In silico Design of an Epitope-Based Vaccine Ensemble for Chagas Disease

Development of a novel multiepitope vaccine against Menangle virus (MenV) using in-silico approaches by targeting its transmembrane proteins

Menangle Virus (MenV) is a zoonotic pathogenic virus that can penetrate humans, causing infection in them. It is an essential threat to public health due to the possibility of cross-species transmissions. Traditional vaccine development methods are time-consuming and resource-intensive, highlighting the need for innovative approaches. This study uses computational techniques to develop a multiepitope vaccine for MenV. We employed in silico tools to find potential B-cell, T-cell, and MHC-binding epitopes in the viral proteome. To ensure their safety and efficacy, these epitopes were evaluated for antigenicity, allergenicity, and toxicity. The selected epitopes were assembled into a multiepitope construct optimized for molecular stability and immunogenicity. Studies of molecular docking and MD simulations were considered to assess the interaction of vaccine candidates with human receptors, indicating a strong immune response. The goal of developing this vaccine is to prepare for potential future outbreaks. This in-silico vaccine design proposes a promising, efficient path to developing effective preventative measures against MenV, potentially expediting vaccine production and contributing to world health security.

In silico identification and ex vivo evaluation of Toxoplasma gondii peptides restricted to HLA-A*02, HLA-A*24 and HLA-B*35 alleles in human PBMC from a Colombian population

Toxoplasma gondii infects approximately 30% of the population, and there is currently no approved vaccine. Identifying immunogenic peptides with high affinity to different HLA molecules is a promising vaccine strategy. This study used an in silico approach using artificial neural networks to identify T. gondii peptides restricted to HLA-A*02, HLA-A*24, and HLA-B*35 alleles. Proteomes from seven T. gondii strains and transcriptomic data of overexpressed genes from T. gondii-RH in human PBMC were also used. Parasite protein sequences were analyzed with R 'Epitope Prediction' library. Peptide candidates were evaluated in the artificial neural networks based on the probabilities of output neurons (p > 0.5). The IFN-γ responses in PBMC from T. gondii seronegative and seropositive individuals were evaluated by ELISpot. Peptides with higher IFN-γ induction were evaluated to identify cytotoxic response in CD8+ T cells (CD107a). In silico analysis identified 36 peptides from T. gondii proteins with predicted affinity to HLA-A*02, A*24, and B*35 alleles. Experiments with PBMCs revealed that a peptide restricted to HLA-A02 (P1: FLFAWITYV) induced a significant increase in IFN-γ-producing cells (p = 0.004). For HLA-A24, a peptide (P8: VFAFAFAFFLI) also induced a significant IFN-γ response (p = 0.004), while for the HLA-B*35 allele, the P6 peptide (YPIAPSFAM) induced a response that differed significantly from the control (p = 0.05). These peptides induced also a significant percentage of central memory CD8 + T cells expressing the degranulation marker CD107a (p < 0.05). Finally, we identified three T. gondii peptides that induced IFN-γ response, and a cytotoxic response measured by CD107a expression on CD45RAneg-CD8 cells. These peptides could be considered part of a multi-epitope vaccine against toxoplasmosis in humans.

Designing and comparative analysis of anti-oxidant and heat shock proteins based multi-epitopic filarial vaccines

BACKGROUND

Lymphatic Filariasis (LF) is a neglected tropical disease affecting more than 882 million people in 44 countries of the world. A multi-epitope prophylactic/therapeutic vaccination targeting filarial defense proteins would be invaluable to achieve the current LF elimination goal.

METHOD

Two groups of proteins, namely Anti-oxidant (AO) and Heat shock proteins (HSPs), have been implicated in the effective survival of the filarial parasites in their hosts. Several B-cell, CTL, and T-helper epitopes were predicted from the three anti-oxidant proteins GST, GPx, and SOD. Likewise, epitopes were also predicted for HSP110, HSP90, and HSP70. Among the predicted epitopes, screening was applied to include only non-allergenic, non-toxic epitopes to construct two MEVs, PVAO and PVHSP. The epitopes for each group of proteins were connected to each other by the inclusion of suitable linkers and an adjuvant. The 3D models for PVAO and PVHSP were predicted, and validated, followed by prediction of physicochemical properties using bioinformatics tools. The binding free energy of PVAO and PVHSP with Toll like Receptors (TLR) TLR1/2, TLR4, TLR5, TLR6, and TLR9 was calculated with HawkDock. The immunogenicity of both the MEVs were assessed by Immune simulation after which codon adaptation and in-silico cloning were carried out.

RESULTS

Conservation of the selected AOs and HSPs in other parasitic nematode species suggested that both the generated chimera could be helpful in cross-protection too. The 3D models of both MEVs contained more than 97% residues in allowed regions, as predicted by PROCHECK server. High MMGBSA and docking scores were obtained between MEVs and TLR4, TLR1/2, TLR6, and TLR9. Molecular dynamics simulation confirmed the stability of candidate vaccines in dynamic conditions present in the biological systems. The in-silico immune simulation indicated significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2 responses following immunization with PVAO and PVHSP.

CONCLUSION

The immunoinformatics approaches used in this study confirmed that, the designed vaccines are capable of eliciting sustained immunity against LF, however, additional in-vivo studies would be required to confirm their efficacy. Furthermore, by employing multi-epitope structures and constructing two different cocktail vaccines for LF, this study can form an important milestone in the development of future LF vaccine/s.

