Immunoinformatics Aided Design and In-Vivo Validation of a Cross-Reactive Peptide Based Multi-Epitope Vaccine Targeting Multiple Serotypes of Dengue Virus

Development and evaluation of recombinant multi-epitopes vaccine against nervous necrosis virus

Capsid protein (CP) is the antigen of nervous necrosis virus (NNV), a fatal microorganism for almost marine fishes. Antigen epitope is a special chemical group in an antigen molecule that determines the specificity of the antigen, usually consisting of 5-15 amino acid residues. However, the antigen epitope of NNV antigen remains unclear. In this study, using immunoinformatic method, we analyzed the antigen epitope of CP and designed a multi-epitopes vaccine of NNV (PA). Furthermore, we evaluated the immune responses induced by PA vaccine. The results showed that three cytotoxic T lymphocyte epitopes and six B-cell lymphocyte epitopes were predicted, with high antigenicity, non-allergen, and non-toxin. Based on these epitopes, a multi-epitopes vaccine of NNV (PA) was designed and prepared. After immunization, the mRNA expression levels of IL-1β, TNF-α, CD4, CD8, MHC-Ⅰ, and MHC-Ⅱ in PA group were significantly up-regulated. Moreover, it has been proven that PA could significantly activate antigen presenting cells. Importantly, PA could induce similar levels of antibodies secretion and immune protection, compared to CP group. The survival rate reached 77.22 % in PA group. This study provides a cheap and effective strategy for aquatic vaccine design, which will be beneficial in application to development of vaccine in aquaculture industry.

Discovery of Bioactive Peptides Through Peptide Scanning

Therapeutic peptides targeted at various diseases are becoming increasingly relevant for the pharmaceutical industry. Several of these drugs were originally designed by mimicking a segment of a protein of interest. As such, protein mimicry represents a promising strategy both in immunology, for the identification of B- and T-cell epitopes, as well as for the modulation of protein activity, including the disruption of protein-protein interactions (PPIs) and the interference with biological or pathological cellular functions. Several methods have been developed to pinpoint the (binding) epitopes of a protein or the regions responsible for biological activity. One of such strategies is the scanning of the protein or selected domains with synthetic overlapping peptides. As the mechanism of action of a mimetic peptide can be similar to that of the whole protein, this method offers a powerful tool for the investigation of protein function, along with providing a solid basis for the development of therapeutic candidates. This review gives a general overview of different applications of the peptide scanning methodology, describing a comparison of the preparation and use of solid-phase libraries (peptide arrays) with isolated peptide libraries and highlighting their strengths and most common applications.

Bioinformatics-guided decoding of the Ancylostoma duodenale genome for the identification of potential vaccine targets

Ancylostoma duodenale, a parasitic nematode worm, is found to be involved in various infections, including intestinal blood loss, protein malnutrition, and anemia. Antimicrobial resistance to the available therapeutics has prompted the search for new drug and vaccine targets against A. duodenale. Despite significant advances in vaccine development against A. duodenale, no commercial and FDA-approved vaccine exists to safeguard humans from infections caused by this pathogen. In this investigation, a stringent bioinformatics analysis identified 36 unique essential and host-interacting proteins. Based on their subcellular localization, 6 proteins located in the extracellular space and outer membrane were categorized as vaccine targets, while the remaining proteins were predicted to act as potential drug candidates. These vaccine candidates were further assessed for antigenicity, allergenicity, and physicochemical analysis to determine their suitability for the designing of a multi-epitope vaccine. Two candidate proteins were chosen as optimal targets in the development of vaccine design. The identified T- and B-cell epitopes from these proteins were then combined with appropriate linkers and adjuvants to design chimeric vaccine constructs aimed at inducing both cellular and humoral immune responses. Molecular docking, molecular dynamic simulations, PCA analysis, DCCM analysis, and binding free energy calculations proved stable interactions of the designed vaccine with human immune cell receptors. Within a bacterial cloning system, the vaccine constructs demonstrated the ability to be cloned and expressed. The immunological stimulation elicited significant immunological responses to the proposed vaccine. Our investigation identified new therapeutic targets and developed a peptide-based multi-epitope vaccine against A. duodenale infection. Additional experimental verification will open up new therapeutic alternatives for this emerging resistant pathogen.

Immmunoinformatics-based design of T and B-cell multi-epitope vaccine to combat Borrelia burgdorferi infection

Lyme disease is one of the most common vector-borne infectious diseases globally, partly due to the absence of a vaccine for humans. Hence, in this study, an immunoinformatics method was used to design a multi-epitope vaccine (MEV) against Borrelia burgdorferi. The optimal B- and T-cell epitopes from Borrelia burgdorferi proteins (BmpA and OspC) were joined with the appropriate linkers to construct a MEV. In addition, β-defensin was included as an adjuvant in the vaccine construct. Secondary and tertiary structures of MEV were predicted, refined and validated. The developed vaccine was high antigenicity, non-allergenicity, solubility and stability. The Ramachandran plot, ProSA-web and ERRAT were employed to ensure the final model's authenticity. The immune simulation confirmed acceptable responses of both cellular and humoral immune. The vaccine's binding stability with Toll-like receptor 2 (TLR2) was confirmed using molecular docking and molecular dynamics (MD) simulation. Furthermore, MEV effectively stimulated high-level antibody production in mice, significantly promoted splenocyte proliferation in immunized mice, and markedly enhanced splenic IFN-γand IL-4 mRNA transcription levels. These results suggest that MEV, as a novel vaccine candidate, holds significant potential for future prevention and control of Borrelia burgdorferi infections.

