EpiJen: a server for multistep T cell epitope prediction

Vaccination with OprB porin, and its epitopes offers protection against A. baumannii infections in mice

A. baumannii is a deadly antimicrobial resistance pathogen that acquires drug resistance through different mechanisms. Therefore, it is necessary to investigate all its virulence factors and design effective vaccines against it. For this purpose, OprB, an outer membrane porin, was investigated in this study, and its secondary and tertiary structures, physicochemical properties, and B-T epitopes were determined. The vaccine potential of this protein and its linear, non-continuous, and chimeric epitopes were also in-vivo analyzed. Based on the results, two surface epitopes and one non-continuous epitope were identified. Surface contiguous epitopes were produced recombinantly and non-continuous epitope sequences were synthesized and then produced. The chimeric epitope was also produced via the SOE-PCR technique. Active and passive immunization of mice with the whole OprB protein, non-continuous epitope, contiguous epitopes, two epitopes in chimeric form, as well as the mixture of two purified epitopes showed that the survival level and total IgG titer of the mice compared to non-vaccinated mice or mice that were vaccinated with an internal fragment increased significantly. The bacterial load in the immunized mice's lung, liver, kidney, and spleen was much lower than in the control groups, and the TNF-α, IFN-γ, and IL-6 cytokines levels were also lower in these groups and were similar to the naive mice. On the other hand, subunit vaccines showed acceptable safety and due to their minimal cross-activity, their use is much safer.

A review on the development of bacterial multi-epitope recombinant protein vaccines via reverse vaccinology

Bacterial vaccines play a crucial role in combating bacterial infectious diseases. Apart from the prevention of disease, bacterial vaccines also help to reduce the mortality rates in infected populations. Advancements in vaccine development technologies have addressed the constraints of traditional vaccine design, providing novel approaches for the development of next-generation vaccines. Advancements in reverse vaccinology, bioinformatics, and comparative proteomics have opened horizons in vaccine development. Specifically, the use of protein structural data in crafting multi-epitope vaccines (MEVs) to target pathogens has become an important research focus in vaccinology. In this review, we focused on describing the methodologies and tools for epitope vaccine development, along with recent progress in this field. Moreover, this article also discusses the challenges in epitope vaccine development, providing insights for the future development of bacterial multi-epitope genetically engineered vaccines.

Designing a multi-epitope influenza vaccine: an immunoinformatics approach

Influenza continues to be one of the top public health problems since it creates annual epidemics and can start a worldwide pandemic. The virus's rapid evolution allows the virus to evade the host defense, and then seasonal vaccines need to be reformulated nearly annually. However, it takes almost half a year for the influenza vaccine to become accessible. This delay is especially concerning in the event of a pandemic breakout. By producing the vaccine through reverse vaccinology and phage display vaccines, this time can be reduced. In this study, epitopes of B lymphocytes, cytotoxic T lymphocytes, and helper T lymphocytes of HA, NA, NP, and M2 proteins from two strains of Influenza A were anticipated. We found two proper epitopes (ASFIYNGRL and LHLILWITDRLFFKC) in Influenza virus proteins for CTL and HTL cells, respectively. Optimal epitopes and linkers in silico were cloned into the N-terminal end of M13 protein III (pIII) to create a multi-epitope-pIII construct, i.e., phage display vaccine. Also, prediction of tertiary structure, molecular docking, molecular dynamics simulation, and immune simulation were performed and showed that the designed multi-epitope vaccine can bind to the receptors and stimulate the immune system response.

A therapeutic epitopes-based vaccine engineering against Salmonella enterica XDR strains for typhoid fever: a Pan-vaccinomics approach

A prevalent food-borne pathogen, Salmonella enterica serotypes Typhi, is responsible for gastrointestinal and systemic infections globally. Salmonella vaccines are the most effective, however, producing a broad-spectrum vaccine remains challenging due to Salmonella's many serotypes. Efforts are urgently required to develop a novel vaccine candidate that can tackle all S. Typhi strains because of their high resistance to multiple kinds of antibiotics (particularly the XDR H58 strain). In this work, we used a computational pangenome-based vaccine design technique on all available (n = 119) S. Typhi reference genomes and identified one TonB-dependent siderophore receptor (WP_001034967.1) as highly conserved and prospective vaccine candidates from the predicted core genome (n = 3,351). The applied pan-proteomics and Immunoinformatic approaches help in the identification of four epitopes that may trigger adequate host body immune responses. Furthermore, the proposed vaccine ensemble demonstrates a stable binding conformation with the examined immunological receptor (HLAs and TRL2/4) and has large interaction energy determined via molecular docking and molecular dynamics simulation techniques. Eventually, an expression vector for the Escherichia. coli K12 strain was constructed from the vaccine sequence. Additional analysis revealed that the vaccine may help to elicit strong immune responses for typhoid infections, however, experimental analysis is required to verify the vaccine's effectiveness based on these results. Moreover, the applied computer-assisted vaccine design may considerably decrease vaccine development costs and speed up the process. The study's findings are intriguing, but they must be evaluated in the experimental labs to confirm the developed vaccine's biological efficiency against XDR S. Typhi.Communicated by Ramaswamy H. Sarma.

Intranasal Multiepitope PD-L1-siRNA-Based Nanovaccine: The Next-Gen COVID-19 Immunotherapy

The first approved vaccines for human use against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are nanotechnology-based. Although they are modular, rapidly produced, and can reduce disease severity, the currently available vaccines are restricted in preventing infection, stressing the global demand for novel preventive vaccine technologies. Bearing this in mind, we set out to develop a flexible nanovaccine platform for nasal administration to induce mucosal immunity, which is fundamental for optimal protection against respiratory virus infection. The next-generation multiepitope nanovaccines co-deliver immunogenic peptides, selected by an immunoinformatic workflow, along with adjuvants and regulators of the PD-L1 expression. As a case study, we focused on SARS-CoV-2 peptides as relevant antigens to validate the approach. This platform can evoke both local and systemic cellular- and humoral-specific responses against SARS-CoV-2. This led to the secretion of immunoglobulin A (IgA), capable of neutralizing SARS-CoV-2, including variants of concern, following a heterologous immunization strategy. Considering the limitations of the required cold chain distribution for current nanotechnology-based vaccines, it is shown that the lyophilized nanovaccine is stable for long-term at room temperature and retains its in vivo efficacy upon reconstitution. This makes it particularly relevant for developing countries and offers a modular system adaptable to future viral threats.

