Reverse vaccinology

Computer Integrated Dominant Epitopes Evoke Protective Immune Response Against Streptococcus pneumoniae

Streptococcus pneumoniae is a gram-positive bacterium responsible for various diseases like pneumonia, acute otitis media, sinusitis, meningitis and bacteraemia. These diseases cause a significant amount of morbidity and mortality. Although polysaccharide vaccines are available, the protection provided by these vaccines is serotype-dependent and not enough in sensitive populations like children and older people. Designing a subunit vaccine by using proteins that are responsible for the pathogenesis of diseases can provide better protection against bacterial infections. In this study, we present the design of a novel multi-epitope vaccine against Streptococcus pneumoniae using an immunoinformatic approach. More than 1170 epitopes were identified against B cells, cytotoxic T lymphocytes and helper T lymphocytes from more than 60 pneumococcal proteins. Epitopes were further screened, and potential epitopes were selected for vaccine development. Seven different vaccine combinations that harbour the 15 dominant B-cell, cytotoxic T cell and helper T cell epitopes were evaluated with linker and β-defensin adjuvant to finalise the best vaccine construct. Bioinformatics tools were used to analyse the construct's physicochemical properties, secondary and tertiary structures, allergenicity, antigenicity and immunogenicity. Docking studies with the TLR-4 receptor and molecular dynamics simulations indicated strong binding affinity and stability. In silico immune response simulations predicted robust IgG immune response generation and observed more than 200 000 IgG1 + IgG2 counts per mL. Similarly, cell-mediated immunity was also enhanced by the designed vaccine construct. The construct was codon-optimised and cloned in silico for expression in Escherichia coli. These findings suggest that the construct is a promising candidate for further experimental validation.

Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii

Vaccine discovery against eukaryotic parasites is not trivial and few exist. Reverse vaccinology is an in silico vaccine discovery approach, designed to identify vaccine candidates from the thousands of protein sequences encoded by a target genome. Previously, we produced the Vacceed bioinformatics pipeline for identification of parasite membrane and excreted/secreted proteins that were likely be exposed to the hosts immune system. More recently, we improved upon machine learning as the final decision-making process to identify parasite proteins that induce a protective response in an animal model. Subsequently, we combined Vacceed with metrics on B and T cell epitope types to produce a new in silico discovery workflow. In this study we extend this in silico workflow to the developability of proteins as vaccines by the incorporation of metrics on the physicochemical properties of proteins. To demonstrate this process, every Toxoplasma gondii protein was ranked in its capacity to provide exposure to the immune system (Vacceed exposure score), presence of epitopes and solubility characteristics by several multicriteria decision making (MCDM) tools (such as TOPSIS, VIKOR and MABAC). A consensus rank was subsequently generated from the results of these tools using a variety of aggregate ranking methods. Levels of uncertainty in the aggregate protein rankings was assessed by conformal interval prediction in association with a machine learning model. Several of the top ranked proteins identified by this approach were novel, uncharacterized membrane transporters or proteins associated with RNA metabolism. In conclusion, MCDM automated the decision making using well known algorithms while conformal prediction intervals varied significantly across the 8000+ proteins of T. gondii. Highly ranked proteins (e.g. the top 100) typically generated low prediction intervals, providing high levels of confidence in their ranks.

In silico approaches for predicting natural compounds with therapeutic potential and vaccine candidates against Streptococcus equi

Equine strangles is a prevalent disease that affects the upper respiratory in horses and is caused by the Gram-positive bacterium Streptococcus equi. In addition to strangles, other clinical conditions are caused by the two S. equi subspecies, equi and zooepidemicus, which present relevant zoonotic potential. Treatment of infections caused by S. equi has become challenging due to the worldwide spreading of infected horses and the unavailability of effective therapeutics and vaccines. Penicillin treatment is often recommended, but multidrug resistance issues arised. We explored the whole genome sequence of 18 S. equi isolates to identify candidate proteins to be targeted by natural drug-like compounds or explored as immunogens. We considered only proteins shared among the sequenced strains of subspecies equi and zooepidemicus, absent in the equine host and predicted to be essential and involved in virulence. Of these, 4 proteins with cytoplasmic subcellular location were selected for molecular docking with a library of 5008 compounds, while 6 proteins were proposed as prominent immunogens against S. equi due to their probabilities of behaving as adhesins. The molecular docking analyses revealed the best ten ligands for each of the 4 drug target candidates, and they were ranked according to their binding affinities and the number of hydrogen bonds for complex stability. Finally, the natural 5-ring compound C25H20F3N5O3 excelled in molecular dynamics simulations for the increased stability in the interaction with UDP-N-acetylenolpyruvoylglucosamine reductase (MurB). This research paves the way to developing new therapeutics to minimize the impacts caused by S. equi infections.

Genome of the invasive North American Haemaphysalis longicornis tick as a template for bovine anti-tick vaccine discovery

BACKGROUND

The ixodid tick Haemaphysalis longicornis Neumann, commonly referred to as the Asian longhorned tick, has expanded its range outside of East Asia into countries such as Australia, New Zealand, and the United States. Since the first U.S. detection in 2017, H. longicornis has spread to 21 states and the District of Columbia and has been implicated as a vector of various human and animal pathogens including Theileria orientalis Ikeda genotype, a causal agent of bovine theileriosis. Facilitated in part by the parthenogenetic nature of invasive populations, this tick has become a paramount threat to agricultural rangelands and U.S. livestock production. Reliance on traditional acaricides for vector control selects for resistant individuals, reducing the effectiveness of many chemical tools over time. Thus, focus has shifted to alternative control mechanisms including anti-tick vaccine development. To further such research, here we sequence and assemble a high-quality H. longicornis genome and robust gene catalog from invasive North American ticks while also providing an organ-specific transcriptomic expression catalog and in-depth informatic screening of the tick proteome for potential bovine antigenic molecules with potential utility as vaccine candidates.

RESULTS

Using a combination of PacBio HiFi single-molecule sequencing and Hi-C chromosome conformation capture data, our genome assembly contains 270 scaffolds and spans a haploid genome size of 3.09 Gbp with an N50 of 213.4 Mbp. Gene prediction identified 21,947 high-confidence gene structures containing 96.2% of the core Arthropoda odb10 orthologs. Our organ-specific transcriptome library comprising salivary glands, midgut, ovaries, foreleg and hindleg additionally highlights potential anti-tick vaccine candidates and metabolic pathways to target for future in vitro trials.

CONCLUSIONS

Single-molecule sequencing of a triploid, parthenogenetic North American Haemaphysalis longicornis tick allowed for the generation of a highly contiguous genome assembly that, when coupled with extensive transcriptome profiling, resulted in a robust gene catalog containing multiple candidates for further study as anti-tick vaccine antigens.

