Immuno-informatics design of a multimeric epitope peptide based vaccine targeting SARS-CoV-2 spike glycoprotein

Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development

BACKGROUND

The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches?

METHOD

Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers.

RESULTS

Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs).

CONCLUSIONS

Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.

Immunoinformatics-driven approach for development of potential multi-epitope vaccine against the secreted protein FlaC of Campylobacter jejuni

Campylobacter jejuni causes a leading human gastrointestinal infection which is associated with foodborne diarrhea, stomach cramping, and fever. In the recent years, numerous multidrug-resistant strains of C. jejuni has evolved and is considered in the priority pathogens category. Therefore, an increasing demand exists to develop an effective vaccine against Campylobacteriosis. The T cell and B cell epitopes from the FlaC protein were predicted using comprehensive immunoinformatics tools. The predicted epitopes were chosen based on their antigenicity, allergenicity, and toxicity profiles. Using the bioinformatics approach various physicochemical properties of the constructed vaccine were determined. The molecular docking analysis of the vaccine with the TLRs demonstrated that TLR5 has a higher binding affinity of -1159.0 kcal/mol. Molecular dynamics simulation has confirmed the stable association of the vaccine with TLR5. The immune response of the constructed vaccine was validated using immunostimulation. Based on this study, we recommend the formulation of a multi-epitope vaccine as a promising agent to effectively combat the dreadful campylobacteriosis infection.Communicated by Ramaswamy H. Sarma.

Proteome based analysis of circulating SARS-CoV-2 variants: approach to a universal vaccine candidate

The discovery of the first infectious variant in Wuhan, China, in December 2019, has posed concerns over global health due to the spread of COVID-19 and subsequent variants. While the majority of patients experience flu-like symptoms such as cold and fever, a small percentage, particularly those with compromised immune systems, progress from mild illness to fatality. COVID-19 is caused by a RNA virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach involved utilizing immunoinformatic to identify vaccine candidates with multiple epitopes and ligand-binding regions in reported SARS-CoV-2 variants. Through analysis of the spike glycoprotein, we identified dominant epitopes for T-cells and B-cells, resulting in a vaccine construct containing two helper T-cell epitopes, six cytotoxic T-cell epitopes, and four linear B-cell epitopes. Prior to conjugation with adjuvants and linkers, all epitopes were evaluated for antigenicity, toxicity, and allergenicity. Additionally, we assessed the vaccine Toll-Like Receptors complex (2, 3, and 4). The vaccine construct demonstrated antigenicity, non-toxicity, and non-allergenicity, thereby enabling the host to generate antibodies with favorable physicochemical characteristics. Furthermore, the 3D structure of the B-cell construct exhibited a ProSA-web z-score plot with a value of -1.71, indicating the reliability of the designed structure. The Ramachandran plot analysis revealed that 99.6% of the amino acid residues in the vaccine subunit were located in the high favored observation region, further establishing its strong candidacy as a vaccination option.

Prediction of an immunogenic peptide ensemble and multi-subunit vaccine for Visceral leishmaniasis using bioinformatics approaches

Visceral Leishmaniasis (VL) is a neglected tropical disease of public health importance in the Indian subcontinent. Despite consistent elimination initiatives, the disease has not yet been eliminated and there is an increased risk of resurgence from active VL reservoirs including asymptomatic, post kala azar dermatitis leishmaniasis (PKDL) and HIV-VL co-infected individuals. To achieve complete elimination and sustain it in the long term, a prophylactic vaccine, which can elicit long lasting immunity, is desirable. In this study, we employed immunoinformatic tools to design a multi-subunit epitope vaccine for the Indian population by targeting antigenic secretory proteins screened from the Leishmania donovani proteome. Out of 8014 proteins, 277 secretory proteins were screened for their cellular location and proteomic evidence. Through NCBI BlastP, unique fragments of the proteins were cropped, and their antigenicity was evaluated. B-cell, HTL and CTL epitopes as well as IFN-ɣ, IL-17, and IL-10 inducers were predicted, manually mapped to the fragments and common regions were tabulated forming a peptide ensemble. The ensemble was evaluated for Class I MHC immunogenicity and toxicity. Further, immunogenic peptides were randomly selected and used to design vaccine constructs. Eight vaccine constructs were generated by linking random peptides with GS linkers. Synthetic TLR-4 agonist, RS09 was used as an adjuvant and linked with the constructs using EAAK linkers. The predicted population coverage of the constructs was ∼99.8 % in the Indian as well as South Asian populations. The most antigenic, nontoxic, non-allergic construct was chosen for the prediction of secondary and tertiary structures. The 3D structures were refined and analyzed using Ramachandran plot and Z-scores. The construct was docked with TLR-4 receptor. Molecular dynamic simulation was performed to check for the stability of the docked complex. Comparative in silico immune simulation studies showed that the predicted construct elicited humoral and cell-mediated immunity in human host comparable to that elicited by Leish-F3, which is a promising vaccine candidate for human VL.