Immunopeptidomic MHC-I profiling and immunogenicity testing identifies Tcj2 as a new Chagas disease mRNA vaccine candidate

Trypanosoma cruzi is a protozoan parasite that causes Chagas disease. Globally 6 to 7 million people are infected by this parasite of which 20-30% will progress to develop Chronic Chagasic Cardiomyopathy (CCC). Despite its high disease burden, no clinically approved vaccine exists for the prevention or treatment of CCC. Developing vaccines that can stimulate T. cruzi-specific CD8+ cytotoxic T cells and eliminate infected cells requires targeting parasitic antigens presented on major histocompatibility complex-I (MHC-I) molecules. We utilized mass spectrometry-based immunopeptidomics to investigate which parasitic peptides are displayed on MHC-I of T. cruzi infected cells. Through duplicate experiments, we identified an array of unique peptides that could be traced back to 17 distinct T. cruzi proteins. Notably, six peptides were derived from Tcj2, a trypanosome chaperone protein and member of the DnaJ (heat shock protein 40) family, showcasing its potential as a viable candidate vaccine antigen with cytotoxic T cell inducing capacity. Upon testing Tcj2 as an mRNA vaccine candidate in mice, we observed a strong memory cytotoxic CD8+ T cell response along with a Th1-skewed humoral antibody response. In vitro co-cultures of T. cruzi infected cells with splenocytes of Tcj2-immunized mice restricted the replication of T. cruzi, demonstrating the protective potential of Tcj2 as a vaccine target. Moreover, antisera from Tcj2-vaccinated mice displayed no cross-reactivity with DnaJ in lysates from mouse and human indicating a decreased likelihood of triggering autoimmune reactions. Our findings highlight how immunopeptidomics can identify new vaccine targets for Chagas disease, with Tcj2 emerging as a promising new mRNA vaccine candidate.

Genomics in parasitology: Accomplishments and challenges

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Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity

The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.

Trypanosoma cruzi Tc24 Antigen Expressed and Orally Delivered by Schizochytrium sp. Microalga is Immunogenic in Mice

Chagas disease-caused by the parasite Trypanosoma cruzi-is a neglected tropical disease for which available drugs are not fully effective in the chronic stage and a vaccine is not available yet. Microalgae represent a promising platform for the production and oral delivery of low-cost vaccines. Herein, we report a vaccine prototype against T. cruzi produced in a microalgae platform, based on the candidate antigen Tc24 with a C terminus fusion with the Co1 peptide (Tc24:Co1 vaccine prototype). After modeling the tertiary structure, in silico studies suggested that the chimeric protein is antigenic, not allergenic, and molecular docking indicated binding with Toll-like receptors 2 and 4. Thus, Tc24:Co1 was expressed in the marine microalga Schizochytrium sp., and Western blot confirmed the expression at 48 h after induction, with a yield of 632 µg/L of algal culture (300 μg/g of lyophilized algal cells) as measured by the enzyme-linked immunosorbent assay (ELISA). Upon oral administration of whole-cell Schizochytrium sp. expressing Tc24:Co1 (7.5 µg or 15 µg of Tc24:Co1 doses) in mice, specific serum IgG and intestinal mucosa IgA responses were detected in addition to an increase in serum Th1/Th2 cytokines. In conclusion, Schizochytrium sp.-expressing Tc24:Co1 is a promising oral vaccine prototype to be evaluated in an animal model of Trypanosoma cruzi infection.

In silico design and analysis of a multiepitope vaccine against Chlamydia

Chlamydia trachomatis (Ct) is the most common sexually transmitted bacterial infection worldwide, potentially leading to severe pathologies including pelvic inflammatory disease, ectopic pregnancy, and tubal infertility if left untreated. Current strategies, including screening and antibiotics, have limited effectiveness due to high rates of asymptomatic cases and logistical challenges. A multiepitope prophylactic vaccine could afford long-term protection against infection. Immunoinformatic analyses were employed to design a multiepitope Chlamydia vaccine antigen. B- and T-cell epitopes from five highly conserved and immunogenic Ct antigens were predicted and selected for the vaccine design. The final construct, adjuvanted with cholera toxin A1 subunit (CTA1), was further screened for immunogenicity. CTA1-MECA (multiepitope Chlamydia trachomatis antigen) was identified as antigenic and nonallergenic. A tertiary structure was predicted, refined, and validated as a good quality model. Molecular docking exhibited strong interactions between the vaccine and toll-like receptor 4 (TLR4). Additionally, immune responses consistent with protection including IFN-γ, IgG + IgM antibodies, and T- and B-cell responses were predicted following vaccination in an immune simulation. Expression of the construct in an Escherichia coli expression vector proved efficient. To further validate the vaccine efficacy, we assessed its immunogenicity in mice. Immunization with CTA1-MECA elicited high levels of Chlamydia-specific antibodies in mucosal and systemic compartments.

Immunoinformatics Approach to Design a Chimeric CD70-Peptide Vaccine against Renal Cell Carcinoma

Renal cell carcinoma (RCC) accounts for the majority of cancer-related deaths worldwide. Overexpression of CD70 has been linked to advanced stages of RCC. Therefore, this study aims to develop a multiepitope vaccine targeting the overexpressed CD70 using immunoinformatics techniques. In this investigation, in silico multiepitope vaccines were constructed by linking specific CD70 protein epitopes for helper T lymphocytes and CD8+ T lymphocytes. To enhance immunogenicity, sequences of cell-penetrating peptide (CPP), penetratin (pAntp), along with the entire sequence of tumor necrosis factor-α (TNF-α), were attached to the N-terminal and C-terminal of the CD70 epitopes. Computational assessments were performed on these chimeric vaccines for antigenicity, allergenicity, peptide toxicity, population coverage, and physicochemical properties. Furthermore, refined 3D constructs were subjected to a range of analyses, encompassing structural B-cell epitope prediction and molecular docking. The chosen vaccine construct underwent diverse assessments such as molecular dynamics simulation, immune response simulation, and in silico cloning. All vaccines comprised antigenic, nontoxic, and nonallergenic epitopes, ensuring extensive global population coverage. The vaccine constructs demonstrated favorable physicochemical characteristics. The binding affinity of chimeric vaccines to the TNF receptor remained relatively stable, influenced by the alignment of vaccine components. Molecular docking and dynamics analyses predicted stable interactions between CD70-CPP-TNF and the TNF receptor, indicating potential efficacy. In silico codon optimization and cloning of the vaccine nucleic acid sequence were accomplished using the pET28a plasmid. Furthermore, this vaccine displayed the capacity to modulate humoral and cellular immune responses. Overall, the results suggest therapeutic potential for the chimeric CD70-CPP-TNF vaccine against RCC. However, validation through in vitro and in vivo experiments is necessary. This trial is registered with NCT04696731 and NCT04046445.

