Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

In silico advancements in Peptide-MHC interaction: A molecular dynamics study of predicted glypican-3 peptides and HLA-A*11:01

Our study employed molecular dynamics (MD) simulations to assess the binding affinity between short peptides derived from the tumor-associated antigen glypican 3 (GPC3) and the major histocompatibility complex (MHC) molecule HLA-A*11:01 in hepatocellular carcinoma. We aimed to improve the reliability of in silico predictions of peptide-MHC interactions, which are crucial for developing targeted cancer therapies. We used five algorithms to discover four peptides (TTDHLKFSK, VINTTDHLK, KLIMTQVSK, and STIHDSIQY), demonstrating the substantial potential for HLA-A11:01 presentation. The Anchored Peptide-MHC Ensemble Generator (APE-Gen) was used to create the initial structure of the peptide-MHC complex. This was followed by a 200 ns molecular dynamics (MD) simulation using AMBER22, which verified the precise positioning of the peptides in the binding groove of HLA-A*11:01, specifically at the A and F pockets. Notably, the 2nd residue, which serves as a critical anchor within the 2nd pocket, played a pivotal role in stabilising the binding interactions.VINTTDHLK (ΔG SIE = -14.46 ± 0.53 kcal/mol and ΔG MM/GBSA = -30.79 ± 0.49 kcal/mol) and STIHDSIQY (ΔG SIE and ΔG MM/GBSA = -14.55 ± 0.16 and -23.21 ± 2.23 kcal/mol) exhibited the most effective binding potential among the examined peptides, as indicated by both their binding free energies and its binding affinity on the T2 cell line (VINTTDHLK: IC50 = 0.45 nM; STIHDSIQY: IC50 = 0.35 nM). The remarkable concordance between in silico and in vitro binding affinity results was of particular significance, indicating that MD simulation is a potent instrument capable of bolstering confidence in in silico peptide predictions. By employing MD simulation as a method, our study provides a promising avenue for improving the prediction of potential peptide-MHC interactions, thereby facilitating the development of more effective and targeted cancer therapies.

Immunoinformatic approach to design an efficient multi-epitope peptide vaccine against melanoma

Melanoma is known to be the most hazardous and life-threatening type of skin cancer. Although numerous treatments have been authorized in recent years, they often result in severe side effects and may not fully cure the disease. To combat this issue, immunotherapy has emerged as a promising approach for the prevention and treatment of melanoma. Specifically, the use of epitope melanoma vaccine, a subset of immunotherapy, has recently gained attention. The aim of this study was to create a multi-epitope melanoma vaccine using immunoinformatic methods. Two well-known antigens, NYESO-1 and MAGE-C2, were selected due to their strong immunogenicity and high expression in melanoma. To enhance the immunogenicity of the peptide vaccine, Brucella cell-surface protein 31 (BCSP31), the G5 domain of resuscitation-promoting factor B (RpfB) adjuvants, and the helper epitope of pan HLADR-binding epitope (PADRE) were incorporated to vaccine construct. These different segments were connected with suitable linkers and the resulting vaccine structure was evaluated for its physicochemical, structural, and immunological properties using computational tools. The designed vaccine was found to have satisfactory allergenicity, antigenicity, and physicochemical parameters. Additionally, a high-quality tertiary structure of the vaccine was achieved through modeling, refinement, and validation. Docking and molecular dynamics studies showed that the vaccine had a stable and appropriate interaction with the cognate TLR2 and TLR4 receptors during the simulation period. Finally, in silico immune simulation analysis revealed a significant increase in the levels of helper and cytotoxic T cells, as well as the cytokines interferon-gamma and interleukin-2, after repeated exposure to the melanoma vaccine. These results suggest that the designed vaccine has the potential to be an effective therapeutic option for melanoma. However, additional in vitro and in vivo validations are crucial to assess real-world efficacy and safety.

Reverse Vaccinology and Immunoinformatic Approach for Designing a Bivalent Vaccine Candidate Against Hepatitis A and Hepatitis B Viruses

Hepatitis A and B are two crucial viral infections that still dramatically affect public health worldwide. Hepatitis A Virus (HAV) is the main cause of acute hepatitis, whereas Hepatitis B Virus (HBV) leads to the chronic form of the disease, possibly cirrhosis or liver failure. Therefore, vaccination has always been considered the most effective preventive method against pathogens. At this moment, we aimed at the immunoinformatic analysis of HAV-Viral Protein 1 (VP1) as the major capsid protein to come up with the most conserved immunogenic truncated protein to be fused by HBV surface antigen (HBs Ag) to achieve a bivalent vaccine against HAV and HBV using an AAY linker. Various computational approaches were employed to predict highly conserved regions and the most immunogenic B-cell and T-cell epitopes of HAV-VP1 capsid protein in both humans and BALB/c. Moreover, the predicted fusion protein was analyzed regarding primary and secondary structures and also homology validation. Afterward, the three-dimensional structure of vaccine constructs docked with various toll-like receptors (TLR) 2, 4 and 7. According to the bioinformatics tools, the region of 99-259 amino acids of VP1 was selected with high immunogenicity and conserved epitopes. T-cell epitope prediction showed that this region contains 32 antigenic peptides for Human leukocyte antigen (HLA) class I and 20 antigenic peptides in terms of HLA class II which are almost fully conserved in the Iranian population. The vaccine design includes 5 linear and 4 conformational B-cell lymphocyte (BCL) epitopes to induce humoral immune responses. The designed VP1-AAY-HBsAg fusion protein has the potency to be constructed and expressed to achieve a bivalent vaccine candidate, especially in the Iranian population. These findings led us to claim that the designed vaccine candidate provides potential pathways for creating an exploratory vaccine against Hepatitis A and Hepatitis B Viruses with high confidence for the identified strains.