In Vivo Validation of Novel Synthetic tbp1 Peptide-Based Vaccine Candidates against Haemophilus influenzae Strains in BALB/c Mice

Haemophilus influenzae is a Gram-negative bacterium characterized as a small, nonmotile, facultative anaerobic coccobacillus. It is a common cause of a variety of invasive and non-invasive infections. Among six serotypes (a-f), H. influenzae type b (Hib) is the most familiar and predominant mostly in children and immunocompromised individuals. Following Hib vaccination, infections due to other serotypes have increased in number, and currently, there is no suitable effective vaccine to induce cross-strain protective antibody responses. The current study was aimed to validate the capability of two 20-mer highly conserved synthetic tbp1 (transferrin-binding protein 1) peptide-based vaccine candidates (tbp1-E1 and tbp1-E2) predicted using in silico approaches to induce immune responses against H. influenzae strains. Cytokine induction ability, immune simulations, and molecular dynamics (MD) simulations were performed to confirm the candidacy of epitopic docked complexes. Synthetic peptide vaccine formulations in combination with two different adjuvants, BGs (Bacterial Ghosts) and CFA/IFA (complete/incomplete Freund's adjuvant), were used in BALB/c mouse groups in three booster shots at two-week intervals. An indirect ELISA was performed to determine endpoint antibody titers using the Student's t-distribution method. The results revealed that the synergistic use of both peptides in combination with BG adjuvants produced better results. Significant differences in absorbance values were observed in comparison to the rest of the peptide-adjuvant combinations. The findings of this study indicate that these tbp1 peptide-based vaccine candidates may present a preliminary set of peptides for the development of an effective cross-strain vaccine against H. influenzae in the future due to their highly conserved nature.

Advances of Reverse Vaccinology for mRNA Vaccine Design against SARS-CoV-2: A Review of Methods and Tools

mRNA vaccines are a new class of vaccine that can induce potent and specific immune responses against various pathogens. However, the design of mRNA vaccines requires the identification and optimization of suitable antigens, which can be challenging and time consuming. Reverse vaccinology is a computational approach that can accelerate the discovery and development of mRNA vaccines by using genomic and proteomic data of the target pathogen. In this article, we review the advances of reverse vaccinology for mRNA vaccine design against SARS-CoV-2, the causative agent of COVID-19. We describe the steps of reverse vaccinology and compare the in silico tools used by different studies to design mRNA vaccines against SARS-CoV-2. We also discuss the challenges and limitations of reverse vaccinology and suggest future directions for its improvement. We conclude that reverse vaccinology is a promising and powerful approach to designing mRNA vaccines against SARS-CoV-2 and other emerging pathogens.

Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2

In the recent past several vaccines were developed to combat the COVID-19 disease. Unfortunately, the protective efficacy of the current vaccines has been reduced due to the high mutation rate in SARS-CoV-2. Here, we successfully implemented a coevolution based immunoinformatics approach to design an epitope-based peptide vaccine considering variability in spike protein of SARS-CoV-2. The spike glycoprotein was investigated for B- and T-cell epitope prediction. Identified T-cell epitopes were mapped on previously reported coevolving amino acids in the spike protein to introduce mutation. The non-mutated and mutated vaccine components were constructed by selecting epitopes showing overlapping with the predicted B-cell epitopes and highest antigenicity. Selected epitopes were linked with the help of a linker to construct a single vaccine component. Non-mutated and mutated vaccine component sequences were modelled and validated. The in-silico expression level of the vaccine constructs (non-mutated and mutated) in E. coli K12 shows promising results. The molecular docking analysis of vaccine components with toll-like receptor 5 (TLR5) demonstrated strong binding affinity. The time series calculations including root mean square deviation (RMSD), radius of gyration (RGYR), and energy of the system over 100 ns trajectory obtained from all atom molecular dynamics simulation showed stability of the system. The combined coevolutionary and immunoinformatics approach used in this study will certainly help to design an effective peptide vaccine that may work against different strains of SARS-CoV-2. Moreover, the strategy used in this study can be implemented on other pathogens.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

A multiepitope vaccine candidate against infectious bursal disease virus using immunoinformatics-based reverse vaccinology approach