Targeted vaccine development against Bilophila wadsworthia to curb colon diseases: A multiepitope approach based on reverse vaccinology and computational analysis

The presence of excessive hydrogen sulfide (H2S)-producing bacteria, particularly Bilophila wadsworthia in appendices, is linked to a weaker colonic mucus barrier, inflammatory bowel disease, and colorectal cancer. Thus, targeting this bacterium could reduce sulfide levels and address associated health concerns. Here, we utilized reverse vaccinology and immunoinformatics to design a chimeric vaccine against B. wadsworthia, focusing on membrane-bound and extracellular proteins. Subtractive proteome analysis identified 18 potential vaccine candidates (PVCs), from which six B-cell, eight CD8+ T cell, and six CD4+ T cell epitopes were predicted. Chosen epitopes were assessed for immunological properties and cross-reactivity with human and mouse proteomes. Subsequently, these epitopes were fused with appropriate linkers, PADRE epitope, TAT peptide, and Cholera Toxin B subunit adjuvant to form a robust multi-epitope vaccine (MEV). The MEV's tertiary structure was modelled and validated for reliable analysis. Molecular docking and dynamics simulations demonstrated stable binding of MEV with Toll-like receptor 4. The MEV showed favorable physicochemical characteristics, high expression potential in Escherichia coli, broad population coverage (∼98 %), and cross-protection against different B. wadsworthia strains. Immune simulation suggested induction of strong B and T cell responses, including primary, secondary, and tertiary immune responses. Further experimental studies are necessary to validate these findings.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Immunization with a Trypanosoma cruzi cyclophilin-19 deletion mutant protects against acute Chagas disease in mice

Human infection with the protozoan parasite Trypanosoma cruzi causes Chagas disease for which there are no prophylactic vaccines. Cyclophilin 19 is a secreted cis-trans peptidyl isomerase expressed in all life stages of Trypanosoma cruzi. This protein in the insect stage leads to the inactivation of insect anti-parasitic peptides and parasite transformation whereas in the intracellular amastigotes it participates in generating ROS promoting the growth of parasites. We have generated a parasite mutant with depleted expression of Cyp19 by removal of 2 of 3 genes encoding this protein using double allelic homologous recombination. The mutant parasite line failed to replicate when inoculated into host cells in vitro or in mice indicating that Cyp19 is critical for infectivity. The mutant parasite line also fails to replicate in or cause clinical disease in immuno-deficient mice further validating their lack of virulence. Repeated inoculation of mutant parasites into immuno-competent mice elicits parasite-specific trypanolytic antibodies and a Th-1 biased immune response and challenge of mutant immunized mice with virulent wild-type parasites is 100% effective at preventing death from acute disease. These results suggest that parasite Cyp19 may be candidate for small molecule drug targeting and that the mutant parasite line may warrant further immunization studies for prevention of Chagas disease.

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

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

Immunoinformatics analysis for design of multi-epitope subunit vaccine by using heat shock proteins against Schistosoma mansoni

The development of T cell and B cell that able provide long-term immune response against the schistosomiasisis to the people belongs to the epidemic area. Heat Shock Proteins (HSPs) are up-regulated in schistosomes as their environment changes owing to the developmental cycle, assisting the parasite in living with the adverse circumstances related with its life cycle. Schistosomiasis is still a severe health problem in the people of many countries in worldwide. In this work, to develop a chimeric antigen, we used an advanced and powerful immunoinformatics technique that targeted Schistosoma mansoni (S. mansoni) Heat shock protein (HSPs). Antigenicity, immunogenicity, allergenicity, and physicochemical characteristics were all assessed in silico for the developed subunit vaccine. The 3D structure of the vaccine was constructed and the stability of the vaccine construct was increased by using disulphide engineering. The protein-protein docking and simulation were performed between the vaccine construct and Toll-like receptor-4. The antigenicity probability value obtained for the vaccine construct was 0.93, which indicates that vaccine is non-allergenic and safe for human consumption. Communicated by Ramaswamy H. Sarma.

Immunoinformatics design of multivalent epitope vaccine against monkeypox virus and its variants using membrane-bound, enveloped, and extracellular proteins as targets

Introduction

The current monkeypox (MPX) outbreak, caused by the monkeypox virus (MPXV), has turned into a global concern, with over 59,000 infection cases and 23 deaths worldwide.

Objectives

Herein, we aimed to exploit robust immunoinformatics approach, targeting membrane-bound, enveloped, and extracellular proteins of MPXV to formulate a chimeric antigen. Such a strategy could similarly be applied for identifying immunodominant epitopes and designing multi-epitope vaccine ensembles in other pathogens responsible for chronic pathologies that are difficult to intervene against.

Methods

A reverse vaccinology pipeline was used to select 11 potential vaccine candidates, which were screened and mapped to predict immunodominant B-cell and T-cell epitopes. The finalized epitopes were merged with the aid of suitable linkers, an adjuvant (Resuscitation-promoting factor), a PADRE sequence (13 aa), and an HIV TAT sequence (11 aa) to formulate a multivalent epitope vaccine. Bioinformatics tools were employed to carry out codon adaptation and computational cloning. The tertiary structure of the chimeric vaccine construct was modeled via I-TASSER, and its interaction with Toll-like receptor 4 (TLR4) was evaluated using molecular docking and molecular dynamics simulation. C-ImmSim server was implemented to examine the immune response against the designed multi-epitope antigen.