Construction of an immunoinformatics-based multi-epitope vaccine candidate targeting Kyasanur forest disease virus

Kyasanur forest disease (KFD) is one of the neglected tick-borne viral zoonoses. KFD virus (KFDV) was initially considered endemic to the Western Ghats region of Karnataka state in India. Over the years, there have been reports of its spread to newer areas within and outside Karnataka. The absence of an effective treatment for KFD mandates the need for further research and development of novel vaccines. The present study was designed to develop a multi-epitope vaccine candidate against KFDV using immunoinformatics approaches. A total of 74 complete KFDV genome sequences were analysed for genetic recombination followed by phylogeny. Computational prediction of B- and T-cell epitopes belonging to envelope protein was performed and epitopes were prioritised based on IFN-Gamma, IL-4, IL-10 stimulation and checked for allergenicity and toxicity. The eight short-listed epitopes (three MHC-Class 1, three MHC-Class 2 and two B-cell) were then combined using various linkers to construct the vaccine candidate. Molecular docking followed by molecular simulations revealed stable interactions of the vaccine candidate with immune receptor complex namely Toll-like receptors (TLR2-TLR6). Codon optimization followed by in-silico cloning of the designed multi-epitope vaccine construct into the pET30b (+) expression vector was carried out. Immunoinformatics analysis of the multi-epitope vaccine candidate in the current study has potential to significantly accelerate the initial stages of vaccine development. Experimental validation of the potential multi-epitope vaccine candidate remains crucial to confirm effectiveness and safety in real-world conditions.

Do Porto D, Custódio FL, Trevizani R, Nicolás MF. PAPreC: A Pipeline for Antigenicity Prediction Comparison Methods across Bacteria

Antigenicity prediction plays a crucial role in vaccine development, antibody-based therapies, and diagnostic assays, as this predictive approach helps assess the potential of molecular structures to induce and recruit immune cells and drive antibody production. Several existing prediction methods, which target complete proteins and epitopes identified through reverse vaccinology, face limitations regarding input data constraints, feature extraction strategies, and insufficient flexibility for model evaluation and interpretation. This work presents PAPreC (Pipeline for Antigenicity Prediction Comparison), an open-source, versatile workflow (available at https://github.com/YasCoMa/paprec_nx_workflow) designed to address these challenges. PAPreC systematically examines three key factors: the selection of training data sets, feature extraction methods (including physicochemical descriptors and ESM-2 encoder-derived embeddings), and diverse classifiers. It provides automated model evaluation, interpretability through SHapley Additive exPlanations (SHAP) analysis, and applicability domain assessments, enabling researchers to identify optimal configurations for their specific data sets. Applying PAPreC to IEDB data as a reference, we demonstrate its effectiveness across the ESKAPE pathogen group. A case study involving Pseudomonas aeruginosa and Staphylococcus aureus shows that specific feature configurations are more suitable for different sequence types, and that ESM-2 embeddings enhance model performance. Moreover, our results indicate that separate models for Gram-positive and Gram-negative bacteria are not required. PAPreC offers a comprehensive, adaptable, and robust framework to streamline and improve antigenicity prediction for diverse bacterial data sets.

Epitope mapping of recombinant Salmonella enterica serotype Heidelberg flagellar hook-associated protein by in silico and in vivo approaches

BACKGROUND

Salmonella is a leading cause of human acute bacterial gastroenteritis worldwide. Outbreaks of human salmonellosis have often been associated with consumption of contaminated poultry products. Various strategies have been explored to control this microorganism during poultry production and processing. Vaccination of broiler chickens is regarded as one of the effectives means to control this microorganism. The aim of the present study was to compare the epitope identification in the Salmonella enterica serotype Heidelberg FlgK protein by in silico prediction and in vivo experiment with mass spectrometry in association with immunoprecipitation proteomics.

RESULTS

The Salmonella serotype Heidelberg FlgK protein contains 553 amino acids with a molecular mass of 61 kDa. This protein is conserved among Salmonella serotype Heidelberg isolates. The results show that both approaches identified three common shared consensus peptide epitope sequences at the positions of 77-95, 243-255 and 358-373 in the Salmonella serotype Heidelberg FlgK protein.

CONCLUSIONS

These findings provide a rational for further evaluation of these shared linear epitopes in vaccine development to cover the chicken population.

Computational design, expression, and characterization of a Plasmodium falciparum multi-epitope, multi-stage vaccine candidate (PfCTMAG)

Malaria, a tropical disease, claims the lives of thousands of people annually and the development of resistance to insecticides and antimalarial drugs poses a great challenge to current prevention and control strategies. Current malaria vaccines are limited in efficacy, duration of protection, and safety, due to the high antigenic diversity and complex life cycle of the Plasmodium parasite. This study sought to design and assess a more effective multi-stage, multi-epitope vaccine candidate for the control of malaria. A multi-epitope malaria vaccine candidate was designed in silico using multiple antigens from both the pre-erythrocytic and erythrocytic stages, expressed in bacteria, and its sero-reactivity to antibodies in plasma from malaria-positive (cases) and negative individuals (controls) was assessed using enzyme-linked immunosorbent assay (ELISA). Immunization experiments were equally conducted with BALB/c mice. In-silico analysis revealed that the designed antigen, PfCTMAG (Plasmodium falciparum Circumsporozoite, Thrombospondin-related adhesion protein, Merozoite surface protein 2, Apical asparagine (Asn)-rich protein and Glutamate-Rich Protein), effectively bound to Toll-like receptor 4 (TLR-4) and triggered a strong immune response. In sero-reactivity studies, malaria-positives (cases) had higher anti-PfCTMAG IgG ( p  = 0.024) and IgM ( p < 0.001) levels compared to malaria negatives (controls). The mice immunized with PfCTMAG did not show adverse reactions and had higher levels of IgG antibodies (p = 0.002) compared to controls, thereby validating the safety and immunogenicity of PfCTMAG as a promising vaccine candidate.

Computational epitope-based vaccine design with bioinformatics approach; a review

The significance of vaccine development has gained heightened importance in light of the COVID-19 pandemic. In such critical circumstances, global citizens anticipate researchers in this field to swiftly identify a vaccine candidate to combat the pandemic's root cause. It is widely recognized that the vaccine design process is traditionally both time-consuming and costly. However, a specialized subfield within bioinformatics, known as "multi-epitope vaccine design" or "reverse vaccinology," has significantly decreased the time and costs of the vaccine design process. The methodology reverses itself in this subfield and finds a potential vaccine candidate by analyzing the pathogen's genome. Leveraging the tools available in this domain, we strive to pinpoint the most suitable antigen for crafting a vaccine against our target. Once the optimal antigen is identified, the next step involves uncovering epitopes within this antigen. The immune system recognizes particular areas of an antigen as epitopes. By characterizing these crucial segments, we gain the opportunity to design a vaccine centered around these epitopes. Subsequently, after identifying and assembling the vital epitopes with the assistance of linkers and adjuvants, our vaccine candidate can be formulated. Finally, employing computational techniques, we can thoroughly evaluate the designed vaccine. This review article comprehensively covers the entire multi-epitope vaccine development process, starting from obtaining the pathogen's genome to identifying the relevant vaccine candidate and concluding with an evaluation. Furthermore, we will delve into the essential tools needed at each stage, comparing and introducing them.