Enhancing Wound Healing and Anti-Inflammatory Effects by Combination of CIGB-258 and Apolipoprotein A-I against Carboxymethyllysine Toxicity in Zebrafish: Insights into Structural Stabilization and Antioxidant Properties

CIGB-258 is known to exert anti-inflammatory activity via structural stabilization of apolipoprotein A-I (apoA-I) and functional enhancement of high-density lipoproteins (HDL) against acute toxicity of carboxymethyllysine (CML). The co-presence of CIGB-258 in reconstituted HDL (rHDL) formed larger rHDL particles and enhanced anti-inflammatory activity in a dose-dependent manner of apoA-I:CIGB-258, 1:0, 1:0.1, 1:0.5, and 1:1 of molar ratio, in the synthesis of the rHDL. However, no study has evaluated the enhancement of HDL functionality by the co-presence of lipid-free apoA-I and CIGB-258. The present study was therefore designed to compare the structural stabilization and functional improvement of HDL in the presence of lipid-free apoA-I and CIGB-258 in molar ratios of 1:0, 1:0.1, 1:0.5, and 1:1 within both HDL2 and HDL3. As the concentration of CIGB-258 increased, it effectively inhibited the cupric-ion-induced oxidation of HDL, thereby safeguarding apoA-I from proteolytic degradation. Additionally, the wound-healing activity of zebrafish was significantly (p < 0.01) enhanced by the co-addition of apoA-I:CIGB-258 (1:1) up to 1.6-fold higher than apoA-I alone (1:0) under the presence of CML. ApoA-I:CIGB-258 (1:1) treatment exhibited the lowest apoptosis and production of reactive oxygen species against CML-induced damage in the wound site. Also, an increase in wounded tissue granulation and epidermis thickness was observed with increasing concentration of CIGB-258 during 48 h post-treatment via the healing process. Intraperitoneal injection of apoA-I:CIGB-258 mixture remarkably ameliorated the acute paralysis and restored zebrafish swimming ability impaired by the acute toxicity of CML. The increase of CIGB-258 content, especially co-injection of apoA-I:CIGB-258 (1:1), leads to a significant 2.3-fold (p < 0.001) and 4.1-fold (p < 0.001) higher zebrafish survivability and recovery of swimming ability, respectively, than those of CML-control. In the apoA-I:CIGB-258 (1:1) group, neutrophil infiltration and interleukin (IL)-6 production was lowest in the hepatic tissue with the least cellular damage and apoptosis. Additionally, the group treated with apoA-I:CIGB-258 (1:1) demonstrated the lowest plasma levels of total cholesterol (TC) and triglycerides (TG), along with minimal damage to the kidney, ovary, and testicular cells. Conclusively, co-treatment of CIGB-258 with apoA-I effectively mitigated acute inflammation in zebrafish, safeguarded vital organs, structurally stabilized apoA-I, and enhanced HDL functionality.

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.

Tetanus-diphtheria vaccine can prime SARS-CoV-2 cross-reactive T cells

Vaccines containing tetanus-diphtheria antigens have been postulated to induce cross-reactive immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which could protect against coronavirus disease (COVID-19). In this work, we investigated the capacity of Tetanus-diphtheria (Td) vaccine to prime existing T cell immunity to SARS-CoV-2. To that end, we first collected known SARS-CoV-2 specific CD8+ T cell epitopes targeted during the course of SARS-CoV-2 infection in humans and identified as potentially cross-reactive with Td vaccine those sharing similarity with tetanus-diphtheria vaccine antigens, as judged by Levenshtein edit distances (≤ 20% edits per epitope sequence). As a result, we selected 25 potentially cross-reactive SARS-CoV-2 specific CD8+ T cell epitopes with high population coverage that were assembled into a synthetic peptide pool (TDX pool). Using peripheral blood mononuclear cells, we first determined by intracellular IFNγ staining assays existing CD8+ T cell recall responses to the TDX pool and to other peptide pools, including overlapping peptide pools covering SARS-CoV-2 Spike protein and Nucleocapsid phosphoprotein (NP). In the studied subjects, CD8+ T cell recall responses to Spike and TDX peptide pools were dominant and comparable, while recall responses to NP peptide pool were less frequent and weaker. Subsequently, we studied responses to the same peptides using antigen-inexperienced naive T cells primed/stimulated in vitro with Td vaccine. Priming stimulations were carried out by co-culturing naive T cells with autologous irradiated peripheral mononuclear cells in the presence of Td vaccine, IL-2, IL-7 and IL-15. Interestingly, naive CD8+ T cells stimulated/primed with Td vaccine responded strongly and specifically to the TDX pool, not to other SARS-CoV-2 peptide pools. Finally, we show that Td-immunization of C57BL/6J mice elicited T cells cross-reactive with the TDX pool. Collectively, our findings support that tetanus-diphtheria vaccines can prime SARS-CoV-2 cross-reactive T cells and likely contribute to shape the T cell responses to the virus.

In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis

Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.

Structure-aware deep model for MHC-II peptide binding affinity prediction

The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.

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.

Immunoinformatics analysis of Brucella melitensis to approach a suitable vaccine against brucellosis

BACKGROUND

Brucellosis caused by B. melitensis is one of the most important common diseases between humans and livestock. Currently, live attenuated vaccines are used for this disease, which causes many problems, and unfortunately, there is no effective vaccine for human brucellosis. The aim of our research was to design a recombinant vaccine containing potential immunogenic epitopes against B. melitensis.

METHODS

In this study, using immunoinformatics approaches, 3 antigens Omp31, Omp25, and Omp28 were identified and the amino acid sequence of the selected antigens was determined in NCBI. Signal peptides were predicted by SignaIP-5.0 server. To predict B-cell epitopes from ABCpred and Bcepred servers, to predict MHC-I epitopes from RANKPEP and SYFPEITHI servers, to predict MHC-II epitopes from RANKPEP and MHCPred servers, and to predict CTL epitopes were used from the CTLPred server. Potentially immunogenic final epitopes were joined by flexible linkers. Finally, allergenicity (AllerTOP 2.0 server), antigenicity (Vaxijen server), physicochemical properties (ProtParam server), solubility (Protein-sol server), secondary (PSIPRED and GRO4 servers) and tertiary structure (I-TASSER server), refinement (GalaxyWEB server), validation (ProSA-web, Molprobity, and ERRAT servers), and optimization of the codon sequence (JCat server) of the structure of the multi-epitope vaccine were analyzed.

RESULTS

The analysis of immunoinformatics tools showed that the designed vaccine has high quality, acceptable physicochemical properties, and can induce humoral and cellular immune responses against B. melitensis bacteria. In addition, the high expression level of recombinant antigens in the E. coli host was observed through in silico simulation.

