In silico design and validation of a novel multi-epitope vaccine candidate against structural proteins of Chikungunya virus using comprehensive immunoinformatics analyses

Immunoinformatics-driven design of a multi-epitope vaccine against nipah virus: A promising approach for global health protection

This study focuses on developing a multi-epitope vaccine against the highly pathogenic Nipah virus using immunoinformatics. It aims to design a vaccine targeting the viral nucleoprotein to elicit robust immune responses. The approach integrates epitope prediction, vaccine construction, and validation through computational tools to address the lack of effective vaccines and mitigate global health threats posed by Nipah virus outbreaks. Immunoinformatics approaches have been utilized for epitope prediction, focusing on B-cell and T-cell epitopes of the Nipah virus nucleoprotein. The multi-epitope vaccine was constructed using linkers and adjuvants to enhance immunogenicity. Structural refinement, molecular docking with human ephrin B2 receptor, and immune simulations were performed to validate the vaccine's stability, binding efficiency, and immune response potential. The designed multi-epitope vaccine exhibited high antigenicity (0.56), non-allergenicity, and non-toxicity. Docking analysis showed a strong binding affinity with the ephrin B2 receptor (binding energy: -920 kcal/mol). Immune simulations indicated significant immune responses with high IgG and IgM levels and memory B-cell activation. Population coverage analysis revealed a global coverage of 88.3 %, supporting its potential for broad immunization. The designed vaccine against the Nipah virus demonstrates promising antigenicity, stability, and strong binding with the ephrin B2 receptor. With global population coverage and a robust immune response, it holds potential for clinical development. Further experimental validation and in vitro studies are recommended to confirm its efficacy as a viable vaccine candidate for the Nipah virus.

Revolutionizing Chikungunya Vaccines: mRNA Breakthroughs With Molecular and Immune Simulations

With the ability to cause massive epidemics that have consequences on millions of individuals globally, the Chikungunya virus (CHIKV) emerges as a severe menace. Developing an effective vaccine is urgent as no effective therapeutics are available for such viral infections. Therefore, we designed a novel mRNA vaccine against CHIKV with a combination of highly antigenic and potential MHC-I, MHC-II, and B-cell epitopes from the structural polyprotein. The vaccine demonstrated well-characterized physicochemical properties, indicating its solubility and potential functional stability within the body (GRAVY score of -0.639). Structural analyses of the vaccine revealed a well-stabilized secondary and tertiary structure (Ramachandran score of 82.8% and a Z-score of -4.17). Docking studies of the vaccine with TLR-2 (-1027.7 KJ/mol) and TLR-4 (-1212.4 KJ/mol) exhibited significant affinity with detailed hydrogen bond interactions. Molecular dynamics simulations highlighted distinct conformational dynamics among the vaccine, "vaccine-TLR-2" and "vaccine-TLR-4" complexes. The vaccine's ability to elicit both innate and adaptive immune responses, including the presence of memory B-cells and T-cells, persistent B-cell immunity for a year, and the activation of TH cells leading to the release of IFN-γ and IL-2, has significant implications for its potential effectiveness. The CHIKV vaccine developed in this study shows promise as a potential candidate for future vaccine production against CHIKV, suggesting its suitability for further clinical advancement, including in vitro and in vivo experiments.

Designing of a multiepitope-based vaccine against echinococcosis utilizing the potent Ag5 antigen: Immunoinformatics and simulation approaches

