Design of a multi-epitope-based vaccine candidate against Bovine Genital Campylobacteriosis using a reverse vaccinology approach.

BACKGROUND: Bovine Genital Campylobacteriosis (BGC), a worldwide distributed venereal disease caused by Campylobacter fetus subsp. venerealis (Cfv), has a relevant negative economic impact in cattle herds. The control of BGC is hampered by the inexistence of globally available effective vaccines. The present in silico study aimed to develop a multi-epitope vaccine candidate against Cfv through reverse vaccinology. RESULTS: The analysis of Cfv strain NCTC 10354 proteome allowed the identification of 9 proteins suitable for vaccine development. From these, an outer membrane protein, OmpA, and a flagellar protein, FliK, were selected for prediction of B-cell and T-cell epitopes. The top-ranked epitopes conservancy was assessed in 31 Cfv strains. The selected epitopes were integrated to form a multi-epitope fragment of 241 amino acids, which included 2 epitopes from OmpA and 13 epitopes from FliK linked by GPGPG linkers and connected to the cholera toxin subunit B by an EAAAK linker. The vaccine candidate was predicted to be antigenic, non-toxic, non-allergenic, and soluble upon overexpression. The protein structure was predicted and optimized, and the sequence was successfully cloned in silico into a plasmid vector. Additionally, immunological simulations demonstrated the vaccine candidate's ability to stimulate an immune response. CONCLUSIONS: This study developed a novel vaccine candidate suitable for further in vitro and in vivo experimental validation, which may become a useful tool for the control of BGC.

Identification of potential vaccine targets for elicitation of host immune cells against SARS-CoV-2 by reverse vaccinology approach.

The COVID-19 pandemic has emerged as a critical global health crisis, demanding urgent and effective strategies for containment. While some knowledge exists about epitope sequences recognized by human immune cells and their activation of CD8+ T cells within the HLA context, comprehensive information remains limited. This study employs reverse vaccinology to explore antigenic HLA-restricted T-cell epitopes capable of eliciting durable immunity. Screening reveals 187 consensus epitopes, with 23 offering broad population coverage worldwide, spanning over 5000 HLA alleles. Sequence alignment analysis highlights the genetic distinctiveness of these peptides from Homo sapiens and their intermediate to high TAP binding efficiency. Notably, these epitopes share 100 % sequence identity across strains from nine countries, indicating potential for a uniform protective immune response among diverse ethnic populations. Docking simulations further confirm their binding capacity with the HLA allele, validating them as promising targets for SARS-CoV-2 immune recognition. The anticipated epitopes are connected with suitable linkers and adjuvant, and then assessed for its translational efficacy within a bacterial expression vector through computational cloning. Through docking, it is observed that the chimeric vaccine construct forms lasting hydrogen bonds with Toll-like receptor (TLR4), while immune simulation illustrates an increased cytotoxic response aimed at CD8+ T cells. This comprehensive computational analysis suggests the chimeric vaccine construct's potential to provoke a robust immune response against SARS-CoV-2. By delineating these antigenic fragments, our study offers valuable insights into effective vaccine and immunotherapy development against COVID-19, contributing significantly to global efforts in combating this infectious threat.

In silico prediction of CD8+ and CD4+ T cell epitopes in Leishmania major proteome: Using immunoinformatics.

The leishmaniases are NDTs (neglected tropical diseases) that affect people all over the world. They are brought on by protozoans from the genus Leishmania and disseminated by phlebotomine flies that are afflicted with the disease. The best option to manage and lower the incidence of these diseases has been thought by the creation of a safe and effective vaccination. This research used an in silico based mining approach to look for high potential epitopes that might bind to MHC Class I and MHC Class II molecules (mainly; HLA-A*02:01 & HLA-DRB1*03:01) from human population in order to promote vaccine development. Based on the presence of signal peptides, GPI anchors, antigenicity predictions, and a subtractive proteomic technique, we have screened 17 putative antigenic proteins from the 8083 total proteins of L. major. After that thorough immunogenic epitope prediction were done using IEDB-AR tools. We isolated five immunogenic epitopes (three 9-mer & two 15-mer) from five antigenic proteins through docking and MD simulation analysis. Finally, these five anticipated epitopes, viz., TLPEIPVNV, ELMAPVFGL, TLAAAVALL, NSINIRLDGVTSAGF and NVPLVVDASSLFRVA have considerably stronger binding potential with their respective alleles and may trigger immunological responses. The goal of this work was to identify MHC restricted epitopes for CD8+ and CD4+ T cells activation using immunoinformatics in order to identify potential vaccine candidates against L. major parasites.