CONCLUSION

According to the results in silico, the designed vaccine can be a suitable candidate to fight brucellosis and in vitro and in vivo studies are needed to evaluate the research of this study.

In Silico Analyses, Experimental Verification and Application in DNA Vaccines of Ebolavirus GP-Derived pan-MHC-II-Restricted Epitopes

(1) Background and Purpose: Ebola virus (EBOV) is the causative agent of Ebola virus disease (EVD), which causes extremely high mortality and widespread epidemics. The only glycoprotein (GP) on the surface of EBOV particles is the key to mediating viral invasion into host cells. DNA vaccines for EBOV are in development, but their effectiveness is unclear. The lack of immune characteristics resides in antigenic MHC class II reactivity. (2) Methods: We selected MHC-II molecules from four human leukocyte antigen II (HLA-II) superfamilies with 98% population coverage and eight mouse H2-I alleles. IEDB, NetMHCIIpan, SYFPEITHI, and Rankpep were used to screen MHC-II-restricted epitopes with high affinity for EBOV GP. Further immunogenicity and conservation analyses were performed using VaxiJen and BLASTp, respectively. EpiDock was used to simulate molecular docking. Cluster analysis and binding affinity analysis of EBOV GP epitopes and selected MHC-II molecules were performed using data from NetMHCIIpan. The selective GP epitopes were verified by the enzyme-linked immunospot (ELISpot) assay using splenocytes of BALB/c (H2d), C3H, and C57 mice after DNA vaccine pVAX-GPEBO immunization. Subsequently, BALB/c mice were immunized with Protein-GPEBO, plasmid pVAX-GPEBO, and pVAX-LAMP/GPEBO, which encoded EBOV GP. The dominant epitopes of BALB/c (H-2-I-AdEd genotype) mice were verified by the enzyme-linked immunospot (ELISpot) assay. It is also used to evaluate and explore the advantages of pVAX-LAMP/GPEBO and the reasons behind them. (3) Results: Thirty-one HLA-II-restricted and 68 H2-I-restricted selective epitopes were confirmed to have high affinity, immunogenicity, and conservation. Nineteen selective epitopes have cross-species reactivity with good performance in MHC-II molecular docking. The ELISpot results showed that pVAX-GPEBO could induce a cellular immune response to the synthesized selective peptides. The better immunoprotection of the DNA vaccines pVAX-LAMP/GPEBO coincides with the enhancement of the MHC class II response. (4) Conclusions: Promising MHC-II-restricted candidate epitopes of EBOV GP were identified in humans and mice, which is of great significance for the development and evaluation of Ebola vaccines.

Structural Framework for Analysis of CD4+ T-Cell Epitope Dominance in Viral Fusion Proteins

Antigen conformation shapes CD4+ T-cell specificity through mechanisms of antigen processing, and the consequences for immunity may rival those from conformational effects on antibody specificity. CD4+ T cells initiate and control immunity to pathogens and cancer and are at least partly responsible for immunopathology associated with infection, autoimmunity, and allergy. The primary trigger for CD4+ T-cell maturation is the presentation of an epitope peptide in the MHC class II antigen-presenting protein (MHCII), most commonly on an activated dendritic cell, and then the T-cell responses are recalled by subsequent presentations of the epitope peptide by the same or other antigen-presenting cells. Peptide presentation depends on the proteolytic fragmentation of the antigen in an endosomal/lysosomal compartment and concomitant loading of the fragments into the MHCII, a multistep mechanism called antigen processing and presentation. Although the role of peptide affinity for MHCII has been well studied, the role of proteolytic fragmentation has received less attention. In this Perspective, we will briefly summarize evidence that antigen resistance to unfolding and proteolytic fragmentation shapes the specificity of the CD4+ T-cell response to selected viral envelope proteins, identify several remarkable examples in which the immunodominant CD4+ epitopes most likely depend on the interaction of processing machinery with antigen conformation, and outline how knowledge of antigen conformation can inform future efforts to design vaccines.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

Combining different bacteria in vaccine formulations enhances the chance for antiviral cross-reactive immunity: a detailed in silico analysis for influenza A virus

Bacteria are well known to provide heterologous immunity against viral infections through various mechanisms including the induction of innate trained immunity and adaptive cross-reactive immunity. Cross-reactive immunity from bacteria to viruses is responsible for long-term protection and yet its role has been downplayed due the difficulty of determining antigen-specific responses. Here, we carried out a systematic evaluation of the potential cross-reactive immunity from selected bacteria known to induce heterologous immunity against various viruses causing recurrent respiratory infections. The bacteria selected in this work were Bacillus Calmette Guerin and those included in the poly-bacterial preparation MV130: Streptococcus pneumoniae, Staphylococcus aureus, Staphylococcus epidermidis, Klebisella pneumoniae, Branhamella catarrhalis and Haemophilus influenzae. The virus included influenza A and B viruses, human rhinovirus A, B and C, respiratory syncytial virus A and B and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Through BLAST searches, we first identified the shared peptidome space (identity ≥ 80%, in at least 8 residues) between bacteria and viruses, and subsequently predicted T and B cell epitopes within shared peptides. Interestingly, the potential epitope spaces shared between bacteria in MV130 and viruses are non-overlapping. Hence, combining diverse bacteria can enhance cross-reactive immunity. We next analyzed in detail the cross-reactive T and B cell epitopes between MV130 and influenza A virus. We found that MV130 contains numerous cross-reactive T cell epitopes with high population protection coverage and potentially neutralizing B cell epitopes recognizing hemagglutinin and matrix protein 2. These results contribute to explain the immune enhancing properties of MV130 observed in the clinic against respiratory viral infections.

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.