Allergies to Allergens from Cats and Dogs: A Review and Update on Sources, Pathogenesis, and Strategies

Inhalation allergies caused by cats and dogs can lead to a range of discomforting symptoms, such as rhinitis and asthma, in humans. With the increasing popularity of and care provided to these companion animals, the allergens they produce pose a growing threat to susceptible patients' health. Allergens from cats and dogs have emerged as significant risk factors for triggering asthma and allergic rhinitis worldwide; however, there remains a lack of systematic measures aimed at assisting individuals in recognizing and preventing allergies caused by these animals. This review provides comprehensive insights into the classification of cat and dog allergens, along with their pathogenic mechanisms. This study also discusses implementation strategies for prevention and control measures, including physical methods, gene-editing technology, and immunological approaches, as well as potential strategies for enhancing allergen immunotherapy combined with immunoinformatics. Finally, it presents future prospects for the prevention and treatment of human allergies caused by cats and dogs. This review will improve knowledge regarding allergies to cats and dogs while providing insights into potential targets for the development of next-generation treatments.

Design of a novel multiepitope vaccine with CTLA-4 extracellular domain against Mycoplasma pneumoniae: A vaccine-immunoinformatics approach

BACKGROUND

Community-acquired pneumonia often stems from the macrolide-resistant strain of Mycoplasma pneumoniae, yet no effective vaccine exists against it.

METHODS

This study proposes a vaccine-immunoinformatics strategy for Mycoplasma pneumoniae and other pathogenic microbes. Specifically, dominant B and T cell epitopes of the Mycoplasma pneumoniae P30 adhesion protein were identified through immunoinformatics method. The vaccine sequence was then constructed by coupling with CTLA-4 extracellular region, a novel molecular adjuvant for antigen-presenting cells. Subsequently, the vaccine's physicochemical properties, antigenicity, and allergenicity were verified. Molecular dynamics modeling was employed to confirm interaction with TLR-2, TLR-4, B7-1, and B7-2. Finally, the vaccine underwent in silico cloning for expression.

RESULTS

The vaccine exhibited both antigenicity and non-allergenicity. Molecular dynamics simulation, post-docking with TLR-2, TLR-4, B7-1, and B7-2, demonstrated stable interaction between the vaccine and these molecules. In silico cloning confirmed effective expression of the vaccine gene in insect baculovirus vectors.

CONCLUSION

This vaccine-immunoinformatics approach holds promise for the development of vaccines against Mycoplasma pneumoniae and other pathogenic non-viral and non-bacterial microbes.

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

A novel vaccine candidate against A. baumannii based on a new OmpW family protein (OmpW2); structural characterization, antigenicity and epitope investigation, and in-vivo analysis

A. baumannii is an MDR pathogen whose SARS-CoV-2 has recently increased its mortality rate in hospitalized patients. So, the virulence factors investigation and design of a vaccine against this bacterium seem to be critical. In this regard, the OmpW2 protein was structurally characterized by this study, and its B-T cell epitopes were mapped by bioinformatic tools. In-vivo analyses were employed to verify the immunogenicity of this protein and its selected epitopes. The results indicated that OmpW2 is a conserved virulent antigen, not toxic for the host, and not similar to the human or mouse proteome. A putative interaction between OmpW2 and a Fe-S-cluster redox enzyme was detected. Based on the results, OmpW2 belongs to the OmpW superfamily and eight beta sheets have been predicted in its tight beta-barrel structure. Several exposed epitopes were detected in the OmpW2 sequence and structure, and a sub-unit potential vaccine was generated based on the epitopes. The ELISA results indicated that after the second booster vaccination of BALB/c mice with the whole OmpW2 protein or its sub-unit fragment, the IgG titer significantly raised (p < 0.05). The mortality rate and the bacterial burden in the lung, liver, kidney, and spleen in both passive and active immunized mice were significantly decreased (p ≤ 0.001). In-vivo experiments confirmed that the OmpW2 whole protein and its sub-unit fragment induce the host immune system and can be applied to design a commercial vaccine or diagnostic kit.

An immunoinformatic approach employing molecular docking and molecular dynamics simulation for evaluation of l-asparaginase produced by Bacillus velezensis

l-Asparaginase is one of the most important treatments for acute lymphoblastic leukemia. In this study, l-asparaginase-producing bacteria were isolated from the effluent and soil of the Isfahan slaughterhouse using M9 specific medium. Isolates were identified by 16SrRNA phylogenetic analysis. The immune characteristics were predicted. Molecular docking was performed between l-asparaginase and l-asparagine substrate using AutoDock tools 4.2 and AutoDock Vina. Molecular dynamics simulation studies were fulfilled using GROMACS. Five l-asparaginase-producing bacteria isolated that belonging to Stenotrophomonas maltophilia, Chryseobacterium sp. Chryseobacterium indologenes, Bacillus velezensis and Bacillus safensis. Predictions showed B. velezensis has better immune characteristics than B. safensis. The binding energies of the docked complex were calculated to be -4.34 and -4.9 kcal/mol. Molecular docking confirmed the interaction of l-asparaginase with its substrate. It was observed that the residues Thr36, Tyr50, Ala47, Thr116, Asp117, Met142, Thr193 and Thr192 were fundamental in protein-ligand complexation. Also, RMSD, RMSF, Rg, DSSP, SASA and MM-PBSA analysis showed that when l-asparaginase is bound to l-asparagine, it did not lose stability, secondary structure and compactness. Slaughterhouse soils and effluents are a potential source of l-asparaginase-producing bacteria that probably can probably produce l-asparaginase with more favorable immune properties than commercial enzymes.Communicated by Ramaswamy H. Sarma.

Design of a novel multiepitope vaccine against Chlamydia pneumoniae using the extracellular protein as a target

Chlamydia pneumoniae (C. pneumoniae) infection in humans is universal and causes various respiratory infectious diseases, making a safe and effective preventive vaccine essential. In this study, a multi-epitope vaccine with CTLA-4 extracellular structure was constructed by an immunoinformatics approach. Since MOMP protein is the major extracellular protein in C. pneumoniae and has good immunogenicity and high conservation, we selected the MOMP protein of C. pneumoniae as the antigen target, predicted the T and B cell epitopes of the MOMP protein and then connected the CTLA-4 extracellular structure with the predicted dominant epitopes by various linkers to construct a multi-epitope vaccine. The biochemical characterization of the multi-epitope vaccine showed its immunogenicity and anti-allergic properties. The tertiary structure of this vaccine, along with molecular docking, molecular dynamics simulation, and principal component analysis, showed that the multi-epitope vaccine structure interacted with B7 (B7-1, B7-2) and toll-like receptors (TLR-2, TLR-4). Ultimately, the vaccine was cloned and effectively expressed in silico on an insect baculovirus expression vector (pFastBac1). These analyses showed that the designed vaccine could potentially target antigen-presenting cells and was immune to C. pneumoniae, which provided novel strategies for developing the vaccine.