Echinococcosis is a significant parasitic zoonotic disease with severe implications for human and animal health. To date, there has been no effective vaccine candidate available for echinococcosis. Therefore, we employed computational approaches to develop a multiepitope-based vaccine using the most potent epitopes of MHC-I, MHC-II, and B-cell derived from the Ag5 protein of Echinococcus spp. The final vaccine construct containing the epitopes, linkers, and adjuvant exhibited potent antigenicity (score > 0.1) with no evidence of allergenicity (score < 0) and toxicity (score < 0) in several computational platforms. The vaccine also exhibited favorable physicochemical characteristics such as being highly soluble (SOLpro score of 0.781243) and hydrophilic (Grand average of hydropathy of -0.433). Moreover, the tertiary structure of the vaccine was also found to be structurally stable, with a Z score of -5.71. Further, the molecular docking analysis confirmed the vaccine's significant binding affinity to the RP-105 (docking score of -1252.7) and TLR-9 (docking score of -970.9). The molecular dynamic simulations confirmed the structural stability of the docked complexes under a virtual physiological system. The negative ΔTOTAL values derived from the MM-PBSA and MM-GBSA analyses confirmed a spontaneous and thermodynamically favorable binding process between the vaccine and receptors. Moreover, the vaccine demonstrated high potentiality to elicit both innate (natural killer cell, dendritic and macrophage) and adaptive (B-cell, helper T cell and cytotoxic T cell) immune responses with sustained humoral immune responses evidenced by increased IFN-γ and IL-2 levels. Following codon optimization and in silico cloning, the vaccine was successfully expressed (CAI value of 0.9607 and average GC content of 52.34%) after being inserted into the pET-28a (+) plasmid of E. coli. These findings highlight the potential of the designed vaccine candidate to combat echinococcosis and lay the groundwork for future preclinical and clinical studies.

A novel multi-epitope peptide vaccine targeting immunogenic antigens of Ebola and monkeypox viruses with potential of immune responses provocation in silico

The emergence or reemergence of monkeypox (Mpox) and Ebola virus (EBOV) agents causing zoonotic diseases remains a huge threat to human health. Our study aimed at designing a multi-epitope vaccine (MEV) candidate to target both the Mpox and EBOV agents using immunoinformatics tools. Viral protein sequences were retrieved, and potential nonallergenic, nontoxic, and antigenic epitopes were obtained. Next, cytotoxic and helper T-cell (CTL and HTL, respectively) and B-cell (BCL) epitopes were predicted, and those potential epitopes were fused utilizing proper linkers. The in silico cloning and expression processes were implemented using Escherichia coli K12. The immune responses were prognosticated using the C-ImmSim server. The MEV construct (29.53 kDa) included four BCL, two CTL, and four HTL epitopes and adjuvant. The MEV traits were pertinent in terms of antigenicity, non-allergenicity, nontoxicity, physicochemical characters, and stability. The MEV candidate was also highly expressed in E. coli K12. The strong affinity of MEV-TLR3 was confirmed using molecular docking and molecular dynamics simulation analyses. Immune simulation analyses unraveled durable activation and responses of cellular and humoral arms alongside innate immune responses. The designed MEV candidate demonstrated appropriate traits and was promising in the prediction of immune responses against both Mpox and EBOV agents. Further experimental assessments of the MEV are required to verify its efficacy.

A Universal Multi-Epitope Vaccine Design Against Porcine Reproductive and Respiratory Syndrome Virus via Bioinformatics and Immunoinformatics Approaches

Porcine reproductive and respiratory syndrome virus (PRRSV) causes reproductive disorders in sows and severe pneumonia in piglets, alongside immunosuppressive effects on the host. It poses a significant global threat to the swine industry, with no effective control measures currently available due to its complex pathogenesis and high variability. Conventional inactivated and attenuated vaccines provide inadequate protection and carry biosafety risks. In this study, we designed a universal multi-epitope peptide vaccine against PRRSV using bioinformatics and immunoinformatics approaches to address these limitations. By selecting sequences from seven representative PRRSV strains, we predicted highly conserved and immunogenic T cell (Th and CTL) epitopes across all encoded proteins. These were rationally concatenated with reported B cell neutralizing epitopes into a multi-epitope vaccine construct. We performed comprehensive assessments of the construct's physicochemical and biochemical properties, along with predictions and refinements of its secondary and tertiary structures. Molecular docking simulations with TLR2 and TLR4 revealed strong potential binding interactions. Immune simulations indicated that the multi-epitope vaccine could induce robust humoral and cellular immune responses. This study provides a scientific foundation for the development of safe and effective PRRSV subunit vaccines and offers new perspectives for designing vaccines against other viral diseases.