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for developing immunotherapies, including neoantigen vaccine and drugs. Accurate prediction of TCR-epitope binding specificity via deep learning remains challenging, especially in test cases which are unseen in the training set. Here, we propose TEPCAM (TCR-EPitope identification based on Cross-Attention and Multi-channel convolution), a deep learning model that incorporates self-attention, cross-attention mechanism, and multi-channel convolution to improve the generalizability and enhance the model interpretability. Experimental results demonstrate that our model outperformed several state-of-the-art models on two challenging tasks including a strictly split dataset and an external dataset. Furthermore, the model can learn some interaction patterns between TCR and epitope by extracting the interpretable matrix from cross-attention layer and mapping them to the three-dimensional structures. The source code and data are freely available at https://github.com/Chenjw99/TEPCAM.

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.

Rational design of a multivalent vaccine targeting arthropod-borne viruses using reverse vaccinology strategies.

Viruses transmitted by arthropods, such as Dengue, Zika, and Chikungunya, represent substantial worldwide health threats, particularly in countries like India. The lack of approved vaccines and effective antiviral therapies calls for developing innovative strategies to tackle these arboviruses. In this study, we employed immunoinformatics methodologies, incorporating reverse vaccinology, to design a multivalent vaccine targeting the predominant arboviruses. Epitopes of B and T cells were recognized within the non-structural proteins of Dengue, Zika, and Chikungunya viruses. The predicted epitopes were enhanced with adjuvants ß-defensin and RS-09 to boost the vaccine's immunogenicity. Sixteen distinct vaccine candidates were constructed, each incorporating epitopes from all three viruses. FUVAC-11 emerged as the most promising vaccine candidate through molecular docking and molecular dynamics simulations, demonstrating favorable binding interactions and stability. Its effectiveness was further evaluated using computational immunological studies confirming strong immune responses. The in silico cloning performed using the pET-28a(+) plasmid facilitates the future experimental implementation of this vaccine candidate, paving the way for potential advancements in combating these significant arboviral threats. However, further in vitro and in vivo studies are warranted to confirm the results obtained in this computational study, which highlights the effectiveness of immunoinformatics and reverse vaccinology in creating vaccines against major Arboviruses, offering a promising model for developing vaccines for other vector-borne diseases and enhancing global health security.

Immunoinformatics-based multi-epitope containing fused polypeptide vaccine design against visceral leishmaniasis with high immunogenicity and TLR binding.

Visceral leishmaniasis (VL) is the most lethal among all leishmaniasis diseases and remains categorized as a neglected tropical disease (NTD). This study aimed to develop a peptide-based multi-epitope vaccine construct against VL using immunoinformatics methodologies. To achieve this, four distinct proteins were screened to identify peptides consisting of 9-15 amino acids with high binding affinity to toll-like receptors (TLRs), strong antigenicity, low allergenicity, and minimal toxicity. The resulting multi-epitope vaccine construct was fused in a tandem arrangement with appropriate linker peptides and exhibited superior properties related to cytotoxic T lymphocytes (CTLs), helper T lymphocytes (HTLs), and B-cell epitopes. Subsequently, a three-dimensional (3D) model of the vaccine construct was generated, refined, and validated for structural stability and immune response capabilities. Molecular docking and simulations confirmed the vaccine construct's stability and binding affinities with TLRs, with TLR4 displaying the highest binding affinity, followed by TLR2 and TLR3. Additionally, simulations predicted robust cellular and humoral antibody-mediated immune responses elicited by the designed vaccine construct. Notably, this vaccine construct includes proteins from various pathways of Leishmania donovani (LD), which have not been previously utilized in VL vaccine design. Thus, this study opens new avenues for the development of vaccines against diverse protozoan diseases.