DeepTAP: An RNN-based method of TAP-binding peptide prediction in the selection of tumor neoantigens

The transport of peptides from the cytoplasm to the endoplasmic reticulum (ER) by transporters associated with antigen processing (TAP) is a critical step in the intracellular presentation of cytotoxic T lymphocyte (CTL) epitopes. The development and application of computational methods, especially deep learning methods and new neural network strategies that can automatically learn feature representations with limited knowledge, provide an opportunity to develop fast and efficient methods to identify TAP-binding peptides. Herein, this study presents a comprehensive analysis of TAP-binding peptide sequences to derive TAP-binding motifs and preferences for N-terminal and C-terminal amino acids. A novel recurrent neural network (RNN)-based method called DeepTAP, using bidirectional gated recurrent unit (BiGRU), was developed for the accurate prediction of TAP-binding peptides. Our results demonstrated that DeepTAP achieves an optimal balance between prediction precision and false positives, outperforming other baseline models. Furthermore, DeepTAP significantly improves the prediction accuracy of high-confidence neoantigens, especially the top-ranked ones, making it a valuable tool for researchers studying antigen presentation processes and T-cell epitope screening. DeepTAP is freely available at https://github.com/zjupgx/deeptap and https://pgx.zju.edu.cn/deeptap.

Peptide-based vaccine for cancer therapies

Different strategies based on peptides are available for cancer treatment, in particular to counter-act the progression of tumor growth and disease relapse. In the last decade, in the context of therapeutic strategies against cancer, peptide-based vaccines have been evaluated in different tumor models. The peptides selected for cancer vaccine development can be classified in two main type: tumor-associated antigens (TAAs) and tumor-specific antigens (TSAs), which are captured, internalized, processed and presented by antigen-presenting cells (APCs) to cell-mediated immunity. Peptides loaded onto MHC class I are recognized by a specific TCR of CD8+ T cells, which are activated to exert their cytotoxic activity against tumor cells presenting the same peptide-MHC-I complex. This process is defined as active immunotherapy as the host's immune system is either de novo activated or restimulated to mount an effective, tumor-specific immune reaction that may ultimately lead to tu-mor regression. However, while the preclinical data have frequently shown encouraging results, therapeutic cancer vaccines clinical trials, including those based on peptides have not provided satisfactory data to date. The limited efficacy of peptide-based cancer vaccines is the consequence of several factors, including the identification of specific target tumor antigens, the limited immunogenicity of peptides and the highly immunosuppressive tumor microenvironment (TME). An effective cancer vaccine can be developed only by addressing all such different aspects. The present review describes the state of the art for each of such factors.

Design and computational analysis of an effective multi-epitope vaccine candidate using subunit B of cholera toxin as a build-in adjuvant against urinary tract infections

Introduction

Urinary tract infection (UTI) is one of the most common infections, usually caused by uropathogenic Escherichia coli (UPEC). However, antibiotics are a usual treatment for UTIs; because of increasing antibiotic-resistant strains, vaccination can be beneficial in controlling UTIs. Using immunoinformatics techniques is an effective and rapid way for vaccine development.

Methods

Three conserved protective antigens (FdeC, Hma, and UpaB) were selected to develop a novel multi-epitope vaccine consisting of subunit B of cholera toxin (CTB) as a mucosal build-in adjuvant to enhance the immune responses. Epitopes-predicted B and T cells and suitable linkers were used to separate them and effectively increase the vaccine's immunogenicity. The vaccine protein's primary, secondary, and tertiary structures were evaluated, and the best 3D model was selected. Since CTB is the TLR2 ligand, molecular docking was made between the vaccine protein and TLR2. Molecular dynamic (MD) simulation was employed to evaluate the stability of the vaccine protein-TLR2 complex. The vaccine construct was subjected to in silico cloning.

Results

The designed vaccine protein has multiple properties in the analysis. The HADDOCK outcomes show an excellent interaction between vaccine protein and TLR2. The MD results confirm the stability of the vaccine protein- TLR2 complex during the simulation. In silico cloning verified the expression efficiency of our vaccine protein.

Conclusion

The results of this study suggest that our designed vaccine protein could be a promising vaccine candidate against UTI, but further in vitro and in vivo studies are needed.

Characterization and expression of domains of Alphaherpesvirus bovine 1/5 envelope glycoproteins B in Komagataella phaffi

BACKGROUND

Bovine herpes virus (BoHV 1 and BoHV-5) are the causative agents of infectious bovine rhinotracheitis (IBR). IBR is responsible for important economic losses in the cattle industry. The envelope glycoprotein B (gB) is essential for BoHV infection of cattle's upper respiratory and genital tract. gB is one of the main candidate antigens for a potential recombinant vaccine since it induces a strong and persistent immune response.

RESULTS

In this study, gB of BoHV-1 and BoHV-5 was characterized in terms of function, structure, and antigenicity through bioinformatics tools. gB showed conserved sequence and structure, so, both domains named PH Like 1 and 2 domains of each virus were selected for the design of a bivalent vaccine candidate. The immunoinformatic study showed that these two domains have epitopes recognizable by B and T lymphocytes, followed by this, the cDNA domains from BoHV-1/5 gB (Domains-gB) were transformed into the yeast Komagataella phaffii GS115 (previously known as Pichia pastoris). A recombinant protein with molecular weight of about 110 kDa was obtained from the culture media. The vaccine candidate protein (Domains-gB) was recognized by a monoclonal antibody from a commercial ELISA kit used for IBR diagnostic, which may suggest that the epitopes are conserved of the entire infectious virus.

CONCLUSION

Overall, it was shown that the recombinant domains of BoHV-1/5 gB have antigenic and immunogenic properties similar to the native gB. This vaccine candidate is promising to be used in future studies to assess its immunogenicity in an animal model.

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.

Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity, and Toxicity: An Immunoinformatics Approach

Immunoinformatics is a modern branch of science formed as a result of the intersection between immunology and computer science. One of the important steps in the design of multi-epitope vaccines is the prediction of B cell epitopes. B cell epitopes are of two types, linear and discontinuous. Linear epitope residues lie next to each other in the primary structure of a protein. The amino acids that constitute discontinuous epitopes lie close to each other in the three-dimensional structure of the protein. Recognition of B cell epitopes by antibodies on an antigen constitutes an important event in the immune responses toward the antigenic challenge and also forms the basis for several immunological applications. Prediction of B cell epitopes in an antigen constitutes one of the important steps in the design of multi-epitope-based vaccines. This chapter explains the prediction of linear B cell epitopes in an antigen as well as their allergenicity, antigenicity, and toxicity by using online tools.