In Silico Subtractive Proteome Analysis to Design Multi-Epitope-Based Subunit Vaccine against Eikenella corrodens

Eikenella corrodens is a gram-negative, facultatively anaerobic bacterium typically found in the oropharynx and respiratory tract of humans. It is responsible for various infections, including head-and-neck infections, pericarditis, and abscesses of the deltoid, perirenal tissue, brain, and liver. Increasing antibiotic resistance requires urgent identification of novel drug targets to fight this bacterium. In this study, subtractive proteomics and immunoinformatics approaches were used to identify the most suitable candidates for multi-epitope vaccine development. A non-homologous and pathogenic protein, penicillin-binding protein 1A (PBP1A), was identified after extracting the entire proteome sequence of E. corrodens NCTC 10596. PBP1A is antigenic and necessary for pathogen survival. Helper T-cell (HTL), cytotoxic T-cell (CTL), and B-cell lymphocyte-inducing epitopes were integrated through immunoinformatic methods and rigorous immunological screening processes. Various physicochemical, allergenic, and antigenic properties were also evaluated to ensure the safety and immunogenicity of the vaccine candidates. Dynamic modeling and molecular docking techniques were used to examine the molecular interactions, thermodynamic stability, and binding affinities. The vaccine demonstrated a robust and consistent interaction with Toll-like receptors (TLRs), and its potential to elicit an immunological response was evaluated in silico. For in silico cloning, the final vaccine candidates were back-translated and cloned into an E. coli host to achieve high expression of the predicted protein. Computational analyses suggested that the proposed vaccine candidate shows promise for combating bacterial infections and eliciting a robust immune response. However, experimental validation is crucial to authenticate the precise safety and immunogenicity profiles of this vaccine.

Rational design of novel peptide-based vaccine against the emerging OZ virus

Oz virus (OZV) belongs to the Orthomyxoviridae family which includes viruses with a negative-sense, single-stranded, and segmented RNA genome. OZV is a zoonotic pathogen, particularly since the virus can cause deadly illness when injected intracerebrally into nursing mice. OZV is an emerging pathogen with the potential to spark a pandemic as there is no preventive and licensed treatment against this virus. The goal of this study was to develop a novel multi-epitope vaccination against OZV proteins utilizing immunoinformatics and immunological simulation analysis. This work evaluated immunological epitopes (B cells, MHC-I, and MHC-II) to identify highly antigenic OZV target proteins. Shortlisted epitopes were joined together by using appropriate linkers and adjuvants to design multi-epitope vaccine constructs (MEVC). The vaccine models were designed, improved, validated, and the globular regions and post-translational modifications (PTMs) were also evaluated in the vaccine's structure. Molecular docking analysis with the Toll-like receptor (TLR4) showed strong interactions and appropriate binding energies. Molecular dynamics (MD) simulation confirmed stable interactions between the vaccines and TLR4. Bioinformatics tools helped optimize codons, resulting in successful cloning into appropriate host vectors. This study showed that the developed vaccines are stable and non-allergenic in the human body and successfully stimulated immunological responses against OZV. Finally, a mechanism of action for the designed vaccine construct was also proposed. Further experimental validations of the designed vaccine construct will pave the way to create a potentially effective vaccine against this emerging pathogen.

Novel dual-pathogen multi-epitope mRNA vaccine development for Brucella melitensis and Mycobacterium tuberculosis in silico approach