Epitopes mapping for identification of potential cross-reactive peptide against leptospirosis

Leptospira, the pathogenic helical spirochetes that cause leptospirosis, is an emerging zoonotic disease with effective dissemination tactics in the host and can infect humans and animals with moderate or severe illnesses. Thus, peptide-based vaccines may be the most effective strategy to manage the immune response against Leptospira to close these gaps. In the current investigation, highly immunogenic proteins from the proteome of Leptospira interorgan serogroup Icterohaemorrhagie serovar Lai strain 56601 were identified using immunoinformatic methods. It was discovered that the conserved and most immunogenic outer membrane Lepin protein was both antigenic and non-allergenic by testing 15 linear B-cells and the ten best T-cell (Helper-lymphocyte (HTL) with the most significant number of HLA-DR binding alleles and the eight cytotoxic T lymphocyte (CTL)) epitopes. Furthermore, a 3D structural model of CTL epitopes was created using the Pep-Fold3 platform. Using the Autodock 4.2 docking server, research was conducted to determine how well the top-ranked CTL peptide models attach to HLA-A*0201 (PDB ID: 4U6Y). With HLA-A*0201, the epitope SSGTGNLHV binds with a binding energy of -1.29 kcal/mol. Utilizing molecular dynamics modeling, the projected epitope-allele docked complex structure was optimized, and the stability of the complex system was assessed. Therefore, this epitope can trigger an immunological response and produce effective Leptospira vaccine candidates. Overall, this study offers a unique vaccination candidate and may encourage additional research into leptospirosis vaccines.Communicated by Ramaswamy H. Sarma.

Design and evaluation of a multi-epitope DNA vaccine against HPV16

Cervical cancer, among the deadliest cancers affecting women globally, primarily arises from persistent infection with high-risk human papillomavirus (HPV). To effectively combat persistent infection and prevent the progression of precancerous lesions into malignancy, a therapeutic HPV vaccine is under development. This study utilized an immunoinformatics approach to predict epitopes of cytotoxic T lymphocytes (CTLs) and helper T lymphocytes (HTLs) using the E6 and E7 oncoproteins of the HPV16 strain as target antigens. Subsequently, through meticulous selection of T-cell epitopes and other necessary elements, a multi-epitope vaccine was constructed, exhibiting good immunogenic, physicochemical, and structural characteristics. Furthermore, in silico simulations showed that the vaccine not only interacted well with toll-like receptors (TLR2/TLR3/TLR4), but also induced a strong innate and adaptive immune response characterized by elevated Th1-type cytokines, such as interferon-gamma (IFN-γ) and interleukin-2 (IL2). Additionally, our study investigated the effects of different immunization intervals on immune responses, aiming to optimize a time-efficient immunization program. In animal model experiments, the vaccine exhibited robust immunogenic, therapeutic, and prophylactic effects. Administered thrice, it consistently induced the expansion of specific CD4 and CD8 T cells, resulting in substantial cytokines release and increased proliferation of memory T cell subsets in splenic cells. Overall, our findings support the potential of this multi-epitope vaccine in combating HPV16 infection and signify its candidacy for future HPV vaccine development.

Designing multi-epitope vaccines against Echinococcus granulosus: an in-silico study using immuno-informatics

Cystic echinococcosis (CE) is a worldwide zoonotic public health issue. The reasons for this include a lack of specific therapy options, increasing antiparasitic drug resistance, a lack of control strategies, and the absence of an approved vaccine. The aim of the current study is to develop a multiepitope vaccine against CE by in-silico identification and using different Antigen B subunits. The five Echinococcus granulosus antigen B (EgAgB) subunits were examined for eminent antigenic epitopes, and then the best B-cell and Major Histocompatibility Complex MHC-binding epitopes were predicted. Most significant epitopes were combined to create an effective multi-epitope vaccine, which was then validated by testing its secondary and tertiary structures, physicochemical properties, and molecular dynamics (MD) modelling. A multi-epitope vaccine construct of 483 amino acid sequences was designed. It contains B-cell, Helper T Lymphocyte (HTL), and Cytotoxic T Lymphocyte (CTL) epitopes as well as the appropriate adjuvant and linker molecules. The resultant vaccinal construct had a GDT-HA value of 0.9725, RMSD of 0.299, MolProbity of 1.891, Clash score of 13.1, Poor rotamers of 0.9, and qualifying features with Rama favoured of 89.9. It was also highly immunogenic and less allergic. The majority of the amino acids were positioned in the Ramachandran plot's favourable area, and during the molecular dynamic simulation at 100 ns, no notable structural abnormalities were noticed. The resultant construct was significantly expressed and received good endorsement in the pIB2-SEC13-mEGFP expressional vector. In conclusion, the current in-silico multi-epitope vaccine may be evaluated in-vitro, in-vivo, and in clinical trials as an immunogenic vaccine model. It can also play a vital role in preventing this zoonotic parasite infection.

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.

NERVE 2.0: boosting the new enhanced reverse vaccinology environment via artificial intelligence and a user-friendly web interface

BACKGROUND

Vaccines development in this millennium started by the milestone work on Neisseria meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills.

RESULTS

We report here on the development and release of NERVE 2.0 (available at: https://nerve-bio.org ) which keeps the original integrative and modular approach of NERVE, while showing higher predictive performance than its previous version and other web-RV programs (Vaxign and Vaxijen). We renewed some of its modules and added innovative ones, such as Loop-Razor, to recover fragments of promising vaccine candidates or Epitope Prediction for the epitope prediction binding affinities and population coverage. Along with two newly built AI (Artificial Intelligence)-based models: ESPAAN and Virulent. To improve user-friendliness, NERVE was shifted to a tutored, web-based interface, with a noSQL-database to consent the user to submit, obtain and retrieve analysis results at any moment.

CONCLUSIONS

With its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.

Immunoinformatics design of a multi-epitope vaccine for Chlamydia trachomatis major outer membrane proteins

Chlamydia trachomatis (CT) remains a significant infectious cause of blindness and sexually transmitted infections worldwide. The objective and novelty of this study lie in using different serovars of CT to design a broad-spectrum multi-epitope vaccine that might confer immunity against different CT infections. As the major outer membrane protein in CT has good immunodominance properties and high conservation and also determines the several serotypes of CT, it is selected as an antibody target in this study. T-cell and B-cell epitopes from serovars A, B, D, E, L1, and L2 were predicted and combined into a single construct by incorporating adjuvants and linkers to enhance immunogenicity and stability. Physicochemical characterization confirmed the constructed vaccine's anti-allergic, immunogenicity, and thermostable characteristics, followed by structural modeling to refine its 3D configuration. The 3D model structure of the vaccine was validated through the Ramachandran plot and ProSA z-score. Molecular docking studies of the vaccine demonstrated stable binding with toll-like receptor 3, along with molecular dynamics simulations and binding free energy calculations supporting the complex's stability. In silico cloning has indicated a high potential for expression in Escherichia coli. Lastly, immune simulations revealed robust activation of B cells, cytotoxic T cells, and antigen-presenting cells, alongside significant production of IgM, IgG antibodies, and balanced Th1/Th2 cytokine response, which is crucial for effective immunity. These results suggest the multi-epitope vaccine could effectively induce comprehensive immune responses against CT, highlighting the need for further in vivo validation to advance this promising candidate toward clinical use.