Reverse engineering approach: a step towards a new era of vaccinology with special reference to Salmonella

INTRODUCTION

Salmonella is responsible for causing enteric fever, septicemia, and gastroenteritis in humans. Due to high disease burden and emergence of multi- and extensively drug-resistant Salmonella strains, it is becoming difficult to treat the infection with existing battery of antibiotics as we are not able to discover newer antibiotics at the same pace at which the pathogens are acquiring resistance. Though vaccines against Salmonella are available commercially, they have limited efficacy. Advancements in genome sequencing technologies and immunoinformatics approaches have solved the problem significantly by giving rise to a new era of vaccine designing, i.e. 'Reverse engineering.' Reverse engineering/vaccinology has expedited the vaccine identification process. Using this approach, multiple potential proteins/epitopes can be identified and constructed as a single entity to tackle enteric fever.

AREAS COVERED

This review provides details of reverse engineering approach and discusses various protein and epitope-based vaccine candidates identified using this approach against typhoidal Salmonella.

EXPERT OPINION

Reverse engineering approach holds great promise for developing strategies to tackle the pathogen(s) by overcoming the limitations posed by existing vaccines. Progressive advancements in the arena of reverse vaccinology, structural biology, and systems biology combined with an improved understanding of host-pathogen interactions are essential components to design new-generation vaccines.

Refining the DC-targeting vaccination for preventing emerging infectious diseases

The development of safe, long-term, effective vaccines is still a challenge for many infectious diseases. Thus, the search of new vaccine strategies and production platforms that allow rapidly and effectively responding against emerging or reemerging pathogens has become a priority in the last years. Targeting the antigens directly to dendritic cells (DCs) has emerged as a new approach to enhance the immune response after vaccination. This strategy is based on the fusion of the antigens of choice to monoclonal antibodies directed against specific DC surface receptors such as CD40. Since time is essential, in silico approaches are of high interest to select the most immunogenic and conserved epitopes to improve the T- and B-cells responses. The purpose of this review is to present the advances in DC vaccination, with special focus on DC targeting vaccines and epitope mapping strategies and provide a new framework for improving vaccine responses against infectious diseases.

Combined treatment with epigenetic agents enhances anti-tumor activity of MAGE-D4 peptide-specific T cells by upregulating the MAGE-D4 expression in glioma

Objective

The study evaluated the efficacy of combined epigenetic drugs of decitabine (DAC), valproic acid (VPA), and trichostatin A (TSA) on immunotherapy against glioma.

Methods

The expression and prognosis of MAGE-D4 in glioma were analyzed online, and the expression of MAGE-D4 and HLA-A2 in glioma induced by epigenetic drugs was detected by qRT-PCR, Western blot, and flow cytometry. The methylation status of the MAGE-D4 promoter was determined by pyrosequencing. An HLA-A2 restricted MAGE-D4 peptide was predicted and synthesized. An affinity assay and a peptide/HLA complex stability assay were performed to determine the affinity between peptide and HLA. CCK8 assay, CFSE assay, ELISA and ELISPOT were performed to detect the function of MAGE-D4 peptide-specific T cells. Flow cytometry, ELISA, and cytotoxicity assays were used to detect the cytotoxicity effect of MAGE-D4 peptide-specific T cells combined with epigenetic drugs against glioma in vitro. Finally, the glioma-loaded mouse model was applied to test the inhibitory effect of specific T cells on gliomas in vivo.

Results

MAGE-D4 was highly expressed in glioma and correlated with poor prognosis. Glioma cells could be induced to express MAGE-D4 and HLA-A2 by epigenetic drugs. MAGE-D4-associated peptides were found that induce DCs to stimulate the highest T-cell activities of proliferation, IL-2 excretion, and IFN-γ secretion. MAGE-D4 peptide-specific T cells treated with TSA only or combining TSA and DAC had the most cytotoxicity effect, and its cytotoxicity effect on glioma cells decreased significantly after HLA blocking. In vivo experiments also confirmed that MAGE-D4-specific T cells inhibit TSA-treated glioma.

Conclusion

MAGE-D4 is highly expressed in glioma and correlated with the prognosis of glioma. The novel MAGE-D4 peptide identified was capable of inducing MAGE-D4-specific T cells that can effectively inhibit glioma growth, and the epigenetic drug application can enhance this inhibition.

Immunoinformatic approach for the construction of multi-epitopes vaccine against omicron COVID-19 variant

The newly discovered SARS-CoV-2 Omicron variant B.1.1.529 is a Variant of Concern (VOC) announced by the World Health Organization (WHO). It's becoming increasingly difficult to keep these variants from spreading over the planet. The fifth wave has begun in several countries because of Omicron variant, and it is posing a threat to human civilization. As a result, we need effective vaccination that can tackle Omicron SARS-CoV-2 variants that are bound to emerge. Therefore, the current study is an initiative to design a peptide-based chimeric vaccine that may potentially battle SARS-CoV-2 Omicron variant. As a result, the most relevant epitopes present in the mutagenic areas of Omicron spike protein were identified using a set of computational tools and immunoinformatic techniques to uncover common MHC-1, MHC-II, and B cell epitopes that may have the ability to influence the host immune mechanism. A final of three epitopes from CD8+ T-cell, CD4+ T-cell epitopes, and B-cell were shortlisted from spike protein, and that are highly antigenic, IFN-γ inducer, as well as overlapping for the construction of twelve vaccine models. As a result, the antigenic epitopes were coupled with a flexible and stable peptide linker, and the adjuvant was added at the N-terminal end to create a unique vaccine candidate. The structure of a 3D vaccine candidate was refined, and its quality was assessed by using web servers. However, the applied immunoinformatic study along with the molecular docking and simulation of 12 modeled vaccines constructs against six distinct HLAs, and TLRs (TLR2, and TLR4) complexes revealed that the V1 construct was non-allergenic, non-toxic, highly immunogenic, antigenic, and most stable. The vaccine candidate's stability was confirmed by molecular dynamics investigations. Finally, we studied the expression of the suggested vaccination using codon optimization and in-silico cloning. The current study proposed V1 Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that may help the scientific community to treat SARS-CoV-2 infections.

