Proteome-wide mapping and reverse vaccinology-based B and T cell multi-epitope subunit vaccine designing for immune response reinforcement against Porphyromonas gingivalis

Immunoinformatic based designing of highly immunogenic multi-epitope subunit vaccines to stimulate an adaptive immune response against Junin virus

The Junin virus causes Argentine hemorrhagic fever, leading to severe complications such as high fever, malaise, muscle pain, and bleeding disorders, including hemorrhages in the skin and mucous membranes. Neurological issues like confusion, seizures, and coma can also occur. Without prompt and effective treatment, the disease can be fatal, with mortality rates reaching up to 30%. Taking serious measures is essential to mitigate the spread of the disease. Vaccination is the most effective choice to neutralize the Junin virus in the current situation. Consequently, to design the highly immunogenic and non-allergenic multi-epitope subunit vaccine against the Junin virus, we employed the immunoinformatic approach to screen the glycoprotein, nucleoprotein, and RDRP protein for potential immunogenic CTL (Cytotoxic T Lymphocyte), HTL (Helper T Lymphocyte) and B (B Lymphocyte) cell epitopes. Afterward, the predicted epitopes were subjected to 3D modeling and validation. The strong binding affinity of the constructed vaccines with the human TLR3 was confirmed through molecular docking, with scores of - 333 kcal/mol for glycoprotein, - 297 kcal/mol for nucleoprotein, - 308 kcal/mol for RDRP, and - 305 kcal/mol for combined vaccines. Additionally, the binding free energies recorded were - 63.54 kcal/mol, - 64.16 kcal/mol, - 56.81 kcal/mol, and - 51.52 kcal/mol, respectively. Furthermore, the dynamic stability, residual fluctuation, and compactness of vaccine-TLR-3 complexes were confirmed by the molecular dynamic simulation. The codon adaptation index (CAI) values and high GC content confirmed the stable expression of constructed vaccines in the pET-28a ( +) expression vector. The immune simulation analysis demonstrated that administering booster doses of the developed vaccines resulted in a notable increase in IgG, IgM, interleukins, and cytokines levels, indicating effective antigen clearance over time. In conclusion, our study provides preclinical evidence for designing a highly effective Junin virus vaccine, necessitating further in-vitro and in-vivo experiments.

Multi-epitope Based Peptide Vaccine Candidate Against Babesia Infection From Rhoptry-Associated Protein 1 (RAP-1) Antigen Using Immuno-Informatics: An In Silico Approach

Objective

Babesiosis is a significant haemoparasitic infection caused by apicomplexan parasites of the genus Babesia. This infection has continuously threatened cattle farmers owing to its devastating effects on productivity and severe economic implications. Failure to curb the increase of the infection has been attributed to largely ineffective vaccines. This study was designed to develop a potential vaccine candidate.

Method

Rhoptry-associated protein-1 (RAP-1) was used to identify and design a potential multi-epitope vaccine candidate due to its immunogenic properties through an immunoinformatics approach.

Results and conclusions

A multi-epitope vaccine comprising 11 CD8+, 17 CD4+, and 3 B-cell epitopes was constructed using the AAY, GPGPG, and KK linkers. Beta-defensin-3 was added as an adjuvant to potentiate the immune response using the EAAK linker. The designed vaccine was computationally predicted to be antigenic (antigenicity scores: 0.6), soluble (solubility index: 0.730), and non-allergenic. The vaccine construct comprises 595 amino acids with a molecular weight of 64 152 kDa, an instability and aliphatic index of 13.89 and 65.82, which confers stability with a Grand average of hydropathicity (GRAVY) value of 0.122, indicating the hydrophobicity of the construct. Europe has the highest combined class population coverage, with a percentage of 96.07%, while Central America has the lowest population coverage, with a value of 22.94%. The DNA sequence of the vaccine construct was optimized and successfully cloned into a pET-28a (+) plasmid vector. Analysis of binding interactions indicated the stability of the complex when docked with Toll-like receptor-2 (TLR-2). The subunit vaccine construct was predicted to induce and boost sufficient host cellular and humoral responses in silico. However, further experimental research and analysis is required to validate the findings.

Limitation

This study is purely computational, and further experimental validation of these findings through in vivo and in vitro conditions is required.