Immunoinformatics-driven design of a multi-epitope vaccine targeting neonatal rotavirus with focus on outer capsid proteins VP4 and VP7 and non structural proteins NSP2 and NSP5

Rotaviral gastroenteritis remains a major global health concern, particularly for infants and young children under five years old. Prior to the introduction of the WHO-prequalified rotavirus vaccine, rotavirus (RV) was responsible for approximately 800,000 child deaths annually in developing countries. Although vaccination efforts have reduced this number, RV still causes around 200,000 child deaths each year worldwide. The current WHO-prequalified vaccines are live attenuated and offer limited efficacy of 40-60%, with a slight risk of intussusception in young children. To overcome these limitations, we employed immunoinformatics to design a novel multi-epitope vaccine (MEV) targeting rotavirus outer capsid proteins VP4 and VP7, as well as crucial non-structural proteins NSP2 and NSP5. The RV-MEV incorporates 10 epitopes, including 4 CD8 + T-cell, 5 CD4 + T-cell, and 1 B-cell epitope, all of which are antigenic, non-allergenic, and non-toxic. These epitopes also showed potential to induce interferon-γ (IFN-γ). Molecular simulation studies confirmed stable interactions between RV-MEV and human TLR5 and integrin αvβ5 complexes. The RV-MEV was successfully cloned into a pET28a(+) vector during in-silico cloning. Immune simulation studies predict a strong immune response to the RV-MEV. Future in vitro and in vivo studies are necessary to validate the vaccine's effectiveness in providing protection against various rotavirus strains in neonates.

Multi-epitope-based vaccine models prioritization against Astrovirus MLB1 using immunoinformatics and reverse vaccinology approaches

Astrovirus MLB1 (HAstV-MLB1) is non-enveloped RNA virus that cause acute gastroenteritis infection. Despite research progress about infection and pathogenesis of HAstV-MLB1, Currently, no vaccine has been developed to effectively combat this pathogen. The current study is based on immunoinformatics and reverse vaccinology approaches to design next-generation, multi-epitope-based vaccine models against HAstV-MLB1. Genome-wide whole proteome data of HAstV-MLB1 strain was retrieved, and a series of analyses were conducted to explore effective B and T-cell epitopes that hold significant antigenic nature with no toxicity and allergenicity. A set of vaccine constructs were designed by different combination of lead B and T-cell epitopes with diverse linkers and adjuvants sequences. The model vaccine structures were analyzed via rigorous criteria of physiochemical properties, antigenicity, and molecular docking with HLA and TLR4 immune receptors to ensure their efficacy and safety. Based on the lowest binding energy of -82.48 kcal/mol against the HLA receptor, the MLB1-C2 vaccine model with β-definsin adjuvant was prioritized for molecular dynamic and immune simulations analyses to assess its stability and immunogenic potential. These analyses revealed that the MLB1-C2 construct has feasible molecular stability and potential to boost strong immune responses in the host cell. Besides, the model was predicted to be non-toxic, non-allergenic, and antigenic, ensuring broad population coverage and capable to elicit a robust immune response. The in-silico cloning analysis highlighted a possible gene expression potential of the MLB1-C2 construct in E.coli commercial recombinant vector molecule. The findings of the current study provide an essential template for the development of a advanced next-generation effective vaccine against HAstV-MLB1.

A computational approach for MHC-restricted multi-epitope vaccine design targeting Oropouche virus structural proteins

In recent years, Brazil has recorded approximately 500,000 Oropouche virus (OROV) cases in the Amazon region, underscoring the growing global threat posed by emerging and reemerging viruses. Symptoms of OROV closely resemble those of Dengue virus and Zika virus, contributing to underreporting and underestimation of its true impact. In the absence of specific treatments, the development of vaccines becomes essential. This study aimed to identify immunogenic epitopes in three structural proteins of OROV and develop a multi-epitope vaccine candidate. RefSeq sequences of the nucleocapsid protein and the Gn and Gc glycoproteins were obtained from the National Center for Biotechnology Information Virus and submitted to epitope search in Immune Epitope Database. Antigenicity, allergenicity, stability, and toxicity analyses were conducted, and the approved epitopes were aligned to the global protein to remove transmembrane regions and N-glycosylation sites. Thirteen epitopes were selected and used to construct a multi-epitope vaccine candidate, with β-defensin and PADRE adjuvants. The protein demonstrated optimal antigenicity, low allergenicity, and satisfactory stability and solubility. Predictions of humoral and cellular immune responses were performed, indicating satisfactory results for three doses of the vaccine candidate. 3D modeling of the protein was performed, evaluating the molecular docking of the multi-epitope protein with TLR-2, TLR-3, TLR-6, and TLR-8 receptors. Our findings present a promising vaccine candidate against OROV, potentially protecting immunocompromised individuals and high-risk populations, and establishing a foundation for both in vitro and in vivo testing. The identified epitopes could also aid in immunodiagnostic test development, advancing surveillance and control strategies.

Computational identification of B and T-cell epitopes for designing a multi-epitope vaccine against SARS-CoV-2 spike glycoprotein

Although the peak of the COVID-19 pandemic has passed, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose a significant global threat and remains a public health concern. Given the ongoing risk and the substantial loss of life caused by the virus, continuous research into vaccine development is essential. This study employs immunoinformatics approaches to identify T-cell and B-cell epitopes for designing a multi-epitope peptide vaccine candidate targeting the Omicron variant. The proposed vaccine construct comprises 1435 amino acids, including eight linear B lymphocyte, seven cytotoxic T lymphocyte, and five helper T lymphocyte epitopes, along with appropriate adjuvants and linkers. The evaluation of the vaccine revealed high antigenicity, non-allergenicity, non-toxicity, and favorable physicochemical properties. To further assess its efficacy, molecular docking studies were performed to investigate interactions between the vaccine and key immune components, including Toll-like receptors and major histocompatibility complex molecules. Stability of these interactions was confirmed using molecular dynamics simulations in triplicate, conducted over 100 ns using GROMACS 2023 to compute key metrics, such as root mean square deviation, root mean square fluctuation, solvent-accessible surface area, and radius of gyration. The results demonstrate that the multi-epitope vaccine has the potential to elicit strong immune responses against the Omicron variant, providing a promising foundation for further experimental validation and clinical development in COVID-19 vaccine research.