Brucellosis and Tuberculosis, both of which are contagious diseases, have presented significant challenges to global public health security in recent years. Delayed treatment can exacerbate the conditions, jeopardizing patient lives. Currently, no vaccine has been approved to prevent these two diseases simultaneously. In contrast to traditional vaccines, mRNA vaccines offer advantages such as high efficacy, rapid development, and low cost, and their applications are gradually expanding. This study aims to develop multi-epitope mRNA vaccines argeting Brucella melitensis and Mycobacterium tuberculosis H37Rv (L4 strain) utilizing immunoinformatics approaches. The proteins Omp25, Omp31, MPT70, and MPT83 from the specified bacteria were selected to identify the predominant T- and B-cell epitopes for immunological analysis. Following a comprehensive evaluation, a vaccine was developed using helper T lymphocyte epitopes, cytotoxic T lymphocyte epitopes, linear B-cell epitopes, and conformational B-cell epitopes. It has been demonstrated that multi-epitope mRNA vaccines exhibit increased antigenicity, non-allergenicity, solubility, and high stability. The findings from molecular docking and molecular dynamics simulation revealed a robust and enduring binding affinity between multi-epitope peptides mRNA vaccines and TLR4. Ultimately, Subsequently, following the optimization of the nucleotide sequence, the codon adaptation index was calculated to be 1.0, along with an average GC content of 54.01%. This indicates that the multi-epitope mRNA vaccines exhibit potential for efficient expression within the Escherichia coli(E. coli) host. Analysis through immune modeling indicates that following administration of the vaccine, there may be variation in immunecell populations associated with both innate and adaptive immune reactions. These types encompass helper T lymphocytes (HTL), cytotoxic T lymphocytes (CTL), regulatory T lymphocytes, natural killer cells, dendritic cells and various immune cell subsets. In summary, the results suggest that the newly created multi-epitope mRNA vaccine exhibits favorable attributes, offering novel insights and a conceptual foundation for potential progress in vaccine development.

Designing novel multiepitope mRNA vaccine targeting Hendra virus (HeV): An integrative approach utilizing immunoinformatics, reverse vaccinology, and molecular dynamics simulation

Human and animal health is threatened by Hendra virus (HeV), which has few treatments. This in-silico vaccine design study focuses on HeV G (glycoprotein), F (fusion protein), and M (matrix protein). These proteins were computationally assessed for B and T-cell epitopes after considering HeV strain conservation, immunogenicity, and antigenicity. To improve vaccination immunogenicity, these epitopes were selectively ligated into a multiepitope construct. To improve vaccination longevity and immunological response, adjuvants and linkers were ligated. G, F, and M epitopes were used to create an mRNA HeV vaccine. Cytotoxic, helper, and linear B-lymphocytes' epitopes are targeted by this vaccine. The population coverage analysis demonstrates that multi-epitope vaccination covers 91.81 percent of CTL and 98.55 percent of HTL epitopes worldwide. GRAVY evaluated the vaccine's well-characterized physicochemical properties -0.503, indicating solubility and functional stability. Structure analysis showed well-stabilized 2° and 3° structures in the vaccine, with alpha helix, beta sheet, and coil structures (Ramachandran score of 88.5% and Z score of -3.44). There was a strong affinity as shown by docking tests with TLR-4 (central score of -1139.4 KJ/mol) and TLR-2 (center score of -1277.9 KJ/mol). The coupled V-apo, V-TLR2, and V-TLR4 complexes were tested for binding using molecular dynamics simulation where extremely stable complexes were found. The predicted mRNA structures provided significant stability. Codon optimization for Escherichia. coli synthesis allowed the vaccine to attain a GC content of 46.83% and a CAI score of 1.0, which supports its significant expression. Immunological simulations indicated vaccine-induced innate and adaptive immune reactions. Finally, this potential HeV vaccine needs more studies to prove its efficacy and safety.

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.

In Silico Design of a Trans-Amplifying RNA-Based Vaccine against SARS-CoV-2 Structural Proteins

Nucleic acid-based vaccines allow scalable, rapid, and cell-free vaccine production in response to an emerging disease such as the current COVID-19 pandemic. Here, we objected to the design of a multiepitope mRNA vaccine against the structural proteins of SARS-CoV-2. Through an immunoinformatic approach, promising epitopes were predicted for the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins. Fragments rich in overlapping epitopes were selected based on binding affinities with HLA classes I and II for the specific presentation to B and T lymphocytes. Two constructs were designed by fusing the fragments in different arrangements via GG linkers. Construct 1 showed better structural properties and interactions with toll-like receptor 2 (TLR-2), TLR-3, and TLR-4 during molecular docking and dynamic simulation. A 50S ribosomal L7/L12 adjuvant was added to its N-terminus to improve stability and immunogenicity. The final RNA sequence was used to design a trans-amplifying RNA (taRNA) vaccine in a split-vector system. It consists of two molecules: a nonreplicating RNA encoding a trans-acting replicase to amplify the second one, a trans-replicon (TR) RNA encoding the vaccine protein. Overall, the immune response simulation detected that activated B and T lymphocytes and increased memory cell formation. Macrophages and dendritic cells proliferated continuously, and IFN-γ and cytokines like IL-2 were released highly.