Results and discussion

The designed chimeric vaccine construct included 21 immunodominant epitopes (six B-cell, eight cytotoxic T lymphocyte, and seven helper T-lymphocyte) and is predicted non-allergen, antigenic, soluble, with suitable physicochemical features, that can promote cross-protection among the MPXV strains. The selected epitopes indicated a wide global population coverage (93.62%). Most finalized epitopes have 70%-100% sequence similarity with the experimentally validated immune epitopes of the vaccinia virus, which can be helpful in the speedy progression of vaccine design. Lastly, molecular docking and molecular dynamics simulation computed stable and energetically favourable interaction between the putative antigen and TLR4.

Conclusion

Our results show that the multi-epitope vaccine might elicit cellular and humoral immune responses and could be a potential vaccine candidate against the MPXV infection. Further experimental testing of the proposed vaccine is warranted to validate its safety and efficacy profile.

Development and Application of an Assay to Evaluate the Anti-Parasitic Effect of Humoral Responses against Trypanosoma cruzi

Mounting a balanced and robust humoral immune response is of utmost importance for reducing the infectivity of Trypanosoma cruzi. While the role of such a response in controlling the infection is well known, there is a lack of tools that can be used to quickly evaluate it. We developed a serum parasite inhibition assay (to evaluate changes in the parasite infection after exposing infective T. cruzi trypomastigotes to serum samples from infected patients). It is based on Vero cells as the hosts and the Tulahuen β-galactosidase parasite strain, genetically engineered to be quantifiable by spectrophotometry. In parallel, we developed an in-house ELISA to correlate the anti-T. cruzi antibody titres of the clinical samples with their observed anti-parasitic effect in the serum parasite inhibition assay. Serum samples from chronically T. cruzi-infected patients significantly inhibited parasite invasion in a titre-dependant manner, regardless of the patient's clinical status, compared to samples from the non-infected controls. In addition, there was a clear correlation between the reactivity of the samples to the whole-parasite lysates by ELISA and the inhibitory effect. The results of this work confirm the previously described anti-parasitic effect of the serum of individuals exposed to T. cruzi and present a framework for its large-scale evaluation in further studies. The serum parasite inhibition assay represents a reproducible way to evaluate the intensity and anti-parasitic effect of humoral responses against T. cruzi, which could be applied to the evaluation of candidate antigens/epitopes in the design of Chagas disease vaccine candidates.

Current Update on Rotavirus in-Silico Multiepitope Vaccine Design

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

Trypanosoma cruzi-specific CD8+ T cells and other immunological hallmarks in chronic Chagas cardiomyopathy: Two decades of research

Trypanosoma cruzi, the causal agent of Chagas disease, has coexisted with humans for thousands of years. Therefore, the parasite has developed several mechanisms of antigenic variability that has allowed it to live inside the cells and evade the host immune response. Since T. cruzi displays an intracellular cycle-stage, our research team focused on providing insights into the CD8+ T cells immune response in chronic Chagas cardiomyopathy. We began our work in the 2000s studying parasite antigens that induce natural immune responses such as the KMP11 protein and TcTLE, its N-terminal derived peptide. Different approaches allowed us to reveal TcTLE peptide as a promiscuous CD8+ T cell epitope, able of inducing multifunctional cellular immune responses and eliciting a humoral response capable of decreasing parasite movement and infective capacity. Next, we demonstrated that as the disease progresses, total CD8+ T cells display a dysfunctional state characterized by a prolonged hyper-activation state along with an increase of inhibitory receptors (2B4, CD160, PD-1, TIM-3, CTLA-4) expression, an increase of specific terminal effector T cells (TTE), a decrease of proliferative capacity, a decrease of stem cell memory (TSCM) frequency, and a decrease of CD28 and CD3ζ expression. Thus, parasite-specific CD8+ T cells undergo clonal exhaustion, distinguished by an increase in late-differentiated cells, a mono-functional response, and enhanced expression of inhibitory receptors. Finally, it was found that anti-parasitic treatment induces an improved CD8+ T cell response in asymptomatic individuals, and a mouse animal model led us to establish a correlation between the quality of the CD8+ T cell responses and the outcome of chronic infection. In the future, using OMICs strategies, the identification of the specific cellular signals involved in disease progression will provide an invaluable resource for discovering new biomarkers of progression or new vaccine and immunotherapy strategies. Also, the inclusion of the TcTLE peptide in the rational design of epitope-based vaccines, the development of immunotherapy strategies using TSCM or the blocking of inhibitory receptors, and the use of the CD8+ T cell response quality to follow treatments, immunotherapies or vaccines, all are alternatives than could be explored in the fight against Chagas disease.

Computational Modeling and Evaluation of Potential mRNA and Peptide-Based Vaccine against Marburg Virus (MARV) to Provide Immune Protection against Hemorrhagic Fever

A hemorrhagic fever caused by the Marburg virus (MARV) belongs to the Filoviridae family and has been classified as a risk group 4 pathogen. To this day, there are no approved effective vaccinations or medications available to prevent or treat MARV infections. Reverse vaccinology-based approach was formulated to prioritize B and T cell epitopes utilizing a numerous immunoinformatics tools. Potential epitopes were systematically screened based on various parameters needed for an ideal vaccine such as allergenicity, solubility, and toxicity. The most suitable epitopes capable of inducing immune response were shortlisted. Epitopes with population coverage of 100% and fulfilling set parameters were selected for docking with human leukocyte antigen molecules, and binding affinity of each peptide was analyzed. Finally, 4 CTL and HTL each while 6 B cell 16-mers were used for designing multiepitope subunit (MSV) and mRNA vaccine joined via suitable linkers. Immune simulations were used to validate the constructed vaccine's capacity to induce a robust immune response whereas molecular dynamics simulations were used to confirm epitope-HLA complex stability. Based on these parameter's studies, both the vaccines constructed in this study offer a promising choice against MARV but require further experimental verification. This study provides a rationale point to begin with the development of an efficient vaccine against Marburg virus; however, the findings need further experimental validation to confirm the computational finding of this study.