Design of a Multi-Epitope Vaccine against Tuberculosis from Mycobacterium tuberculosis PE_PGRS49 and PE_PGRS56 Proteins by Reverse Vaccinology

Tuberculosis is a disease caused by Mycobacterium tuberculosis, representing the second leading cause of death by an infectious agent worldwide. The available vaccine against this disease has insufficient coverage and variable efficacy, accounting for a high number of cases worldwide. In fact, an estimated third of the world's population has a latent infection. Therefore, developing new vaccines is crucial to preventing it. In this study, the highly antigenic PE_PGRS49 and PE_PGRS56 proteins were analyzed. These proteins were used for predicting T- and B-cell epitopes and for human leukocyte antigen (HLA) protein binding efficiency. Epitopes GGAGGNGSLSS, FAGAGGQGGLGG, GIGGGTQSATGLG (PE_PGRS49), and GTGWNGGKGDTG (PE_PGRS56) were selected based on their best physicochemical, antigenic, non-allergenic, and non-toxic properties and coupled to HLA I and HLA II structures for in silico assays. A construct with an adjuvant (RS09) plus each epitope joined by GPGPG linkers was designed, and the stability of the HLA-coupled construct was further evaluated by molecular dynamics simulations. Although experimental and in vivo studies are still necessary to ensure its protective effect against the disease, this study shows that the vaccine construct is dynamically stable and potentially effective against tuberculosis.

Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics

Fungal infections represent a serious global health problem, causing damage to health and the economy on the scale of millions. Although vaccines are the most effective therapeutic approach used to combat infectious agents, at the moment, no fungal vaccine has been approved for use in humans. However, the scientific community has been working hard to overcome this challenge. In this sense, we aim to describe here an update on the development of fungal vaccines and the progress of methodological and experimental immunotherapies against fungal infections. In addition, advances in immunoinformatic tools are described as an important aid by which to overcome the difficulty of achieving success in fungal vaccine development. In silico approaches are great options for the most important and difficult questions regarding the attainment of an efficient fungal vaccine. Here, we suggest how bioinformatic tools could contribute, considering the main challenges, to an effective fungal vaccine.

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.

In-silico design and evaluation of an epitope-based serotype-independent promising vaccine candidate for highly cross-reactive regions of pneumococcal surface protein A

BACKGROUND

The pathogenicity of pneumococcus with high morbidity, mortality, and multi-drug resistance patterns has been increasing. The limited coverage of the licensed polysaccharide-based vaccines and the replacement of the non-vaccine serotypes are the main reasons for producing a successful serotype-independent vaccine. Pneumococcal surface protein A (PspA) is an extremely important virulence factor and an interesting candidate for conserved protein-based pneumococcal vaccine classified into two prominent families containing five clades. PspA family-elicited immunity is clade-dependent, and the level of the PspA cross-reactivity is restricted to the same family.

METHODS

To cover and overcome the clade-dependent immunity of the PspAs in this study, we designed and tested a PspA_1-5c+p vaccine candidate composed of the highest immunodominant coverage of B- and T-cell epitope truncated domain of each clade focusing on two cross-reactive B and C regions of the PspAs. The antigenicity, toxicity, physicochemical properties, 3D structure prediction, stability and flexibility of the designed protein using molecular dynamic (MD) simulation, molecular docking of the construct withHLADRB1*(01:01) and human lactoferrin N-lop, and immune simulation were assessed using immunoinformatics tools. In the experimental section, after intraperitoneal immunization of the mice with Alum adjuvanted recombinant PspA_1-5c+p, we evaluated the immune response, cross-reactivity, and functionality of the Anti-PspA_1-5c+p antibody using ELISA, Opsonophagocytic killing activity, and serum bactericidal assay.

RESULTS

For the first time, this work suggested a novel PspA-based vaccine candidate using immunoinformatics tools. The designed PspA_1-5c+p protein is predicted to be highly antigenic, non-toxic, soluble, stable with low flexibility in MD simulation, and able to stimulate both humoral and cellular immune responses. The designed protein also could interact strongly with HLADRB1*(01:01) and human lactoferrin N-lop in the docking study. Our immunoinformatics predictions were validated using experimental data. Results showed that the anti-PspA_1-5c+p IgG not only had a high titer with strong and same cross-reactivity coverage against all pneumococcal serotypes used but also had high and effective bioactivity for pneumococcal clearance using complement system and phagocytic cells.

CONCLUSION

Our findings elucidated the potential application of the PspA_1-5c+p vaccine candidate as a serotype-independent pneumococcal vaccine with a strong cross-reactivity feature. Further in-vitro and in-vivo investigations against other PspA clades should be performed to confirm the full protection of the PspA_1-5c+p vaccine candidate.

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.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

A peptide derived from HSP60 reduces proinflammatory cytokines and soluble mediators: a therapeutic approach to inflammation

Cytokines are secretion proteins that mediate and regulate immunity and inflammation. They are crucial in the progress of acute inflammatory diseases and autoimmunity. In fact, the inhibition of proinflammatory cytokines has been widely tested in the treatment of rheumatoid arthritis (RA). Some of these inhibitors have been used in the treatment of COVID-19 patients to improve survival rates. However, controlling the extent of inflammation with cytokine inhibitors is still a challenge because these molecules are redundant and pleiotropic. Here we review a novel therapeutic approach based on the use of the HSP60-derived Altered Peptide Ligand (APL) designed for RA and repositioned for the treatment of COVID-19 patients with hyperinflammation. HSP60 is a molecular chaperone found in all cells. It is involved in a wide diversity of cellular events including protein folding and trafficking. HSP60 concentration increases during cellular stress, for example inflammation. This protein has a dual role in immunity. Some HSP60-derived soluble epitopes induce inflammation, while others are immunoregulatory. Our HSP60-derived APL decreases the concentration of cytokines and induces the increase of FOXP3+ regulatory T cells (Treg) in various experimental systems. Furthermore, it decreases several cytokines and soluble mediators that are raised in RA, as well as decreases the excessive inflammatory response induced by SARS-CoV-2. This approach can be extended to other inflammatory diseases.

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.

Structural analysis of PpSP15 and PsSP9 sand fly salivary proteins designed with a self-cleavable linker as a live vaccine candidate against cutaneous leishmaniasis

Background

Leishmania parasites are deposited in the host through sand fly bites along with sand fly saliva. Therefore, salivary proteins are promising vaccine candidates for controlling leishmaniasis. Herein, two immunogenic salivary proteins, PpSP15 from Phlebotomus papatasi and PsSP9 from Phlebotomus sergenti, were selected as vaccine candidates to be delivered by live Leishmania tarentolae as vector. The stepwise in silico protocol advantaged in this study for multi-protein design in L. tarentolae is then described in detail.