In Silico Driven Multi-Epitope Subunit Candidate Vaccine against Bovine Tuberculosis

Bovine tuberculosis (bTB), caused by Mycobacterium bovis, poses significant zoonotic and economic challenges globally. The current prevention and treatment options are limited and increasingly complicated by the emergence of multidrug-resistant strains. This study employs reverse vaccinology and immunoinformatics to design a multi-epitope subunit vaccine targeting the MPB83, ArfA, DnaK, GrpE, and LpqH proteins of M. bovis. The T-cell and B-cell epitopes of the candidate vaccine were predicted and evaluated for antigenicity, allergenicity, and toxicity. The promising epitopes were then assembled into three vaccine constructs (bTBV1, bTBV2, and bTBV3) using appropriate adjuvants, pan HLA DR-binding epitope (PADRE), and linkers. The constructs were analyzed for physicochemical properties, 3D structure, cytokines induction and stability, followed by molecular docking with bovine CD molecules and toll-like receptor, TLR-9. Among the candidates, bTBV3 was chosen as one of the most promising vaccine candidates due to its high aliphatic index (67.60), lowest instability score (27.26), and a strong binding affinity. Molecular dynamics simulations and the results of interactions between the vaccine-receptor complexes (eigenvalue 2.318704e-06) show that the vaccine construct bTBV3 is stable. In silico immune simulation findings, such as elevated IgM levels and increased Th cell populations, suggest that the designed multi-epitope vaccine candidate bTBV3 elicits robust humoral and cellular immune responses, confirming the vaccine's potential efficacy. Additionally, codon optimization (CAI: 0.997 and GC: 54.687%) and in silico cloning facilitated efficient expression in E. coli. This study highlights the potential of bioinformatics-driven approaches in developing effective subunit vaccines against bTB, providing a foundation for experimental validation and future applications in combating this pervasive zoonotic disease, bovine tuberculosis.

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Pancreatic ductal adenocarcinoma is the most prevalent pancreatic cancer, which is considered a significant global health concern. Chemotherapy and surgery are the mainstays of current pancreatic cancer treatments; however, a few cases are suitable for surgery, and most of the cases will experience recurrent episodes. Compared to DNA or peptide vaccines, mRNA vaccines for pancreatic cancer have more promise because of their delivery, enhanced immune responses, and lower proneness to mutation. We constructed an mRNA vaccine by analyzing S100 family proteins, which are all major activators of receptors for advanced glycation end products. We applied immunoinformatic approaches, including physicochemical properties analysis, structural prediction and validation, molecular docking study, in silico cloning, and immune simulations. The designed mRNA vaccine was estimated to have a molecular weight of 165023.50 Da and was highly soluble (grand average of hydropathicity of -0.440). In the structural assessment, the vaccine seemed to be a well-stable and functioning protein (Z score of -8.94). Also, the docking analysis suggested that the vaccine had a high affinity for TLR-2 and TLR-4 receptors. Additionally, the molecular mechanics with generalized Born and surface area solvation analysis of the "Vaccine-TLR-2" (-141.07 kcal/mol) and "Vaccine-TLR-4" (-271.72 kcal/mol) complexes also suggests a strong binding affinity for the receptors. Codon optimization also provided a high expression level with a GC content of 47.04% and a codon adaptation index score 1.0. The appearance of memory B-cells and T-cells was also observed over a while, with an increased level of helper T-cells and immunoglobulins (IgM and IgG). Moreover, the minimum free energy of the mRNA vaccine was predicted at -1760.00 kcal/mol, indicating the stability of the vaccine following its entry, transcription, and expression. This hypothetical vaccine offers a groundbreaking tool for future research and therapeutic development of pancreatic cancer.