Natural Compounds Targeting Thymic Stromal Lymphopoietin (TSLP): A Promising Therapeutic Strategy for Atopic Dermatitis

Atopic dermatitis (AD) is a chronic inflammatory skin disease with rising prevalence, marked by eczematous lesions, itching, and a weakened skin barrier often tied to filaggrin gene mutations. This breakdown allows allergen and microbe entry, with thymic stromal lymphopoietin (TSLP) playing a crucial role by activating immune pathways that amplify the allergic response. TSLP's central role in AD pathogenesis makes it a promising therapeutic target. Consequently, in this study, we used the virtual drug screening, molecular dynamics simulation, and binding free energies calculation approaches to explore the African Natural Product Database against the TSLP protein. The molecular screening identified four compounds with high docking scores, namely SA_0090 (-7.37), EA_0131 (-7.10), NA_0018 (-7.03), and WA_0006 (-6.99 kcal/mol). Furthermore, the KD analysis showed a strong binding affinity of these compounds with TSLP, with values of -5.36, -5.36, -5.34, and -5.32 kcal/mol, respectively. Moreover, the strong binding affinity of these compounds was further validated by molecular dynamic simulation analysis, which revealed that the WA_0006-TSLP is the most stable complex with the lowest average RMSD. However, the total binding free energies were -40.5602, -41.0967, -27.3293, and -51.3496 kcal/mol, respectively, showing the strong interaction between the selected compounds and TSLP. Likewise, these compounds showed excellent pharmacokinetics characteristics. In conclusion, this integrative approach provides a foundation for the development of safe and effective treatments for AD, potentially offering relief to millions of patients worldwide.

Computational proteomics analysis of Taphrina deformans for the identification of antifungal drug targets and validation with commercial fungicides

Taphrina deformans is a plant-pathogenic fungus and a responsible agent for causing peach leaf curl disease. Taphrina deformans affects peach fruit production and contributes to global economic losses. Commercial fungicides may provide temporary relief; however, their overuse resulted in adverse environmental consequences as well as led to drug-resistant strains of T. deformans. Therefore, the discovery of novel drug targets for the future synthesis of antifungal drugs against Taphrina deformans is needed. Here we studied Taphrina deformans by computational proteomics approaches. The whole genome and proteome of T. deformans were subjected to subtractive proteomics, high-throughput virtual screening, and molecular dynamic simulations. We employed subtractive proteomics analysis of 4,659 proteins extracted from UniProtKB database; after filtering out homologous and non-essential proteins, we identified 189 essential ones, including nine that participated in the crucial metabolic pathways of the pathogen. These proteins were categorized as nuclear (n = 116), cytoplasmic (n = 37), and membrane (n = 36). Of those essential proteins, glutamate-cysteine ligase (GCL) emerged as one promising target due to its essential function for glutathione biosynthesis process which facilitates T. deformans survival and pathogenicity. To validate GCL as an antifungal target, virtual screening and molecular docking studies with various commercial fungicides were carried out to better characterize GCL as a drug target. The data showed strong binding affinities for polyoxin D, fluoxastrobin, trifloxystrobin, and azoxystrobin within the active site of GCL. Polyoxin D showed a strong affinity when the measured docking score was at -7.34 kcal/mol, while molecular dynamics simulations confirmed stable interactions (three hydrogen bonds, two hydrophobic bonds, and one salt bridge interaction), supporting our findings that GCL represents an excellent target for antifungal drug development efforts. The results showed that GCL, as an innovative target for future fungicide designs to combat T. deformans infections, provides an avenue toward creating more effective peach leaf curl disease treatments while mitigating environmental harm caused by its current use.

Advances and challenges in the development of periodontitis vaccines: A comprehensive review

Periodontitis is a prevalent polymicrobial disease. It damages soft tissues and alveolar bone, and causes a significant public-health burden. Development of an advanced therapeutic approach and exploration of vaccines against periodontitis hold promise as potential treatment avenues. Clinical trials for a periodontitis vaccine are lacking. Therefore, it is crucial to address the urgent need for developing strategies to implement vaccines at the primary level of prevention in public health. A deep understanding of the principles and mechanisms of action of vaccines plays a crucial role in the successful development of vaccines and their clinical translation. This review aims to provide a comprehensive summary of potential directions for the development of highly efficacious periodontitis vaccines. In addition, we address the limitations of these endeavors and explore future possibilities for the development of an efficacious vaccine against periodontitis.

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.