Promising Acinetobacter baumannii Vaccine Candidates and Drug Targets in Recent Years

In parallel to the uncontrolled use of antibiotics, the emergence of multidrug-resistant bacteria, like Acinetobacter baumannii, has posed a severe threat. A. baumannii predominates in the nosocomial setting due to its ability to persist in hospitals and survive antibiotic treatment, thereby eventually leading to an increasing prevalence and mortality due to its infection. With the increasing spectra of drug resistance and the incessant collapse of newly discovered antibiotics, new therapeutic countermeasures have been in high demand. Hence, recent research has shown favouritism towards the long-term solution of designing vaccines. Therefore, being a realistic alternative strategy to combat this pathogen, anti-A. Baumannii vaccines research has continued unearthing various antigens with variable results over the last decade. Again, other approaches, including pan-genomics, subtractive proteomics, and reverse vaccination strategies, have shown promise for identifying promiscuous core vaccine candidates that resulted in chimeric vaccine constructs. In addition, the integration of basic knowledge of the pathobiology of this drug-resistant bacteria has also facilitated the development of effective multiantigen vaccines. As opposed to the conventional trial-and-error approach, incorporating the in silico methods in recent studies, particularly network analysis, has manifested a great promise in unearthing novel vaccine candidates from the A. baumannii proteome. Some studies have used multiple A. baumannii data sources to build the co-functional networks and analyze them by k-shell decomposition. Additionally, Whole Genomic Protein Interactome (GPIN) analysis has utilized a rational approach for identifying essential proteins and presenting them as vaccines effective enough to combat the deadly pathogenic threats posed by A. baumannii. Others have identified multiple immune nodes using network-based centrality measurements for synergistic antigen combinations for different vaccination strategies. Protein-protein interactions have also been inferenced utilizing structural approaches, such as molecular docking and molecular dynamics simulation. Similar workflows and technologies were employed to unveil novel A. baumannii drug targets, with a similar trend in the increasing influx of in silico techniques. This review integrates the latest knowledge on the development of A. baumannii vaccines while highlighting the in silico methods as the future of such exploratory research. In parallel, we also briefly summarize recent advancements in A. baumannii drug target research.

Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.

The Use of Molecular Dynamics Simulation Method to Quantitatively Evaluate the Affinity between HBV Antigen T Cell Epitope Peptides and HLA-A Molecules

Chronic hepatitis B virus (HBV), a potentially life-threatening liver disease, makes people vulnerable to serious diseases such as cancer. T lymphocytes play a crucial role in clearing HBV virus, while the pathway depends on the strong binding of T cell epitope peptide and HLA. However, the experimental identification of HLA-restricted HBV antigenic peptides is extremely time-consuming. In this study, we provide a novel prediction strategy based on structure to assess the affinity between the HBV antigenic peptide and HLA molecule. We used residue scanning, peptide docking and molecular dynamics methods to obtain the molecular docking model of HBV peptide and HLA, and then adopted the MM-GBSA method to calculate the binding affinity of the HBV peptide–HLA complex. Overall, we collected 59 structures of HLA-A from Protein Data Bank, and finally obtained 352 numerical affinity results to figure out the optimal bind choice between the HLA-A molecules and 45 HBV T cell epitope peptides. The results were highly consistent with the qualitative affinity level determined by the competitive peptide binding assay, which confirmed that our affinity prediction process based on an HLA structure is accurate and also proved that the homologous modeling strategy for HLA-A molecules in this study was reliable. Hence, our work highlights an effective way by which to predict and screen for HLA-peptide binding that would improve the treatment of HBV infection.

Harnessing Fuzzy Rule Based System for Screening Major Histocompatibility Complex Class I Peptide Epitopes from the Whole Proteome: An Implementation on the Proteome of Leishmania donovani

The development of peptide-based vaccines is enhanced by immunoinformatics, which predicts the patterns that B cells and T cells recognize. Although several tools are available for predicting the Major histocompatibility complex (MHC-I) binding peptides, the wide variants of human leucocyte antigen allele make it challenging to choose a peptide that will induce an immune response in a majority of people. In addition, for a peptide to be considered a potential vaccine candidate, factors such as T cell affinity, proteasome cleavage, and similarity to human proteins also play a major role. Identifying peptides that satisfy the earlier cited measures across the entire proteome is, therefore, challenging. Hence, the fuzzy inference system (FIS) is proposed to detect each peptide's potential as a vaccine candidate and assign it either a very high, high, moderate, or low ranking. The FIS includes input features from 6 modules (binding of 27 major alleles, T cell propensity, pro-inflammatory response, proteasome cleavage, transporter associated with antigen processing, and similarity with human peptide) and rules derived from an observation of features on positive samples. On validation of experimentally verified peptides, a balanced accuracy of ∼80% was achieved, with a Mathew's correlation coefficient score of 0.67 and an F-1 score of 0.74. In addition, the method was implemented on complete proteome of Leishmania donovani, which contains ∼4,800,000 peptides. Lastly, a searchable database of the ranked results of the L. donovani proteome was made and is available online (MHC-FIS-LdDB). It is hoped that this method will simplify the identification of potential MHC-I binding candidates from a large proteome.

Development of multivalent vaccine targeting M segment of Crimean Congo Hemorrhagic Fever Virus (CCHFV) using immunoinformatic approaches

Crimean-Congo Hemorrhagic Fever (CCHF) is a tick-borne viral infection with no licensed vaccine or therapeutics available for its treatment. In the present study we have developed the first multi-epitope subunit vaccine effective against all the seven genotypes of CCHF virus (CCHFV). The vaccine contains five B-cell, two MHC-II (HTL), and three MHC-I (CTL) epitopes screened from two structural glycoproteins (Gc and Gn in M segment) of CCHFV with an N-terminus human β-defensin as an adjuvant, as well as an N-terminus EAAAK sequence. The epitopes were rigorously investigated for their antigenicity, allergenicity, IFN gamma induction, anti-inflammatory responses, stability, and toxicity. The three-dimensional structure of the vaccine was predicted and docked with TLR-3, TLR-8, and TLR-9 receptors to find the strength of the binding complexes via molecular dynamics simulation. After codon adaptation, the subunit vaccine construct was developed in a pDual-GC plasmid and has population coverage of 98.47% of the world's population (HLA-I & II combined). The immune simulation studies were carried out on the C-ImmSim in-silico interface showing a marked increase in the production of cellular and humoral response (B-cell and T-cell) as well as TGFβ, IL-2, IL-10, and IL-12 indicating that the proposed vaccine would be able to sufficiently provoke both humoral and cell-mediated immune responses. Thus, making it a new and promising vaccine candidate against CCHFV.

Immmunoinformatics-based design of a multi-epitope vaccine with CTLA-4 extracellular domain to combat Helicobacter pylori

In view of the high infection rate of Helicobacter pylori, a safe and effective vaccine is urgently needed. Recent trends in vaccine design have shifted toward safe and specific epitope-based vaccines. In this study, by using different immunoinformatics approaches, a total of eight linear B cell epitopes, four HTL and three CTL epitopes of FlaA and UreB proteins of H. pylori G27 strain were screened out, we also predicted the conformational epitopes of the two proteins. Then, the dominant epitopes were sequentially linked by appropriate linkers, and the cytotoxic T lymphocyte-associated antigen 4 extracellular domain was attached to the N-terminal of the epitope sequence. Meanwhile, molecular docking, molecular dynamics simulations and principal component analysis were performed to show that the multi-epitope vaccine structure had strong interactions with B7 (B7-1, B7-2) and Toll-like receptors (TLR-2, -4). Eventually, the effectiveness of the vaccine was validated using in silico cloning. These analyses suggested that the designed vaccine could target antigen-presenting cells and had high potency against H. pylori, which could provide a reference for the future development of efficient H. pylori vaccines.

