In silico vaccine design for Yersinia enterocolitica: A comprehensive approach to enhanced immunogenicity, efficacy and protection.

Yersinia enterocolitica, a foodborne pathogen, has emerged as a significant public health concern due to its increased prevalence and multidrug resistance. This study employed reverse vaccinology to identify novel vaccine candidates against Y. enterocolitica through comprehensive in silico analyses. The core genome's conserved protein translocase subunit SecY was selected as the target, and potential B-cell, MHC class I, and MHC class II epitopes were mapped. 3B-cell epitopes, 3 MHCI and 11 MHCII epitopes were acquired. A multi-epitope vaccine construct was designed by incorporating the identified epitopes, TLR4 Agonist was used as adjuvants to enhance the immunogenic response. EAAAK, CPGPG and AYY linkers were used to form a vaccine construct, followed by extensive computational evaluations. The vaccine exhibited desirable physicochemical properties, stable secondary and tertiary structures as evaluated by PDBSum and trRosetta. Moreover, favorable interactions with the human Toll-like receptor 4 (TLR4) was observed by ClusPro. Population coverage analysis estimated the vaccine's applicability across 99.74 % in diverse populations. In addition, molecular dynamics simulations and normal mode analysis confirmed the vaccine's structural stability and dynamics in a simulated biological environment. Furthermore, codon optimization and in silico cloning facilitated the evaluation of the vaccine's expression potential in E. coli and pET-28a was used a recombinant plasmid. This study provides a promising foundation for the development of an efficacious vaccine against Y. enterocolitica infections.

Design a novel of Brucellosis preventive vaccine based on IgV_CTLA-4 and multiple epitopes via immunoinformatics approach.

Brucellosis is a zoonotic disease caused by Brucella, which is difficult to eliminate by conventional drugs. Therefore, a novel multi-epitope vaccine (MEV) was designed to prevent human Brucella infection. Based on the method of "reverse vaccinology", cytotoxic T lymphocyte epitopes (CTLEs), helper T lymphocyte epitopes (HTLEs), linear B-cell epitopes (LBEs) and conformational B-cell epitopes (CBEs) of four Brucella proteins (VirB9, VirB10, Omp 19 and Omp 25) were obtained. In order to keep the correct protein folding, the multiple epitopes was constructed by connecting epitopes through linkers. In view of the significant connection between human leukocyte antigen CTLA-4 and B7 molecules found on antigen presenting cells (APCs), a new vaccine (V_C4MEV) for preventing brucellosis was created by combining CTLA-4 immunoglobulin variable region (IgV_CTLA-4) with MEV protein. Immunoinformatics analysis showed that V_C4MEV has a good secondary and tertiary structure. Additionally, molecular docking and molecular dynamics simulation (MD) revealed a robust binding affinity between IgV_ CTLA-4 and the B7 molecule. Notably, the vaccine V_C4MEV was demonstrated favorable immunogenicity and antigenicity in both in vitro and in vivo experiments. V_C4MEV had the potential to activate defensive cells and immune responses, offering a hopeful approach for developing vaccines against Brucella in the upcoming years.

Genome-level therapeutic targets identification and chimeric Vaccine designing against the Blastomyces dermatitidis.