Abrogation of ORF8-IRF3 binding interface with Carbon nanotube derivatives to rescue the host immune system against SARS-CoV-2 by using molecular screening and simulation approaches

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway. To block the binding of ORF8-IRF3 and inhibit viral pathogenesis a quick discovery of an inhibitor molecule is needed. Therefore, in the present study, the interface between the ORF8 and IRF3 was targeted on a high-affinity carbon nanotube by using computational tools. After analysis of 62 carbon nanotubes by multiple docking with the induced fit model, the top five compounds with high docking scores of - 7.94 kcal/mol, - 7.92 kcal/mol, - 7.28 kcal/mol, - 7.19 kcal/mol and - 7.09 kcal/mol (top hit1-5) were found to have inhibitory activity against the ORF8-IRF3 complex. Molecular dynamics analysis of the complexes revealed the high compactness of residues, stable binding, and strong hydrogen binding network among the ORF8-nanotubes complexes. Moreover, the total binding free energy for top hit1-5 was calculated to be - 43.21 ± 0.90 kcal/mol, - 41.17 ± 0.99 kcal/mol, - 48.85 ± 0.62 kcal/mol, - 43.49 ± 0.77 kcal/mol, and - 31.18 ± 0.78 kcal/mol respectively. These results strongly suggest that the identified top five nanotubes (hit1-5) possess significant potential for advancing and exploring innovative drug therapies. This underscores their suitability for subsequent in vivo and in vitro experiments, marking them as promising candidates worthy of further investigation.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Computational approach for identifying immunogenic epitopes and optimizing peptide vaccine through in-silico cloning against Mycoplasma genitalium

Mycoplasma genitalium is a pathogenic microorganism linked to a variety of severe health conditions including ovarian cancer, prostate cancer, HIV transmission, and sexually transmitted diseases. A more effective approach to address the challenges posed by this pathogen, given its high antibiotic resistance rates, could be the development of a peptide vaccine. In this study, we used experimentally validated 13 membrane proteins and their immunogenicity to identify suitable vaccine candidates. Thus, based on immunogenic properties and high conservation among other Mycoplasma genitalium sub-strains, the P110 surface protein is considered for further investigation. Later on, we identified T-cell epitopes and B-cell epitopes from the P110 protein to construct a multiepitope-based vaccine. As a result, the 'NIAPISFSFTPFTAA' T-cell epitope and 'KVKYESSGSNNISFDS' B-cell epitope have shown 99.53% and 87.50% population coverage along with 100% conservancy among the subspecies, and both epitopes were found to be non-allergenic. Furthermore, focusing on molecular docking analysis showed the lowest binding energy for MHC-I (-137.5 kcal/mol) and MHC-II (-183.3 kcal/mol), leading to a satisfactory binding strength between the T-cell epitopes and the MHC molecules. However, the constructed multiepitope vaccine (MEV) consisting of 54 amino acids demonstrates favorable characteristics for a vaccine candidate, including a theoretical pI of 4.25 with a scaled solubility of 0.812 and high antigenicity probabilities. Additionally, structural analyses reveal that the MEV displays substantial alpha helices and extended strands, vital for its immunogenicity. Molecular docking with the human Toll-like receptors TLR1/2 heterodimer shows strong binding affinity, reinforcing its potential to elicit an immune response. Our immune simulation analysis demonstrates immune memory development and robust immunity, while codon adaptation suggests optimal expression in E. coli using the pET-28a(+) vector. These findings collectively highlight the MEV's potential as a valuable vaccine candidate against M. genitalium.

Structural vaccinology, molecular simulation and immune simulation approaches to design multi-epitopes vaccine against John Cunningham virus

The JCV (John Cunningham Virus) is known to cause progressive multifocal leukoencephalopathy, a condition that results in the formation of tumors. Symptoms of this condition such as sensory defects, cognitive dysfunction, muscle weakness, homonosapobia, difficulties with coordination, and aphasia. To date, there is no specific and effective treatment to completely cure or prevent John Cunningham polyomavirus infections. Since the best way to control the disease is vaccination. In this study, the immunoinformatic tools were used to predict the high immunogenic and non-allergenic B cells, helper T cells (HTL), and cytotoxic T cells (CTL) epitopes from capsid, major capsid, and T antigen proteins of JC virus to design the highly efficient subunit vaccines. The specific immunogenic linkers were used to link together the predicted epitopes and subjected to 3D modeling by using the Robetta server. MD simulation was used to confirm that the newly constructed vaccines are stable and properly fold. Additionally, the molecular docking approach revealed that the vaccines have a strong binding affinity with human TLR-7. The codon adaptation index (CAI) and GC content values verified that the constructed vaccines would be highly expressed in E. coli pET28a (+) plasmid. The immune simulation analysis indicated that the human immune system would have a strong response to the vaccines, with a high titer of IgM and IgG antibodies being produced. In conclusion, this study will provide a pre-clinical concept to construct an effective, highly antigenic, non-allergenic, and thermostable vaccine to combat the infection of the John Cunningham virus.