Identification of a Potential mRNA-based Vaccine Candidate against the SARS-CoV-2 Spike Glycoprotein: A Reverse Vaccinology Approach

The emergence of the novel coronavirus (SARS-CoV-2) in December 2019 has generated a devastating global consequence which makes the development of a rapidly deployable, effective and safe vaccine candidate an imminent global health priority. The design of most vaccine candidates has been directed at the induction of antibody responses against the trimeric spike glycoprotein of SARS-CoV-2, a class I fusion protein that aids ACE2 (angiotensin-converting enzyme 2) receptor binding. A variety of formulations and vaccinology approaches are being pursued for targeting the spike glycoprotein, including simian and human replication-defective adenoviral vaccines, subunit protein vaccines, nucleic acid vaccines and whole-inactivated SARS-CoV-2. Here, we directed a reverse vaccinology approach towards the design of a nucleic acid (mRNA-based) vaccine candidate. The "YLQPRTFLL" peptide sequence (position 269-277) which was predicted to be a B cell epitope and likewise a strong binder of the HLA*A-0201 was selected for the design of the vaccine candidate, having satisfied series of antigenicity assessments. Through the codon optimization protocol, the nucleotide sequence for the vaccine candidate design was generated and targeted at the human toll-like receptor 7 (TLR7). Bioinformatics analyses showed that the sequence "UACCUGCAGCCGCGUACCUUCCUGCUG" exhibited a strong affinity and likewise was bound to a stable cavity in the TLR7 pocket. This study is therefore expected to contribute to the research efforts directed at securing definitive preventive measures against the SARS-CoV-2 infection.

An integrated in silico based subtractive genomics and reverse vaccinology approach for the identification of novel vaccine candidate and chimeric vaccine against XDR Salmonella typhi H58

Salmonella typhi is notorious for causing enteric fever which is also known as typhoid fever. It emerged as an extreme drug resistant strain that requires urgent attention to prevent its global spread. Statistically, about 11-17 million typhoid illnesses are reported worldwide annually. The only alternative approach for the control of this illness is proper vaccination. However, available typhoid vaccine has certain limitations such as poor long-term efficacy, and non-recommendation for below 6 years children, which opens the avenues for designing new vaccines to overcome such limitations. Computational-based reverse vaccinology along with subtractive genomics analysis is one of the robust approaches used for the prioritization of vaccine candidates through direct screening of genome sequence assemblies. In the current study, we have successfully designed a peptide-based novel antigen chimeric vaccine candidate against the XDR strain of S. typhi H58. The pipeline revealed four peptides from WP_001176621.1 i.e., peptidoglycan-associated lipoprotein Pal and two peptides from WP_000747548.1 i.e., OmpA family lipoprotein as promising target for the induction of immune response against S. typhi. The six epitopes from both proteins were found as immunogenic, antigenic, virulent, highly conserved, nontoxic, and non-allergenic among whole Salmonella H58 proteome. Furthermore, the binding interaction between a chimeric vaccine and human population alleles was unveiled through structure-based studies. So far, these proteins have never been characterized as vaccine targets against S. typhi. The current study proposed that construct V2 could be a significant vaccine candidate against S. typhi H58. However, to ascertain this, future experimental holistic studies are recommended as follow-up.

Peptide-Based Vaccines for Tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO's End TB Strategy.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

Review on Approved and Inprogress COVID-19 Vaccines

The last generation of Coronavirus named COVID-19 is responsible for the recent worldwide outbreak. Concerning the widespread and quick predominance, there is a critical requirement for designing appropriate vaccines to surmount this grave problem. Correspondingly, in this revision, COVID-19 vaccines (which are being developed until March 29th, 2021) are classified into specific and non-specific categories. Specific vaccines comprise genetic-based vaccines (mRNA, DNA), vector-based, protein/recombinant protein vaccines, inactivated viruses, live-attenuated vaccines, and novel strategies including microneedle arrays (MNAs), and nanoparticles vaccines. Moreover, specific vaccines such as BCG, MRR, and a few other vaccines are considered Non-specific. What is more, according to the significance of Bioinformatic sciences in the cutting-edge vaccine design and rapid outbreak of COVID-19, herein, Bioinformatic principles including reverse vaccinology, epitopes prediction/selection and, their further applications in the design of vaccines are discussed. Last but not least, safety, challenges, advantages, and future prospects of COVID-19 vaccines are highlighted.

Identification of vaccine and drug targets in Shigella dysenteriae sd197 using reverse vaccinology approach

Shigellosis is characterized as diarrheal disease that causes a high mortality rate especially in children, elderly and immunocompromised patients. More recently, the World Health Organization advised safe vaccine designing against shigellosis due to the emergence of Shigella dysenteriae resistant strains. Therefore, the aim of this study is to identify novel drug targets as well as the design of the potential vaccine candidates and chimeric vaccine models against Shigella dysenteriae. A computational based Reverse Vaccinology along with subtractive genomics analysis is one of the robust approaches used for the prioritization of drug targets and vaccine candidates through direct screening of genome sequence assemblies. Herein, a successfully designed peptide-based novel highly antigenic chimeric vaccine candidate against Shigella dysenteriae sd197 strain is proposed. The study resulted in six epitopes from outer membrane WP_000188255.1 (Fe (3+) dicitrate transport protein FecA) that ultimately leads to the construction of twelve vaccine models. Moreover, V9 construct was found to be highly immunogenic, non-toxic, non-allergenic, highly antigenic, and most stable in terms of molecular docking and simulation studies against six HLAs and TLRS/MD complex. So far, this protein and multiepitope have never been characterized as vaccine targets against Shigella dysenteriae. The current study proposed that V9 could be a significant vaccine candidate against shigellosis and to ascertain that further experiments may be applied by the scientific community focused on shigellosis.

Bioinformatic Techniques for Vaccine Development: Epitope Prediction and Structural Vaccinology

Structural vaccinology involves characterizing the interactions between an antigen and antibodies or host immune receptors. Central to this is the task of epitope prediction, which involves describing the binding affinity and interactions of a given peptide typically to the major histocompatibility complex in the case of T-cells or to the antibodies in the case of B-cells. Several computational models exist for this purpose which we will review here. Generally, epitope predictions for MHC-I and MHC-II are substantially different tasks as well as epitope prediction for continuous versus discontinuous B-cell epitopes. Overall, these models suffer from overprediction of epitopes although general themes support both the use of neural networks as well as the incorporation of more abundant and more varied experimental annotation into model training as valuable in improving predictive performance.