Blastomyces dermatitidis is a thermally dimorphic fungus that can cause serious and sometimes fatal infections, including blastomycosis. After spore inhalation, a pulmonary infection develops, which can be asymptomatic and have lethal effects, such as acute respiratory distress syndrome. Its most common extra-pulmonary sites are the central nervous system, bones, skin, and genito-urinary systems. Currently, no vaccine has been approved by the FDA to prevent this infection. In the study, a peptide-based vaccine was developed against blastomycosis by using subtractive proteomics and reverse vaccinology approaches. It focuses on mining the whole genome of B. dermatitidis, identifying potential therapeutic targets, and pinpointing potential epitopes for both B- and T-cells that are immunogenic, non-allergenic, non-toxic, and highly antigenic. Multi-epitope constructs were generated by incorporating appropriate linker sequences. A linker (EAAAK) was also added to incorporate an adjuvant sequence to increase immunological potential. The addition of adjuvants and linkers ultimately resulted in the formation of a vaccine construct in which the number of amino acids was 243 and the molecular weight was 26.18 kDa. The designed antigenic and non-allergenic vaccine constructs showed suitable physicochemical properties. The vaccine's structures were predicted, and further analysis verified their interactions with the human TLR-4 receptor through protein-protein docking. Additionally, MD simulation showed a potent interaction between prioritized vaccine-receptor complexes. Immune simulation predicted that the final vaccine injections resulted in significant immune responses for the T- and B-cell immune responses. Moreover, in silico cloning ensured a high expression possibility of the lead vaccine in the E. coli (K12) vector. This study offers an initiative for the development of effective vaccines against B. dermatitidis; however, it is necessary to validate the designed vaccine's immunogenicity experimentally.

Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines.

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

The Evolution of Vaccines Development across Salmonella Serovars among Animal Hosts: A Systematic Review.

Salmonella is a significant zoonotic foodborne pathogen, and the global spread of multidrug-resistant (MDR) strains poses substantial challenges, necessitating alternatives to antibiotics. Among these alternatives, vaccines protect the community against infectious diseases effectively. This review aims to summarize the efficacy of developed Salmonella vaccines evaluated in various animal hosts and highlight key transitions for future vaccine studies. A total of 3221 studies retrieved from Web of Science, Google Scholar, and PubMed/Medline databases between 1970 and 2023 were evaluated. One hundred twenty-seven qualified studies discussed the vaccine efficacy against typhoidal and nontyphoidal serovars, including live-attenuated vaccines, killed inactivated vaccines, outer membrane vesicles, outer membrane complexes, conjugate vaccines, subunit vaccines, and the reverse vaccinology approach in different animal hosts. The most efficacious vaccine antigen candidate found was recombinant heat shock protein (rHsp60) with an incomplete Freund's adjuvant evaluated in a murine model. Overall, bacterial ghost vaccine candidates demonstrated the highest efficacy at 91.25% (95% CI = 83.69-96.67), followed by the reverse vaccinology approach at 83.46% (95% CI = 68.21-94.1) across animal hosts. More than 70% of vaccine studies showed significant production of immune responses, including humoral and cellular, against Salmonella infection. Collectively, the use of innovative methods rather than traditional approaches for the development of new effective vaccines is crucial and warrants in-depth studies.

Deep Intraclonal Analysis for the Development of Vaccines against Drug-Resistant Klebsiella pneumoniae Lineages.

Despite its medical relevance, there is no commercial vaccine that protects the population at risk from multidrug-resistant (MDR) Klebsiella pneumoniae infections. The availability of massive omic data and novel algorithms may improve antigen selection to develop effective prophylactic strategies. Up to 133 exposed proteins in the core proteomes, between 516 and 8666 genome samples, of the six most relevant MDR clonal groups (CGs) carried conserved B-cell epitopes, suggesting minimized future evasion if utilized for vaccination. Antigens showed a range of epitopicity, functional constraints, and potential side effects. Eleven antigens, including three sugar porins, were represented in all MDR-CGs, constitutively expressed, and showed limited reactivity with gut microbiota. Some of these antigens had important interactomic interactions and may elicit adhesion-neutralizing antibodies. Synergistic bivalent to pentavalent combinations that address expression conditions, interactome location, virulence activities, and clone-specific proteins may overcome the limiting protection of univalent vaccines. The combination of five central antigens accounted for 41% of all non-redundant interacting partners of the antigen dataset. Specific antigen mixtures represented in a few or just one MDR-CG further reduced the chance of microbiota interference. Rational antigen selection schemes facilitate the design of high-coverage and "magic bullet" multivalent vaccines against recalcitrant K. pneumoniae lineages.

Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features.