Design of a novel multi-epitope vaccine against Marburg virus using immunoinformatics studies

Marburg virus (MARV) is a highly contagious and virulent agent belonging to Filoviridae family. MARV causes severe hemorrhagic fever in humans and non-human primates. Owing to its highly virulent nature, preventive approaches are promising for its control. There is currently no approved drug or vaccine against MARV, and management mainly involves supportive care to treat symptoms and prevent complications. Our aim was to design a novel multi-epitope vaccine (MEV) against MARV using immunoinformatics studies. In this study, various proteins (VP35, VP40 and glycoprotein precursor) were used and potential epitopes were selected. CTL and HTL epitopes covered 79.44% and 70.55% of the global population, respectively. The designed MEV construct was stable and expressed in Escherichia coli (E. coli) host. The physicochemical properties were also acceptable. MARV MEV candidate could predict comprehensive immune responses such as those of humoral and cellular in silico. Additionally, efficient interaction to toll-like receptor 3 (TLR3) and its agonist (β-defensin) was predicted. There is a need for validation of these results using further in vitro and in vivo studies.

In silico identification of chikungunya virus replication inhibitor validated using biochemical and cell-based approaches

Discovering an alternative therapy with a long-lasting effect on symptoms caused by chikungunya virus (CHIKV) infection is prompted by the lack of a vaccine and the absence of safe, effective and non-toxic medications. One potential strategy is synthesizing or identifying small compounds that can specifically target the active site of an essential enzyme and prevent virus replication. Previous site-directed mutagenesis studies have demonstrated the crucial role of the macrodomain, which is a part of non-structural protein 3 (nsP3), in virus replication. Exploiting this fact, the macrodomain can be targeted to discover a natural substance that can inhibit its function and thereby impede virus replication. With this aim, the present study focused on potential CHIKV nsP3 macrodomain (nsP3MD) inhibitors through in silico, in vitro and cell-based methods. Through virtual screening of the natural compound library, nine nsP3MD inhibitors were initially identified. Molecular dynamics (MD) simulations were employed to evaluate these nine compounds based on the stability of their ligand-receptor complexes and energy parameters. Target analysis and ADMET (i.e. absorption, distribution, metabolism, excretion and toxicity) prediction of the selected compounds revealed their drug-like characteristics. Subsequent in vitro investigation allowed us to narrow the selection down to one compound, N-[2-(5-methoxy-1H-indol-3-yl) ethyl]-2-oxo-1,2-dihydroquinoline-4-carboxamide, which exhibited potent inhibition of CHIKV growth. This molecule effectively inhibited CHIKV replication in the stable embryonal rhabdomyosarcoma cell line capable of producing CHIKV. Our findings demonstrate that the selected compound possesses substantial anti-CHIKV nsP3MD activity both in vitro and in vivo. This work provides a promising molecule for further preclinical studies to develop a potential drug against the CHIKV.

Designing a multi-epitope vaccine against Shigella dysenteriae using immuno-informatics approach

Shigella dysenteriae has been recognized as the second most prevalent pathogen associated with diarrhea that contains blood, contributing to 12.9% of reported cases, and it is additionally responsible for approximately 200,000 deaths each year. Currently, there is no S. dysenteriae licensed vaccine. Multidrug resistance in all Shigella spp. is a growing concern. Current vaccines, such as O-polysaccharide (OPS) conjugates, are in clinical trials but are ineffective in children but protective in adults. Thus, innovative treatments and vaccines are needed to combat antibiotic resistance. In this study, we used immuno-informatics to design a new multiepitope vaccine and identified S. dysenteriae strain SD197's membrane protein targets using in-silico methods. The target protein was prioritized using membrane protein topology analysis to find membrane proteins. B and T-cell epitopes were predicted for vaccine formulation. The epitopes were shortlisted based on an IC50 value <50, antigenicity, allergenicity, and a toxicity analysis. In the final vaccine construct, a total of 8 B-cell epitopes, 12 MHC Class I epitopes, and 7 MHC Class II epitopes were identified for the Lipopolysaccharide export system permease protein LptF. Additionally, 17 MHC Class I epitopes and 14 MHC Class II epitopes were predicted for the Lipoprotein-releasing ABC transporter permease subunit LolE. These epitopes were selected and linked via KK, AAY, and GGGS linkers, respectively. To enhance the immunogenic response, RGD (arginine-glycine-aspartate) adjuvant was incorporated into the final vaccine construct. The refined vaccine structure exhibits a Ramachandran score of 91.5% and demonstrates stable interaction with TLR4. Normal Mode Analysis (NMA) reveals low eigenvalues (3.925996e-07), indicating steady and flexible molecular mobility of docked complexes. Codon optimization was carried out in an effective microbial expression system of the Escherichia coli K12 strain using the recombinant plasmid pET-28a (+). Finally, the entire in-silico analysis suggests that the suggested vaccine may induce a significant immune response against S. dysenteriae, making it a promising option for additional experimental trials.