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host. The objective of this study is to design and construct effective and protective vaccines guided by structural proteomics and machine learning algorithms to provide protection against the M. pulmonis infection. Through a thorough examination of the entire proteome of M. pulmonis, four specific targets Membrane protein P80, Lipoprotein, Uncharacterized protein and GGDEF domain-containing protein have been identified as appropriate for designing a vaccine. The proteins underwent mapping of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) (IFN)-γ ±, and B-cell epitopes using artificial and recurrent neural networks. The design involved the creation of mRNA and peptide-based vaccine, which consisted of 8 CTL epitopes associated by GGS linkers, 7 HTL (IFN-positive) epitopes, and 8 B-cell epitopes joined by GPGPG linkers. The vaccine designed exhibit antigenic behavior, non-allergenic qualities, and exceptional physicochemical attributes. Structural modeling revealed that correct folding is crucial for optimal functioning. The coupling of the MEVC and Toll-like Receptors (TLR)1, TLR2, and TLR6 was examined through molecular docking experiments. This was followed by molecular simulation investigations, which included binding free energy estimations. The results indicated that the dynamics of the interaction were stable, and the binding was strong. In silico cloning and optimization analysis revealed an optimized sequence with a GC content of 49.776 % and a CAI of 0.982. The immunological simulation results showed strong immune responses, with elevated levels of active and plasma B-cells, regulatory T-cells, HTL, and CTL in both IgM+IgG and secondary immune responses. The antigen was completely cleared by the 50th day. This study lays the foundation for creating a potent and secure vaccine candidate to combat the newly identified M. pulmonis infection in people.

Unlocking Hope: Paving the Way for a Cutting-Edge Multi-Epitope Dengue Virus Vaccine

Dengue fever is a significant health issue in Pakistan, demanding a vaccine effective against all the viral strains. This study employs reverse vaccinology to develop potential dengue vaccine candidates (DVAX I-III). The study thoroughly examined conserved areas of dengue virus serotypes 1-4's structural and non-structural proteins. Key viral proteins were analyzed to find antigenic peptides, which were incorporated into vaccine candidates and potentiated with adjuvants. Computational methods predicted peptide structures and evaluated their binding to immune receptors TLR 2, TLR 4, HLA *A1101, and DRB*401. A molecular dynamics simulation lasting 100 ns of the DVAX II-TLR4 complex at different time intervals clearly indicated that the ligand is attached to the receptor. Normal mode analysis assessed the stability and flexibility of these interactions. Encouragingly, all three vaccine candidates demonstrated favorable interactions with these immune receptors and the potential to induce a robust immune response. These findings suggest their safety and warrant further in vivo studies to evaluate their efficacy for clinical development.

Structural proteomics guided annotation of vaccine targets and designing of multi-epitopes vaccine to instigate adaptive immune response against Francisella tularensis

Francisella tularensis can cause severe disease in humans via the respiratory or cutaneous routes and a case fatality ratio of up to 10 % is reported due to lack of proper antibiotic treatment, while F. novicida causes disease in severely immunocompromised individuals. Efforts are needed to develop effective vaccine candidates against Francisella species. Thus, in this study, a systematic computational work frame was used to deeply investigate the whole proteome of Francisella novicida containing 1728 proteins to develop vaccine against F. tularensis and related species. Whole-proteome analysis revealed that four proteins including (A0Q492) (A0Q7Y4), (A0Q4N4), and (A0Q5D9) are the suitable vaccine targets after the removal of homologous, paralogous and prediction of subcellular localization. These proteins were used to predict the T cell, B cell, and HTL epitopes which were joined together through suitable linkers to construct a multi-epitopes vaccine (MEVC). The MEVC was found to be highly immunogenic and non-allergenic while the physiochemical properties revealed the feasible expression and purification. Moreover, the molecular interaction of MEVC with TLR2, molecular simulation, and binding free energy analyses further validated the immune potential of the construct. According to Jcat analysis, the refined sequence demonstrates GC contents of 41.48 % and a CAI value of 1. The in-silico cloning and optimization process ensured compatibility with host codon usage, thereby facilitating efficient expression. Computational immune simulation studies underscored the capacity of MEVC to induce both primary and secondary immune responses. The conservation analysis further revealed that the selected epitopes exhibit 100 % conservation across different species and thus provides wider protection against Francisella.

Integrating immunoinformatics and computational epitope prediction for a vaccine candidate against respiratory syncytial virus

Respiratory syncytial virus (RSV) poses a significant global health threat, especially affecting infants and the elderly. Addressing this, the present study proposes an innovative approach to vaccine design, utilizing immunoinformatics and computational strategies. We analyzed RSV's structural proteins across both subtypes A and B, identifying potential helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes. Criteria such as antigenicity, allergenicity, toxicity, and cytokine-inducing potential were rigorously examined. Additionally, we evaluated the conservancy of these epitopes and their population coverage across various RSV strains. The comprehensive analysis identified six major histocompatibility complex class I (MHC-I) binding, five MHC-II binding, and three B-cell epitopes. These were integrated with suitable linkers and adjuvants to form the vaccine. Further, molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors (TLR4 and TLR5), with a notable preference for TLR4. Immune simulation analysis underscored the vaccine's potential to elicit a strong immune response. This study presents a promising RSV vaccine candidate and offers theoretical support, marking a significant advancement in vaccine development efforts. However, the promising in silico findings need to be further validated through additional in vivo studies.

Novel prophylactic and therapeutic multi-epitope vaccine based on Ag85A, Ag85B, ESAT-6, and CFP-10 of Mycobacterium tuberculosis using an immunoinformatics approach

BACKGROUND

Current tuberculosis (TB) control strategies face limitations, such as low antibiotic treatment compliance and a rise in multidrug resistance. Furthermore, the lack of a safe and effective vaccine compounds these challenges. The limited efficacy of existing vaccines against TB underscores the urgency for innovative strategies, such as immunoinformatics. Consequently, this study aimed to design a targeted multi-epitope vaccine against TB infection utilizing an immunoinformatics approach.