Immunoinformatics-based potential multi-peptide vaccine designing against Jamestown Canyon Virus (JCV) capable of eliciting cellular and humoral immune responses.

Jamestown Canyon virus (JCV) is a deadly viral infection transmitted by various mosquito species. This mosquito-borne virus belongs to Bunyaviridae family, posing a high public health threat in the in tropical regions of the United States causing encephalitis in humans. Common symptoms of JCV include fever, headache, stiff neck, photophobia, nausea, vomiting, and seizures. Despite the availability of resources, there is currently no vaccine or drug available to combat JCV. The purpose of this study was to develop an epitope-based vaccine using immunoinformatics approaches. The vaccine aimed to be secure, efficient, bio-compatible, and capable of stimulating both innate and adaptive immune responses. In this study, the protein sequence of JCV was obtained from the NCBI database. Various bioinformatics methods, including toxicity evaluation, antigenicity testing, conservancy analysis, and allergenicity assessment were utilized to identify the most promising epitopes. Suitable linkers and adjuvant sequences were used in the design of vaccine construct. 50s ribosomal protein sequence was used as an adjuvant at the N-terminus of the construct. A total of 5 CTL, 5 HTL, and 5 linear B cell epitopes were selected based on non-allergenicity, immunological potential, and antigenicity scores to design a highly immunogenic multi-peptide vaccine construct. Strong interactions between the proposed vaccine and human immune receptors, i.e., TLR-2 and TLR-4, were revealed in a docking study using ClusPro software, suggesting their possible relevance in the immunological response to the vaccine. Immunological and physicochemical properties assessment ensured that the proposed vaccine demonstrated high immunogenicity, solubility and thermostability. Molecular dynamics simulations confirmed the strong binding affinities, as well as dynamic and structural stability of the proposed vaccine. Immune simulation suggest that the vaccine has the potential to effectively stimulate cellular and humoral immune responses to combat JCV infection. Experimental and clinical assays are required to validate the results of this study.

Exploring the whole proteome of monkeypox virus to design B cell epitope-based oral vaccines using immunoinformatics approaches.

In the last few months 85,536 cases and 91 deaths were reported for monkeypox disease from 110 and 71 locations from all over the world, correspondingly. The vaccines of other viruses that belong to the Poxviridae family were recommended for monkeypox. There is no licensed vaccine available for monkeypox that originated from monkeypox virus. In the present study, using the reverse vaccinology approach we have performed whole proteome analysis of monkeypox virus to screen out the potential antigenic proteins that can be used as vaccine candidates. We have also designed 12 B cell epitopes-based vaccine candidates using immunoinformatics approach. We have found a total 15 potential antigenic proteins out of which 14 antigens are novel and can be used for further vaccine development against monkeypox. We have performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidates MPOXVs (MPOXV1-MPOXV12). Further, we have performed molecular docking, in silico immune simulation and cloning of MPOXVs. All MPOXVs are potential vaccine candidate that can potentially activate the innate, cellular, and humoral immune response. However, further experimental validation is required before moving to clinical trials. This is the first oral vaccine reported for monkeypox virus derived from monkeypox proteins.

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

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

Structural Framework for Analysis of CD4+ T-Cell Epitope Dominance in Viral Fusion Proteins.

Antigen conformation shapes CD4+ T-cell specificity through mechanisms of antigen processing, and the consequences for immunity may rival those from conformational effects on antibody specificity. CD4+ T cells initiate and control immunity to pathogens and cancer and are at least partly responsible for immunopathology associated with infection, autoimmunity, and allergy. The primary trigger for CD4+ T-cell maturation is the presentation of an epitope peptide in the MHC class II antigen-presenting protein (MHCII), most commonly on an activated dendritic cell, and then the T-cell responses are recalled by subsequent presentations of the epitope peptide by the same or other antigen-presenting cells. Peptide presentation depends on the proteolytic fragmentation of the antigen in an endosomal/lysosomal compartment and concomitant loading of the fragments into the MHCII, a multistep mechanism called antigen processing and presentation. Although the role of peptide affinity for MHCII has been well studied, the role of proteolytic fragmentation has received less attention. In this Perspective, we will briefly summarize evidence that antigen resistance to unfolding and proteolytic fragmentation shapes the specificity of the CD4+ T-cell response to selected viral envelope proteins, identify several remarkable examples in which the immunodominant CD4+ epitopes most likely depend on the interaction of processing machinery with antigen conformation, and outline how knowledge of antigen conformation can inform future efforts to design vaccines.