TriTrypDB: An integrated functional genomics resource for kinetoplastida

Parasitic diseases caused by kinetoplastid parasites are a burden to public health throughout tropical and subtropical regions of the world. TriTrypDB (https://tritrypdb.org) is a free online resource for data mining of genomic and functional data from these kinetoplastid parasites and is part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org). As of release 59, TriTrypDB hosts 83 kinetoplastid genomes, nine of which, including Trypanosoma brucei brucei TREU927, Trypanosoma cruzi CL Brener and Leishmania major Friedlin, undergo manual curation by integrating information from scientific publications, high-throughput assays and user submitted comments. TriTrypDB also integrates transcriptomic, proteomic, epigenomic, population-level and isolate data, functional information from genome-wide RNAi knock-down and fluorescent tagging, and results from automated bioinformatics analysis pipelines. TriTrypDB offers a user-friendly web interface embedded with a genome browser, search strategy system and bioinformatics tools to support custom in silico experiments that leverage integrated data. A Galaxy workspace enables users to analyze their private data (e.g., RNA-sequencing, variant calling, etc.) and explore their results privately in the context of publicly available information in the database. The recent addition of an annotation platform based on Apollo enables users to provide both functional and structural changes that will appear as 'community annotations' immediately and, pending curatorial review, will be integrated into the official genome annotation.

An in silico approach to study the role of epitope order in the multi-epitope-based peptide (MEBP) vaccine design

With different countries facing multiple waves, with some SARS-CoV-2 variants more deadly and virulent, the COVID-19 pandemic is becoming more dangerous by the day and the world is facing an even more dreadful extended pandemic with exponential positive cases and increasing death rates. There is an urgent need for more efficient and faster methods of vaccine development against SARS-CoV-2. Compared to experimental protocols, the opportunities to innovate are very high in immunoinformatics/in silico approaches, especially with the recent adoption of structural bioinformatics in peptide vaccine design. In recent times, multi-epitope-based peptide vaccine candidates (MEBPVCs) have shown extraordinarily high humoral and cellular responses to immunization. Most of the publications claim that respective reported MEBPVC(s) assembled using a set of in silico predicted epitopes, to be the computationally validated potent vaccine candidate(s) ready for experimental validation. However, in this article, for a given set of predicted epitopes, it is shown that the published MEBPVC is one among the many possible variants and there is high likelihood of finding more potent MEBPVCs than the published candidates. To test the same, a methodology is developed where novel MEBP variants are derived by changing the epitope order of the published MEBPVC. Further, to overcome the limitations of current qualitative methods of assessment of MEBPVC, to enable quantitative comparison and ranking for the discovery of more potent MEBPVCs, novel predictors, Percent Epitope Accessibility (PEA), Receptor specific MEBP vaccine potency (RMVP), MEBP vaccine potency (MVP) are introduced. The MEBP variants indeed showed varied MVP scores indicating varied immunogenicity. Further, the MEBP variants with IDs, SPVC_446 and SPVC_537, had the highest MVP scores indicating these variants to be more potent MEBPVCs than the published MEBPVC and hence should be preferred candidates for immediate experimental testing and validation. The method enables quicker selection and high throughput experimental validation of vaccine candidates. This study also opens the opportunity to develop new software tools for designing more potent MEBPVCs in less time.

Accuracy of Diagnostic Tests for the Detection of Chagas Disease: A Systematic Review and Meta-Analysis

The present systematic review and meta-analysis about the accuracy of diagnostic tests aim to describe the findings of literature over the last thirty years for the diagnosis of Chagas disease (CD). This work aimed to determine the accuracy of diagnostic techniques for CD in the disease's acute and chronic phases. The PubMed database was searched for studies published between 1990 and 2021 on CD diagnostics. Fifty-six published studies that met the criteria were analyzed and included in the meta-analysis, evaluating diagnostic accuracy through sensitivity and specificity. For Enzyme-Linked Immunosorbent Assay (ELISA), Fluorescent Antibody Technique (IFAT), Hemagglutination Test (HmT), Polymerase Chain Reaction (PCR), and Real-Time Polymerase Chain Reaction (qPCR) diagnosis methods, the sensitivity had a median of 99.0%, 78.0%, 75.0%, 76.0%, and 94.0%, respectively; while specificity presented a median of 99.0%, 99.0%, 99.0%, 98.0%, and 98.0%, respectively. This meta-analysis showed that ELISA and qPCR techniques had a higher performance compared to other methods of diagnosing CD in the chronic and acute phases, respectively. It was concluded utilizing the Area Under the Curve restricted to the false positive rates (AUCFPR), that the ELISA diagnostic test presents the highest performance in diagnosing acute and chronic CD, compared to serological and molecular tests. Future studies focusing on new CD diagnostics approaches should be targeted.

Trypanosoma evansi RoTat 1.2 variant surface antigen mimotopes selected by panning of the random peptide phage-display library against monoclonal antibodies

Mimotope peptides of native antigens are valuable for diverse applications such as diagnostics, therapeutics and modern vaccine design. Here, we report for the first time the selection and identification of peptide mimotopes of Trypanosoma evansi RoTat 1.2 variant surface glycoprotein (VSG) for their potential uses in surra diagnostics and multi-epitope vaccine research. First, we produced the mouse monoclonal antibodies (mAbs), designated as 2E11 (IgG1) and 1C2 (IgG1), against the antigens in T. evansi RoTat 1.2 lysates. We then used 2E11 mAb to immunoprecipitate the target antigen. The immunoprecipitated antigen was then identified to be the VSG by mass spectrometry. Both 2E11 and 1C2 mAbs reacted with the VSG in immunoblots. The surface plasmon resonance immunosensors developed with both the mAbs detected VSG in the parasite lysates as well as in the rodent sera. Further, the mAbs were biotinylated and used in three rounds of panning to select peptide mimotopes from the random peptide phage display library (PhD-12; New England Biolabs, USA). The phage clones selected against each mAb were amplified and tested by phage capture ELISA for specificity. The peptide coding regions of the selected phages were sequenced and the protein blast search of the deduced amino acid sequences was performed by accessing the non-redundant protein database at https://blast.ncbi.nlm.nih.gov/. The conformational B epitope prediction of the selected mimotope sequences was done by using 3D Pepitope algorithms accessed at: http://pepitope.tau.ac.il/. The potential applications of the selected mimotopes in surra diagnostics and research are being explored.