Multi-epitope vaccine design using in silico analysis of glycoprotein and nucleocapsid of NIPAH virus

According to the 2018 WHO R&D Blueprint, Nipah virus (NiV) is a priority disease, and the development of a vaccine against NiV is strongly encouraged. According to criteria used to categorize zoonotic diseases, NiV is a stage III disease that can spread to people and cause unpredictable outbreaks. Since 2001, the NiV virus has caused annual outbreaks in Bangladesh, while in India it has caused occasional outbreaks. According to estimates, the mortality rate for infected individuals ranges from 70 to 91%. Using immunoinformatic approaches to anticipate the epitopes of the MHC-I, MHC-II, and B-cells, they were predicted using the NiV glycoprotein and nucleocapsid protein. The selected epitopes were used to develop a multi-epitope vaccine construct connected with linkers and adjuvants in order to improve immune responses to the vaccine construct. The 3D structure of the engineered vaccine was anticipated, optimized, and confirmed using a variety of computer simulation techniques so that its stability could be assessed. According to the immunological simulation tests, it was found that the vaccination elicits a targeted immune response against the NiV. Docking with TLR-3, 7, and 8 revealed that vaccine candidates had high binding affinities and low binding energies. Finally, molecular dynamic analysis confirms the stability of the new vaccine. Codon optimization and in silico cloning showed that the proposed vaccine was expressed to a high degree in Escherichia coli. The study will help in identifying a potential epitope for a vaccine candidate against NiV. The developed multi-epitope vaccine construct has a lot of potential, but they still need to be verified by in vitro & in vivo studies.

Designing a Novel Multiepitope Vaccine from the Human Papilloma Virus E1 and E2 Proteins for Indonesia with Immunoinformatics and Molecular Dynamics Approaches

One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico. In this study, we develop a novel multiepitope vaccine candidate using an immunoinformatic approach. We predicted the epitopes of the cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) and evaluated their immunogenic properties. Population coverage analysis of qualified epitopes was conducted to determine the successful use of the vaccine worldwide. The epitopes were constructed into a multiepitope vaccine by using AAY linkers between the CTL epitopes and GPGPG linkers between the HTL epitopes. The tertiary structure of the multiepitope vaccine was modeled with AlphaFold and was evaluated by Prosa-web. The results of vaccine construction were analyzed for B-cell epitope prediction, molecular docking with Toll like receptor-4 (TLR4), and molecular dynamics simulation. The results of epitope prediction obtained 4 CTL epitopes and 7 HTL epitopes that are eligible for construction of multiepitope vaccines. Prediction of the physicochemical properties of multiepitope vaccines obtained good results for recombinant protein production. The interaction showed that the interaction of the multiepitope vaccine-TLR4 complex is stable based on the binding free energy value -106.5 kcal/mol. The results of the immune response simulation show that multiepitope vaccine candidates could activate the adaptive and humoral immune systems and generate long-term B-cell memory. According to these results, the development of a multiepitope vaccine with a reverse vaccinology approach is a breakthrough to develop potential cervical cancer therapeutic vaccines.

In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model

Background

In-silico analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants.

Objective

To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online in-silico (IS) tools with AURKA gene as a model.