Many vaccine design programs have been developed, including our own machine learning approaches Vaxign-ML and Vaxign-DL. Using deep learning techniques, Vaxign-DL predicts bacterial protective antigens by calculating 509 biological and biomedical features from protein sequences. In this study, we first used the protein folding ESM program to calculate a set of 1,280 features from individual protein sequences, and then utilized the new set of features separately or in combination with the traditional set of 509 features to predict protective antigens. Our result showed that the usage of ESM-derived features alone was able to accurately predict vaccine antigens with a performance similar to the orginal Vaxign-DL prediction method, and the usage of the combined ESM-derived and orginal Vaxign-DL features significantly improved the prediction performance according to a set of seven scores including specificity, sensitivity, and AUROC. To further evaluate the updated methods, we conducted a Leave-One-Pathogen-Out Validation (LOPOV) study, and found that the usage of ESM-derived features significantly improved the the prediction of vaccine antigens from 10 bacterial pathogens. This research is the first reported study demonstrating the added value of protein folding features for vaccine antigen prediction.

A novel therapeutic multiepitope vaccine based on oncoprotein E6 and E7 of HPV 16 and 18: An in silico approach.

Introduction: The current vaccine strategies to prevent cervical cancer are effective only for individuals unexposed to HPV, lacking therapeutic effects against pre-existing infections. Multiepitope vaccines, using an immunoinformatic approach, are promising against tumors and viral infections because of their high specificity, safety, and stability, as well as the cheap cost of development. Methods: This study employed computer-based immunoinformatic analysis to design therapeutic multiepitope vaccines against cervical cancer using oncoproteins E6 and E7 of HPV 16 and 18. Several immunoinformatic tools were applied to analyze potential vaccine constructs capable of stimulating immune responses against both oncoproteins. Results: The constructed vaccine exhibited antigenic, immunogenic, nonallergenic, nontoxic, stable, and soluble characteristics. Additionally, it effectively interacted with TLR2 and TLR4, showing high binding capacity. Computational analysis indicated the vaccine could induce immune responses through the elevation of cytokine levels after the third injection, antibody production, activation of memory B and T cells, and promotion of increased dendritic cell counts. Conclusion: The novel multiepitope vaccine based on E6 and E7 presented as a promising candidate for combating HPV infections and associated cervical cancer. Further in vitro and in vivo studies were essential to validate the efficacy and safety of the vaccine.

VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

Designing and development of efficient multi-epitope-based peptide vaccine candidate against emerging avian rotavirus strains: A vaccinomic approach.

BACKGROUND: Enteric avian rotavirus (ARV) is the etiological agent of several health problems that pose a global threat to commercial chickens. Therefore, to avoid these widespread epidemics and high mortality rates, only vaccine and strict biosecurity are required. METHOD: The present study employs computational techniques to design a unique multi-epitope-based vaccine candidate that successfully activates immune cells against the ARV by combining adjuvant, linker, and B and T-cell epitopes. Starting, homologous sequences in the various ARV serotypes were revealed in the NCBI BLAST database, and then the two surface proteins (VP4 and VP7) of the ARV were retrieved from the UniprotKB database. The Clustal Omega server was then used to identify the conserved regions among the homologous sequences, and the B and T-cell epitopes were predicted using IEDB servers. Then, superior epitopes-2 MHC-1 epitopes, 2 MHC-2 epitopes, and 3B-cell epitopes-were combined with various adjuvants to create a total of four unique vaccine candidates. Afterward, the designed vaccine candidates underwent computational validation to assess their antigenicity, allergenicity, and stability. The vaccine candidate (V2) that demonstrated non-antigenicity, a high VaxiJen score, and non-allergenicity was ultimately chosen for molecular docking and dynamic simulation. RESULTS: Although the V2 and V4 vaccine candidates were highly immunogenic, V2 had a higher solubility rate. The predicted values of the aliphatic index and GRAVY value were 30.4 and 0.417, respectively. In terms of binding energy, V2 outperformed V4. Being successfully docked with TLRs, V2 was praised as the finest. After adaptation, the sequence's 50.73 % GC content outside of the BglII or ApaI restriction sites indicated that it was equivalently safe to clone. The chosen sequence was then inserted into the pET28a(+) vector within the BglII and ApaI restriction sites. This resulted in a final clone that was 4914 base pairs long, with the inserted sequence accounting for 478 bp and the vector accounting for the remainder. CONCLUSIONS: The immune-mediated simulation results for the selected vaccine construct showed significant response; thus, the study confirmed that the selected V2 vaccine candidate could enhance the immune response against ARV.