A novel multi-epitope recombinant protein elicits an antigen-specific CD8+ T cells response in Trypanosoma cruzi-infected mice

About 6.5 million people worldwide are afflicted by Chagas disease, which is caused by the protozoan parasite Trypanosoma cruzi. The development of a therapeutic vaccine to prevent the progression of Chagasic cardiomyopathy has been proposed as an alternative for antiparasitic chemotherapy. Bioinformatics tools can predict MHC class I CD8 + epitopes for inclusion in a single recombinant protein with the goal to develop a multivalent vaccine. We expressed a novel recombinant protein Tc24-C4.10E harboring ten nonameric CD8 + epitopes and using Tc24-C4 protein as scaffold to evaluate the therapeutic effect in acute T. cruzi infection. T. cruzi-infected mice were immunized with Tc24-C4.10E or Tc24-C4 in a 50-day model of acute infection. Tc24-C4.10E-treated mice showed a decreased parasitemia compared to the Tc24-C4 (non-adjuvant) immunized mice or control group. Moreover, Tc24-C4.10E induced a higher stimulation index of CD8 + T cells producing IFNγ and IL-4 cytokines. These results suggest that the addition of the MHC Class I epitopes to Tc24-C4 can synergize the antigen-specific cellular immune responses, providing proof-of-concept that this approach could lead to the development of a promising vaccine candidate for Chagas disease.

Contriving multi-epitope vaccine ensemble for monkeypox disease using an immunoinformatics approach

The current global outbreak of monkeypox (MPX) disease, caused by Monkeypox virus (MPXV), has resulted in 16 thousand infection cases, five deaths, and has been declared a global health emergency of international concern by the World Health Organization. Given current challenges in the safety of existing vaccines, a vaccine to prevent MPX infection and/or onset of symptoms would significantly advance disease management. In this context, a multi-epitope-based vaccine could be a well-suited approach. Herein, we searched a publicly accessible database (Virus Pathogen Database and Analysis Resource) for MPXV immune epitopes from various antigens. We prioritized a group of epitopes (10 CD8+ T cells and four B-cell epitopes) using a computer-aided technique based on desirable immunological and physicochemical properties, sequence conservation criteria, and non-human homology. Three multi-epitope vaccines were constructed (MPXV-1–3) by fusing finalized epitopes with the aid of appropriate linkers and adjuvant (beta-defensin 3, 50S ribosomal protein L7/L12, and Heparin-binding hemagglutinin). Codon optimization and in silico cloning in the pET28a (+) expression vector ensure the optimal expression of each construct in the Escherichia Coli system. Two and three-dimensional structures of the constructed vaccines were predicted and refined. The optimal binding mode of the construct with immune receptors [Toll-like receptors (TLR2, TLR3, and TLR4)] was explored by molecular docking, which revealed high docking energies of MPXV-1–TLR3 (–99.09 kcal/mol), MPXV-2–TLR3 (–98.68 kcal/mol), and MPXV-3–TLR2 (–85.22 kcal/mol). Conformational stability and energetically favourable binding of the vaccine-TLR2/3 complexes were assessed by performing molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). In silico immune simulation suggested that innate, adaptive, and humoral responses will be elicited upon administration of such potent multi-epitope vaccine constructs. The vaccine constructs are antigenic, non-allergen, non-toxic, soluble, topographically exposed, and possess favourable physicochemical characteristics. These results may help experimental vaccinologists design a potent MPX vaccine.

Computer-Aided Analysis of West Sub-Saharan Africa Snakes Venom towards the Design of Epitope-Based Poly-Specific Antivenoms

Snakebite envenomation is a neglected tropical disease that causes over 100,000 deaths each year. The only effective treatment consists of antivenoms derived from animal sera, but these have been deemed with highly variable potency and are usually inaccessible and too costly for victims. The production of antivenoms by venom-independent techniques, such as the immunization with multi-epitope constructs, could circumvent those drawbacks. Herein, we present a knowledge-based pipeline to prioritize potential epitopes of therapeutic relevance from toxins of medically important snakes in West Sub-Saharan Africa. It is mainly based on sequence conservation and protein structural features. The ultimately selected 41 epitopes originate from 11 out of 16 snake species considered of highest medical importance in the region and 3 out of 10 of those considered as secondary medical importance. Echis ocellatus, responsible for the highest casualties in the area, would be covered by 12 different epitopes. Remarkably, this pipeline is versatile and customizable for the analysis of snake venom sequences from any other region of the world.

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.

Consensus Enolase of Trypanosoma Cruzi: Evaluation of Their Immunogenic Properties Using a Bioinformatics Approach

There is currently no vaccine against American trypanosomiasis, caused by the parasite Trypanosoma cruzi. This is due to the genomic variation observed in the six DTUs of T. cruzi. This work aims to propose a consensus sequence of the enolase protein from different strains of T. cruzi and mainly evaluate its immunogenic properties at the bioinformatic level. From specialized databases, 15 sequences of the enolase gene were aligned to obtain a consensus sequence, where this sequence was modeled and then evaluated and validated through different bioinformatic programs to learn their immunogenic potential. Finally, chimeric peptides were designed with the most representative epitopes. The results showed high immunogenic potential with six epitopes for MHC-I, and seven epitopes for MHC-II, all of which were highly representative of the enolase present in strains from the American continent as well as five epitopes for B cells. Regarding the computational modeling, molecular docking with Toll-like receptors showed a high affinity and low constant of dissociation, which could lead to an innate-type immune response that helps to eliminate the parasite. In conclusion, the consensus sequence proposed for enolase is capable of providing an ideal immune response; however, the experimental evaluation of this enolase consensus and their chimeric peptides should be a high priority to develop a vaccine against Chagas disease.