Methods

All possible combinations of two salivary proteins, PpSP15 and PsSP9, with or without T2A peptide were designed at the mRNA and protein levels. Then, the best combination for the vaccine candidate was selected based on mRNA and protein stability along with peptide analysis.

Results

At the mRNA level, the most favored secondary structure was PpSP15-T2A-PsSP9. At the protein level, the refined three-dimensional models of all combinations were structurally valid; however, local quality estimation showed that the PpSp15-T2A-PsSP9 fusion had higher stability for each amino acid position, with low root-mean-square deviation (RMSD), compared with the original proteins. In silico evaluation confirmed the PpSP15-T2A-PsSP9 combination as a good Th1-polarizing candidate in terms of high IFN-γ production and low IL-10/TGF-β ratio in response to three consecutive immunizations. Potential protein expression was then confirmed by Western blotting.

Conclusions

The approach presented herein is among the first studies to have privileged protein homology modeling along with mRNA analysis for logical live vaccine design-coding multi-proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05437-x.

Chemoenzymatic Synthesis of SARS-CoV-2 Homogeneous O-Linked Glycopeptides for Exploring Their Inhibition Functions

Harnessing highly conserved peptides derived from the receptor binding domain (RBD) of spike (S) protein to construct peptide-based inhibitors is one of the most effective strategies to fight against the ever-mutating coronavirus SARS-CoV-2. But how the O-glycosylation affects their inhibition abilities has not been intensively explored. Herein, an intrinsic O-glycosylated peptide P_320-334 derived from RBD was screened and homogeneous O-linked glycopeptides containing Tn (GalNAcα1-O-Ser/Thr), T (Galβ1-3GalNAcα1-O-Ser/Thr), sialyl-Tn (sTn, Siaα2-6GalNAcα1-O-Ser/Thr), and sialyl-T (sT, Siaα2-3Galβ1-3GalNAcα1-O-Ser/Thr) structures were first synthesized via chemoenzymatic strategies. Compared with the unglycosylated peptide, the binding of sT-P_320-334 to hACE2 was enhanced to 133% and the inhibition capacity against RBD-hACE2 binding of sTn- and sT-P_320-334 was significantly increased up to 150-410%. Thus, our results suggest the sialic acid residue on the terminal of short O-glycan structures might strengthen the inhibition capacities of these peptide-based inhibitors, which might provide novel optimization directions for the inhibitor design.

Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

A multi-epitope vaccine candidate developed from unique immunogenic epitopes against Cryptosporidium hominis by utilizing an immunoinformatics-driven approach

An immunoinformatics-based strategy is being investigated to identify prospective multi-subunit vaccine candidates against Cryptosporidium hominis (C. hominis). We used a systematic technique based on protein structure to create a competent multi-subunit vaccine candidate against C. hominis, with the likelihood of antigenicity, allergenicity, and transmembrane helices as the screening criteria. Using the suitable linkers, the best-screened epitopes such as B-cell epitopes (BCL), Helper T-lymphocytes (HTL), and cytotoxic T-lymphocytes (CTL) were linked together to intensify and develop the presentation and processing of the antigenic molecules. The greatest 3 D model of the component protein was created with the help of modeling software called Raptorax. The validation of the modeled protein was accomplished via the use of PROCHECK. Furthermore, using the ClusPro web server, the projected modeled structure was docked with known receptor TLR-4 to determine their interactions. A molecular dynamics (MD) simulation was used to investigate the stability of the multi-subunit vaccine bound with TLR-4 based on the docking score. Aside from that, the codon optimization and in silico expression demonstrate the possibility of high expression and simple purification of the vaccine product resulting from codon optimization. The overall findings indicated that the multi-subunit vaccine might be a viable vaccination candidate against C. hominis.Communicated by Ramaswamy H. Sarma.

Comparative Immunoreactivity Analyses of Hantaan Virus Glycoprotein-Derived MHC-I Epitopes in Vaccination

MHC-I antigen processes and presentation trigger host-specific anti-viral cellular responses during infection, in which epitope-recognizing cytotoxic T lymphocytes eliminate infected cells and contribute to viral clearance through a cytolytic killing effect. In this study, Hantaan virus (HTNV) GP-derived 9-mer dominant epitopes were obtained with high affinity to major HLA-I and H-2 superfamilies. Further immunogenicity and conservation analyses selected 11 promising candidates, and molecule docking (MD) was then simulated with the corresponding MHC-I alleles. Two-way hierarchical clustering revealed the interactions between GP peptides and MHC-I haplotypes. Briefly, epitope hotspots sharing good affinity to a wide spectrum of MHC-I molecules highlighted the biomedical practice for vaccination, and haplotype clusters represented the similarities among individuals during T-cell response establishment. Cross-validation proved the patterns observed through both MD simulation and public data integration. Lastly, 148 HTNV variants yielded six types of major amino acid residue replacements involving four in nine hotspots, which minimally influenced the general potential of MHC-I superfamily presentation. Altogether, our work comprehensively evaluates the pan-MHC-I immunoreactivity of HTNV GP through a state-of-the-art workflow in light of comparative immunology, acknowledges present discoveries, and offers guidance for ongoing HTNV vaccine pursuit.

Dual-Function Semaphorin 4D Released by Platelets: Suppression of Osteoblastogenesis and Promotion of Osteoclastogenesis

Effects of the antiosteoblastogenesis factor Semaphorin 4D (Sema4D), expressed by thrombin-activated platelets (TPs), on osteoblastogenesis, as well as osteoclastogenesis, were investigated in vitro. Intact platelets released both Sema4D and IGF-1. However, in response to stimulation with thrombin, platelets upregulated the release of Sema4D, but not IGF-1. Anti-Sema4D-neutralizing monoclonal antibody (mAb) upregulated TP-mediated osteoblastogenesis in MC3T3-E1 osteoblast precursors. MC3T3-E1 cells exposed to TPs induced phosphorylation of Akt and ERK further upregulated by the addition of anti-sema4D-mAb, suggesting the suppressive effects of TP-expressing Sema4D on osteoblastogenesis. On the other hand, TPs promoted RANKL-mediated osteoclastogenesis in the primary culture of bone-marrow-derived mononuclear cells (BMMCs). Among the known three receptors of Sema4D, including Plexin B1, Plexin B2 and CD72, little Plexin B2 was detected, and no Plexin B1 was detected, but a high level of CD72 mRNA was detected in RANKL-stimulated BMMCs by qPCR. Both anti-Sema4D-mAb and anti-CD72-mAb suppressed RANKL-induced osteoclast formation and bone resorptive activity, suggesting that Sema4D released by TPs promotes osteoclastogenesis via ligation to a CD72 receptor. This study demonstrated that Sema4D released by TPs suppresses osteogenic activity and promotes osteoclastogenesis, suggesting the novel property of platelets in bone-remodeling processes.