METHODS

The multi-epitope vaccine targeted Ag85A, Ag85B, ESAT-6, and CFP-10 proteins. The design adopted various immunoinformatics tools for cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and linear B lymphocyte (LBL) epitope prediction, the assessment of vaccine characteristics, structure modeling, population coverage analysis, disulfide engineering, solubility prediction, molecular docking/dynamics with toll-like receptors (TLRs), codon optimization/cloning, and immune simulation.

RESULTS

The multi-epitope vaccine, which was assembled using 12 CTL, 25 HTL, and 21 LBL epitopes associated with CpG adjuvants, showed promising characteristics. The immunoinformatics analysis confirmed the antigenicity, immunogenicity, and lack of allergenicity. Physicochemical evaluations indicated that the proteins were stable, thermostable, hydrophilic, and highly soluble. Docking simulations suggested high-affinity binding to TLRs, including TLR2, TLR4, and TLR9. In silico immune simulation predicted strong T cell (cytokine release) and B cell (immunoglobulin release) responses.

CONCLUSION

This immunoinformatics-designed multi-epitope vaccine targeting Ag85A, Ag85B, ESAT-6, and CFP-10 proteins showed promising characteristics in terms of stability, immunogenicity, antigenicity, solubility, and predicted induction of humoral and adaptive immune responses. This suggests its potential as a prophylactic and therapeutic vaccine against TB.

T cell epitope based vaccine design while targeting outer capsid proteins of rotavirus strains infecting neonates: an immunoinformatics approach

Gastrointestinal diarrhea is majorly caused by the rotavirus (RV) in the children who generally are under the age group of 5 years. WHO estimates that ∼95% of the children contract RV infection, by this age. The disease is highly contagious; notably in many cases, it is proven fatal with high mortality rates especially in the developing countries. In India alone, an estimated 145,000 yearly deaths occurs due to RV related gastrointestinal diarrhea. WHO pre-qualified vaccines that are available for RV are all live attenuated vaccines with modest efficacy range between 40 and 60%. Further, the risk of intussusceptions has been reported in some children on RV vaccination. Thus, in a quest to develop alternative candidate to overcome challenges associated with these oral vaccines, we chose immunoinformatics approach to design a multi-epitope vaccine (MEV) while targeting the outer capsid viral proteinsVP4 and VP7 of the neonatal strains of rotavirus. Interestingly, ten epitopes, that is, six CD8+T-cells and four CD4+T-cell epitopes were identified which were predicted to be antigenic, non-allergic, non-toxic and stable. These epitopes were then linked to adjuvants, linkers, and PADRE sequences to create a multi-epitope vaccine for RV. The in silico designed RV-MEV and human TLR5 complex displayed stable interactions during molecular dynamics simulations. Further, the immune simulation studies of RV-MEV corroborated that the vaccine candidate emerges as a promising immunogen. Future investigations while performing in vitro and in vivo analyses with designed RV-MEV construct are highly desirable to warrant the potential of this vaccine candidate in protective immunity against different strains of RVs infecting neonates.Communicated by Ramaswamy H. Sarma.

Designing a Conserved Immunogenic Peptide Construct from the Nucleocapsid Protein of Puumala orthohantavirus

Puumala orthohantavirus (PUUV) is an emerging zoonotic virus endemic to Europe and Russia that causes nephropathia epidemica, a mild form of hemorrhagic fever with renal syndrome (HFRS). There are limited options for treatment and diagnosis of orthohantavirus infection, making the search for potential immunogenic candidates crucial. In the present work, various bioinformatics tools were employed to design conserved immunogenic peptides containing multiple epitopes of PUUV nucleocapsid protein. Eleven conserved peptides (90% conservancy) of the PUUV nucleocapsid protein were identified. Three conserved peptides containing multiple T and B cell epitopes were selected using a consensus epitope prediction algorithm. Molecular docking using the HPEP dock server demonstrated strong binding interactions between the epitopes and HLA molecules (ten alleles for each class I and II HLA). Moreover, an analysis of population coverage using the IEDB database revealed that the identified peptides have over 90% average population coverage across six continents. Molecular docking and simulation analysis reveal a stable interaction with peptide constructs of chosen immunogenic peptides and Toll-like receptor-4. These computational analyses demonstrate selected peptides' immunogenic potential, which needs to be validated in different experimental systems.

Immune targeting of filarial glutaredoxin through a multi-epitope peptide-based vaccine: A reverse vaccinology approach

Despite the efforts of global programme to eliminate lymphatic filariasis (GPELF), the threat of lymphatic filariasis (LF) still looms over humanity in terms of long-term disabilities, and morbidities across the globe. In light of this situation, investigators have chosen to focus on the development of immunotherapeutics targeting the physiologically important filarial-specific proteins. Glutaredoxin (16.43 kDa) plays a pivotal role in filarial redox biology, serving as a vital contributor. In the context of the intra-host survival of filarial parasites, this antioxidant helps in mitigating the oxidative stress imposed by the host immune system. Given its significant contribution, the development of a vaccine targeting glutaredoxin holds promise as a new avenue for achieving a filaria-free world. Herein, multi-epitope-based vaccine was designed using advanced immunoinformatics approach. Initially, 4B-cell epitopes and 6 T-cell epitopes (4 MHC I and 2 MHC II) were identified from the 146 amino acid long sequence of glutaredoxin of the human filarid, Wuchereria bancrofti. Subsequent clustering of these epitopes with linker peptides finalized the vaccine structure. To boost TLR-mediated innate immunity, TLR-specific adjuvants were incorporated into the designed vaccine. After that, experimental analyses confirm the designed vaccine, Vac4 as anefficient ligand of human TLR5 to elicit protective innate immunity against filarial glutaredoxin. Immune simulation further demonstrated abundant levels of IgG and IgM as crucial contributors in triggering vaccine-induced adaptive responses in the recipients. Hence, to facilitate the validation of immunogenicity of the designed vaccine, Vac4 was cloned in silico in pET28a(+) expression vector for recombinant production. Taken together, our findings suggest that vaccine-mediated targeting of filarial glutaredoxin could be a future option for intervening LF on a global scale.