Infectious bursal disease virus is the causative agent of infectious bursal disease (Gumboro disease), a highly contagious immunosuppressive disease of chicken with a substantial economic impact on small- and large-scale poultry industries worldwide. Currently, live attenuated vaccines are widely used to control the disease in chickens despite their issues with safety (immunosuppression and bursal atrophy) and efficiency (breaking through the maternally-derived antibody titer). To overcome the drawbacks, the current study has, for the first time, attempted to construct a computational model of a multiepitope based vaccine candidate against infectious bursal disease virus, which has the potential to overcome the safety and protection issues found in the existing live-attenuated vaccines. The current study used a reverse vaccinology based immunoinformatics approach to construct the vaccine candidate using major and minor capsid proteins of the virus, VP2 and VP3, respectively. The vaccine construct was composed of four CD8⁺ epitopes, seven CD4⁺ T-cell epitopes, 11 B-cell epitopes and a Cholera Toxin B adjuvant, connected using appropriate flexible peptide linkers. The vaccine construct was evaluated as antigenic with VaxiJen Score of 0.6781, immunogenic with IEDB score of 2.89887 and non-allergenic. The 55.64 kDa construct was further evaluated for its physicochemical characteristics, which revealed that it was stable with an instability index of 16.24, basic with theoretical pI of 9.24, thermostable with aliphatic index of 86.72 and hydrophilic with GRAVY score of -0.256. The docking and molecular dynamics simulation studies of the vaccine construct with Toll-like receptor-3 revealed fair structural interaction (binding affinity of -295.94 kcal/mol) and complex stability. Further, the predicted induction of antibodies and cytokines by the vaccine construct indicated the possible elicitation of the host's immune response against the virus. The work is a significant attempt to develop next-generation vaccines against the infectious bursal disease virus though further experimental studies are required to assess the efficacy and protectivity of the proposed vaccine candidate in vivo.

Exploiting reverse vaccinology approach for the design of a multiepitope subunit vaccine against the major SARS-CoV-2 variants

The current COVID-19 pandemic, an infectious disease caused by the novel coronavirus (SARS-CoV-2), poses a threat to global health because of its high rate of spread and death. Currently, vaccination is the most effective method to prevent the spread of this disease. In the present study, we developed a novel multiepitope vaccine against SARS-CoV-2 containing Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (BA.1) variants. To this end, we performed a robust immunoinformatics approach based on multiple epitopes of the four structural proteins of SARS-CoV-2 (S, M, N, and E) from 475 SARS-CoV-2 genomes sequenced from the regions with the highest number of registered cases, namely the United States, India, Brazil, France, Germany, and the United Kingdom. To investigate the best immunogenic epitopes for linear B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL), we evaluated antigenicity, allergenicity, conservation, immunogenicity, toxicity, human population coverage, IFN-inducing, post-translational modifications, and physicochemical properties. The tertiary structure of a vaccine prototype was predicted, refined, and validated. Through docking experiments, we evaluated its molecular coupling to the key immune receptor Toll-Like Receptor 3 (TLR3). To improve the quality of docking calculations, quantum mechanics/molecular mechanics calculations (QM/MM) were used, with the QM part of the simulations performed using the density functional theory formalism (DFT). Cloning and codon optimization were performed for the successful expression of the vaccine in E. coli. Finally, we investigated the immunogenic properties and immune response of our SARS-CoV-2 multiepitope vaccine. The results of the simulations show that administering our prototype three times significantly increases the antibody response and decreases the amount of antigens. The proposed vaccine candidate should therefore be tested in clinical trials for its efficacy in neutralizing SARS-CoV-2.

Bioinformatic Analysis of B- and T-cell Epitopes from SARS-CoV-2 Structural Proteins and their Potential Cross-reactivity with Emerging Variants and other Human Coronaviruses

Background

The mutations in SARS-CoV-2 variants of concern (VOC) facilitate the virus’ escape from the neutralizing antibodies induced by vaccines. However, the protection from hospitalization and death is not significantly diminished. Both vaccine boosters and infection improve immune responses and provide protection, suggesting that conserved and/or cross-reactive epitopes could be involved. While several important T- and B-cell epitopes have been identified, mainly in the S protein, the M and N proteins and their potential cross-reactive epitopes with other coronaviruses remain largely unexplored.

Aims

To identify and map new potential B- and T-cell epitopes within the SARS-CoV-2 S, M and N proteins, as well as cross-reactive epitopes with human coronaviruses.

Methods

Different bioinformatics tools were used to: i) Identify new and compile previously-reported B-and T-cell epitopes from SARS-CoV-2 S, M and N proteins; ii) Determine the mutations in S protein from VOC that affect B- and T-cell epitopes, and; iii) Identify cross-reactive epitopes with coronaviruses relevant to human health.

Results

New, potential B- and T-cell epitopes from S, M and N proteins as well as cross-reactive epitopes with other coronaviruses were found and mapped within the proteins’ structures.