Potential Immunogenic Activity of Computationally Designed mRNA- and Peptide-Based Prophylactic Vaccines against MERS, SARS-CoV, and SARS-CoV-2: A Reverse Vaccinology Approach

The continued emergence of human coronaviruses (hCoVs) in the last few decades has posed an alarming situation and requires advanced cross-protective strategies against these pandemic viruses. Among these, Middle East Respiratory Syndrome coronavirus (MERS-CoV), Severe Acute Respiratory Syndrome coronavirus (SARS-CoV), and Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) have been highly associated with lethality in humans. Despite the challenges posed by these viruses, it is imperative to develop effective antiviral therapeutics and vaccines for these human-infecting viruses. The proteomic similarity between the receptor-binding domains (RBDs) among the three viral species offers a potential target for advanced cross-protective vaccine designs. In this study, putative immunogenic epitopes including Cytotoxic T Lymphocytes (CTLs), Helper T Lymphocytes (HTLs), and Beta-cells (B-cells) were predicted for each RBD-containing region of the three highly pathogenic hCoVs. This was followed by the structural organization of peptide- and mRNA-based prophylactic vaccine designs. The validated 3D structures of these epitope-based vaccine designs were subjected to molecular docking with human TLR4. Furthermore, the CTL and HTL epitopes were processed for binding with respective human Lymphocytes Antigens (HLAs). In silico cloning designs were obtained for the prophylactic vaccine designs and may be useful in further experimental designs. Additionally, the epitope-based vaccine designs were evaluated for immunogenic activity through immune simulation. Further studies may clarify the safety and efficacy of these prophylactic vaccine designs through experimental testing against these human-pathogenic coronaviruses.

Computational mining of MHC class II epitopes for the development of universal immunogenic proteins

The human leukocyte antigen (HLA) gene complex, one of the most diverse gene complexes found in the human genome, largely dictates how our immune systems recognize pathogens. Specifically, HLA genetic variability has been linked to vaccine effectiveness in humans and it has likely played some role in the shortcomings of the numerous human vaccines that have failed clinical trials. This variability is largely impossible to evaluate in animal models, however, as their immune systems generally 1) lack the diversity of the HLA complex and/or 2) express major histocompatibility complex (MHC) receptors that differ in specificity when compared to human MHC. In order to effectively engage the majority of human MHC receptors during vaccine design, here, we describe the use of HLA population frequency data from the USA and MHC epitope prediction software to facilitate the in silico mining of universal helper T cell epitopes and the subsequent design of a universal human immunogen using these predictions. This research highlights a novel approach to using in silico prediction software and data processing to direct vaccine development efforts.

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design

Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

Immunoinformatics and molecular dynamics approaches: Next generation vaccine design against West Nile virus

West Nile Virus (WNV) is a life threatening flavivirus that causes significant morbidity and mortality worldwide. No preventive therapeutics including vaccines against WNV are available for human use. In this study, immunoinformatics approach was performed to design a multi epitope-based subunit vaccine against this deadly pathogen. Human (HLA) and Mice (H-2) allele specific potential T-cell and B-cell epitopes were shortlisted through a stringent procedure. Molecular docking showed selected epitopes that have stronger binding affinity with human TLR-4. Molecular dynamics simulation confirmed the stable nature of the docked complex. Furthermore, in silico cloning analysis ensures efficient expression of desired gene in the microbial system. Interestingly, previous studies showed that two of our selected epitopes have strong immune response against WNV. Therefore, selected epitopes could be strong vaccine candidates to prevent WNV infections in human. However, further in vitro and in vivo investigations could be strengthening the validation of the vaccine candidate against WNV.

Mining the Mycobacterium tuberculosis proteome for identification of potential T-cell epitope based vaccine candidates

Identification of protective antigens for designing a high-efficacy tuberculosis vaccine is the need of the hour. Till date only 7% of the Mycobacterium tuberculosis proteome has been explored for discovering antigens capable of activating T-cell responses. Therefore, it becomes crucial to screen the remaining Mycobacterium tuberculosis proteome for more immunodominant T-cell epitopes. An extensive knowledge of the epitopes recognized by our immune system can aid this process of finding potential T cell antigens for development of a better TB vaccine. In the present in-silico study, 237 proteins belonging to the 'virulence, detoxification, and adaptation' category of Mycobacterium tuberculosis proteome were targeted for T-cell epitope screening. 50825 MHC Class I and 49357 MHC Class II epitopes were generated using NetMHC3.4 and IEDB servers respectively and tested for their antigenicity and cytokine stimulation. The highest antigenic epitopes were analyzed for their world population coverage and epitope conservancy. Molecular docking and molecular dynamics simulation studies were performed to corroborate the binding affinities and structural stability of the peptide-MHC complexes. We predicted a total of 3 MHC Class I (ILLKMCWPA, FAVGMNVYV, and SLAGNSAKV) and 7 MHC Class II (DLTIGFFLHIPFPPV, RPDLTIGFFLHIPFP, LTIGFFLHIPFPPVE, VLVFALVVALVYLQF, LVFALVVALVYLQFR, PNLVAARFIQLTPVY, and LVLVFALVVALVYLQ) epitopes that can be promising vaccine candidates. These predicted epitopes belong to 6 distinct proteins: Rv0169 (mce1a), Rv3490 (ostA), Rv3496 (mce4D), Rv1085c, Rv0563 (HtpX), Rv3497c (mce4C). All these proteins are expressed at different stages in the life cycle of Mycobacterium tuberculosis and thus, the predicted epitopes could be employed as candidates for designing a multistage-multiepitopic vaccine.