Computational strategies in Klebsiella pneumoniae vaccine design: navigating the landscape of in silico insights.

The emergence of multidrug-resistant Klebsiella pneumoniae poses a grave threat to global public health, necessitating urgent strategies for vaccine development. In this context, computational tools have emerged as indispensable assets, offering unprecedented insights into klebsiellal biology and facilitating the design of effective vaccines. Here, a review of the application of computational methods in the development of K. pneumoniae vaccines is presented, elucidating the transformative impact of in silico approaches. Through a systematic exploration of bioinformatics, structural biology, and immunoinformatics techniques, the complex landscape of K. pneumoniae pathogenesis and antigenicity was unravelled. Key insights into virulence factors, antigen discovery, and immune response mechanisms are discussed, highlighting the pivotal role of computational tools in accelerating vaccine development efforts. Advancements in epitope prediction, antigen selection, and vaccine design optimisation are examined, highlighting the potential of in silico approaches to update vaccine development pipelines. Furthermore, challenges and future directions in leveraging computational tools to combat K. pneumoniae are discussed, emphasizing the importance of multidisciplinary collaboration and data integration. This review provides a comprehensive overview of the current state of computational contributions to K. pneumoniae vaccine development, offering insights into innovative strategies for addressing this urgent global health challenge.

Immunoinformatics and Reverse Vaccinology Approach for the Identification of Potential Vaccine Candidates against Vandammella animalimors.

Vandammella animalimorsus is a Gram-negative and non-motile bacterium typically transmitted to humans through direct contact with the saliva of infected animals, primarily through biting, scratches, or licks on fractured skin. The absence of a confirmed post-exposure treatment of V. animalimorsus bacterium highlights the imperative for developing an effective vaccine. We intended to determine potential vaccine candidates and paradigm a chimeric vaccine against V. animalimorsus by accessible public data analysis of the strain by utilizing reverse vaccinology. By subtractive genomics, five outer membranes were prioritized as potential vaccine candidates out of 2590 proteins. Based on the instability index and transmembrane helices, a multidrug transporter protein with locus ID A0A2A2AHJ4 was designated as a potential candidate for vaccine construct. Sixteen immunodominant epitopes were retrieved by utilizing the Immune Epitope Database. The epitope encodes the strong binding affinity, nonallergenic properties, non-toxicity, high antigenicity scores, and high solubility revealing the more appropriate vaccine construct. By utilizing appropriate linkers and adjuvants alongside a suitable adjuvant molecule, the epitopes were integrated into a chimeric vaccine to enhance immunogenicity, successfully eliciting both adaptive and innate immune responses. Moreover, the promising physicochemical features, the binding confirmation of the vaccine to the major innate immune receptor TLR-4, and molecular dynamics simulations of the designed vaccine have revealed the promising potential of the selected candidate. The integration of computational methods and omics data has demonstrated significant advantages in discovering novel vaccine targets and mitigating vaccine failure rates during clinical trials in recent years.

Reverse vaccinology-based multi-epitope vaccine design against Indian group A rotavirus targeting VP7, VP4, and VP6 proteins.