Analysis of E2F1 single-nucleotide polymorphisms reveals deleterious non-synonymous substitutions that disrupt E2F1-RB protein interaction in cancer

Cancer is a medical condition that is caused by the abnormal growth and division of cells, leading to the formation of tumors. The E2F1 and RB pathways are critical in regulating cell cycle, and their dysregulation can contribute to the development of cancer. In this study, we analyzed experimentally reported SNPs in E2F1 and assessed their effects on the binding affinity with RB. Out of 46, nine mutations were predicted as deleterious, and further analysis revealed four highly destabilizing mutations (L206W, R232C, I254T, A267T) that significantly altered the protein structure. Molecular docking of wild-type and mutant E2F1 with RB revealed a docking score of -242 kcal/mol for wild-type, while the mutant complexes had scores ranging from -217 to -220 kcal/mol. Molecular simulation analysis revealed variations in the dynamics features of both mutant and wild-type complexes due to the acquired mutations. Furthermore, the total binding free energy for the wild-type E2F1-RB complex was -64.89 kcal/mol, while those of the L206W, R232C, I254T, and A267T E2F1-RB mutants were -45.90 kcal/mol, -53.52 kcal/mol, -55.67 kcal/mol, and -61.22 kcal/mol, respectively. Our study is the first to extensively analyze E2F1 gene mutations and identifies candidate mutations for further validation and potential targeting for cancer therapeutics.

Elucidating the binding mechanism of SARS-CoV-2 NSP6-TBK1 and structure-based designing of phytocompounds inhibitors for instigating the host immune response

SARS-CoV-2, also referred to as severe acute respiratory syndrome coronavirus 2, is the virus responsible for causing COVID-19, an infectious disease that emerged in Wuhan, China, in December 2019. Among its crucial functions, NSP6 plays a vital role in evading the human immune system by directly interacting with a receptor called TANK-binding kinase (TBK1), leading to the suppression of IFNβ production. Consequently, in the present study we used the structural and biophysical approaches to analyze the effect of newly emerged mutations on the binding of NSP6 and TBK1. Among the identified mutations, four (F35G, L37F, L125F, and I162T) were found to significantly destabilize the structure of NSP6. Furthermore, the molecular docking analysis highlighted that the mutant NSP6 displayed its highest binding affinity with TBK1, exhibiting docking scores of -1436.2 for the wildtype and -1723.2, -1788.6, -1510.2, and -1551.7 for the F35G, L37F, L125F, and I162T mutants, respectively. This suggests the potential for an enhanced immune system evasion capability of NSP6. Particularly, the F35G mutation exhibited the strongest binding affinity, supported by a calculated binding free energy of -172.19 kcal/mol. To disrupt the binding between NSP6 and TBK1, we conducted virtual drug screening to develop a novel inhibitor derived from natural products. From this screening, we identified the top 5 hit compounds as the most promising candidates with a docking score of -6.59 kcal/mol, -6.52 kcal/mol, -6.32 kcal/mol, -6.22 kcal/mol, and -6.21 kcal/mol. The molecular dynamic simulation of top 3 hits further verified the dynamic stability of drugs-NSP6 complexes. In conclusion, this study provides valuable insight into the higher infectivity of the SARS-CoV-2 new variants and a strong rationale for the development of novel drugs against NSP6.

Mutational analysis of SARS-CoV-2 ORF6-KPNA2 binding interface and identification of potent small molecule inhibitors to recuse the host immune system

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surfaced on 31 December, 2019, and was identified as the causative agent of the global COVID-19 pandemic, leading to a pneumonia-like disease. One of its accessory proteins, ORF6, has been found to play a critical role in immune evasion by interacting with KPNA2 to antagonize IFN signaling and production pathways, resulting in the inhibition of IRF3 and STAT1 nuclear translocation. Since various mutations have been observed in ORF6, therefore, a comparative binding, biophysical, and structural analysis was used to reveal how these mutations affect the virus's ability to evade the human immune system. Among the identified mutations, the V9F, V24A, W27L, and I33T, were found to have a highly destabilizing effect on the protein structure of ORF6. Additionally, the molecular docking analysis of wildtype and mutant ORF6 and KPNA2 revealed the docking score of - 53.72 kcal/mol for wildtype while, -267.90 kcal/mol, -258.41kcal/mol, -254.51 kcal/mol and -268.79 kcal/mol for V9F, V24A, W27L, and I33T respectively. As compared to the wildtype the V9F showed a stronger binding affinity with KPNA2 which is further verified by the binding free energy (-42.28 kcal/mol) calculation. Furthermore, to halt the binding interface of the ORF6-KPNA2 complex, we used a computational molecular search of potential natural products. A multi-step virtual screening of the African natural database identified the top 5 compounds with best docking scores of -6.40 kcal/mol, -6.10 kcal/mol, -6.09 kcal/mol, -6.06 kcal/mol, and -6.03 kcal/mol for tophit1-5 respectively. Subsequent all-atoms simulations of these top hits revealed consistent dynamics, indicating their stability and their potential to interact effectively with the interface residues. In conclusion, our study represents the first attempt to establish a foundation for understanding the heightened infectivity of new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.