A novel immunoinformatics approach for developing a poly-epitope vaccine targeting foot and mouth disease virus, exploiting structural VP proteins

Foot and mouth Disease virus (FMDV) belongs to Picornaviridae family and Aphthovirus genus causing Foot and mouth disease (FMD) in cloven-hoofed animals. FMDV, a prevalent virus induces both acute and chronic infections with high mutation rates resulting in seven primary serotypes, making vaccine development indispensable. Due to time and cost effectiveness of the immunoinformatic approach, we designed in-silico polyepitope vaccine (PEV) for the curtailment of FMDV. Structural and immunogenic parts of FMDV (Viral Protein 1 (VP1), Viral Protein 2 (VP2), Viral Protein 3 (VP3), and Viral Protein 4 (VP4)) were used to design the cytotoxic T Lymphocyte (CTL), Helper T Lymphocyte (HTL), and B-cell epitopes, followed by screening for antigenic, non-allergenic, Interferon (IFN) simulator, and non-toxicity, which narrowed down to 7 CTL, 3 HTL, and 12 B-cell epitopes. These selected epitopes were linked using appropriate linkers and Cholera Toxin B (CTB) adjuvant for immunological modulation. The physiochemical analyses followed by the structure prediction demonstrated the stability, hydrophilicity and solubility of the PEV. The interactions and stability between the vaccine, Toll like Receptor 3 (TLR3) and Toll like receptor 7 (TLR7) were revealed by molecular docking and Molecular Mechanics/Poisson Boltzmann Surface Area (MMPBSA) with high stability and compactness verified by MD simulation. In-silico immune simulation demonstrated a strong immunological response. FMDV-PEV (Poly epitope vaccine) will be effectively produced in an E. coli system, as codon optimization and cloning in an expression vector was performed. The effectiveness, safety, and immunogenicity profile of FMDV-PEV may be confirmed by further experimental validations.

Immunoinformatics and computational approaches driven designing a novel vaccine candidate against Powassan virus

Powassan virus (POWV) is an arthropod-borne virus (arbovirus) capable of causing severe illness in humans for severe neurological complications, and its incidence has been on the rise in recent years due to climate change, posing a growing public health concern. Currently, no vaccines to prevent or medicines to treat POWV disease, emphasizing the urgent need for effective countermeasures. In this study, we utilize bioinformatics approaches to target proteins of POWV, including the capsid, envelope, and membrane proteins, to predict diverse B-cell and T-cell epitopes. These epitopes underwent screening for critical properties such as antigenicity, allergenicity, toxicity, and cytokine induction potential. Eight selected epitopes were then conjugated with adjuvants using various linkers, resulting in designing of a potentially stable and immunogenic vaccine candidate against POWV. Moreover, molecular docking, molecular dynamics simulations, and immune simulations revealed a stable interaction pattern with the immune receptor, suggesting the vaccine's potential to induce robust immune responses. In conclusion, our study provided a set of derived epitopes from POWV's proteins, demonstrating the potential for a novel vaccine candidate against POWV. Further in vitro and in vivo studies are warranted to advance our efforts and move closer to the goal of combatting POWV and related arbovirus infections.

Designing a Multiepitope Vaccine against Eastern Equine Encephalitis Virus: Immunoinformatics and Computational Approaches

Eastern equine encephalitis virus (EEEV) is a significant threat to human and animal populations, causing severe encephalitis, often leading to long-term neurological complications and even mortality. Despite this, no approved antiviral treatments or EEEV human vaccines currently exist. In response, we utilized immunoinformatics and computational approaches to design a multiepitope vaccine candidate for EEEV. By screening the structural polyprotein of EEEV, we predicted both T-cell and linear B-cell epitopes. These epitopes underwent comprehensive evaluations for their antigenicity, toxicity, and allergenicity. From these evaluations, we selected ten epitopes highly suitable for vaccine design, which were connected with adjuvants using a stable linker. The resulting vaccine construct demonstrated exceptional antigenic, nontoxic, nonallergenic, and physicochemical properties. Subsequently, we employed molecular docking and molecular dynamics simulations to reveal a stable interaction pattern between the vaccine candidate and Toll-like receptor 5. Besides, computational immune simulations predicted the vaccine's capability to induce robust immune responses. Our study addresses the urgent need for effective EEEV preventive strategies and offers valuable insights for EEEV vaccine development. As EEEV poses a severe threat with potential spread due to climate change, our research provides a crucial step in enhancing public health defenses against this menacing zoonotic disease.

In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Influenza remains a global health concern due to its potential to cause pandemics as a result of rapidly mutating influenza virus strains. Existing vaccines often struggle to keep up with these rapidly mutating flu viruses. Therefore, the development of a broad-spectrum peptide vaccine that can stimulate an optimal antibody response has emerged as an innovative approach to addressing the influenza threat. In this study, an immunoinformatic approach was employed to rapidly predict immunodominant epitopes from different antigens, aiming to develop an effective multiepitope influenza vaccine (MEV). The immunodominant B-cell linear epitopes of seasonal influenza strains hemagglutinin (HA) and neuraminidase (NA) were predicted using an antibody-peptide microarray, involving a human cohort including vaccinees and infected patients. On the other hand, bioinformatics tools were used to predict immunodominant cytotoxic T-cell (CTL) and helper T-cell (HTL) epitopes. Subsequently, these epitopes were evaluated by various immunoinformatic tools. Epitopes with high antigenicity, high immunogenicity, non-allergenicity, non-toxicity, as well as exemplary conservation were then connected in series with appropriate linkers and adjuvants to construct a broad-spectrum MEV. Moreover, the structural analysis revealed that the MEV candidates exhibited good stability, and the docking results demonstrated their strong affinity to Toll-like receptors 4 (TLR4). In addition, molecular dynamics simulation confirmed the stable interaction between TLR4 and MEVs. Three injections with MEVs showed a high level of B-cell and T-cell immune responses according to the immunological simulations in silico. Furthermore, in-silico cloning was performed, and the results indicated that the MEVs could be produced in considerable quantities in Escherichia coli (E. coli). Based on these findings, it is reasonable to create a broad-spectrum MEV against different subtypes of influenza A and B viruses in silico.