In silico analyses and experimental validation of the MHC class-I restricted epitopes of Ebolavirus GP

Ebolavirus (EBOV) causes an extremely high mortality and prevalence disease called Ebola virus disease (EVD). There is only one glycoprotein (GP) on the virus particle surface, which mediates entry into the host cell. MHC class-I restricted CD8 + T cell responses are important antiviral immune responses. Therefore, it is of great importance to understand EBOV GP-specific MHC class-I restricted epitopes within immunogenicity. In this study, computational approaches were employed to predict the dominant MHC class-I molecule epitopes of EBOV GP for mouse H2 and major alleles of HLA class-I supertypes. Our results yielded 42 dominant epitopes in H2 haplotypes and 301 dominant epitopes in HLA class-I haplotypes. After validation by ELISpot assay, in-depth analyses to ascertain their nature of conservation, immunogenicity, and docking with the corresponding MHC class-I molecules were undertaken. Our study predicted MHC class-I restricted epitopes that may aid the advancement of anti-EBOV immune responses. And the integrated strategy of epitope prediction, validation, and comparative analyses were postulated, promising for epitope-based immunotherapy development and application to viral epidemics.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

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.

Molecular Basis for Global Incidence of Pemphigoid Diseases and Differences in Phenotypes

Pemphigoid (Pg) diseases are a group of potentially fatal autoimmune mucocutaneous diseases. They have different clinical phenotypes, involving only the skin or multiple mucous membranes. They occur globally and frequently affect the elderly. The common marker among all variants is the presence of autoantibodies targeting the dermal-epidermal or mucosal-submucosal junctions, or basement membrane zone (BMZ). Four target antigens in the BMZ were studied. These included BPAG1, BPAG2 and subunits of α6 and β4 human integrins. Our objective was to find a molecular basis for the global incidence of Pg diseases and a mechanism that will explain the vast differences in clinical phenotypes and outcomes. All the variants of Pg that were analyzed had a statistically significant association with HLA-DQβ1*03:01 in ten countries on four continents. This explains the reason for global incidence. Prediction models discovered multiple peptides in each of the four antigens that serve as T cell epitopes. These T cell epitopes were shown to bind to HLA-DQβ1*03:01. In addition, structure modelling demonstrated the peptide-HLA complex bound to the T cell receptor. These autoreactive T cells would stimulate B cells to produce specific anti-BMZ autoantibodies. Anti-BMZ autoantibodies with different specificities will produce different phenotypes, which will account for involvement of different tissues and organs in different molecules. The contribution this study makes is that it provides a molecular basis of why a similar disease occurs in different racial groups. Furthermore, it provides the basis for the production of autoantibodies with different specificities, which resultantly produces different phenotypes.

Prediction of Potential Drug Targets and Vaccine Candidates Against Antibiotic-Resistant Pseudomonas aeruginosa

Pseudomonas aeruginosa is one of the leading causes of nosocomial infections, characterized by increasing antibiotic resistance, severity and mortality. Therefore, numerous efforts have been made nowadays to identify new therapeutic targets. This study aimed to find potential drug targets and vaccine candidates in drug-resistant strains of P. aeruginosa. Extensive antibiotic-resistant and carbapenem-resistant strains of P. aeruginosa with complete genome were selected and ten common hypothetical proteins (HPs) containing more than 200 amino acids were obtained. The structural, functional and immunological predictions of these HPs were performed with the utility of bioinformatics approaches. Two common HPs (Gene ID: 2877781645 and 2877781936) among other investigated proteins were revealed as potential candidates for pharmaceutical and vaccine purposes based on structural and physicochemical properties, functional domains, subcellular localizations, signal peptides, toxicity, virulence factor, antigenicity, allergenicity and immunoinformatic predictions. The consequence of this predictive study will assist in novel drug and vaccine design through experimental investigations.

Epitope-Based Vaccine of a Brucella abortus Putative Small RNA Target Induces Protection and Less Tissue Damage in Mice

Brucella spp. are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Currently available live attenuated vaccines against brucellosis still have drawbacks. Therefore, subunit vaccines, produced using epitope-based antigens, have the advantage of being safe, cost-effective and efficacious. Here, we identified B. abortus small RNAs expressed during early infection with bone marrow-derived macrophages (BMDMs) and an apolipoprotein N-acyltransferase (Int) was identified as the putative target of the greatest expressed small RNA. Decreased expression of Int was observed during BMDM infection and the protein sequence was evaluated to rationally select a putative immunogenic epitope by immunoinformatic, which was explored as a vaccinal candidate. C57BL/6 mice were immunized and challenged with B. abortus, showing lower recovery in the number of viable bacteria in the liver, spleen, and axillary lymph node and greater production of IgG and fractions when compared to non-vaccinated mice. The vaccinated and infected mice showed the increased expression of TNF-α, IFN-γ, and IL-6 following expression of the anti-inflammatory genes IL-10 and TGF-β in the liver, justifying the reduction in the number and size of the observed granulomas. BMDMs stimulated with splenocyte supernatants from vaccinated and infected mice increase the CD86+ marker, as well as expressing greater amounts of iNOS and the consequent increase in NO production, suggesting an increase in the phagocytic and microbicidal capacity of these cells to eliminate the bacteria.