Exploring the natural products chemical space to abrogate the F3L-dsRNA interface of monkeypox virus to enhance the immune responses using molecular screening and free energy calculations

Amid the ongoing monkeypox outbreak, there is an urgent need for the rapid development of effective therapeutic interventions capable of countering the immune evasion mechanisms employed by the monkeypox virus (MPXV). The evasion strategy involves the binding of the F3L protein to dsRNA, resulting in diminished interferon (IFN) production. Consequently, our current research focuses on utilizing virtual drug screening techniques to target the RNA binding domain of the F3L protein. Out of the 954 compounds within the South African natural compound database, only four demonstrated notable docking scores: -6.55, -6.47, -6.37, and -6.35 kcal/mol. The dissociation constant (KD) analysis revealed a stronger binding affinity of the top hits 1-4 (-5.34, -5.32, -5.29, and -5.36 kcal/mol) with the F3L in the MPXV. All-atom simulations of the top-ranked hits 1 to 4 consistently exhibited stable dynamics, suggesting their potential to interact effectively with interface residues. This was further substantiated through analyses of parameters such as radius of gyration (Rg), Root Mean Square Fluctuation, and hydrogen bonding. Cumulative assessments of binding free energy confirmed the top-performing candidates among all the compounds, with values of -35.90, -52.74, -28.17, and -32.11 kcal/mol for top hits 1-4, respectively. These results indicate that compounds top hit 1-4 could hold significant promise for advancing innovative drug therapies, suggesting their suitability for both in vivo and in vitro experiments.

Integrated immunoinformatics and subtractive proteomics approach for multi-epitope vaccine designing to combat S. pneumoniae TIGR4

Streptococcus pneumoniae is one of the major precarious pathogens accountable for over 1.2 million fatalities annually. The key drivers for pneumococcal vaccine development involve high morbidity and mortality in over one million cases, especially in very young children and the elderly. In this study, immunoinformatics was integrated with subtractive proteomics to find antigenic proteins for designing a multi-epitope vaccine against S. pneumoniae. As prospective vaccine targets, the developed pipeline identified two antigenic proteins, i.e., penicillin-binding protein and ATP synthase subunit. Several immunoinformatics and bioinformatics resources were used to forecast T- and B-cell epitopes from specific proteins. By employing a mixture of five cytotoxic T-cell lymphocytes, six helper T-cell lymphocytes, and seven linear B-cell lymphocyte epitopes, a 392 amino acid-long vaccine was designed. To enhance immune responses, the designed vaccine was coupled with a cholera enterotoxin subunit B adjuvant. The designed vaccine was highly antigenic, non-allergenic, and stable for human usage. The stability of the vaccine with toll-like receptor-4 was evaluated by molecular docking and molecular dynamic simulation. In addition, immunological simulation was performed to test its real-world potency. The vaccine codon was then cloned in silico. Overall, this study paves the way for the development of a multi-epitope S. pneumoniae vaccine under laboratory conditions. Furthermore, the current findings warrant for the experimental validation of the final multi-epitope vaccine construct to demonstrate its immunological reinforcing capability and clinical applicability.

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 K_d 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.

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

BACKGROUND

Streptococcus pneumoniae (Pneumococcus) has remained a leading cause of fatal infections such as pneumonia, meningitis, and sepsis. Moreover, this pathogen plays a major role in bacterial co-infection in patients with life-threatening respiratory virus diseases such as influenza and COVID-19. High morbidity and mortality in over one million cases, especially in very young children and the elderly, are the main motivations for pneumococcal vaccine development. Due to the limitations of the currently marketed polysaccharide-based vaccines, non-serotype-specific protein-based vaccines have received wide research interest in recent years. One step further is to identify high antigenic regions within multiple highly-conserved proteins in order to develop peptide vaccines that can affect various stages of pneumococcal infection, providing broader serotype coverage and more effective protection. In this study, immunoinformatics tools were used to design an effective multi-epitope vaccine in order to elicit neutralizing antibodies against multiple strains of pneumococcus.