Peptide-based immunoprotection against Rhipicephalus microplus tick

The main agents for tick control are chemical acaricides. However, when used without technical guidance, they can lead to environmental damage and the development of resistant tick strains. In this context, vaccines are alternative o be used in integrated tick management format by combining with other effective tools. We isolated RNA from ticks Rhipicephalus microplus, prepared the library, and performed next-generation sequencing; a pipeline analysis was applied to identify the hypothetical proteins having immunogenic potential and their predicted immunogenic peptides. Twelve peptides, ranging from 12 to 38 amino acid residues, containing the selected epitopes from different targets were selected and synthesized in two forms: the pure peptide; and the peptide conjugated to keyhole limpet hemocyanin (KLH) carrier. These peptides were divided into two groups of six peptides each. The antigen formulations (groups 1 and 2) were prepared with conjugated peptides containing 200 µg of each peptide per dose emulsified with Montanide ISA 61VG (SEPPIC); the control treatment had the adjuvant formulation without peptides (group 3). To evaluate the protective efficacy, 15 weaned male calves (Angus breed) aged around 6 months to one year and weighing approximately 200-250 kg were divided into three groups of five animals each; they were immunized thrice, at an interval of 28 days. After immunization, all the calves infested with 15,000 larvae of Rhipicephalus microplus. Peptide epitopes were recognized by antibodies against host-specific IgGs using indirect ELISA. The mean of the antibody level was determined for each group and compared using analysis of variance with two factors (ANOVA). F-test was used to determine the significance of differences observed between the groups. The percentage efficacy was calculated based on the number of ticks, the weight of teleoginas, and the weight and hatchability of the eggs, compared to that in the control group. The evaluation of immunoprotection indicated efficacies of 69 and 51 %, respectively in Group 1 and 2.

Designing a multi-epitope subunit vaccine against Toxoplasma gondii through reverse vaccinology approach

The parasite Toxoplasma gondii, or T. gondii, is zoonotic that both individuals as well as animals can contract resulting in toxoplasmosis, a life-threatening illness. We used an immunoinformatic technique in our research to construct a vaccine with multi-epitopes so that it can decrease the devastating impact caused by this dangerous parasite. In order to construct the vaccine, GRA6 and MIC3 proteins were targeted, which are engaged in T. gondii identification, infection, and immune response. Novel epitopes for linear B lymphocytes (LBL), cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL) were found by epitope mapping, every anticipated epitope was assessed through rigorous screening to determine the top choices for epitopes which were entirely preserved, very antigenic in nature, nonallergenic, and nontoxic. 4 CTLs, 3 HTLs and 4 LBL epitopes were chosen and combined along with proper linkers and adjuvants to design a vaccine with several epitopes. Linkers as well as adjuvants were provided to make the vaccine more immunogenic, antigenic, and stable. The proposed vaccination was identified to possess the necessary biophysical properties, be soluble, extremely antigenic, and non-allergic. Reliability of the vaccine design was demonstrated by secondary along with tertiary structure prediction. It was anticipated that the vaccine's three-dimensional structure would likely link up with TLR-2 and TLR-4 via the investigation of molecular docking. TLR-2 and TLR-4 are crucial for the parasite's invasion and the body's response. In our docking investigation, both TLRs demonstrated strong binding affinities utilizing the vaccine structure. After that, the vaccine construct's elevated expression rate, which was observed in Escherichia coli strain K12, was confirmed by an investigation using in silico cloning and codon adaptation. The results of the research are really encouraging and some properties of the vaccine were found to be significantly better than existing the T. gondii multi-epitope vaccination based on the same proteins. Nonetheless, in vivo trials are strongly suggested for potential future studies.

Helicobacter pylori-targeted AI-driven vaccines: a paradigm shift in gastric cancer prevention

Helicobacter pylori (H. pylori), a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial resistance and strain diversity. This comprehensive review critically analyzes the limitations of conventional antibiotic-based therapies and explores cutting-edge approaches to combat H. pylori infections and associated gastric carcinogenesis. We emphasize the pressing need for innovative therapeutic strategies, with a particular focus on precision medicine and tailored vaccine development. Despite promising advancements in enhancing host immunity, current Helicobacter pylori vaccine clinical trials have yet to achieve long-term efficacy or gain approval regulatory approval. We propose a paradigm-shifting approach leveraging artificial intelligence (AI) to design precision-targeted, multiepitope vaccines tailored to multiple H. pylori subtypes. This AI-driven strategy has the potential to revolutionize antigen selection and optimize vaccine efficacy, addressing the critical need for personalized interventions in H. pylori eradication efforts. By leveraging AI in vaccine design, we propose a revolutionary approach to precision therapy that could significantly reduce H. pylori -associated gastric cancer burden.

Advances of computational methods enhance the development of multi-epitope vaccines

Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data

Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of only the unlabeled proteins from the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.

Prophylactic and therapeutic vaccine development: advancements and challenges

Biomedical research is fundamental in developing preventive and therapeutic vaccines, serving as a cornerstone of global public health. This review explores the key concepts, methodologies, tools, and challenges in the vaccine development landscape, focusing on transitioning from basic biomedical sciences to clinical applications. Foundational disciplines such as virology, immunology, and molecular biology lay the groundwork for vaccine creation, while recent innovations like messenger RNA (mRNA) technology and reverse vaccinology have transformed the field. Additionally, it highlights the role of pharmaceutical advancements in translating lab discoveries into clinical solutions. Techniques like CRISPR-Cas9, genome sequencing, monoclonal antibodies, and computational modeling have significantly enhanced vaccine precision and efficacy, expediting the development of vaccines against infectious diseases. The review also discusses challenges that continue to hinder progress, including stringent regulatory pathways, vaccine hesitancy, and the rapid emergence of new pathogens. These obstacles underscore the need for interdisciplinary collaboration and the adoption of innovative strategies. Integrating personalized medicine, nanotechnology, and artificial intelligence is expected to revolutionize vaccine science further. By embracing these advancements, biomedical research has the potential to overcome existing challenges and usher in a new era of therapeutic and prophylactic vaccines, ultimately improving global health outcomes. This review emphasizes the critical role of vaccines in combating current and future health threats, advocating for continued investment in biomedical science and technology.

Novel Antibody-Based Protection/Therapeutics in Staphylococcus aureus

Staphylococcus aureus is a commensal of the skin and nares of humans as well as the causative agent of infections associated with significant mortality. The acquisition of antibiotic resistance traits complicates the treatment of such infections and has prompted the development of monoclonal antibodies. The selection of protective antigens is typically guided by studying the natural antibody responses to a pathogen. What happens when the pathogen masks these antigens and subverts adaptive responses, or when the pathogen inhibits or alters the effector functions of antibodies? S. aureus is constantly exposed to its human host and has evolved all these strategies. Here, we review how anti-S. aureus targets have been selected and how antibodies have been engineered to overcome the formidable immune evasive activities of this pathogen. We discuss the prospects of antibody-based therapeutics in the context of disease severity, immune competence, and history of past infections.

Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing

Even though the use of SARS-CoV-2 vaccines during the COVID-19 pandemic showed unprecedented success in a short time, it also exposed a flaw in the current vaccine design strategy to offer broad protection against emerging variants of concern. However, developing broad-spectrum vaccines is still a challenge for immunologists. The development of universal vaccines against emerging pathogens and their variants appears to be a practical solution to mitigate the economic and physical effects of the pandemic on society. Very few reports are available to explain the basic concept of universal vaccine design and development. This review provides an overview of the innate and adaptive immune responses generated against vaccination and essential insight into immune mechanisms helpful in designing universal vaccines targeting influenza viruses and coronaviruses. In addition, the characteristics, safety, and factors affecting the efficacy of universal vaccines have been discussed. Furthermore, several advancements in methods worthy of designing universal vaccines are described, including chimeric immunogens, heterologous prime-boost vaccines, reverse vaccinology, structure-based antigen design, pan-reactive antibody vaccines, conserved neutralizing epitope-based vaccines, mosaic nanoparticle-based vaccines, etc. In addition to the several advantages, significant potential constraints, such as defocusing the immune response and subdominance, are also discussed.

Use of recombinant proteins for the diagnosis and prevention of Mycoplasma bovis: a systematic review

Introduction

Mycoplasma bovis is a highly contagious pathogen that causes various diseases in herd animals, negatively impacting reproduction, production, and milk yield. Effective diagnostic methods and vaccine development are critical for controlling M. bovis outbreaks. This systematic review aimed to evaluate diagnostic alternatives and vaccine compounds based on recombinant proteins.

Methods

Following the PRISMA protocol, a systematic search was conducted in the SciELO, PubMed, and CAPES Periodicals Portal databases. Inclusion criteria included studies published between 2008 and 2023 that involved (1) the use of recombinant proteins for M. bovis identification or vaccine production, (2) biological samples, (3) availability in the selected databases, (4) in vitro or in vivo experimental designs, and (5) English-language publications.

Results

Ten of the initial 53 studies screened met the inclusion criteria. Of these, four studies focused on diagnostic approaches and six on vaccine development. Diagnostic studies predominantly used an indirect enzyme-linked immunosorbent assay (ELISA) with recombinant proteins, achieving over 90% sensitivity and specificity in detecting M. bovis infections. In contrast, the development of recombinant vaccines has shown limited success, with challenges in identifying effective adjuvants and optimizing conditions for protective immunity.

Discussion

While recombinant protein-based diagnostics have proven effective, developing a successful vaccine against M. bovis remains elusive. Further research is necessary to refine vaccine formulations, including selecting suitable adjuvants and challenge models to enhance protective efficacy against M. bovis infections.

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.

Molecular dynamics simulation and purification of chimeric L1/L2 protein from human papillomavirus type 52 expressed in Escherichia coli BL21 (DE3)

The available prophylactic vaccines for human papillomavirus (HPV) in the market are only effective against specific types of HPV, rendering them ineffective for other types of HPV infections. The objective of this research is to investigate the stability of the recombinant protein constructed, namely chimeric L1/L2 protein from HPV type 52, with improved cross-neutralization ability. The 3D model, predicted using Alphafold, Robetta, I-Tasser, and refined with Galaxy Refinement, is validated using Ramachandran plot analysis. The stability is verified through molecular dynamics simulations, considering parameters such as RMSD, RMSF, Rg, and SASA, where stable conditions are observed. The chimeric L1/L2 protein from HPV type 52 is purified using affinity chromatography, and the His-tag is cleaved using SUMO protease to obtain pure chimeric protein with the size of ~ 55 kDa. Western blot analysis confirms binding to anti-L1 HPV type 52 polyclonal antibody. The obtained vaccine candidate can be utilized as an effective prophylactic vaccine against HPV.

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.

The clinical applications of immunosequencing

Technological advances in high-throughput sequencing have opened the door for the interrogation of adaptive immune responses at unprecedented scale. It is now possible to determine the sequences of antibodies or T-cell receptors produced by individual B and T cells in a sample. This capability, termed immunosequencing, has transformed the study of both infectious and non-infectious diseases by allowing the tracking of dynamic changes in B and T cell clonal populations over time. This has improved our understanding of the pathology of cancers, autoimmune diseases, and infectious diseases. However, to date there has been only limited clinical adoption of the technology. Advances over the last decade and on the horizon that reduce costs and improve interpretability could enable widespread clinical use. Many clinical applications have been proposed and, while most are still undergoing research and development, some methods relying on immunosequencing data have been implemented, the most widespread of which is the detection of measurable residual disease. Here, we review the diagnostic, prognostic, and therapeutic applications of immunosequencing for both infectious and non-infectious diseases.

An In Silico Design of a Vaccine against All Serotypes of the Dengue Virus Based on Virtual Screening of B-Cell and T-Cell Epitopes

Dengue virus poses a significant global health challenge, particularly in tropical and subtropical regions. Despite the urgent demand for vaccines in the control of the disease, the two approved vaccines, Dengvaxia and TV003/TV005, there are current questions regarding their effectiveness due to an increased risk of antibody-dependent enhancement (ADE) and reduced protection. These challenges have underscored the need for further development of improved vaccines for Dengue Virus. This study presents a new design using an in silico approach to generate a more effective dengue vaccine. Initially, our design process began with the collection of Dengue polyprotein sequences from 10 representative countries worldwide. And then conserved fragments of viral proteins were retrieved as the bases for epitope screening. The selection of epitopes was then carried out with criteria such as antigenicity, immunogenicity, and binding affinity with MHC molecules, while the exclusion criteria were according to their allergenicity, toxicity, and potential for antibody-dependent enhancement. We then constructed a core antigen with the selected epitopes and linked the outcomes with distinct adjuvant proteins, resulting in three candidate vaccines: PSDV-1, PSDV-2, and PSDV-3. Among these, PSDV-2 was selected for further validation due to its superior physicochemical and structural properties. Extensive simulations demonstrated that PSDV-2 exhibited strong binding to pattern recognition receptors, high stability, and robust immune induction, confirming its potential as a high-quality vaccine candidate. For its recombinant expression, a plasmid was subsequently designed. Our new vaccine design offers a promising additional option for Dengue virus protection. Further experimental validations will be conducted to confirm its protective efficacy and safety.

TgVax452, an epitope-based candidate vaccine targeting Toxoplasma gondii tachyzoite-specific SAG1-related sequence (SRS) proteins: immunoinformatics, structural simulations and experimental evidence-based approaches

BACKGROUND

The highly expressed surface antigen 1 (SAG1)-related sequence (SRS) proteins of T. gondii tachyzoites, as a widespread zoonotic parasite, are critical for host cell invasion and represent promising vaccine targets. In this study, we employed a computer-aided multi-method approach for in silico design and evaluation of TgVax452, an epitope-based candidate vaccine against T. gondii tachyzoite-specific SRS proteins.

METHODS

Using immunoinformatics web-based tools, structural modeling, and static/dynamic molecular simulations, we identified and screened B- and T-cell immunodominant epitopes and predicted TgVax452's antigenicity, stability, safety, adjuvanticity, and physico-chemical properties.

RESULTS

The designed protein possessed 452 residues, a MW of 44.07 kDa, an alkaline pI (6.7), good stability (33.20), solubility (0.498), and antigenicity (0.9639) with no allergenicity. Comprehensive molecular dynamic (MD) simulation analyses confirmed the stable interaction (average potential energy: 3.3799 × 106 KJ/mol) between the TLR4 agonist residues (RS09 peptide) of the TgVax452 in interaction with human TLR4, potentially activating innate immune responses. Also, a dramatic increase was observed in specific antibodies (IgM and IgG), cytokines (IFN-γ), and lymphocyte responses, based on C-ImmSim outputs. Finally, we optimized TgVax452's codon adaptation and mRNA secondary structure for efficient expression in E. coli BL21 expression machinery.