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

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

A reverse vaccinology approach to design an mRNA-based vaccine to provoke a robust immune response against HIV-1.

There have been substantial advances in HIV research over the past three decades, but we are still far from our goal of eliminating HIV-1 infection entirely. Numerous ever-evolving antigens are produced as a result of HIV-1's genetic variability. Developing an effective vaccination is challenging because of the structural properties of the viral envelope glycoprotein that obscure conserved receptor-binding sites and the presence of carbohydrate moieties that prevent antibodies from reaching potential epitopes. To work on an HIV-specific vaccine, this study identified 5 HIV-surface proteins, from the literature, to screen potential epitopes and construct an mRNA vaccine. A wide range of immunological-informatics techniques were utilized to develop a construct that efficiently stimulated cellular and humoral immune responses. The vaccine was produced with 31 epitopes, a TLR4 agonist termed RpfE that acts as an adjuvant, secretion boosters, subcellular trafficking structures, and linkers. It was determined that this suggested vaccine would cover 98.9 percent of the population, making it widely available. We, furthermore, carried out an immunological simulation of the vaccine illustrating the active and stable responses from innate and adaptive immune cells, the memory cells remained active for up to 350 days after vaccine injection, whereas the antigen was excreted from the body within 24 hours. Docking performed with TLR-4 and TLR-3 showed significant interaction with -11.9kcal/mol and -18.2kcal/mol-1 respectively. Molecular dynamics simulations further validated the vaccine's stability, with a dissociation constant of 1.7E-11 for the TLR3-vaccine complex and 5.8E-11 for the TLR4-vaccine complex. Lastly, codon optimization was carried out to guarantee that the designed mRNA construct would be translated into the host successfully. This vaccine adaptation, if tested in-vitro, would be efficacious and potent as predicted.

[A multi-stage and multi-epitope vaccine against Mycobacterium tuberculosis based on an immunoinformatics approach].

Objectives To develop a multi-stage and multi-epitope vaccine, which consists of epitopes from the early secretory and latency-associated antigens of Mycobacterium tuberculosis (MTB). Methods The B-cell, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes of 12 proteins were predicted using an immunoinformatics. The epitopes with antigenicity, without cytotoxicity and sensitization, were further screened to construct the multi-epitope vaccine. Furthermore, the proposed vaccine underwent physicochemical properties analysis and secondary structure prediction as well as 3D structure modeling, refinement and validation. Then the refined model was docked with TLR4. Finally, an immune simulation of the vaccine was carried out. Results The proposed vaccine, which consists of 12 B-cell, 11 CTL and 12 HTL epitopes, had a flexible and stable globular conformation as well as a thermostable and hydrophilic structure. A stable interaction of the vaccine with TLR4 was confirmed by molecular docking. The efficiency of the candidate vaccine to trigger effective cellular and humoral immune responses was assessed by immune simulation. Conclusion A multi-stage multi-epitope MTB vaccine construction strategy based on immunoinformatics is proposed, which is expected to prevent both active and latent MTB infection.

Structural immunoinformatics approach for rational design of a multi-epitope vaccine against triple negative breast cancer.