RESULTS

The B- and T-cell epitopes from highly protective antigens PspA (clades 1-5) and PhtD were predicted and immunodominant peptides were linked to each other with proper linkers. The domain 4 of Ply, as a potential TLR4 agonist adjuvant candidate, was attached to the end of the construct to enhance the immunogenicity of the epitope vaccine. The evaluation of the physicochemical and immunological properties showed that the final construct was stable, soluble, antigenic, and non-allergenic. Furthermore, the protein was found to be acidic and hydrophilic in nature. The protein 3D-structure was built and refined, and the Ramachandran plot, ProSA-web, ERRAT, and Verify3D validated the quality of the final model. Molecular docking analysis showed that the designed construct via Ply domain 4 had a strong interaction with TLR4. The structural stability of the docked complex was confirmed by molecular dynamics. Finally, codon optimization was performed for gene expression in E. coli, followed by in silico cloning in the pET28a(+) vector.

CONCLUSION

The computational analysis of the construct showed acceptable results, however, the suggested vaccine needs to be experimentally verified in laboratory to ensure its safety and immunogenicity.

Current Update on Rotavirus in-Silico Multiepitope Vaccine Design

Rotavirus gastroenteritis is one of the leading causes of pediatric morbidity and mortality worldwide in infants and under-five populations. The World Health Organization (WHO) recommended global incorporation of the rotavirus vaccine in national immunization programs to alleviate the burden of the disease. Implementation of the rotavirus vaccination in certain regions of the world brought about a significant and consistent reduction of rotavirus-associated hospitalizations. However, the efficacy of licensed vaccines remains suboptimal in low-income countries where the incidences of rotavirus gastroenteritis continue to happen unabated. The problem of low efficacy of currently licensed oral rotavirus vaccines in low-income countries necessitates continuous exploration, design, and development of new rotavirus vaccines. Traditional vaccine development is a complex, expensive, labor-intensive, and time-consuming process. Reverse vaccinology essentially utilizes the genome and proteome information on pathogens and has opened new avenues for in-silico multiepitope vaccine design for a plethora of pathogens, promising time reduction in the complete vaccine development pipeline by complementing the traditional vaccinology approach. A substantial number of reviews on licensed rotavirus vaccines and those under evaluation are already available in the literature. However, a collective account of rotavirus in-silico vaccines is lacking in the literature, and such an account may further fuel the interest of researchers to use reverse vaccinology to expedite the vaccine development process. Therefore, the main focus of this review is to summarize the research endeavors undertaken for the design and development of rotavirus vaccines by the reverse vaccinology approach utilizing the tools of immunoinformatics.

Immunoinformatics design of B and T-cell epitope-based SARS-CoV-2 peptide vaccination

SARS-COV-2 is a virulent respiratory virus, first identified in China (Wuhan) at the end of 2019. Scientists and researchers are trying to find any possible solution to this deadly viral disease. Different drug source agents have been identified, including western medicine, natural products, and traditional Chinese medicine. They have the potential to counteract COVID-19. This virus immediately affects the liver and causes a decrease in oxygen levels. In this study, multiple vacciome approaches were employed for designing a multi-epitope subunit vaccine for battling against SARS-COV-2. Vaccine designing, immunogenicity, allergenic, and physico-chemical assessment were performed by using the vacciome approach. The vaccine design is likely to be antigenic and produce potent interactions with ACE2 and NSP3 receptors. The developed vaccine has also been given to in-silico cloning models and immune response predictions. A total number of 12 CTL and 12 HTL antigenic epitopes were predicted from three selected covid-19 virulent proteins (spike protein, nucleocapsid protein, and membrane proteins, respectively) based on C-terminal cleavage and MHC binding scores. These predicted epitopes were amalgamated by AYY and GPGPG linkers, and a β-defensins adjuvant was inserted into the N-terminus of this vaccine. This analysis shows that the recommended vaccine can produce immune responses against SARS-COV-2. Designing and developing of the mentioned vaccine will require further experimental validation.

mRNA-Based Vaccine Designing against Epstein-Barr Virus to Induce an Immune Response Using Immunoinformatic and Molecular Modelling Approaches