CONCLUSION

Our findings suggest that TgVax452 is a promising candidate vaccine against T. gondii tachyzoite-specific SRS proteins and requires further experimental studies for its potential use in preclinical trials.

The Toxin of VapBC-1 Toxin-Antitoxin Module from Leptospira interrogans Is a Ribonuclease That Does Not Arrest Bacterial Growth but Affects Cell Viability

Bacterial ubiquitous Toxin-Antitoxin (TA) systems are considered to be important survival mechanisms during stress conditions. In regular environmental conditions, the antitoxin blocks the toxin, whereas during imbalanced conditions, the antitoxin concentration decreases, exposing the bacteria cell to a range of toxic events. The most evident consequence of this disequilibrium is cell growth arrest, which is the reason why TAs are generally described as active in the function of bacterial growth kinetics. Virulence-associated proteins B and C (VapBC) are a family of type II TA system, in which VapC is predicted to display the toxic ribonuclease activity while VapB counteracts this activity. Previously, using in silico data, we designated four VapBC TA modules in Leptospira interrogans serovar Copenhageni, the main etiological agent of human leptospirosis in Brazil. The present study aimed to obtain the proteins and functionally characterize the VapBC-1 module. The expression of the toxin gene vapC in E. coli did not decrease the cell growth rate in broth culture, as was expected to happen within active TA modules. However, interestingly, when the expression of the toxin was compared to that of the complexed toxin and antitoxin, cell viability was strongly affected, with a decrease of three orders of magnitude in colony forming unity (CFU). The assumption of the affinity between the toxin and the antitoxin was confirmed in vivo through the observation of their co-purification from cultivation of E. coli co-expressing vapB-vapC genes. RNAse activity assays showed that VapC-1 cleaves MS2 RNA and ribosomal RNA from L. interrogans. Our results indicate that the VapBC-1 module is a potentially functional TA system acting on targets that involve specific functions. It is very important to emphasize that the common attribution of the functionality of TA modules cannot be defined based merely on their ability to inhibit bacterial growth in a liquid medium.

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.

Chimeric lipoproteins for leptospirosis vaccine: immunogenicity and protective potential

Leptospirosis, a neglected zoonotic disease, is caused by pathogenic spirochetes belonging to the genus Leptospira and has one of the highest morbidity and mortality rates worldwide. Vaccination stands out as one of the most effective preventive measures for susceptible populations. Within the outer membrane of Leptospira spp., we find the LIC12287, LIC11711, and LIC13259 lipoproteins. These are of interest due to their surface location and potential immunogenicity. Thorough examination revealed the conservation of these proteins among pathogenic Leptospira spp.; we mapped the distribution of T- and B-cell epitopes along their sequences and assessed the 3D structures of each protein. This information aided in selecting immunodominant regions for the development of a chimeric protein. Through gene synthesis, we successfully constructed a chimeric protein, which was subsequently expressed, purified, and characterized. Hamsters were immunized with the chimeric lipoprotein, formulated with adjuvants aluminum hydroxide, EMULSIGEN®-D, Sigma Adjuvant System®, and Montanide™ ISA206VG. Another group was vaccinated with an inactivated Escherichia coli bacterin expressing the chimeric protein. Following vaccination, hamsters were challenged with a virulent L. interrogans strain. Our evaluation of the humoral immune response revealed the production of IgG antibodies, detectable 28 days after the second dose, in contrast to pre-immune samples and control groups. This demonstrates the potential of the chimeric protein to elicit a robust humoral immune response; however, no protection against challenge was achieved. While this study provides valuable insights into the subject, further research is warranted to identify protective antigens that could be utilized in the development of a leptospirosis vaccine. KEY POINTS: • Several T- and B-cell epitopes were identified in all the three proteins. • Four different adjuvants were used in vaccine formulations. • Immunization stimulated significant levels of IgG2/3 in vaccinated animals.

Dominant B cell-T cell epitopes instigated robust immune response in-silico against Scrub Typhus

Scrub typhus, a potentially life-threatening infectious disease, is attributed to bacteria Orientia tsutsugamushi (O. tsutsugamushi). The transmission of this illness to humans occurs through the bite of infected chiggers, which are the larval forms of mites belonging to the genus Leptotrombidium. In this research, we developed a subunit vaccine specifically designed to target outer membrane proteins. Immunodominant cytotoxic T-lymphocytes (CTLs), B- lymphocytes (BCLs), and major histocompatibility complex (MHC)- II epitopes were identified using machine learning and bioinformatics approaches. These epitopes were arranged in different combinations with the help of suitable linkers like AAY, KK, GPGPG and adjuvant (cholera toxin B) that resulted in a vaccine construct. Physiochemical properties were assessed, where the predicted solubility (0.571) was higher than threshold value. Tertiary structure was predicted using I-TASSER web server and evaluated using Ramachandran plot (94 % residues in most favourable region) and z-score (-6.04), which had shown the structure to have good stability and residue arrangement. Molecular docking with immune receptors, Toll-like receptor (TLR)-2 and -4 showed good residue interaction with 13 and 5 hydrogen bonds respectively. Molecular dynamics simulations of receptor-ligand complex provided the idea about the strong interaction having 1.524751 × 10-5 eigenvalue. Amino acid sequence of vaccine was converted to nucleotide sequence and underwent codon optimization. The optimized codon sequence was used for in-silico cloning, which provided idea about the possibility of synthesis of vaccine using E. coli as host. Overall, this study provided a promising blueprint for a scrub typhus vaccine, although experimental validation is needed for confirmation. Furthermore, it is crucial to acknowledge that while bioinformatics provides valuable insights, in-vitro and in-vivo studies are imperative for a comprehensive evaluation of vaccine candidate. Thus, the integration of computational predictions with empirical research is essential to validate the efficacy, safety, and real-world applicability of the designed vaccine against Scrub Typhus. Nevertheless, the findings are good to carry forward for in-vitro and in-vivo investigations.

An MCDM approach for Reverse vaccinology model to predict bacterial protective antigens

Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.

Evaluation of humoral and cellular immune responses against Vibrio cholerae using oral immunization by multi-epitope-phage-based vaccine

INTRODUCTION

Cholera is a severe gastrointestinal disease that manifests with rapid onset of diarrhea, vomiting, and high mortality rates. Due to its widespread occurrence in impoverished communities with poor water sanitation, there is an urgent demand for a cost-effective and highly efficient vaccine. Multi-epitope vaccines containing dominant immunological epitopes and adjuvant compounds have demonstrated potential in boosting the immune response.

MATERIAL AND METHODS

B and T epitopes of OMPU, OMPW, TCPA, CTXA, and CTXB proteins were predicted using bioinformatics methods. Subsequently, highly antigenic multi-epitopes that are non-allergenic and non-toxic were synthesized. These multi-epitopes were then cloned into the pCOMB phagemid. A plasmid M13KO7ΔpIII containing all helper phage proteins except pIII was created to produce the recombinant phage. Female Balb/c mice were divided into three groups and immunized accordingly. The mice received the helper phage, recombinant phage or PBS via gavage feeding thrice within two weeks. Serum samples were collected before and after immunization for the ELISA test as well as evaluating immune system induction through ELISpot testing of spleen lymphocytes.