DcaP porin and its epitope-based subunit promise effective vaccines against Acinetobacter baumannii; in-silico and in-vivo approaches

A. baumannii is a multi-drug resistant pathogen with a relatively high mortality rate. To date, no vaccine has been approved against this bacterium. DcaP is a high abundance porin during infection that its structure has been recently determined, but no information about its immunogenic properties has been reported yet. So, in this study DcaP properties were analyzed and its vaccine potential was evaluated. The results showed this porin is an extremely conserved antigen with no allergenicity and toxicity that bears no resemblance to human proteins. Six potential immunogen areas in the DcaP sequence were detected based on in-silico B and T-cell epitope mapping and other approaches. A multiple-epitope potential vaccine was designed based on the predicted linear epitopes and amplified by overlap extension PCR technique. In-vivo results indicated that active and passive immunization of mice with the DcaP protein or its designed subunit vaccine raises the antibody titers and decreases the mortality rate of the immunized mice infected with A. baumannii. Based on the results, DcaP and its indicated immunogen regions can be considered as a peptide or subunit vaccine. The immunogen regions could also be applied in multivalent subunit vaccine candidates against A. baumannii and other bacteria.

Physicochemical and structural characterization, epitope mapping and vaccine potential investigation of a new protein containing Tetratrico Peptide Repeats of Acinetobacter baumannii: An in-silico and in-vivo approach

Acinetobacter baumannii is an opportunistic multidrug-resistant pathogen that causes a significant mortality rate. The proteins containing Tetratrico Peptide Repeats (TPRs) are involved in the pathogenicity and virulence of bacteria and have different roles such as transfer of bacterial virulence factors to host cells, binding to the host cells and inhibition of phagolysosomal maturation. So, in this study, physicochemical properties of a new protein containing TPRs in A. baumannii which was named PcTPRs1 by this study were characterized and its 3D structure was predicted by in-silico tools. The protein B and T cell epitopes were mapped and its vaccine potential was in-silico and in-vivo investigated. Domain analysis indicated that the protein contains the Flp pilus assembly protein TadD domain which has three TPRs. The helix is dominant in the protein structure, and this protein is an outer membrane antigen which, is extremely conserved among A. baumannii strains; thus, has good properties to be applied as a recombinant vaccine. The best-predicted and refined model was applied in ligand-binding sites and conformational epitopes prediction. Based on epitope mapping results, several epitopes were characterized which could stimulate both immune systems. BLAST results showed the introduced epitopes are completely conserved among A. baumannii strains. The in-vivo analysis indicates that a 101 amino acid fragment of the protein which contains the best selected epitope, can produce a good protectivity against A. baumannii as well as the whole TPR protein and thus could be investigated as an effective subunit and potential vaccines.

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.

Antigenic sites in SARS-CoV-2 spike RBD show molecular similarity with pathogenic antigenic determinants and harbors peptides for vaccine development

The spike protein of coronavirus is key target for drug development and other pharmacological interventions. In current study, we performed an integrative approach to predict antigenic sites in SARS-CoV-2 spike receptor binding domain and found nine potential antigenic sites. The predicted antigenic sites were then assessed for possible molecular similarity with other known antigens in different organisms. Out of nine sites, seven sites showed molecular similarity with 54 antigenic determinants found in twelve pathogenic bacterial species (Mycobacterium tuberculosis, Mycobacterium leprae, Bacillus anthracis, Borrelia burgdorferi, Clostridium perfringens, Clostridium tetani, Helicobacter Pylori, Listeria monocytogenes, Staphylococcus aureus, Streptococcus pyogenes, Vibrio cholera and Yersinia pestis), two malarial parasites (Plasmodium falciparum and Plasmodium knowlesi) and influenza virus A. Most of the bacterial antigens that displayed molecular similarity with antigenic sites in SARS-CoV-2 RBD (receptor binding domain) were toxins and virulent factors. Antigens from Mycobacterium that showed similarity were mainly involved in modulating host cell immune response and ensuring persistence and survival of pathogen in host cells. Presence of a large number of antigenic determinants, similar to those in highly pathogenic microorganisms, not merely accounts for complex etiology of the disease but also provides an explanation for observed pathophysiological complications, such as deregulated immune response, unleashed or dysregulated cytokine secretion (cytokine storm), multiple organ failure etc., that are more evident in aged and immune-compromised patients. Over-representation of antigenic determinants from Plasmodium and Mycobacterium in all antigenic sites suggests that anti-malarial and anti-TB drugs can prove to be clinical beneficial for COVID-19 treatment. Besides this, anti-leprosy, anti-lyme, anti-plague, anti-anthrax drugs/vaccine etc. are also expected to be beneficial in COVID-19 treatment. Moreover, individuals previously immunized/vaccinated or had previous history of malaria, tuberculosis or other disease caused by fifteen microorganisms are expected to display a considerable degree of resistance against SARS-CoV-2 infection. Out of the seven antigenic sites predicted in SARS-CoV-2, a part of two antigenic sites were also predicted as potent T-cell epitopes (KVGGNYNYL^444-452 and SVLYNSASF^366-374) against MHC class I and three (KRISNCVADYSVLYN^356-370, DLCFTNVYADSFVI^389-402, and YRVVVLSFELLHA^508-520) against MHC class II. All epitopes possessed significantly lower predicted IC50 value which is a prerequisite for a preferred vaccine candidate for COVID-19.

Complete genome characterization and evolutionary analysis of dengue viruses isolated during 2016-2017 in Pune, India

Dengue is the most common mosquito-borne viral infection in tropical and sub-tropical countries. In the recent years, frequent dengue outbreaks are being reported in many parts of India. DENV circulates as four independent serotypes posing a major public health threat around the globe. Phylogenetic and full genome sequence analyses of 19 complete DENV genome sequences presenting all the four serotypes in Pune, India (2016-2017) revealed no change in the circulating genotypes i.e., genotype V clade C (D1), genotype IVB (D2), genotype III lineage III (D3) and genotype I clade D (D4). Additionally, unique amino acid substitutions that may potentially influence viral fitness and virulence in host cells were identified. Mapping of the unique amino acid substitutions onto the T cell epitopes of the reference strains revealed that 8/10 (D1), 14/15 (D2), 3/4 (D3) and 21/74 (D4), amino acids were involved in T-cell epitope presentation for a maximum number of HLA alleles associated with disease outcome. Selection pressure analysis documented a positive selection pressure to be acting on few amino acid sites indicating continuous evolutionary changes in the viral RNA. Overall, the evolutionary and selection pressure data generated during this study may help in better understanding of DENV evolution and epidemiology.