Rotavirus, a primary contributor to severe cases of infantile gastroenteritis on a global scale, results in significant morbidity and mortality in the under-five population, particularly in middle to low-income countries, including India. WHO-approved live-attenuated vaccines are linked to a heightened susceptibility to intussusception and exhibit low efficacy, primarily attributed to the high genetic diversity of rotavirus, varying over time and across different geographic regions. Herein, molecular data on Indian rotavirus A (RVA) has been reviewed through phylogenetic analysis, revealing G1P[8] to be the prevalent strain of RVA in India. The conserved capsid protein sequences of VP7, VP4 and VP6 were used to examine helper T lymphocyte, cytotoxic T lymphocyte and linear B-cell epitopes. Twenty epitopes were identified after evaluation of factors such as antigenicity, non-allergenicity, non-toxicity, and stability. These epitopes were then interconnected using suitable linkers and an N-terminal beta defensin adjuvant. The in silico designed vaccine exhibited structural stability and interactions with integrins (αvß3 and αIIbß3) and toll-like receptors (TLR2 and TLR4) indicated by docking and normal mode analyses. The immune simulation profile of the designed RVA multiepitope vaccine exhibited its potential to trigger humoral as well as cell-mediated immunity, indicating that it is a promising immunogen. These computational findings indicate potential efficacy of the designed vaccine against rotavirus infection.

Development of an ancestral DC and TLR4-inducing multi-epitope peptide vaccine against the spike protein of SARS-CoV and SARS-CoV-2 using the advanced immunoinformatics approaches.

The oldest human coronavirus that started pandemics is severe acute respiratory syndrome virus (SARS-CoV). While SARS-CoV was eradicated, its new version, SARS-CoV2, caused the global pandemic of COVID-19. Evidence highlights the harmful events orchestrated by these viruses are mediated by Spike (S)P protein. Experimental epitopes of the S protein which were overlapping and ancestral between SARS-CoV and SARS-CoV-2 were obtained from the immune epitopes database (IEDB). The epitopes were then assembled in combination with a 50 S ribosomal protein L7/L12 adjuvant, a Mycobacterium tuberculosis-derived element and mediator of dendritic cells (DCs) and toll-like receptor 4 (TLR4). The immunogenic sequence was modeled by the GalaxyWeb server. After the improvement and validation of the protein structure, the physico-chemical properties and immune simulation were performed. To investigate the interaction with TLR3/4, Molecular Dynamics Simulation (MDS) was used. By merging the 17 B- and T-lymphocyte (HTL/CTL) epitopes, the vaccine sequence was created. Also, the Ramachandran plot presented that most of the residues were located in the most favorable and allowed areas. Moreover, SnapGene was successful in cloning the DNA sequence linked to our vaccine in the intended plasmid. A sequence was inserted between the XhoI and SacI position of the pET-28a (+) vector, and simulating the agarose gel revealed the existence of the inserted gene in the cloned plasmid with SARS vaccine (SARSV) construct, which has a 6565 bp in length overall. In terms of cytokines/IgG response, immunological simulation revealed a strong immune response. The stabilized vaccine showed strong interactions with TLR3/4, according to Molecular Dynamics Simulation (MDS) analysis. The present ancestral vaccine targets common sequences which seem to be valuable targets even for the new variant SARS-CoV-2.

Designing an mRNA Vaccine against P. jirovecii Involved in Fatal Pneumonia Infections via Comparative Proteomics and Reverse Vaccinology Approaches.

BACKGROUND: Pneumocystis jirovecii is the most emerging life-threating health problem that causes acute and fatal pneumonia infection. It is rare and more contagious for patients with leukemia and immune-deficiency disorders. Until now there is no treatment available for this infection therefore, it is needed to develop any treatment against this pathogen. METHODS: In this work, we used comparative proteomics, robust immune-informatics, and reverse vaccinology to create an mRNA vaccine against Pneumocystis jirovecii by targeting outer and transmembrane proteins. Using a comparative subtractive proteomic analysis of two Pneumocystis jirovecii proteomes, a distinct non-redundant Pneumocystis jirovecii (strain SE8) proteome was chosen. Seven Pneumocystis jirovecii transmembrane proteins were chosen from this proteome based on hydrophilicity, essentiality, virulence, antigenicity, pathway interaction, protein-protein network analysis, and allergenicity. OBJECTIVE: The reverse vaccinology approach was used to predict the immunogenic and antigenic epitopes of major histocompatibility complex (MHC) I, II and B-cells from the selected proteins on the basis of their antigenicity, toxicity and allergenicity. These immunogenic epitopes were linked together to construct the mRNA-based vaccine. To enhance the immunogenicity, suitable adjuvant, linkers (GPGPG, KK, and CYY), and PRDRE sequences were used. RESULTS: Through predictive modeling and confirmation via the Ramachandran plot, we assessed secondary and 3D structures. The adjuvant RpfE was incorporated to enhance the vaccine construct's immunogenicity (GRAVY index: -0.271, instability index: 39.53, antigenicity: 1.0428). The physiochemical profiling of vaccine construct was predicted it an antigenic, efficient, and potential vaccine. Notably, strong interactions were observed between the vaccine construct and TLR-3/TLR-4 (-1301.7 kcal/mol-1 and -1374.7 kcal/mol-1). CONCLUSIONS: The results predicted that mRNA-based vaccines trigger a cellular and humoral immune response, making the vaccine potential candidate against Pneumocystis jirovecii and it is more suitable for in-vitro analysis and validation to prove its effectiveness.