Bioinformatics and immunoinformatics assisted multiepitope vaccine construct against Burkholderia anthina

Burkholderia anthina is a pathogenic bacterial species belonging to the Burkholderiaceae family and it is mainly considered the etiological agent of chronic obstructive pulmonary diseases associated with cystic fibrosis, due to being intrinsic antibiotic resistant making it difficult to treat pulmonary infections. Hence increased rate of antibiotic-resistant bacterial species vaccine development is the priority to tackle this problem. In research work, we designed a multi-epitope-based vaccine construct against B. anthina using reverse vaccinology immunoinformatics and biophysical approaches. Based on the subtractive proteomic screening of core proteins we identified 3 probable antigenic proteins and good vaccine targets namely, type VI secretion system tube protein hcp Burkholderia, fimbria/pilus periplasmic chaperone and fimbrial biogenesis outer membrane usher protein. The selected 3 proteins were used for B and B cells B-derived T-cell epitopes prediction. In epitopes prediction, different epitopes were predicted with various lengths and percentile scores and subjected to further immunoinformatics analysis. In immunoinformatics screening a total number of 06, IDDGNANAL, KTVKPDPRY, SEVESGSAP, YGGDLTVEV, SVSHDTNGR, and GSKADGYQR epitopes were considered good vaccine target candidates and shortlisted for vaccine construct designing. The vaccine construct was designed by joining selected epitopes with the help of a GPGPG linker and additionally linked with cholera toxin b subunit adjuvant to increase the efficacy of the vaccine construct the sequence of the said adjuvant were retrieved from protein data bank through its (PDB ID: 5ELD). The designed vaccine construct was evaluated for its physiochemical properties analysis in which we reported that the vaccine construct comprises 216 amino acids with a molecular weight of 22.37499 kilo Dalton, 15.55 instability index (II) is computed, and this classifies that the vaccine construct is properly stable. VaxiJen v2.0 web server predicted that the vaccine construct is probable antigenic in nature with 0.6320 predicted value. Furthermore AllerTOP v. 2.0 tool predicted that the designed vaccine construct is non allergic in nature. Molecular docking analysis was done for analysis of the binding affinity of the vaccine construct with TLR-2 (PDB ID: 6NIG), the docking results predicted 799.2 kcal/mol binding energy score that represents the vaccine construct has a good binding ability with TLR-2. Moreover, molecular dynamic simulation analysis results revealed that the vaccine construct and immune cell receptor has proper binding stability over various environmental condition, i.e. change in pressure range, temperature, and motion. After each analysis, we observed that the vaccine construct is safe stable, and probably antigenic and could generate an immune response against the target pathogen but in the future, experimental analysis is still needed to verify in silico base results.

Immunoinformatics-aided rational design of a multi-epitope vaccine targeting feline infectious peritonitis virus

Feline infectious peritonitis (FIP) is a grave and frequently lethal ailment instigated by feline coronavirus (FCoV) in wild and domestic feline species. The spike (S) protein of FCoV assumes a critical function in viral ingress and infection, thereby presenting a promising avenue for the development of a vaccine. In this investigation, an immunoinformatics approach was employed to ascertain immunogenic epitopes within the S-protein of FIP and formulate an innovative vaccine candidate. By subjecting the amino acid sequence of the FIP S-protein to computational scrutiny, MHC-I binding T-cell epitopes were predicted, which were subsequently evaluated for their antigenicity, toxicity, and allergenicity through in silico tools. Our analyses yielded the identification of 11 potential epitopes capable of provoking a robust immune response against FIPV. Additionally, molecular docking analysis demonstrated the ability of these epitopes to bind with feline MHC class I molecules. Through the utilization of suitable linkers, these epitopes, along with adjuvants, were integrated to design a multi-epitope vaccine candidate. Furthermore, the stability of the interaction between the vaccine candidate and feline Toll-like receptor 4 (TLR4) was established via molecular docking and molecular dynamics simulation analyses. This suggests good prospects for future experimental validation to ascertain the efficacy of our vaccine candidate in inducing a protective immune response against FIP.

Immunoinformatics study to explore dengue (DENV-1) proteome to design multi-epitope vaccine construct by using CD4+ epitopes

BACKGROUND

Immunoinformatics is an emerging interdisciplinary field which integrates immunology, bioinformatics, and computational biology to study the immune system. In this study, we apply immunoinformatics approaches to explore the dengue proteome in order to design a multi-epitope vaccine construct.

METHODS

We used existing databases and algorithms to predict potential epitopes on dengue proteins and used a bioinformatics approach to identify the most promising epitopes. We then used molecular modelling to develop a multi-epitope construct which could be used as a potential vaccine. The results of this study demonstrate that immunoinformatics is a powerful tool for exploring and designing potential vaccines for infectious diseases like dengue.

RESULTS

Here, we found four CD4+ epitopes NLKYSVIVTVHTGDQ, ANPIVTDKEKPVNIE, LDPVVYDAKFEKQL, and VGAIALDFKPGTSGS that were used to design vaccine construct. The vaccine construct docked with TLR5. RMSD values suggest that docked complex of TLR5 and vaccine construct have putative stable interaction to induce immunogenic effects on host.

CONCLUSIONS

Furthermore, our study provides a proof of concept for the use of immunoinformatics approaches in DENV vaccine design. This vaccine can be effective in treating patients infected with DENV virus.