Epstein-Barr Virus (EBV) is a human pathogen that has a morbidity rate of 90% in adults worldwide. Infectious mononucleosis is caused by EBV replication in B cells and epithelial cells of the host. EBV has also been related to autoimmune illnesses, including multiple sclerosis and cancers like nasopharyngeal carcinomas and Burkitt's lymphoma. Currently, no effective medications or vaccinations are available to treat or prevent EBV infection. Thus, the current study focuses on a bioinformatics approach to design an mRNA-based multi-epitope (MEV) vaccine to prevent EBV infections. For this purpose, we selected six antigenic proteins from the EBV proteome based on their role in pathogenicity to predict, extract, and analyze T and B cell epitopes using immunoinformatics tools. The epitopes were directed through filtering parameters including allergenicity, toxicity, antigenicity, solubility, and immunogenicity assessment, and finally, the most potent epitopes able to induce T and B cell immune response were selected. In silico molecular docking of prioritized T cell peptides with respective Human Leukocytes Antigens molecules, were carried out to evaluate the individual peptide's binding affinity. Six CTL, four HTL, and ten linear B cell epitopes fulfilled the set parameters and were selected for MEV-based mRNA vaccine. The prioritized epitopes were joined using suitable linkers to improve epitope presentation. The immune simulation results affirmed the designed vaccine's capacity to elicit a proper immune response. The MEV-based mRNA vaccine constructed in this study offers a promising choice for a potent vaccine against EBV.

Reverse vaccinology approach to identify novel and immunogenic targets against Porphyromonas gingivalis: An in silico study

Porphyromonas gingivalis is a primary causative agent of chronic periodontitis. Moreover, it leads to several systemic diseases, including rheumatoid arthritis, cardiovascular, neurodegenerative, and Alzheimer’s diseases. It seems that the development of a vaccine against this bacterium is necessary. Thus, this study decided to identify novel immunogenic targets and developed multiple epitope-based vaccines against P. gingivalis. For this purpose, the pan/core-proteome of this bacterium was studied, and the suitable vaccine targets were selected based on different properties, including exposed localization of proteins, antigenicity, non-allergenicity, non-similarity to host proteome, stability, B-cell epitopes and MHC II binding sites, sequence conservation, molecular docking, and immune simulation. Through the quartile scoring method, 12 proteins with ≥ 20 scores were considered as suitable immunogenic targets. The results of the protein domain and functional class search showed that most of the immunogenic proteins were involved in the transport and metabolism of inorganic ions and lipids. In addition, two unknown function proteins, including WP_004584259.1 and WP_099780539.1 were detected as immunogenic targets. Three constructions carrying multi-epitopes were generated including Naked, LCL, and as chimeric structures. Among them, FliC chimeric protein had the strongest affinity to the human TLR2, 4, and 6, while the LCL platform represented the highest level of immune stimulation response. The obtained results from this study revealed new insights into prophylactic routes against P. gingivalis by introducing novel immunogenic targets. However, further investigations, including site-directed mutation and immunoassay are needed to confirm the pathogenic role and protectivity of these novel proteins.

Immunoinformatics-Based Proteome Mining to Develop a Next-Generation Vaccine Design against Borrelia burgdorferi: The Cause of Lyme Borreliosis

The tick-borne bacterium, Borrelia burgdorferi has been implicated in Lyme disease-a deadly infection, formerly confined to North America, but currently widespread across Europe and Asia. Despite the severity of this disease, there is still no human Lyme disease vaccine available. A reliable immunoinformatic approach is urgently needed for designing a therapeutic vaccine against this Gram-negative pathogen. Through this research, we explored the immunodominant proteins of B. burgdorferi and developed a novel and reliable vaccine design with great immunological predictability as well as low contamination and autoimmunity risks. Our initial analysis involved proteome-wide analysis to filter out proteins on the basis of their redundancy, homology to humans, virulence, immunogenicity, and size. Following the selection of proteins, immunoinformatic tools were employed to identify MHC class I & II epitopes and B-cell epitopes, which were subsequently subjected to a rigorous screening procedure. In the final formulation, ten common MHC-I and II epitopes were used together with a suitable adjuvant. We predicted that the final chimeric multi-epitope vaccine could invoke B-cell responses and IFN-gamma-mediated immunity as well as being stable and non-allergenic. The dynamics simulations predicted the stable folding of the designed molecule, after which the molecular docking predicted the stability of the interaction between the potential antigenic epitopes and human immune receptors. Our studies have shown that the designed next-generation vaccine stimulates desirable immune responses, thus potentially providing a viable way to prevent Lyme disease. Nevertheless, further experimental studies in a wet lab are needed in order to validate the results.