Unveiling a Comprehensive Multi-epitope Subunit Vaccine Strategy Against Salmonella subsp. enterica: Bridging Core, Subtractive Proteomics, and Immunoinformatics.

Salmonella subsp. enterica (SE) presents a significant global health challenge in both developed and developing countries. Current SE vaccines have limitations, targeting specific strains and demonstrating moderate efficacy in adults, while also being unsuitable for young children and often unaffordable in regions with lower income levels where the disease is prevalent. To address these challenges, this study employed a computational approach integrating core proteomics, subtractive proteomics, and immunoinformatics to develop a universal SE vaccine and identify potential drug targets. Analysis of the core proteome of 185 SE strains revealed 1964 conserved proteins. Subtractive proteomics identified 9 proteins as potential vaccine candidates and 41 as novel drug targets. Using reverse vaccinology-based immunoinformatics, four multi-epitope-based subunit vaccine constructs (MESVCs) were designed, aiming to stimulate cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte responses. These constructs underwent comprehensive evaluations for antigenicity, immunogenicity, toxicity, hydropathicity, and physicochemical properties. Predictive modeling, refinement, and validation were conducted to determine the secondary and tertiary structures of the SE-MESVCs, followed by docking studies with MHC-I, MHC-II, and TLR4 receptors. Molecular docking assessments showed favorable binding with all three receptors, with SE-MESVC-4 exhibiting the most promising binding energy. Molecular dynamics simulations confirmed the binding affinity and stability of SE-MESVC-4 with the TLR4/MD2 complex. Additionally, codon optimization and in silico cloning verified the efficient translation and successful expression of SE-MESVC-4 in Escherichia coli (E. coli) str. K12. Subsequent in silico immune simulation evaluated the efficacy of SE-MESVC-4 in triggering an effective immune response. These results suggest that SE-MESVC-4 may induce both humoral and cellular immune responses, making it a potential candidate for an effective SE vaccine. However, further experimental investigations are necessary to validate the immunogenicity and efficacy of SE-MESVC-4, bringing us closer to effectively combating SE infections.

Immunoinformatics approach for designing a multiepitope subunit vaccine against porcine epidemic diarrhea virus genotype IIA spike protein.

Background: Porcine epidemic diarrhea (PED), caused by the porcine epidemic diarrhea virus (PEDV), is associated with high mortality and morbidity rates, especially in neonatal pigs. This has resulted in significant economic losses for the pig industry. PEDV genotype II-based vaccines were found to confer better immunity against both heterologous and homologous challenges; specifically, spike (S) proteins, which are known to play a significant role during infection, are ideal for vaccine development. Aim: This study aims to design a multi-epitope subunit vaccine targeting the S protein of the PEDV GIIa strain using an immunoinformatics approach. Methods: Various bioinformatics tools were used to predict HTL, CTL, and B-cell epitopes. The epitopes were connected using appropriate linkers and conjugated with the CTB adjuvant and M-ligand. The final multiepitope vaccine construct (fMEVc) was then docked to toll-like receptor 4 (TLR4). The stability of the fMEVc-TLR4 complex was then simulated using GROMACS. C-immsim was then used to predict the in vitro immune response of the fMEVc. Results: Six epitopes were predicted to induce antibody production, ten epitopes were predicted to induce CTL responses, and four epitopes were predicted to induce HTL responses. The assembled epitopes conjugated with the CTB adjuvant and M-ligand, fMEVc, is antigenic, non-allergenic, stable, and soluble. The construct showed a favorable binding affinity for TLR4, and the protein complex was shown to be stable through molecular dynamics simulations. A robust immune response was induced after immunization, as demonstrated through immune stimulation. Conclusion: In conclusion, the multi-epitope subunit vaccine construct for PEDV designed in this study exhibits promising antigenicity, stability, and immunogenicity, eliciting robust immune responses and suggesting its potential as a candidate for further vaccine development.