Chimeric vaccine design against the conserved TonB-dependent receptor-like β-barrel domain from the outer membrane tbpA and hpuB proteins of Kingella kingae ATCC 23330

Kingella kingae is a Gram-negative bacterium that primarily causes pediatric infections such as septicemia, endocarditis, and osteoarticular infections. Its virulence is attributed to the outer membrane proteins having implications in bacterial adhesion, invasion, nutrition, and host tissue damage. TonB-dependent receptors (TBDRs) play an important role in nutrition and were previously implicated as vaccine targets in other bacteria. Therefore, we targeted the conserved β-barrel TBDR domain of these proteins for designing a vaccine construct that could elicit humoral and cellular immune responses. We used bioinformatic tools to mine TBDR-containing proteins from K. kingae ATCC 23330 and then predict B- and T-cell epitopes from their conserved β-barrel TDR domain. A chimeric vaccine construct was designed using three antigenic epitopes, covering >98% of the world population and capable of inciting humoral and adaptive immune responses. The final construct elicited a robust immune response. Docking and dynamics simulation showed good binding affinity of the vaccine construct to various receptors of the immune system. Additionally, the vaccine was predicted to be safe and non-allergenic, making it a promising candidate for further development. In conclusion, our study demonstrates the potential of immunoinformatics approaches in designing chimeric vaccines against K. kingae infections. The chimeric vaccine we designed can serve as a blueprint for future experimental studies to develop an effective vaccine against this pathogen, which can serve as a potential strategy to prevent K. kingae infections.

Designing a polyvalent vaccine targeting multiple strains of varicella zoster virus using integrated bioinformatics approaches

The Varicella Zoster Virus (VZV) presents a global health challenge due to its dual manifestations of chickenpox and shingles. Despite vaccination efforts, incomplete coverage, and waning immunity lead to recurrent infections, especially in aging and immunocompromised individuals. Existing vaccines prevent chickenpox but can trigger the reactivation of shingles. To address these limitations, we propose a polyvalent multiepitope subunit vaccine targeting key envelope glycoproteins of VZV. Through bioinformatics approaches, we selected six glycoproteins that are crucial for viral infection. Epitope mapping led to the identification of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B cell linear (LBL) epitopes. Incorporating strong immunostimulants, we designed two vaccine constructs, demonstrating high antigenicity, solubility, stability, and compatibility with Toll-like receptors (TLRs). Molecular docking and dynamics simulations underscored the stability and affinity of the vaccine constructs with TLRs. These findings lay the foundation for a comprehensive solution to VZV infections, addressing the challenges of incomplete immunity and shingles reactivation. By employing advanced immunoinformatics and dynamics strategies, we have developed a promising polyvalent multiepitope subunit vaccine candidate, poised to enhance protection against VZV and its associated diseases. Further validation through in vivo studies is crucial to confirm the effectiveness and potential of the vaccine to curb the spread of VZV. This innovative approach not only contributes to VZV control but also offers insights into tailored vaccine design strategies against complex viral pathogens.

Deciphering the Immunogenicity of Monkeypox Proteins for Designing the Potential mRNA Vaccine

The Monkeypox virus (MPXV), an orthopox virus, is responsible for monkeypox in humans, a zoonotic disease similar to smallpox. This infection first appeared in the 1970s in humans and then in 2003, after which it kept on spreading all around the world. To date, various antivirals have been used to cure this disease, but now, MPXV has developed resistance against these, thus increasing the need for an alternative cure for this deadly disease. In this study, we devised a reverse vaccinology approach against MPXV using a messenger RNA (mRNA) vaccine by pinning down the antigenic proteins of this virus. By using bioinformatic tools, we predicted prospective immunogenic B and T lymphocyte epitopes. Based on cytokine inducibility score, nonallergenicity, nontoxicity, antigenicity, and conservancy, the final epitopes were selected. Our analysis revealed the stable structure of the mRNA vaccine and its efficient expression in host cells. Furthermore, strong interactions were demonstrated with toll-like receptors 2 (TLR2) and 4 (TLR4) according to the molecular dynamic simulation studies. The in silico immune simulation analyses revealed an overall increase in the immune responses following repeated exposure to the designed vaccine. Based on our findings, the vaccine candidate designed in this study has the potential to be tested as a promising novel mRNA therapeutic vaccine against MPXV infection.

Designing a multi-epitope candidate vaccine by employing immunoinformatics approaches to control African swine fever spread

The African swine fever virus has been circulating for decades and is highly infectious, often fatal to farmed and wild pigs. There is currently no approved vaccine or treatment for the disease, making prevention even more difficult. Therefore, vaccine development is necessary and urgent to limit the consequences of ASF and ensure the food chain and sustainability of the swine industry. This research study was conducted to design a multi-epitope vaccine for controlling veterinary diseases caused by the African swine fever virus. We employed the immunoinformatics approaches to reveal 37 epitopes from different viral proteins of ASFV. These epitopes were linked to adjuvants and linkers to form a full-fledged immunogenic vaccine construct. The tertiary structure of the final vaccine was predicted using a deep-learning approach. The molecular docking and molecular dynamics predicted stable interactions between the vaccine and immune receptor TLR5 of Sus scrofa (Pig). The MD simulation studies reflect that the calculated parameters like RMSD, RMSF, number of hydrogen bonds, and finally, the buried interface surface area for the complex remained stable throughout the simulation time. This analysis suggests the stability of interface interactions between the TLR5 and the multi-epitope vaccine construct. Further, the physiochemical analysis demonstrated that our designed vaccine construct was expected to have high stability and prolonged half-life time in mammalian cells. Traditional vaccine design experiments require significant time and financial input from the development stage to the final product. Studies like this can assist in accelerating vaccine development while minimizing the cost.Communicated by Ramaswamy H. Sarma.