Conclusion

Numerous potential B- and T-cell epitopes were found in S, M and N proteins, some of which are conserved between coronaviruses. VOCs present mutations within important epitopes in the S protein; however, a significant number of other epitopes remain unchanged. The epitopes identified here may contribute to augmenting the protective response to SARS-CoV-2 and its variants induced by infection and/or vaccination, and may also be used for the rational design of novel broad-spectrum coronavirus vaccines.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

In-silico design of an immunoinformatics based multi-epitope vaccine against Leishmania donovani

BACKGROUND

Visceral Leishmaniasis (VL) is a fatal vector-borne parasitic disorder occurring mainly in tropical and subtropical regions. VL falls under the category of neglected tropical diseases with growing drug resistance and lacking a licensed vaccine. Conventional vaccine synthesis techniques are often very laborious and challenging. With the advancement of bioinformatics and its application in immunology, it is now more convenient to design multi-epitope vaccines comprising predicted immuno-dominant epitopes of multiple antigenic proteins. We have chosen four antigenic proteins of Leishmania donovani and identified their T-cell and B-cell epitopes, utilizing those for in-silico chimeric vaccine designing. The various physicochemical characteristics of the vaccine have been explored and the tertiary structure of the chimeric construct is predicted to perform docking studies and molecular dynamics simulations.

RESULTS

The vaccine construct is generated by joining the epitopes with specific linkers. The predicted tertiary structure of the vaccine has been found to be valid and docking studies reveal the construct shows a high affinity towards the TLR-4 receptor. Population coverage analysis shows the vaccine can be effective on the majority of the world population. In-silico immune simulation studies confirms the vaccine to raise a pro-inflammatory response with the proliferation of activated T and B cells. In-silico codon optimization and cloning of the vaccine nucleic acid sequence have also been achieved in the pET28a vector.

CONCLUSION

The above bioinformatics data support that the construct may act as a potential vaccine. Further wet lab synthesis of the vaccine and in vivo works has to be undertaken in animal model to confirm vaccine potency.

SOLVx therapeutics vaccine - Activate T-cell immunity using broad surveillance epitope strategy against mutant strains SARS-COV2

Highly mutable Coronavirus-19 continuously reconstructs its genome and renders prophylactic vaccines ineffective. The objective of the present study was to demonstrate the anti-viral efficacy and safety of the SOLVx therapeutics vaccine. The peptides were designed with Neo7Logix R&D and synthesized with Genescript GLP laboratory with 95 % purity. BALB/C mice were used to develop the HCoV-229E mutant coronavirus model and viral mRNA confirmation in the lung tissue was assessed with qPCR. Mice were euthanized and effects of treatment on various parameters (Viral mRNA in lungs, cytokine levels, PBMC differentiation, hematological and biochemical) were assessed with respective biological samples. Immuno-typing analysis of PBMCs by flowcytometry showed marked increase in T cell subsets, % of B cells and NK cell population in mice treated with SOLVx (Series 1) in a dose dependent manner. Serum immunoglobulin G, and M levels were increased significantly (P < 0.001). In the peptide treatment groups, there was a dose dependent statistically significant decrease in IL-6, IL-10 and TNF-α levels (P < 0.001). IFN-γ was elevated in treatment group significantly (P < 0.001). In conclusion, the qPCR results suggested that the SOLVx vaccine (Series 1) reduced the SARS-COV2 virus infectivity in a dose dependent manner. The humoral, cellular and functional activity of the SOLVx showed that it worked through multi-mechanistic targeting the virus evolution, offering immune response, defense and eradication of the SARS-COV2 virus.

Potential association factors for developing effective peptide-based cancer vaccines

Peptide-based cancer vaccines have been shown to boost immune systems to kill tumor cells in cancer patients. However, designing an effective T cell epitope peptide-based cancer vaccine still remains a challenge and is a major hurdle for the application of cancer vaccines. In this study, we constructed for the first time a library of peptide-based cancer vaccines and their clinical attributes, named CancerVaccine (https://peptidecancervaccine.weebly.com/). To investigate the association factors that influence the effectiveness of cancer vaccines, these peptide-based cancer vaccines were classified into high (HCR) and low (LCR) clinical responses based on their clinical efficacy. Our study highlights that modified peptides derived from artificially modified proteins are suitable as cancer vaccines, especially for melanoma. It may be possible to advance cancer vaccines by screening for HLA class II affinity peptides may be an effective therapeutic strategy. In addition, the treatment regimen has the potential to influence the clinical response of a cancer vaccine, and Montanide ISA-51 might be an effective adjuvant. Finally, we constructed a high sensitivity and specificity machine learning model to assist in designing peptide-based cancer vaccines capable of providing high clinical responses. Together, our findings illustrate that a high clinical response following peptide-based cancer vaccination is correlated with the right type of peptide, the appropriate adjuvant, and a matched HLA allele, as well as an appropriate treatment regimen. This study would allow for enhanced development of cancer vaccines.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

