using B-cell and CD8+ CTL epitopes to generate the complete immunogenic effect. This study opens new avenues for the extension of research on MERS vaccine development.Communicated by Ramaswamy H. Sarma.

Immunoinformatics-aided design of a new multi-epitope vaccine adjuvanted with domain 4 of pneumolysin against Streptococcus pneumoniae strains.

BACKGROUND: Streptococcus pneumoniae (Pneumococcus) has remained a leading cause of fatal infections such as pneumonia, meningitis, and sepsis. Moreover, this pathogen plays a major role in bacterial co-infection in patients with life-threatening respiratory virus diseases such as influenza and COVID-19. High morbidity and mortality in over one million cases, especially in very young children and the elderly, are the main motivations for pneumococcal vaccine development. Due to the limitations of the currently marketed polysaccharide-based vaccines, non-serotype-specific protein-based vaccines have received wide research interest in recent years. One step further is to identify high antigenic regions within multiple highly-conserved proteins in order to develop peptide vaccines that can affect various stages of pneumococcal infection, providing broader serotype coverage and more effective protection. In this study, immunoinformatics tools were used to design an effective multi-epitope vaccine in order to elicit neutralizing antibodies against multiple strains of pneumococcus. RESULTS: The B- and T-cell epitopes from highly protective antigens PspA (clades 1-5) and PhtD were predicted and immunodominant peptides were linked to each other with proper linkers. The domain 4 of Ply, as a potential TLR4 agonist adjuvant candidate, was attached to the end of the construct to enhance the immunogenicity of the epitope vaccine. The evaluation of the physicochemical and immunological properties showed that the final construct was stable, soluble, antigenic, and non-allergenic. Furthermore, the protein was found to be acidic and hydrophilic in nature. The protein 3D-structure was built and refined, and the Ramachandran plot, ProSA-web, ERRAT, and Verify3D validated the quality of the final model. Molecular docking analysis showed that the designed construct via Ply domain 4 had a strong interaction with TLR4. The structural stability of the docked complex was confirmed by molecular dynamics. Finally, codon optimization was performed for gene expression in E. coli, followed by in silico cloning in the pET28a(+) vector. CONCLUSION: The computational analysis of the construct showed acceptable results, however, the suggested vaccine needs to be experimentally verified in laboratory to ensure its safety and immunogenicity.

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 approach of epitope prediction for SARS-CoV-2.

BACKGROUND: The novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide. RESULTS: Molecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate. CONCLUSION: Our predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR.

Screening of potential antigens from whole proteome and development of multi-epitope vaccine against Rhizopus delemar using immunoinformatics approaches.

Mucormycosis is a deadly fungal disease mainly caused by Rhizopus oryzae (strain 99-880), also known as Rhizopus delemar. Previously, mucormycosis occurs in immunocompromised patients of diabetes mellitus, cancer, organ transplant, etc. But there was a drastic increase in mucormycosis cases in the ongoing COVID-19 pandemic. Despite several available therapies and antifungal treatments, the mortality rate of mucormycosis is about more than 50%. Currently, there is no vaccine available in the market for mucormycosis that urgently needs to develop a potential vaccine against mucormycosis with high efficacy. In the present study, we have screened 4 genome-derived predicted antigens (GDPA) through sequential filtration of the whole proteome of R. delemar using different benchmarked bioinformatics tools. These 4 GDPA along with 4 randomly selected experimentally reported antigens (ERA) were sourced for prediction of B- and T- cell epitopes and utilized in designing of two potential multi-epitope vaccine candidates which can induce both innate and adaptive immunity against R. delemar. Besides these, comparative immune simulation studies and in silico cloning were performed using L. lactis as an expression system for their possible uses as oral vaccines. This is the first multi-epitope vaccine designed against R. delemar through systematic pipelined reverse vaccinology and immunoinformatic approaches. Although the wet-lab based experimental validation of designed vaccines is required before testing in the preclinical model, the current study will significantly help in reducing the cost of experimentation as well as improving the efficacy of vaccine therapy against mucormycosis and other pathogenic diseases.Communicated by Ramaswamy H. Sarma.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.