TNBC is a highly malignant breast cancer known for its aggressive behavior affecting young female adults. The standard treatment for TNBC includes surgery, chemotherapy, and radiotherapy, which often have significant side effects. Therefore, novel preventive methods are required to combat TNBC effectively. In this study, we utilized immunoinformatics to construct an in-silico vaccine against TNBC using the TRIM25 molecule via the reverse vaccinology method. Four vaccines were designed by generating T and B-cell epitopes linked with four different linkers. The modeled vaccine was docked and the results showed that vaccine-3 exhibited the highest affinity with the immune receptors. The molecular dynamics results revealed that the binding affinity and stability of Vaccine-3 were greater than those of Vaccine 2 complexes. This study has great potential preventive measures for TNBC, and further research is warranted to evaluate its efficacy in preclinical settings. This study presents an innovative preventive strategy for triple-negative breast cancer (TNBC) through immunoinformatics and reverse vaccinology to develop an in-silico vaccine. Leveraging these innovative techniques offers a novel avenue for combating the complex challenges associated with TNBC. This approach demonstrates considerable potential as a significant breakthrough in preventive measures for this particularly aggressive and malignant form of breast cancer.

Beyond the state of the art of reverse vaccinology: predicting vaccine efficacy with the universal immune system simulator for influenza.

When it was first introduced in 2000, reverse vaccinology was defined as an in silico approach that begins with the pathogen's genomic sequence. It concludes with a list of potential proteins with a possible, but not necessarily, list of peptide candidates that need to be experimentally confirmed for vaccine production. During the subsequent years, reverse vaccinology has dramatically changed: now it consists of a large number of bioinformatics tools and processes, namely subtractive proteomics, computational vaccinology, immunoinformatics, and in silico related procedures. However, the state of the art of reverse vaccinology still misses the ability to predict the efficacy of the proposed vaccine formulation. Here, we describe how to fill the gap by introducing an advanced immune system simulator that tests the efficacy of a vaccine formulation against the disease for which it has been designed. As a working example, we entirely apply this advanced reverse vaccinology approach to design and predict the efficacy of a potential vaccine formulation against influenza H5N1. Climate change and melting glaciers are critical due to reactivating frozen viruses and emerging new pandemics. H5N1 is one of the potential strains present in icy lakes that can raise a pandemic. Investigating structural antigen protein is the most profitable therapeutic pipeline to generate an effective vaccine against H5N1. In particular, we designed a multi-epitope vaccine based on predicted epitopes of hemagglutinin and neuraminidase proteins that potentially trigger B-cells, CD4, and CD8 T-cell immune responses. Antigenicity and toxicity of all predicted CTL, Helper T-lymphocytes, and B-cells epitopes were evaluated, and both antigenic and non-allergenic epitopes were selected. From the perspective of advanced reverse vaccinology, the Universal Immune System Simulator, an in silico trial computational framework, was applied to estimate vaccine efficacy using a cohort of 100 digital patients.

Epitope-based vaccine design against the membrane and nucleocapsid proteins of SARS-CoV-2.

BACKGROUND: The high prevalence and mortality rate of coronavirus disease 2019 (COVID-19) is a major global concern. Bioinformatics approaches have helped to develop new strategies to combat infectious agents, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Indeed, the structural proteins of microorganisms provide suitable epitopes for the development of vaccines to prevent infectious diseases. OBJECTIVES: The present study aimed to use bioinformatics tools to find peptides from the membrane (M) and nucleocapsid (N) proteins with effective cellular and humoral immunogenicity. MATERIAL AND METHODS: Sequences of the M and N proteins were sourced from the National Center for Biotechnology Information (NCBI). The conserved regions of the proteins with the highest immunogenicity were identified and assessed using different servers, and the physicochemical and biochemical properties of the epitopes were evaluated. Finally, allergenicity, antigenicity and docking to human leukocyte antigen (HLA) were investigated. RESULTS: The data indicated that the best epitopes were LVIGFLFLT and LFLTWICLL (as membrane epitopes), and KLDDKDPNFKDQ (as a nucleocapsid epitope), with significant immunogenicity and no evidence of allergenicity. The 3 epitopes are stable peptides that can interact with HLA to induce strong immune responses. CONCLUSIONS: The findings indicate that 3 common epitopes could effectively elicit an immune response against the disease. Hence, in vitro and in vivo studies are recommended to confirm the theoretical information.

Designing a multi-epitopes subunit vaccine against human herpes virus 6A based on molecular dynamics and immune stimulation.