Bioinformatics, Computational Informatics, and Modeling Approaches to the Design of mRNA COVID-19 Vaccine Candidates

This article is devoted to applying bioinformatics and immunoinformatics approaches for the development of a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. The study’s relevance is dictated by the fact that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began its global threat at the end of 2019 and since then has had a devastating impact on the whole world. Measures to reduce threats from the pandemic include social restrictions, restrictions on international travel, and vaccine development. In most cases, vaccine development depends on the spike glycoprotein, which serves as a medium for its entry into host cells. Although several variants of SARS-CoV-2 have emerged from mutations crossing continental boundaries, about 6000 delta variants have been reported along the coast of more than 20 countries in Africa, with South Africa accounting for the highest percentage. This also applies to the omicron variant of the SARS-CoV-2 virus in South Africa. The authors suggest that bioinformatics and immunoinformatics approaches be used to develop a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. Various immunoinformatics tools have been used to predict T- and B-lymphocyte epitopes. The epitopes were further subjected to multiple evaluations to select epitopes that could elicit a sustained immunological response. The candidate vaccine consisted of seven epitopes, a highly immunogenic adjuvant, an MHC I-targeting domain (MITD), a signal peptide, and linkers. The molecular weight (MW) was predicted to be 223.1 kDa, well above the acceptable threshold of 110 kDa on an excellent vaccine candidate. In addition, the results showed that the candidate vaccine was antigenic, non-allergenic, non-toxic, thermostable, and hydrophilic. The vaccine candidate has good population coverage, with the highest range in East Africa (80.44%) followed by South Africa (77.23%). West Africa and North Africa have 76.65% and 76.13%, respectively, while Central Africa (75.64%) has minimal coverage. Among seven epitopes, no mutations were observed in 100 randomly selected SARS-CoV-2 spike glycoproteins in the study area. Evaluation of the secondary structure of the vaccine constructs revealed a stabilized structure showing 36.44% alpha-helices, 20.45% drawn filaments, and 33.38% random helices. Molecular docking of the TLR4 vaccine showed that the simulated vaccine has a high binding affinity for TLR-4, reflecting its ability to stimulate the innate and adaptive immune response.

Designing of a Novel Multi-Antigenic Epitope-Based Vaccine against E. hormaechei: An Intergraded Reverse Vaccinology and Immunoinformatics Approach

Enterobacter hormaechei is involved in multiple hospital-associated infections and is resistant to beta-lactam and tetracycline antibiotics. Due to emerging antibiotics resistance in E. hormaechei and lack of licensed vaccine availability, efforts are required to overcome the antibiotics crisis. In the current research study, a multi-epitope-based vaccine against E. hormaechei was designed using reverse vaccinology and immunoinformatic approaches. A total number of 50 strains were analyzed from which the core proteome was extracted. One extracellular (curlin minor subunit CsgB) and two periplasmic membrane proteins (flagellar basal-body rod protein (FlgF) and flagellar basal body P-ring protein (FlgI) were prioritized for B and T-cell epitope prediction. Only three filtered TPGKMDYTS, GADMTPGKM and RLSAESQAT epitopes were used when designing the vaccine construct. The epitopes were linked via GPGPG linkers and EAAAK linker-linked cholera toxin B-subunit adjuvant was used to enhance the immune stimulation efficacy of the vaccine. Docking studies of the vaccine construct with immune cell receptors revealed better interactions, vital for generating proper immune reactions. Docked complexes of vaccine with MHC-I, MHC-II and Tool-like receptor 4 (TLR-4) reported the lowest binding energy of −594.1 kcal/mol, −706.7 kcal/mol, −787.2 kcal/mol, respectively, and were further subjected to molecular dynamic simulations. Net binding free energy calculations also confirmed that the designed vaccine has a strong binding affinity for immune receptors and thus could be a good vaccine candidate for future experimental investigations.

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.