Materials and Methods

We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using AURKA gene as model. We predicted a protein model and analyzed 209 out of 64,369 AURKA variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.

Results

Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.

Conclusion

This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.

Protective Efficacy Induced by the Common Eimeria Antigen Elongation Factor 2 against Challenge with Three Eimeria Species in Chickens

Avian coccidiosis arises from co-infection involving multiple Eimeria species, which could give rise to substantial economic losses in the global poultry industry. As a result, multivalent anticoccidial vaccines containing common Eimeria antigens offer considerable promise for controlling co-infection in clinical practice. In our previous study, Elongation factor 2 (EF2) was deemed as an immunogenic common antigen across various Eimeria species. This current investigation aimed to further assess the immunogenicity and protective efficacy of EF2 in recombinant subunit vaccine format against three Eimeria species. The EF2 gene cloned from Eimeria maxima (E. maxima) cDNA was designated as EF2 of E. maxima (EmEF2). The immunogenicity of the recombinant protein EmEF2 (rEmEF2) was assessed through Western blot analysis. The evaluation of the vaccine-induced immune response encompassed the determination of T lymphocyte subset proportions, cytokine mRNA transcription levels, and specific IgY concentrations in rEmEF2-vaccinated chickens using flow cytometry, quantitative real-time PCR (qPCR), and indirect enzyme-linked immunosorbent assay (ELISA). Subsequently, the protective efficacy of rEmEF2 was evaluated through vaccination and challenge experiments. The findings demonstrated that rEmEF2 was effectively recognized by the His-tag monoclonal antibody and E. maxima chicken antiserum. Vaccination with rEmEF2 increased the proportions of CD4+ and CD8+ T lymphocytes, elevated IL-4 and IFN-γ mRNA transcription levels, and enhanced IgY antibody levels compared to the control groups. Moreover, compared to the control groups, vaccination with rEmEF2 led to decreased weight loss, reduced oocyst outputs, and alleviated enteric lesions. Furthermore, in the rEmEF2-immunized groups, challenges with E. maxima and E. acervulina resulted in anticoccidial index (ACI) scores of 166.35 and 185.08, showing moderate-to-excellent protective efficacy. Nevertheless, challenges with E. tenella and mixed Eimeria resulted in ACI scores of 144.01 and 127.94, showing low protective efficacy. In conclusion, EmEF2, a common antigen across Eimeria species, demonstrated the capacity to induce a significant cellular and humoral immune response, as well as partial protection against E. maxima, E. acervulina, and E. tenella. These results highlight EmEF2 as a promising candidate antigen for the development of multivalent vaccines targeting mixed infections by Eimeria species.

An updated review on oral protein-based antigen vaccines efficiency and delivery approaches: a special attention to infectious diseases

Various infectious agents affect human health via the oral entrance. The majority of pathogens lack approved vaccines. Oral vaccination is a convenient, safe and cost-effective approach with the potential of provoking mucosal and systemic immunity and maintaining individual satisfaction. However, vaccines should overcome the intricate environment of the gastrointestinal tract (GIT). Oral protein-based antigen vaccines (OPAVs) are easier to administer than injectable vaccines and do not require trained healthcare professionals. Additionally, the risk of needle-related injuries, pain, and discomfort is eliminated. However, OPAVs stability at environmental and GIT conditions should be considered to enhance their stability and facilitate their transport and storage. These vaccines elicit the local immunity, protecting GIT, genital tract and respiratory epithelial surfaces, where numerous pathogens penetrate the body. OPAVs can also be manipulated (such as using specific incorporated ligand and receptors) to elicit targeted immune response. However, low bioavailability of OPAVs necessitates development of proper protein carriers and formulations to enhance their stability and efficacy. There are several strategies to improve their efficacy or protective effects, such as incorporation of adjuvants, enzyme inhibitors, mucoadhesive or penetrating devices and permeation enhancers. Hence, efficient delivery of OPAVs into GIT require proper delivery systems mainly including smart target systems, probiotics, muco-adhesive carriers, lipid- and plant-based delivery systems and nano- and microparticles.