Core-Proteomics-Based Annotation of Antigenic Targets and Reverse-Vaccinology-Assisted Design of Ensemble Immunogen against the Emerging Nosocomial Infection-Causing Bacterium Elizabethkingia meningoseptica

Elizabethkingia meningoseptica is a ubiquitous Gram-negative emerging pathogen that causes hospital-acquired infection in both immunocompromised and immunocompetent patients. It is a multi-drug-resistant bacterium; therefore, an effective subunit immunogenic candidate is of great interest to encounter the pathogenesis of this pathogen. A protein-wide annotation of immunogenic targets was performed to fast-track the vaccine development against this pathogen, and structural-vaccinology-assisted epitopes were predicted. Among the total proteins, only three, A0A1T3FLU2, A0A1T3INK9, and A0A1V3U124, were shortlisted, which are the essential vaccine targets and were subjected to immune epitope mapping. The linkers EAAK, AAY, and GPGPG were used to link CTL, HTL, and B-cell epitopes and an adjuvant was also added at the N-terminal to design a multi-epitope immunogenic construct (MEIC). The computationally predicted physiochemical properties of the ensemble immunogen reported a highly antigenic nature and produced multiple interactions with immune receptors. In addition, the molecular dynamics simulation confirmed stable binding and good dynamic properties. Furthermore, the computationally modeled immune response proposed that the immunogen triggered a strong immune response after several doses at different intervals. Neutralization of the antigen was observed on the 3rd day of injection. Conclusively, the immunogenic construct produces protection against Elizabethkingia meningoseptica; however, further immunological testing is needed to unveil its real efficacy.

Immunoinformatics and Immunogenetics-Based Design of Immunogenic Peptides Vaccine against the Emerging Tick-Borne Encephalitis Virus (TBEV) and Its Validation through In Silico Cloning and Immune Simulation

Tick-borne encephalitis virus (TBEV), belonging to the Flaviviridae family, is transmitted to humans via infected tick bites, leading to serious neurological complications and, in some cases, death. The available vaccines against the TBEV are reported to have low immunogenicity and are associated with adverse effects like swelling, redness and fever. Moreover, these vaccines are whole-organism-based, carry a risk of reactivation and potential for significant mortality. Consequently, to design a potential antigenic and non-allergenic multi-epitope subunit vaccine against the TBEV, we used an immunoinformatic approach to screen the Tick-borne virus proteome for highly antigenic CTL, HTL and B cell epitopes. The proper folding of the constructed vaccine was validated by a molecular dynamic simulation. Additionally, the molecular docking and binding free energy (−87.50 kcal/mol) further confirmed the strong binding affinity of the constructed vaccine with TLR-4. The vaccine exhibited a CAI value of 0.93 and a GC content of 49%, showing a high expression capability in E coli. Moreover, the analysis of immune simulation demonstrated robust immune responses against the injected vaccine and clearance of the antigen with time. In conclusion, our vaccine candidate shows promise for both in vitro and in vivo analyses due to its high immunogenicity, non-allergenicity and stable interaction with the human TLR-4 receptor.

Subtractive Proteomics and Immuno-informatics Approaches for Multi-peptide Vaccine Prediction Against Klebsiella oxytoca and Validation Through In Silico Expression

Klebsiella oxytoca is a gram-negative bacterium. It is opportunistic in nature and causes hospital acquired infections. Subtractive proteomics and reverse vaccinology approaches were employed to screen out the best proteins for vaccine designing. Whole proteome of K. oxytoca strain ATCC 8724, consisting of 5483 proteins, was used for designing the vaccine. Total 1670 cytotoxic T lymphocyte (CTL) epitope were predicted through NetCTL while 1270 helper T lymphocyte (HTL) epitopes were predicted through IEDB server. The epitopes were screened for non-toxicity, allergenicity, antigenicity and water solubility. After epitope screening 300 CTL and 250 HTL epitopes were submitted to IFN-γ epitope server to predict their Interferon-γ induction response. The selected IFN-γ positive epitopes were tested for their binding affinity with MHCI-DRB1 by MHCPred. The 15 CTL and 13 HTL epitopes were joined by linkers AAY and GPGPG respectively in vaccine construct. Chain C of Pam3CSK4 (PDB ID; 2Z7X) was linked to the vaccine construct as an adjuvant. A 450aa long vaccine construct was submitted to I-TASSER server for 3D structure prediction. Thirteen Linear B cells were predicted by ABCPred server and 10 sets of discontinues epitopes for 3D vaccine structure were predicted by DiscoTope server. The modeled 3D vaccine construct was docked with human Toll-like receptor 2 (PDB ID: 6NIG) by PatchDock. The docked complexes were refined by FireDock. The selected docked complex showed five hydrogen bonds and one salt bridge. The vaccine sequence was reverse transcribed to get nucleotide sequence for In silico cloning. The reverse transcribed sequence strand was cloned in pET28a(+) expression vector. A clone containing 6586 bp was constructed including the 450 bp of query gene sequence.