Immunoinformatics and Biophysics Approaches to Design a Novel Multi-Epitopes Vaccine Design against Staphylococcus auricularis

Due to the misuse of antibiotics in our daily lives, antimicrobial resistance (AMR) has become a major health problem. Penicillin, the first antibiotic, was used in the 1930s and led to the emergence of AMR. Due to alterations in the microbe’s genome and the evolution of new resistance mechanisms, antibiotics are losing efficacy against microbes. There are high rates of mortality and morbidity due to antibiotic resistance, so addressing this major health issue requires new approaches. Staphylococcus auricularis is a Gram-positive cocci and is capable of causing opportunistic infections and sepsis. S. auricularis is resistant to several antibiotics and does not currently have a licensed vaccine. In this study, we used bacterial pan-genome analysis (BPGA) to study S. auricularis pan-genome and applied a reverse immunology approach to prioritize vaccine targets against S. auricularis. A total of 15,444 core proteins were identified by BPGA analysis, which were then used to identify good vaccine candidates considering potential vaccine filters. Two vaccine candidates were evaluated for epitope prediction including the superoxide dismutase and gamma-glutamyl transferase protein. The epitope prediction phase involved the prediction of a variety of B-Cell and T-cell epitopes, and the epitopes that met certain criteria, such as antigenicity, immunogenicity, non-allergenicity, and non-toxicity were chosen. A multi-epitopes vaccine construct was then constructed from all the predicted epitopes, and a cholera toxin B-subunit adjuvant was also added to increase vaccine antigenicity. Three-dimensional models of the vaccine were used for downward analyses. Using the best-modeled structure, binding potency was tested with MHC-I, MHC-II and TLR-4 immune cells receptors, proving that the vaccine binds strongly with the receptors. Further, molecular dynamics simulations interpreted strong intermolecular binding between the vaccine and receptors and confirmed the vaccine epitopes exposed to the host immune system. The results support that the vaccine candidate may be capable of eliciting a protective immune response against S. auricularis and may be a promising candidate for experimental in vitro and in vivo studies.

First computational design using lambda-superstrings and in vivo validation of SARS-CoV-2 vaccine

Coronavirus disease 2019 (COVID-19) is the greatest threat to global health at the present time, and considerable public and private effort is being devoted to fighting this recently emerged disease. Despite the undoubted advances in the development of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, uncertainty remains about their future efficacy and the duration of the immunity induced. It is therefore prudent to continue designing and testing vaccines against this pathogen. In this article we computationally designed two candidate vaccines, one monopeptide and one multipeptide, using a technique involving optimizing lambda-superstrings, which was introduced and developed by our research group. We tested the monopeptide vaccine, thus establishing a proof of concept for the validity of the technique. We synthesized a peptide of 22 amino acids in length, corresponding to one of the candidate vaccines, and prepared a dendritic cell (DC) vaccine vector loaded with the 22 amino acids SARS-CoV-2 peptide (positions 50-71) contained in the NTD domain (DC-CoVPSA) of the Spike protein. Next, we tested the immunogenicity, the type of immune response elicited, and the cytokine profile induced by the vaccine, using a non-related bacterial peptide as negative control. Our results indicated that the CoVPSA peptide of the Spike protein elicits noticeable immunogenicity in vivo using a DC vaccine vector and remarkable cellular and humoral immune responses. This DC vaccine vector loaded with the NTD peptide of the Spike protein elicited a predominant Th1-Th17 cytokine profile, indicative of an effective anti-viral response. Finally, we performed a proof of concept experiment in humans that included the following groups: asymptomatic non-active COVID-19 patients, vaccinated volunteers, and control donors that tested negative for SARS-CoV-2. The positive control was the current receptor binding domain epitope of COVID-19 RNA-vaccines. We successfully developed a vaccine candidate technique involving optimizing lambda-superstrings and provided proof of concept in human subjects. We conclude that it is a valid method to decipher the best epitopes of the Spike protein of SARS-CoV-2 to prepare peptide-based vaccines for different vector platforms, including DC vaccines.

A Systematic Immuno-Informatic Approach to Design a Multiepitope-Based Vaccine Against Emerging Multiple Drug Resistant Serratia marcescens

Serratia marcescens is now an important opportunistic pathogen that can cause serious infections in hospitalized or immunocompromised patients. Here, we used extensive bioinformatic analyses based on reverse vaccinology and subtractive proteomics-based approach to predict potential vaccine candidates against S. marcescens. We analyzed the complete proteome sequence of 49 isolate of Serratia marcescens and identified 5 that were conserved proteins, non-homologous from human and gut flora, extracellular or exported to the outer membrane, and antigenic. The identified proteins were used to select 5 CTL, 12 HTL, and 12 BCL epitopes antigenic, non-allergenic, conserved, hydrophilic, and non-toxic. In addition, HTL epitopes were able to induce interferon-gamma immune response. The selected peptides were used to design 4 multi-epitope vaccines constructs (SMV1, SMV2, SMV3 and SMV4) with immune-modulating adjuvants, PADRE sequence, and linkers. Peptide cleavage analysis showed that antigen vaccines are processed and presented via of MHC class molecule. Several physiochemical and immunological analyses revealed that all multiepitope vaccines were non-allergenic, stable, hydrophilic, and soluble and induced the immunity with high antigenicity. The secondary structure analysis revealed the designed vaccines contain mainly coil structure and alpha helix structures. 3D analyses showed high-quality structure. Molecular docking analyses revealed SMV4 as the best vaccine construct among the four constructed vaccines, demonstrating high affinity with the immune receptor. Molecular dynamics simulation confirmed the low deformability and stability of the vaccine candidate. Discontinuous epitope residues analyses of SMV4 revealed that they are flexible and can interact with antibodies. In silico immune simulation indicated that the designed SMV4 vaccine triggers an effective immune response. In silico codon optimization and cloning in expression vector indicate that SMV4 vaccine can be efficiently expressed in E. coli system. Overall, we showed that SMV4 multi-epitope vaccine successfully elicited antigen-specific humoral and cellular immune responses and may be a potential vaccine candidate against S. marcescens. Further experimental validations could confirm its exact efficacy, the safety and immunogenicity profile. Our findings bring a valuable addition to the development of new strategies to prevent and control the spread of multidrug-resistant Gram-negative bacteria with high clinical relevance.

Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains

The discovery of a new SARS-CoV-2 virus strain in South Africa presents a major public health threat, therefore contributing to increased infections and transmission rates during the second wave of the global pandemic. This study lays the groundwork for the development of a novel subunit vaccine candidate from the circulating strains of South African SARS-CoV-2 and provides an understanding of the molecular epidemiological trend of the circulating strains. A total of 475 whole-genome nucleotide sequences from South Africa submitted between December 1, 2020 and February 15, 2021 available at the GISAID database were retrieved based on its size, coverage level and hosts. To obtain the distribution of the clades and lineages of South African SARS-CoV-2 circulating strains, the metadata of the sequence retrieved were subjected to an epidemiological analysis. There was a prediction of the cytotoxic T lymphocytes (CTL), Helper T cells (HTL) and B-cell epitopes. Furthermore, there was allergenicity, antigenicity and toxicity predictions on the epitopes. The analysis of the physicochemical properties of the vaccine construct was performed; the secondary structure, tertiary structure and B-cell 3D conformational structure of the vaccine construct were predicted. Also, molecular binding simulations and dynamics simulations were adopted in the prediction of the vaccine construct's stability and binding affinity with TLRs. Result obtained from the metadata analysis indicated lineage B.1.351 to be in higher circulation among various circulating strains of SARS-CoV-2 in South Africa and GH has the highest number of circulating clades. The construct of the novel vaccine was antigenic, non-allergenic and non-toxic. The Instability index (II) score and aliphatic index were estimated as 41.74 and 78.72 respectively. The computed half-life in mammalian reticulocytes was 4.4 h in vitro, for yeast and in E. coli was >20 h and >10 h in vivo respectively. The grand average of hydropathicity (GRAVY) score is estimated to be -0.129, signifying the hydrophilic nature of the protein. The molecular docking indicates that the vaccine construct has a high binding affinity towards the TLRs with TLR 3 having the highest binding energy (-1203.2 kcal/mol) and TLR 9 with the lowest (-1559.5 kcal/mol). These results show that the vaccine construct is promising and should be evaluated using animal model.

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.

SARS-CoV-2 subunit vaccine adjuvants and their signaling pathways

INTRODUCTION

Vaccines are the agreed upon weapon against the COVID-19 pandemic. This review discusses about COVID-19 subunit vaccines adjuvants and their signaling pathways, which could provide a glimpse into the selection of appropriate adjuvants for prospective vaccine development studies.

AREAS COVERED

In the introduction, a brief background about the SARS-CoV-2 pandemic, the vaccine development race and classes of vaccine adjuvants were provided. . The antigen, trial stage, and types of adjuvants were extracted from the included articles and thun assimilated. Finally, the pattern recognition receptors (PRRs), their classes, cognate adjuvants, and potential signaling pathways were comprehended.

EXPERT OPINION

Adjuvants are unsung heroes of subunit vaccines. The in silico studies are very vital in avoiding several costly trial errors and save much work times. The majority of the (pre)clinical studies are promising. It is encouraging that most of the selected adjuvants are novel. Much emphasis must be paid to the optimal paring of antigen-adjuvant-PRRs for obtaining the desired vaccine effect. A good subunit vaccine/adjuvant is one that has high efficacy, safety, dose sparing, and rapid seroconversion rate and broad spectrum of immune response. In the years to come, COVID-19 adjuvanted subunit vaccines are expected to have superior utility than any other vaccines for various reasons.