Dengue Vaccination: Towards a New Dawn of Curbing Dengue Infection

Dengue is an infectious disease caused by dengue virus (DENV) and is a serious global burden. Antibody-dependent enhancement and the ability of DENV to infect immune cells, along with other factors, lead to fatal Dengue Haemorrhagic Fever and Dengue Shock Syndrome. This necessitates the development of a robust and efficient vaccine but vaccine development faces a number of hurdles. In this review, we look at the epidemiology, genome structure and cellular targets of DENV and elaborate upon the immune responses generated by human immune system against DENV infection. The review further sheds light on various challenges in development of a potent vaccine against DENV which is followed by presenting a current account of different vaccines which are being developed or have been licensed.

High potential for biomass-degrading CAZymes revealed by pine forest soil metagenomics

The undisturbed environment in Netarhat, with its high levels of accumulated lignocellulosic biomass, presents an opportunity to identify microbes for biomass digestion. This study focuses on the bioprospecting of native soil microbes from the Netarhat forest in Jharkhand, India, with the potential for lignocellulosic substrate digestion. These biocatalysts could help overcome the bottleneck of biomass saccharification and reduce the overall cost of biofuel production, replacing harmful fossil fuels. The study used metagenomic analysis of pine forest soil via whole genome shotgun sequencing, revealing that most of the reads matched with the bacterial species, very low percentage of reads (0.1%) belongs to fungal species, with 13% of unclassified reads. Actinobacteria were found to be predominant among the bacterial species. MetaErg annotation identified 11,830 protein family genes and 2 metabolic marker genes in the soil samples. Based on the Carbohydrate Active EnZyme (CAZy) database, 3,996 carbohydrate enzyme families were identified, with family Glycosyl hydrolase (GH) dominating with 1,704 genes. Most observed GH families in the study were GH0, 3, 5, 6. 9, 12. 13, 15, 16, 39, 43, 57, and 97. Modelling analysis of a representative GH 43 gene suggested a strong affinity for cellulose than xylan. This study highlights the lignocellulosic digestion potential of the native microfauna of the lesser-known pine forest of Netarhat.Communicated by Ramaswamy H. Sarma.

Toward α-1,3/4 fucosyltransferases targeted drug discovery: In silico uncovering of promising natural inhibitors of fucosyltransferase 6

Sialyl Lewis X (sLex ) antigen is a fucosylated cell-surface glycan that is normally involved in cell-cell interactions. The enhanced expression of sLex on cell surface glycans, which is attributed to the upregulation of fucosyltransferase 6 (FUT6), has been implicated in facilitating metastasis in human colorectal, lung, prostate, and oral cancers. The role that the upregulated FUT6 plays in the progression of tumor to malignancy, with reduced survival rates, makes it a potential target for anticancer drugs. Unfortunately, the lack of experimental structures for FUT6 has hampered the design and development of its inhibitors. In this study, we used in silico techniques to identify potential FUT6 inhibitors. We first modeled the three-dimensional structure of human FUT6 using AlphaFold. Then, we screened the natural compound libraries from the COCONUT database to sort out potential natural products (NPs) with best affinity toward the FUT6 model. As a result of these simulations, we identified three NPs for which we predicted binding affinities and interaction patterns quite similar to those we calculated for two experimentally tested FUT6 inhibitors, that is, fucose mimetic-1 and a GDP-triazole derived compound. We also performed molecular dynamics (MD) simulations for the FUT6 complexes with identified NPs, to investigate their stability. Analysis of the MD simulations showed that the identified NPs establish stable contacts with FUT6 under dynamics conditions. On these grounds, the three screened compounds appear as promising natural alternatives to experimentally tested FUT6 synthetic inhibitors, with expected comparable binding affinity. This envisages good prospects for future experimental validation toward FUT6 inhibition.

Secreted Aspartyl Proteinases Targeted Multi-Epitope Vaccine Design for Candida dubliniensis Using Immunoinformatics

Candida dubliniensis is an opportunistic pathogen associated with oral and invasive fungal infections in immune-compromised individuals. Furthermore, the emergence of C. dubliniensis antifungal drug resistance could exacerbate its treatment. Hence, in this study a multi-epitope vaccine candidate has been designed using an immunoinformatics approach by targeting C. dubliniensis secreted aspartyl proteinases (SAP) proteins. In silico tools have been utilized to predict epitopes and determine their allergic potential, antigenic potential, toxicity, and potential to elicit interleukin-2 (IL2), interleukin-4 (IL4), and IFN-γ. Using the computational tools, eight epitopes have been predicted that were then linked with adjuvants for final vaccine candidate development. Computational immune simulation has depicted that the immunogen designed emerges as a strong immunogenic candidate for a vaccine. Further, molecular docking and molecular dynamics simulation analyses revealed stable interactions between the vaccine candidate and the human toll-like receptor 5 (TLR5). Finally, immune simulations corroborated the promising candidature of the designed vaccine, thus calling for further in vivo investigation.

Immunoinformatics-Aided Design of a Peptide Based Multiepitope Vaccine Targeting Glycoproteins and Membrane Proteins against Monkeypox Virus

Monkeypox is a self-limiting zoonotic viral disease and causes smallpox-like symptoms. The disease has a case fatality ratio of 3-6% and, recently, a multi-country outbreak of the disease has occurred. The currently available vaccines that have provided immunization against monkeypox are classified as live attenuated vaccinia virus-based vaccines, which pose challenges of safety and efficacy in chronic infections. In this study, we have used an immunoinformatics-aided design of a multi-epitope vaccine (MEV) candidate by targeting monkeypox virus (MPXV) glycoproteins and membrane proteins. From these proteins, seven epitopes (two T-helper cell epitopes, four T-cytotoxic cell epitopes and one linear B cell epitopes) were finally selected and predicted as antigenic, non-allergic, interferon-γ activating and non-toxic. These epitopes were linked to adjuvants to design a non-allergic and antigenic candidate MPXV-MEV. Further, molecular docking and molecular dynamics simulations predicted stable interactions between predicted MEV and human receptor TLR5. Finally, the immune-simulation analysis showed that the candidate MPXV-MEV could elicit a human immune response. The results obtained from these in silico experiments are promising but require further validation through additional in vivo experiments.