An Overview of Current Uses and Future Opportunities for Computer-Assisted Design of Vaccines for Neglected Tropical Diseases

Neglected tropical diseases are infectious diseases that impose high morbidity and mortality rates over 1.5 billion people worldwide. Originally restricted to tropical and subtropical regions, changing climate conditions have increased their potential to emerge elsewhere. Control of their impact suffers from shortages like poor epidemiological surveillance or irregular drug distribution, and some NTDs still lack of appropriate diagnostics and/or efficient therapeutics. For these, availability of vaccines to prevent new infections, or the worsening of those already established, would mean a major breakthrough. However, only dengue and rabies count with approved vaccines at present. Herein, we review the state-of-the-art of vaccination strategies for NTDs, setting the focus on third generation vaccines and the concept of reverse vaccinology. Its capability to address pathogens´ biological complexity, likely contributing to save developmental costs is discussed. The use of computational tools is a fundamental aid to analyze increasingly large datasets aimed at designing vaccine candidates with the highest, possibly, opportunities to succeed. Ultimately, we identify and analyze those studies that took an in silico approach to find vaccine candidates, and experimentally assessed their immunogenicity and/or protection capabilities.

In-silico identification of subunit vaccine candidates against lung cancer-associated oncogenic viruses

Globally, ~20% of cancer malignancies are associated with virus infections. Lung cancer is the most prevalent cancer and has a 10% 5-year survival rate when diagnosed at stage IV. Cancer vaccines and oncolytic immunotherapy are promising treatment strategies for better clinical outcomes in advanced-stage cancer patients. Here, we used a reverse vaccinology approach to devise subunit vaccine candidates against lung cancer-causing oncogenic viruses. Protein components (945) from nine oncogenic virus species were systematically analyzed to identify epitope-based subunit vaccine candidates. Best vaccine candidates were identified based on their predicted ability to stimulate humoral and cell-mediated immunity and avoid self-tolerance. Using a rigorous integrative approach, we identified 125 best antigenic epitopes with predicted B-cell, T-cell, and/or MHC-binding capability and vaccine adjuvant potential. Thirty-two of these antigenic epitopes were predicted to have IL-4/IFN-gamma inducing potential and IL-10 non-inducing potential and were predicted to bind 15 MHC-type I and 49 MHC-type II alleles. All 32 epitopes were non-allergenic and 31 were non-toxic. The identified epitopes showed good conservancy and likely bind a broad class of human HLA alleles, indicating promiscuous potential. The majority of best antigenic epitopes were derived from Human papillomavirus and Epstein-Barr virus proteins. Of the 32 epitopes, 25 promiscuous epitopes were related to E1 and E6 envelope genes and were present in multiple viral strains/species, potentially providing heterologous immunity. Further validating our results, 38 antigenic epitopes were also present in the largest experimentally-validated epitope resource, Immune Epitope Database and Analysis Resource. We further narrowed the selection to 29 antigenic epitopes with the highest immunogenic/immune-boosting potential. These epitopes possess tremendous therapeutic potential as vaccines against lung cancer-causing viruses and should be validated in future experiments. All findings are available at https://webs.iiitd.edu.in/raghava/vlcvirus/.

Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach

SARS-CoV-2 is the deadly virus behind COVID-19, the disease that went on to ravage the world and caused the biggest pandemic 21st century has witnessed so far. On the face of ongoing death and destruction, the urgent need for the discovery of a vaccine against the virus is paramount. This study resorted to the emerging discipline of immunoinformatics in order to design a multi-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2. Various immunoinformatics tools were utilized to predict T and B lymphocyte epitopes. The epitopes were channeled through a filtering pipeline comprised of antigenicity, toxicity, allergenicity, and cytokine inducibility evaluation with the goal of selecting epitopes capable of generating both T and B cell-mediated immune responses. Molecular docking simulation between the epitopes and their corresponding MHC molecules was carried out. 13 epitopes, a highly immunogenic adjuvant, elements for proper sub-cellular trafficking, a secretion booster, and appropriate linkers were combined for constructing the vaccine. The vaccine was found to be antigenic, almost neutral at physiological pH, non-toxic, non-allergenic, capable of generating a robust immune response and had a decent worldwide population coverage. Based on these parameters, this design can be considered a promising choice for a vaccine against SARS-CoV-2.

A Putative Prophylactic Solution for COVID-19: Development of Novel Multiepitope Vaccine Candidate against SARS-COV-2 by Comprehensive Immunoinformatic and Molecular Modelling Approach

The outbreak of 2019-novel coronavirus (SARS-CoV-2) that causes severe respiratory infection (COVID-19) has spread in China, and the World Health Organization has declared it a pandemic. However, no approved drug or vaccines are available, and treatment is mainly supportive and through a few repurposed drugs. The urgency of the situation requires the development of SARS-CoV-2-based vaccines. Immunoinformatic and molecular modelling are time-efficient methods that are generally used to accelerate the discovery and design of the candidate peptides for vaccine development. In recent years, the use of multiepitope vaccines has proved to be a promising immunization strategy against viruses and pathogens, thus inducing more comprehensive protective immunity. The current study demonstrated a comprehensive in silico strategy to design stable multiepitope vaccine construct (MVC) from B-cell and T-cell epitopes of essential SARS-CoV-2 proteins with the help of adjuvants and linkers. The integrated molecular dynamics simulations analysis revealed the stability of MVC and its interaction with human Toll-like receptors (TLRs), which trigger an innate and adaptive immune response. Later, the in silico cloning in a known pET28a vector system also estimated the possibility of MVC expression in Escherichia coli. Despite that this study lacks validation of this vaccine construct in terms of its efficacy, the current integrated strategy encompasses the initial multiple epitope vaccine design concepts. After validation, this MVC can be present as a better prophylactic solution against COVID-19.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.