Immunoinformatics and reverse vaccinomic approaches for effective design

The emergence of mutagenic strains of severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) worst hit the world which already suffered from the Coronavirus disease-2019 (COVID-19) pandemic for 2 years. Due to recent advances in vaccinomics, many vaccine candidates are available but their efficacy against a mutant version of SARS-CoV-2 has remained uncertain. The immune-informatics-based reverse vaccinomic approaches have shown promising investigations recently for the development of cost-effective vaccinomics candidates in a very short period of time. The strategic vaccine development of selected epitopes using artificial intelligence for both B- and T-cells is a very crucial step in this process. This approach provides a highly effective and immunogenic vaccine that offers immunological safety against autoimmunity and other adverse effects over ethnicities, pregnant women, and vulnerable age groups. Several researchers have developed effective vaccine candidates using computational vaccinology and the immune-informatics approach. In this process, a unique peptide sequence of viral proteins such as Nucleocapsid, spike, envelope protein was identified by various in silico tools which are acting as immunological epitopes against TLRs, T-cells, and B-cells. While the conventional immunological vaccine studies take years for vaccine candidature, the immunoinformatics approach is a time-efficient way for the next generation research to study host-pathogen interactions and vaccine development. It is also cost-effective and leads to a better understanding of disease pathogenesis, diagnosis, and immunological response. Owing to the advantage of immunoinformatics-based vaccine approaches the present chapter aimed to discuss vaccine development using immunoinformatics approaches. Besides, the current challenges and future aspects have also been discussed herewith.

ILeukin10Pred: A Computational Approach for Predicting IL-10-Inducing Immunosuppressive Peptides Using Combinations of Amino Acid Global Features

Simple Summary

Interleukin-10 is a cytokine that exhibits potent anti-inflammatory characteristics that play an essential role in limiting the host’s immune response to pathogens and regulating the growth or differentiation of various immune cells. Moreover, interleukin-10 prediction via conventional approaches is time-consuming and labor-intensive. Hence, researchers are inclined towards an alternative approach to predict interleukin-10-inducing peptides. Additionally, numerous in silico tools are available to predict T cell epitopes. These methods generally follow a direct or indirect approach where they directly predict cytotoxic T-lymphocyte epitopes rather than major histocompatibility complex binders or indirectly predict single components of the T cell recognition pathway. However, very few studies are available that address cytokine-specific predictions. Our research utilized a computer-aided approach to develop a model to predict IL-10-inducing peptides. This study outperformed the existing state-of-the-art method and achieved an accuracy of 87.5% and Matthew’s correlation coefficient (MCC) of 0.755 on the hybrid feature types and outperformed an existing state-of-the-art method based on dipeptide compositions that achieved an accuracy of 81.24% and an MCC value of 0.59. Therefore, our model is promising to assist in predicting immunosuppressive peptides that induce interleukin-10 cytokines.

Abstract

Interleukin (IL)-10 is a homodimer cytokine that plays a crucial role in suppressing inflammatory responses and regulating the growth or differentiation of various immune cells. However, the molecular mechanism of IL-10 regulation is only partially understood because its regulation is environment or cell type-specific. In this study, we developed a computational approach, ILeukin10Pred (interleukin-10 prediction), by employing amino acid sequence-based features to predict and identify potential immunosuppressive IL-10-inducing peptides. The dataset comprises 394 experimentally validated IL-10-inducing and 848 non-inducing peptides. Furthermore, we split the dataset into a training set (80%) and a test set (20%). To train and validate the model, we applied a stratified five-fold cross-validation method. The final model was later evaluated using the holdout set. An extra tree classifier (ETC)-based model achieved an accuracy of 87.5% and Matthew’s correlation coefficient (MCC) of 0.755 on the hybrid feature types. It outperformed an existing state-of-the-art method based on dipeptide compositions that achieved an accuracy of 81.24% and an MCC value of 0.59. Our experimental results showed that the combination of various features achieved better predictive performance..

Immunoinformatics Analysis of SARS-CoV-2 ORF1ab Polyproteins to Identify Promiscuous and Highly Conserved T-Cell Epitopes to Formulate Vaccine for Indonesia and the World Population

SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon's entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

A Perspective on Nanotechnology and COVID-19 Vaccine Research and Production in South Africa

Advances in nanotechnology have enabled the development of a new generation of vaccines, which are playing a critical role in the global control of the COVID-19 pandemic and the return to normalcy. Vaccine development has been conducted, by and large, by countries in the global north. South Africa, as a major emerging economy, has made extensive investments in nanotechnology and bioinformatics and has the expertise and resources in vaccine development and manufacturing. This has been built at a national level through decades of investment. In this perspective article, we provide a synopsis of the investments made in nanotechnology and highlight how these could support innovation, research, and development for vaccines for this disease. We also discuss the application of bioinformatics tools to support rapid and cost-effective vaccine development and make recommendations for future research and development in this area to support future health challenges.