Advanced strategies for development of vaccines against human bacterial pathogens

Infectious diseases are one of the main grounds of death and disabilities in human beings globally. Lack of effective treatment and immunization for many deadly infectious diseases and emerging drug resistance in pathogens underlines the need to either develop new vaccines or sufficiently improve the effectiveness of currently available drugs and vaccines. In this review, we discuss the application of advanced tools like bioinformatics, genomics, proteomics and associated techniques for a rational vaccine design.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Whole genome deep sequencing analysis of viral quasispecies diversity and evolution in HBeAg seroconverters

Background & Aims

We aimed to investigate how viral quasispecies of the HBV whole genome evolves and diversifies in response to HBeAg seroconversion and viral control utilising next-generation sequencing (NGS).

Methods

Fifty HBeAg-positive chronic hepatitis B patients, including 18 treatment-naïve and 32 interferon (IFN)-treated individuals, were recruited. Serial HBV whole genomes in serum were analysed by NGS to determine sequence characteristics and viral quasispecies.

Results

HBV quasispecies diversity, measured by nucleotide diversity, was negatively correlated with viral load and hepatitis activity. Spontaneous HBeAg seroconverters exhibited significantly greater viral quasispecies diversity than treatment-naïve non-seroconverters from >1 year before seroconversion (0.0112 vs. 0.0060, p <0.01) to >1 year after seroconversion (0.0103 vs. 0.0068, p <0.01). IFN-induced HBeAg seroconverters tended to have higher viral genetic diversity than non-seroconverters along with treatment. Particularly, the IFN responders, defined as IFN-induced HBeAg seroconversion with low viraemia, exhibited significantly greater genetic diversity of whole HBV genome at 6 months post-IFN treatment than IFN non-responders (0.0148 vs. 0.0106, p = 0.048). Moreover, spontaneous HBeAg seroconverters and IFN responders exhibited significantly higher evolutionary rates and more intra-host single-nucleotide variants. Interestingly, in spontaneous HBeAg seroconverters and IFN responders, there were distinct evolutionary patterns in the HBV genome.

Conclusions

Higher HBV quasispecies diversity is associated with spontaneous HBeAg seroconversion and IFN-induced HBeAg seroconversion with low viraemia, conferring a favourable clinical outcome.

Lay summary

HBeAg seroconversion is a landmark in the natural history of chronic HBV infection. Using next-generation sequencing, we found that the nucleotide diversity of HBV was negatively correlated with viral load and hepatitis activity. Patients undergoing HBeAg seroconversion had more diverse HBV genomes and a faster viral evolution rate. Our findings suggest HBeAg seroconversion is driven by host selection pressure, likely immune selection pressure.

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Deep sequencing of whole HBV genome uncovers the quasispecies changes in chronic hepatitis B patients.

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The nucleotide diversity of HBV negatively correlates with viraemia during HBeAg loss/seroconversion.

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Viral quasispecies diversity is greater in spontaneous HBeAg seroconverters before and after seroconversion than in treatment-naïve non-seroconverters.

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Responders to IFN have greater viral quasispecies diversity than non-responders at 24 weeks after treatment.

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The genome positions of non-synonymous intra-host single nucleotide variants (iSNVs) of HBV tend to be located at possible T cell epitopes.

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.

Design of Multi-Epitope Vaccine against SARS-CoV-2

The ongoing COVID-19 pandemic requires urgently specific therapeutics and approved vaccines. Here, the four structural proteins of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), the causative agent of COVID-19, are screened by in-house immunoinformatic tools to identify peptides acting as potential T-cell epitopes. In order to act as an epitope, the peptide should be processed in the host cell and presented on the cell surface in a complex with the Human Leukocyte Antigen (HLA). The aim of the study is to predict the binding affinities of all peptides originating from the structural proteins of SARS-CoV-2 to 30 most frequent in the human population HLA proteins of class I and class II and to select the high binders (IC50 < 50 nM). The predicted high binders are compared to known high binders from SARS-CoV conserved in CoV-2 and 77% of them coincided. The high binders will be uploaded onto lipid nanoparticles and the multi-epitope vaccine prototype will be tested for ability to provoke T-cell mediated immunity and protection against SARS-CoV-2.

Computational Analysis of African Swine Fever Virus Protein Space for the Design of an Epitope-Based Vaccine Ensemble

African swine fever virus is the etiological agent of African swine fever, a transmissible severe hemorrhagic disease that affects pigs, causing massive economic losses. There is neither a treatment nor a vaccine available, and the only method to control its spread is by extensive culling of pigs. So far, classical vaccine development approaches have not yielded sufficiently good results in terms of concomitant safety and efficacy. Nowadays, thanks to advances in genomic and proteomic techniques, a reverse vaccinology strategy can be explored to design alternative vaccine formulations. In this study, ASFV protein sequences were analyzed using an in-house pipeline based on publicly available immunoinformatic tools to identify epitopes of interest for a prospective vaccine ensemble. These included experimentally validated sequences from the Immune Epitope Database, as well as de novo predicted sequences. Experimentally validated and predicted epitopes were prioritized following a series of criteria that included evolutionary conservation, presence in the virulent and currently circulating variant Georgia 2007/1, and lack of identity to either the pig proteome or putative proteins from pig gut microbiota. Following this strategy, 29 B-cell, 14 CD4⁺ T-cell and 6 CD8⁺ T-cell epitopes were selected, which represent a starting point to investigating the protective capacity of ASFV epitope-based vaccines.

Extent of polymorphism and selection pressure on the Trypanosoma cruzi vaccine candidate antigen Tc24

INTRODUCTION

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, is a major public health problem in the Americas, and existing drugs have severe limitations. In this context, a vaccine would be an attractive alternative for disease control. One of the difficulties in developing an effective vaccine lies in the high genetic diversity of T. cruzi. In this study, we evaluated the level of sequence diversity of the leading vaccine candidate Tc24 in multiple parasite strains.

METHODS AND RESULTS

We quantified its level of polymorphism within and between T. cruzi discrete typing units (DTUs) and how this potential polymorphism is structured by different selective pressures. We observed a low level of polymorphism of Tc24 protein, weakly associated with parasite DTUs, but not with the geographic origin of the strains. In particular, Tc24 was under strong purifying selection pressure and predicted CD8+ T-cell epitopes were mostly conserved. Tc24 strong conservation may be associated with structural/functional constrains to preserve EF hand domains and their calcium-binding loops, and Tc24 is likely important for the parasite fitness.

DISCUSSION

Together, these results show that a vaccine based on Tc24 is likely to be effective against a wide diversity of parasite strains across the American continent, and further development of this vaccine candidate should be a high priority.

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.

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.