Computational Approach for Predicting Common Epitopes in the VP1 Structural Protein of Enterovirus Serotypes EV-D68, EV-D70, and EV-A71

The three human Enterovirus serotypes D-68, D-70, and A-71, are common pathogens that are transmitted by fecal-oral and aerosol routes. These positive RNA viruses were known to exhibit high levels of genetic diversity and variability. Currently, no vaccines are available to protect humans from these three serotypes. Therefore, efforts are needed for the development of a vaccine directed against heterologous viruses. In our study, an immunoinformatics approach is used to identify T- and B-cell epitopes that may help for the generation of a universal vaccine against EV-D70, EV-A71, and EV-D68. B and T cell epitopes were selected based on their length. As a result, 5 B cell epitopes and 18 T cell epitopes were predicted. Our B cell epitope prediction results showed that there are a number of linear regions. Position 150-170 was found to be the most immunogenic for the different strains. Regarding the epitopes of the T lymphocytes, the result of the interactions shows that 95% of the predicted epitopes are common between the 3 sequences and the 5 methods used. These results demonstrate the great immunogenic potential of these sequences and their capacities to trigger immune reactions in people with different HLA alleles. The “VFYDGFAGF” epitope is the most important and most immunogenic for triggering an immune response. Our study results allowed us to identify epitopes to be used in the development of cross-protection vaccines against the three Enterovirus serotypes. However, in vivo and in vitro studies are needed to assess the potential of the epitopes predicted by our study.

In Silico Identification of Chikungunya Virus B- and T-Cell Epitopes with High Antigenic Potential for Vaccine Development

Reverse vaccinology is an outstanding strategy to identify antigens with high potential for vaccine development. Different parameters of five prediction programs were used to assess their sensitivity and specificity to identify B-cell epitopes of Chikungunya virus (CHIKV) strains reported in the IEDB database. The results, based on the use of 15 to 20 mer epitopes and the polyproteins to which they belong, were compared to establish the best parameters to optimize the prediction of antigenic peptides of the Mexican strain CHIKV AJV21562.1. LBtope showed the highest specificity when we used the reported epitopes and polyproteins but the worst sensitivity with polyproteins; ABCpred had similar specificity to LBtope only with the epitopes reported and showed moderate specificity when we used polyproteins for the predictions. Because LBtope was more reliable in predicting true epitopes, it was used as a reference program to predict and select six novel epitopes of the Mexican strain of CHIKV according to prediction frequency, viral genome localization, and non-homology with the human proteome. On the other hand, six bioinformatics programs were used with default parameters to predict T-cell epitopes in the CHIKV strains AJV21562.1 and AJV21561.1. The sequences of the polyproteins were analyzed to predict epitopes present in the more frequent HLA alleles of the Mexican population: DQA1*03011, DQA1*0401, DQA1*0501, DQB1*0201, DQB1*0301, DQB1*0302, and DQB1*0402. Fifteen predicted epitopes in the non-structural and 15 predicted epitopes in the structural polyprotein (9- to 16-mers) with the highest scores of each allele were compared to select epitopes with at least 80% identity. Next, the epitopes predicted with at least two programs were aligned to the human proteome, and 12 sequences without identity with the human proteome were identified as potential antigenic candidates. This strategy would be useful to evaluate vaccine candidates against other viral diseases affecting the countries of the Americas and to increase knowledge about these diseases.

BCEPS: A Web Server to Predict Linear B Cell Epitopes with Enhanced Immunogenicity and Cross-Reactivity

Prediction of linear B cell epitopes is of interest for the production of antigen-specific antibodies and the design of peptide-based vaccines. Here, we present BCEPS, a web server for predicting linear B cell epitopes tailored to select epitopes that are immunogenic and capable of inducing cross-reactive antibodies with native antigens. BCEPS implements various machine learning models trained on a dataset including 555 linearized conformational B cell epitopes that were mined from antibody–antigen protein structures. The best performing model, based on a support vector machine, reached an accuracy of 75.38% ± 5.02. In an independent dataset consisting of B cell epitopes retrieved from the Immune Epitope Database (IEDB), this model achieved an accuracy of 67.05%. In BCEPS, predicted epitopes can be ranked according to properties such as flexibility, accessibility and hydrophilicity, and with regard to immunogenicity, as judged by their predicted presentation by MHC II molecules. BCEPS also detects if predicted epitopes are located in ectodomains of membrane proteins and if they possess N-glycosylation sites hindering antibody recognition. Finally, we exemplified the use of BCEPS in the SARS-CoV-2 Spike protein, showing that it can identify B cell epitopes targeted by neutralizing antibodies.

Characterization of Conserved and Promiscuous Human Rhinovirus CD4 T Cell Epitopes

Human rhinovirus (RV) is the most common cause of upper respiratory infections and exacerbations of asthma. In this work, we selected 14 peptides (6 from RV A and 8 from RV C) encompassing potential CD4 T cell epitopes. Peptides were selected for being highly conserved in RV A and C serotypes and predicted to bind to multiple human leukocyte antigen class II (HLA II) molecules. We found positive T cell recall responses by interferon gamma (IFNγ)-ELISPOT assays to eight peptides, validating seven of them (three from RV A and four from RV C) as CD4 T cell epitopes through intracellular cytokine staining assays. Additionally, we verified their promiscuous binding to multiple HLA II molecules by quantitative binding assays. According to their experimental HLA II binding profile, the combination of all these seven epitopes could be recognized by >95% of the world population. We actually determined IFNγ responses to a pool encompassing these CD4 T cell epitopes by intracellular cytokine staining, finding positive responses in 29 out of 30 donors. The CD4 T cell epitopes identified in this study could be key to monitor RV infections and to develop peptide-based vaccines against most RV A and C serotypes.