RESULTS

The titer of the recombinant phage was determined to be 1011 PFU/ml. The presence of the recombinant phage was confirmed through differences in optical density between sample and control groups in the ELISA phage technique, as well as by observing transduction activity, which demonstrated successful production of a recombinant phage displaying the Vibrio multi-epitope on M13 phage pIII. ELISA results revealed significant differences in phage antibodies before and after inoculation, particularly notable in the negative control mice. Mice treated with multi-epitope phages exhibited antibodies against Vibrio cholerae lysate. Additionally, ELISpot results indicated activation of cellular immunity in mice receiving both Vibrio and helper phage.

CONCLUSION

This study emphasizes the potential of multi-epitope on phage to enhance both cellular and humoral immunity in mice, demonstrating how phages can be used as adjuvants to stimulate mucosal immunity and act as promising candidates for oral vaccination.

Phylogenetic Tracing of Evolutionarily Conserved Zonula Occludens Toxin Reveals a "High Value" Vaccine Candidate Specific for Treating Multi-Strain Pseudomonas aeruginosa Infections

Extensively drug-resistant Pseudomonas aeruginosa infections are emerging as a significant threat associated with adverse patient outcomes. Due to this organism's inherent properties of developing antibiotic resistance, we sought to investigate alternative strategies such as identifying "high value" antigens for immunotherapy-based purposes. Through extensive database mining, we discovered that numerous Gram-negative bacterial (GNB) genomes, many of which are known multidrug-resistant (MDR) pathogens, including P. aeruginosa, horizontally acquired the evolutionarily conserved gene encoding Zonula occludens toxin (Zot) with a substantial degree of homology. The toxin's genomic footprint among so many different GNB stresses its evolutionary importance. By employing in silico techniques such as proteomic-based phylogenetic tracing, in conjunction with comparative structural modeling, we discovered a highly conserved intermembrane associated stretch of 70 amino acids shared among all the GNB strains analyzed. The characterization of our newly identified antigen reveals it to be a "high value" vaccine candidate specific for P. aeruginosa. This newly identified antigen harbors multiple non-overlapping B- and T-cell epitopes exhibiting very high binding affinities and can adopt identical tertiary structures among the least genetically homologous P. aeruginosa strains. Taken together, using proteomic-driven reverse vaccinology techniques, we identified multiple "high value" vaccine candidates capable of eliciting a polarized immune response against all the P. aeruginosa genetic variants tested.

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.

In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches

Infection with the hepatitis C virus (HCV) is one of the causes of liver cancer, which is the world's sixth most prevalent and third most lethal cancer. The current treatments do not prevent reinfection; because they are expensive, their usage is limited to developed nations. Therefore, a prophylactic vaccine is essential to control this virus. Hence, in this study, an immunoinformatics method was applied to design a multi-epitope vaccine against HCV. The best B- and T-cell epitopes from conserved regions of the E2 protein of seven HCV genotypes were joined with the appropriate linkers to design a multi-epitope vaccine. In addition, cholera enterotoxin subunit B (CtxB) was included as an adjuvant in the vaccine construct. This study is the first to present this epitopes-adjuvant combination. The vaccine had acceptable physicochemical characteristics. The vaccine's 3D structure was predicted and validated. The vaccine's binding stability with Toll-like receptor 2 (TLR2) and TLR4 was confirmed using molecular docking and molecular dynamics (MD) simulation. The immune simulation revealed the vaccine's efficacy by increasing the population of B and T cells in response to vaccination. In silico expression in Escherichia coli (E. coli) was also successful.

Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum

Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.

Reverse engineering protection: A comprehensive survey of reverse vaccinology-based vaccines targeting viral pathogens

Vaccines have significantly reduced the impact of numerous deadly viral infections. However, there is an increasing need to expedite vaccine development in light of the recurrent pandemics and epidemics. Also, identifying vaccines against certain viruses is challenging due to various factors, notably the inability to culture certain viruses in cell cultures and the wide-ranging diversity of MHC profiles in humans. Fortunately, reverse vaccinology (RV) efficiently overcomes these limitations and has simplified the identification of epitopes from antigenic proteins across the entire proteome, streamlining the vaccine development process. Furthermore, it enables the creation of multiepitope vaccines that can effectively account for the variations in MHC profiles within the human population. The RV approach offers numerous advantages in developing precise and effective vaccines against viral pathogens, including extensive proteome coverage, accurate epitope identification, cross-protection capabilities, and MHC compatibility. With the introduction of RV, there is a growing emphasis among researchers on creating multiepitope-based vaccines aiming to stimulate the host's immune responses against multiple serotypes, as opposed to single-component monovalent alternatives. Regardless of how promising the RV-based vaccine candidates may appear, they must undergo experimental validation to probe their protection efficacy for real-world applications. The time, effort, and resources allocated to the laborious epitope identification process can now be redirected toward validating vaccine candidates identified through the RV approach. However, to overcome failures in the RV-based approach, efforts must be made to incorporate immunological principles and consider targeting the epitope regions involved in disease pathogenesis, immune responses, and neutralizing antibody maturation. Integrating multi-omics and incorporating artificial intelligence and machine learning-based tools and techniques in RV would increase the chances of developing an effective vaccine. This review thoroughly explains the RV approach, ideal RV-based vaccine construct components, RV-based vaccines designed to combat viral pathogens, its challenges, and future perspectives.

Vaccine development: obligate intracellular bacteria new tools, old pathogens: the current state of vaccines against obligate intracellular bacteria

Obligate intracellular bacteria have remained those for which effective vaccines are unavailable, mostly because protection does not solely rely on an antibody response. Effective antibody-based vaccines, however, have been developed against extracellular bacteria pathogens or toxins. Additionally, obligate intracellular bacteria have evolved many mechanisms to subvert the immune response, making vaccine development complex. Much of what we know about protective immunity for these pathogens has been determined using infection-resolved cases and animal models that mimic disease. These studies have laid the groundwork for antigen discovery, which, combined with recent advances in vaccinology, should allow for the development of safe and efficacious vaccines. Successful vaccines against obligate intracellular bacteria should elicit potent T cell memory responses, in addition to humoral responses. Furthermore, they ought to be designed to specifically induce strong cytotoxic CD8+ T cell responses for protective immunity. This review will describe what we know about the potentially protective immune responses to this group of bacteria. Additionally, we will argue that the novel delivery platforms used during the Sars-CoV-2 pandemic should be excellent candidates to produce protective immunity once antigens are discovered. We will then look more specifically into the vaccine development for Rickettsiaceae, Coxiella burnetti, and Anaplasmataceae from infancy until today. We have not included Chlamydia trachomatis in this review because of the many vaccine related reviews that have been written in recent years.