Selection of vaccine candidates against Pseudomonas koreensis using reverse vaccinology and a preliminary efficacy trial in Empurau (Tor tambroides).

This study marks the first utilization of reverse vaccinology to develop recombinant subunit vaccines against Pseudomonas koreensis infection in Empurau (Tor tambroides). The proteome (5538 proteins) was screened against various filters to prioritize proteins based on features that are associated with virulence, subcellular localization, transmembrane helical structure, antigenicity, essentiality, non-homology with the host proteome, molecular weight, and stability, which led to the identification of eight potential vaccine candidates. These potential vaccine candidates were cloned and expressed, with six achieving successful expression and purification. The antigens were formulated into two distinct vaccine mixtures, Vac A and Vac B, and their protective efficacy was assessed through in vivo challenge experiments. Vac A and Vac B demonstrated high protective efficacies of 100 % and 81.2 %, respectively. Histological analyses revealed reduced tissue damage in vaccinated fish after experimental infection, with Vac A showing no adverse effects, whereas Vac B exhibited mild degenerative changes. Quantitative real-time PCR results showed a significant upregulation of TNF-α and downregulation of IL-1ß in the kidneys, spleen, gills, and intestine in both Vac A- and Vac B-immunized fish after challenged with P. koreensis. Additionally, IL-8 exhibits tissue-specific differential expression, with significant upregulation in the kidney, gills, and intestine, and downregulation in the spleen, particularly notable in Vac A-immunized fish. The research underscores the effectiveness of the reverse vaccinology approach in fish and demonstrates the promising potential of Vac A and Vac B as recombinant subunit vaccines.

An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology.

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.

A novel multi-epitope peptide vaccine candidate targeting hepatitis E virus: An in silico approach.

Hepatitis E virus (HEV) is a foodborne virus transmitted through the faecal-oral route that causes viral hepatitis in humans worldwide. Ever since its discovery as a zoonotic agent, HEV was isolated from several species with an expanding range of hosts. HEV possesses several features of other RNA viruses but also has certain HEV-specific traits that make its viral-host interactions inimitable. HEV leads to severe morbidity and mortality in immunocompromised people and pregnant women across the world. The situation in underdeveloped countries is even more alarming. Even after creating a menace across the world, we still lack an effective vaccine against HEV. Till date, there is only one licensed vaccine for HEV available only in China. The development of an anti-HEV vaccine that can reduce HEV-induced morbidity and mortality is required. Live attenuated and killed vaccines against HEV are not accessible due to the lack of a tolerant cell culture system, slow viral replication kinetics and varying growth conditions. Thus, the main focus for anti-HEV vaccine development is now on the molecular approaches. In the current study, we have designed a multi-epitope vaccine against HEV through a reverse vaccinology approach. For the first time, we have used viral ORF3, capsid protein and polyprotein altogether for epitope prediction. These are crucial for viral replication and persistence and are major vaccine targets against HEV. The proposed in silico vaccine construct comprises of highly immunogenic and antigenic T-cell and B-cell epitopes of HEV proteins. The construct is capable of inducing an effective and long-lasting host immune response as evident from the simulation results. In addition, the construct is stable, non-allergic and antigenic for the host. Altogether, our findings suggest that the in silico vaccine construct may be useful as a vaccine candidate for preventing HEV infections.