Identification of a novel calcium activated potassium channel from Leishmania donovani and in silico predictions of its antigenic features

Visceral Leishmaniasis is a major neglected tropical disease with increasing incidences of drug resistance. This has led to the search for a suitable drug target for chemotherapeutic intervention. Potassium channels are a family of membrane proteins which play a vital role in homeostasis and any perturbation in them alters cell survival which makes them an attractive target. To characterize a calcium-activated potassium channel from Leishmania donovani (LdK_Ca), a putative ion-channel like protein which showed sequence similarity with other Trypanosoma cruzi putative potassium channels was selected. It was cloned and expressed with a histidine tag. MALDI confirmed that it is a potassium channel. Homology model of LdK_Ca was generated by I-TASSER. It is a transmembrane protein localized in the plasma membrane as predicted by DeepLoc tool. In silico analyses of the protein showed that it is a small conductance calcium activated potassium channel. Point mutation in conserved signature domain 'TXGYGD' affects the protein function as predicted by heat map analysis. The LdK_Ca model predicted amino acids S207, T208 and M236 as ligand-binding sites. The sequence HSLRGRSARVIQLAWRLRKARKVGPHAPSLKQKVYTLVLSWLLT was the highest scoring B-cell epitope. The highest scoring T-cell epitope was RLYSVIVYL. This study may provide new insights into antigenicity features of leishmanial calcium-activated potassium channels.

Integrative Analysis of HTNV Glycoprotein Derived MHC II Epitopes by In Silico Prediction and Experimental Validation

Hantaan virus (HTNV), the causative pathogen of hemorrhagic fever with renal syndrome (HFRS), is a negative RNA virus belonging to the Orthohantaviridae family. HTNV envelope glycoprotein (GP), encoded by the genomic medium segment, is immunogenic and is therefore a promising vaccine candidate. Major histocompatibility complex class I (MHC-I) epitopes derived from HTNV has been extensively studied, but little is known of MHC-II epitopes. In silico predictions based on four databases indicated that the full-length HTNV GP has 1121 15-mer epitopes, of which 289 had a high score for binding to the human and murine MHC-II superfamily. It found that epitope ILTVLKFIANIFHTS could potentially bind most MHC-II molecules covering human and murine haplotypes. Dominant epitopes were validated by enzyme-linked immunospot assay of splenocytes from immunized mice; 6 of 10 epitopes supported the predictions including TATYSIVGPANAKVP, TKTLVIGQCIYTITS, FSLLPGVAHSIAVEL, CETYKELKAHGVSCP, CGLYLDRLKPVGSAY, and NLGENPCKIGLQTSS. Conservation analysis of dominant epitopes revealed host–virus interactions without geographic stratification, thus meeting the requirements of candidate vaccines for large-population prophylaxis. These findings provide insight into hantavirus antigenicity and suggest that vaccines targeting MHC-II could provide immune protection in large population to complement symptomatic therapies for the treatment of HFRS.

Immunogenicity and efficacy of the COVID-19 candidate vector vaccine MVA-SARS-2-S in preclinical vaccination

The highly attenuated vaccinia virus MVA is licensed as smallpox vaccine; as a vector it is a component of the approved adenovirus-MVA–based prime-boost vaccine against Ebola virus disease. Here, we provide results from testing the COVID-19 candidate vaccine MVA-SARS-2-S, a poxvirus-based vector vaccine that proceeded to clinical evaluation. When administered by intramuscular inoculation, MVA-SARS-2-S expresses and safely delivers the full-length SARS-CoV-2 S protein, inducing balanced SARS-CoV-2–specific cellular and humoral immunity, and protective efficacy in vaccinated mice. Substantial clinical experience has been gained with MVA vectors using homologous and heterologous prime-boost applications, including the immunization of children and immunocompromised individuals. Thus, MVA-SARS-2-S represents an important resource for developing further optimized COVID-19 vaccines.

Severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) has emerged as the infectious agent causing the pandemic coronavirus disease 2019 (COVID-19) with dramatic consequences for global human health and economics. Previously, we reached clinical evaluation with our vector vaccine based on modified vaccinia virus Ankara (MVA) against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes an infection in humans similar to SARS and COVID-19. Here, we describe the construction and preclinical characterization of a recombinant MVA expressing full-length SARS-CoV-2 spike (S) protein (MVA-SARS-2-S). Genetic stability and growth characteristics of MVA-SARS-2-S, plus its robust expression of S protein as antigen, make it a suitable candidate vaccine for industrial-scale production. Vaccinated mice produced S-specific CD8⁺ T cells and serum antibodies binding to S protein that neutralized SARS-CoV-2. Prime-boost vaccination with MVA-SARS-2-S protected mice sensitized with a human ACE2-expressing adenovirus from SARS-CoV-2 infection. MVA-SARS-2-S is currently being investigated in a phase I clinical trial as aspirant for developing a safe and efficacious vaccine against COVID-19.

T Cell Specificity: A Great Challenge in Chagas Disease

The CD4⁺ and CD8⁺ T cell immune response against T. cruzi, the parasite causing Chagas disease, are relevant for both parasite control and disease pathogenesis. Several studies have been focused on their phenotype and functionally, but only a few have drilled down to identify the parasite proteins that are processed and presented to these cells, especially to CD4⁺ T lymphocytes. Although approximately 10,000 proteins are encoded per haploid T. cruzi genome, fewer than 200 T cell epitopes from 49 T. cruzi proteins have been identified so far. In this context, a detailed knowledge of the specific targets of T cell memory response emerges as a prime tool for the conceptualization and development of prophylactic or therapeutic vaccines, an approach with great potential to prevent and treat this chronic disease. Here, we review the available information about this topic in a comprehensive manner and discuss the future challenges in the field.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Mapping and role of T cell response in SARS-CoV-2-infected mice

Virus-specific T cells play essential roles in protection against multiple virus infections, including SARS-CoV and MERS-CoV. While SARS-CoV-2-specific T cells have been identified in COVID-19 patients, their role in the protection of SARS-CoV-2-infected mice is not established. Here, using mice sensitized for infection with SARS-CoV-2 by transduction with an adenovirus expressing the human receptor (Ad5-hACE2), we identified SARS-CoV-2-specific T cell epitopes recognized by CD4+ and CD8+ T cells in BALB/c and C57BL/6 mice. Virus-specific T cells were polyfunctional and were able to lyse target cells in vivo. Further, type I interferon pathway was proved to be critical for generating optimal antiviral T cell responses after SARS-CoV-2 infection. T cell vaccination alone partially protected SARS-CoV-2-infected mice from severe disease. In addition, the results demonstrated cross-reactive T cell responses between SARS-CoV and SARS-CoV-2, but not MERS-CoV, in mice. Understanding the role of the T cell response will guide immunopathogenesis studies of COVID-19 and vaccine design and validation.

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.