Human Herpesvirus 6A (HHV-6A) is a prevalent virus associated with various clinical manifestations, including neurological disorders, autoimmune diseases, and promotes tumor cell growth. HHV-6A is an enveloped, double-stranded DNA virus with a genome of approximately 160-170 kb containing a hundred open-reading frames. An immunoinformatics approach was applied to predict high immunogenic and non-allergenic CTL, HTL, and B cell epitopes and design a multi-epitope subunit vaccine based on HHV-6A glycoprotein B (gB), glycoprotein H (gH), and glycoprotein Q (gQ). The stability and correct folding of the modeled vaccines were confirmed through molecular dynamics simulation. Molecular docking found that the designed vaccines have a strong binding network with human TLR3, with Kd values of 1.5E-11 mol/L, 2.6E-12 mol/L, 6.5E-13 mol/L, and 7.1E-11 mol/L for gB-TLR3, gH-TLR3, gQ-TLR3, and the combined vaccine-TLR3, respectively. The codon adaptation index values of the vaccines were above 0.8, and their GC content was around 67 % (normal range 30-70 %), indicating their potential for high expression. Immune simulation analysis demonstrated robust immune responses against the vaccine, with approximately 650,000/ml combined IgG and IgM antibody titer. This study lays a strong foundation for developing a safe and effective vaccine against HHV-6A, with significant implications for treating associated conditions.

Proteomics-based vaccine targets annotation and design of subunit and mRNA-based vaccines for Monkeypox virus (MPXV) against the recent outbreak.

Monkeypox Virus (MPXV) is a growing public health threat with increasing cases and fatalities globally. To date, no specific vaccine or small molecule therapeutic choices are available for the treatment of MPXV disease. In this work, we employed proteomics and structural vaccinology approaches to design mRNA and multi-epitopes-based vaccines (MVC) against MPXV. We first identified ten proteins from the whole proteome of MPXV as potential vaccine targets. We then employed structural vaccinology approaches to map potential epitopes of these proteins for B cell, cytotoxic T lymphocytes (CTL), and Helper T lymphocytes (HTL). Finally, 9 CTL, 6 B cell, and 5 HTL epitopes were joined together through suitable linkers to construct MVC (multi-epitope vaccine) and mRNA-based vaccines. Molecular docking, binding free energy calculation, and in silico cloning revealed robust interaction of the designed MVC with toll-like receptor 2 (TLR2) and efficient expression in E. Coli K12 strain. The immune simulation results revealed that the antigen titer after the injection reached to the maximum level on the 5th day and an abrupt decline in the antigen titer was observed upon the production of IgM, IgG and IgM + IgG, dendritic cells, IFN-gamma, and IL (interleukins), which suggested the potential of our designed vaccine candidate for inducing an immune response against MPXV.

Bioinformatics and immunoinformatics approach to develop potent multi-peptide vaccine for coxsackievirus B3 capable of eliciting cellular and humoral immune response.

Coxsackievirus B3 (CVB3) is a viral pathogen of various human disorders with no effective preventative interventions. Herein, we aimed to design a chimeric vaccine construct for CVB3 using reverse vaccinology and immunoinformatics approaches by screening the whole viral polyprotein sequence. Firstly, screening and mapping of viral polyprotein to predict 21 immunodominant epitopes (B-cell, CD8+ and CD4+ T-cell epitopes), fused with an adjuvant (Resuscitation-promoting factor), appropriate linkers, HIV-TAT peptide, Pan DR epitope, and 6His-tag to assemble a multi-epitope vaccine construct. The chimeric construct is predicted as probable antigen, non-allergen, stable, possess encouraging physicochemical features, and indicates a broader population coverage (98 %). The tertiary structure of the constructed vaccine was predicted and refined, and its interaction with the Toll-like receptor 4 (TLR4) was investigated through molecular docking and dynamics simulation. Computational cloning of the construct was carried out in pET28a (+) plasmid to guarantee the higher expression of the vaccine protein. Lastly, in silico immune simulation foreseen that humoral and cellular immune responses would be elicited in response to the administration of such a potent chimeric construct. Thus, the design constructed could vaccinate against CVB3 infection and various CVB serotypes. However, further in vitro/in vivo research must assess its safety and effectiveness.