Immune interface interference vaccines: An evolution-informed approach to anti-bacterial vaccine design

Developing protein-based vaccines against bacteria has proved much more challenging than producing similar immunisations against viruses. Currently, anti-bacterial vaccines are designed using methods based on reverse vaccinology. These identify broadly conserved, immunogenic proteins using a combination of genomic and high-throughput laboratory data. While this approach has successfully generated multiple rationally designed formulations that show promising immunogenicity in animal models, few have been licensed. The difficulty of inducing protective immunity in humans with such vaccines mirrors the ability of many bacteria to recolonise individuals despite recognition by natural polyvalent antibody repertoires. As bacteria express too many antigens to evade all adaptive immune responses through mutation, they must instead inhibit the efficacy of such host defences through expressing surface structures that interface with the immune system. Therefore, 'immune interface interference' (I3) vaccines that target these features should synergistically directly target bacteria and prevent them from inhibiting responses to other surface antigens. This approach may help us understand the efficacy of the two recently introduced immunisations against serotype B meningococci, which both target the Factor H-binding protein (fHbp) that inhibits complement deposition on the bacterial surface. Therefore, I3 vaccine designs may help overcome the current challenges of developing protein-based vaccines to prevent bacterial infections.

Unravelling the genomic secrets of bacterial fish pathogens: a roadmap to aquaculture sustainability

In the field of aquaculture, bacterial pathogens pose significant challenges to fish health and production. Advancements in genomic technologies have revolutionized our understanding of bacterial fish pathogens and their interactions with their host species. This review explores the application of genomic approaches in the identification, classification, and characterization of bacterial fish pathogens. Through an extensive analysis of the literature, we have compiled valuable data on 79 bacterial fish pathogens spanning 13 different phyla, encompassing their whole genome sequences. By leveraging high-throughput sequencing techniques, researchers have gained valuable insights into the genomic makeup of these pathogens, enabling a deeper understanding of their virulence factors and mechanisms of host interaction. Furthermore, genomic approaches have facilitated the discovery of potential vaccine and drug targets, opening up new avenues for the development of effective interventions against fish pathogens. Additionally, the utilization of genomics in fish disease resistance and control in aquaculture has shown promising results, enabling the identification of genetic markers associated with disease resistance traits. This review highlights the significant contributions of genomics to the field of fish pathogen research and underscores its potential for improving disease management strategies and enhancing the sustainability of aquaculture practices.

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Characterisation of Putative Outer Membrane Proteins from Leptospira borgpetersenii Serovar Hardjo-Bovis Identifies Novel Adhesins and Diversity in Adhesion across Genomospecies Orthologs

Leptospirosis is a zoonotic bacterial disease affecting mammalian species worldwide. Cattle are a major susceptible host; infection with pathogenic Leptospira spp. represents a public health risk and results in reproductive failure and reduced milk yield, causing economic losses. The characterisation of outer membrane proteins (OMPs) from disease-causing bacteria dissects pathogenesis and underpins vaccine development. As most leptospire pathogenesis research has focused on Leptospira interrogans, this study aimed to characterise novel OMPs from another important genomospecies, Leptospira borgpetersenii, which has global distribution and is relevant to bovine and human diseases. Several putative L. borgpetersenii OMPs were recombinantly expressed, refolded and purified, and evaluated for function and immunogenicity. Two of these unique, putative OMPs (rLBL0972 and rLBL2618) bound to immobilised fibronectin, laminin and fibrinogen, which, together with structural and functional data, supports their classification as leptospiral adhesins. A third putative OMP (rLBL0375), did not exhibit saturable adhesion ability but, together with rLBL0972 and the included control, OmpL1, demonstrated significant cattle milk IgG antibody reactivity from infected cows. To dissect leptospire host-pathogen interactions further, we expressed alleles of OmpL1 and a novel multi-specific adhesin, rLBL2618, from a variety of genomospecies and surveyed their adhesion ability, with both proteins exhibiting divergences in extracellular matrix component binding specificity across synthesised orthologs. We also observed functional redundancy across different L. borgspetersenii OMPs which, together with diversity in function across genomospecies orthologs, delineates multiple levels of plasticity in adhesion that is potentially driven by immune selection and host adaptation. These data identify novel leptospiral proteins which should be further evaluated as vaccine and/or diagnostic candidates. Moreover, functional redundancy across leptospire surface proteins together with identified adhesion divergence across genomospecies further dissect the complex host-pathogen interactions of a genus responsible for substantial global disease burden.

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning

Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.

Immunoinformatics aided designing of a next generation poly-epitope vaccine against uropathogenic Escherichia coli to combat urinary tract infections

Urinary tract infections (UTIs) are the second most prevalent bacterial infections and uropathogenic Escherichia coli (UPEC) stands among the primary causative agents of UTIs. The usage of antibiotics is the routine therapy being used in various countries to treat UTIs but becoming ineffective because of increasing antibiotic resistance among UPEC strains. Thus, there must be the development of some alternative treatment strategies such as vaccine development against UPEC. In the following study, pan-genomics along with reverse vaccinology approaches is used under the framework of bioinformatics for the identification of core putative vaccine candidates, employing 307 UPEC genomes (complete and draft), available publicly. A total of nine T-cell epitopes (derived from B-cells) of both MHC classes (I and II), were prioritized among three potential protein candidates. These epitopes were then docked together by using linkers (GPGPG and AAY) and an adjuvant (Cholera Toxin B) to form a poly-valent vaccine construct. The chimeric vaccine construct was undergone by molecular modelling, further refinement and energy minimization. We predicted positive results of the vaccine construct in immune simulations with significantly high levels of immune cells. The protein-protein docking analysis of vaccine construct with toll-like receptors predicted efficient binding, which was further validated by molecular dynamics simulation of vaccine construct with TLR-2 and TLR-4 at 120 ns, resulting in stable complexes' conformation throughout the simulation run. Overall, the vaccine construct demonstrated positive antigenic response. In future, this chimeric vaccine construct or the identified epitopes could be experimentally validated for the development of UPEC vaccines against UTIs.Communicated by Ramaswamy H. Sarma.

Computational design of a multi-epitope vaccine candidate against Langya henipavirus using surface proteins

In July 2022, Langya henipavirus (LayV) was identified in febrile patients in China. There is currently no approved vaccine against this virus. Therefore, this research aimed to design a multi-epitope vaccine against LayV using reverse vaccinology. The best epitopes were selected from LayV's fusion protein (F) and glycoprotein (G), and a multi-epitope vaccine was designed using these epitopes, adjuvant, and appropriate linkers. The physicochemical properties, antigenicity, allergenicity, toxicity, and solubility of the vaccine were evaluated. The vaccine's secondary and 3D structures were predicted, and molecular docking and molecular dynamics (MD) simulations were used to assess the vaccine's interaction and stability with toll-like receptor 4 (TLR4). Immune simulation, codon optimization, and in silico cloning of the vaccine were also performed. The vaccine candidate showed good physicochemical properties, as well as being antigenic, non-allergenic, and non-toxic, with acceptable solubility. Molecular docking and MD simulation revealed that the vaccine and TLR4 have stable interactions. Furthermore, immunological simulation of the vaccine indicated its ability to elicit immune responses against LayV. The vaccine's increased expression was also ensured using codon optimization. This study's findings were encouraging, but in vitro and in vivo tests are needed to confirm the vaccine's protective effect.Communicated by Ramaswamy H. Sarma.