A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

A Multi-epitope Vaccine Candidate Against Bolivian Hemorrhagic fever Caused by Machupo Virus

Bolivian hemorrhagic fever (BHF) caused by Machupo virus (MACV) is a New World arenavirus having a reported mortality rate of 25-35%. The BHF starts with fever, followed by headache, and nausea which rapidly progresses to severe hemorrhagic phase within 7 days of disease onset. One of the key promoters for MACV viral entry into the cell followed by viral propagation is performed by the viral glycoprotein (GPC). GPC is post-transcriptionally cleaved into GP1, GP2 and a signal peptide. These proteins all take part in the viral infection in host body. Therefore, GPC protein is an ideal target for developing therapeutics against MACV infection. In this study, GPC protein was considered to design a multi-epitope, multivalent vaccine containing antigenic and immunogenic CTL and HTL epitopes. Different structural validations and physicochemical properties were analysed to validate the vaccine. Docking and molecular dynamics simulations were conducted to understand the interactions of the vaccine with various immune receptors. Finally, the vaccine was codon optimised in silico and along with which immune simulation studies was performed in order to evaluate the vaccine's effectiveness in triggering an efficacious immune response against MACV.

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.

Immunoinformatic Approach for Rational Identification of Immunogenic Peptides Against Host Entry and/or Exit Mpox Proteins and Potential Multiepitope Vaccine Construction

COVID-19 has intensified humanity's concern about the emergence of new pandemics. Since 2018, epidemic outbreaks of the mpox virus have become worrisome. In June 2022, the World Health Organization declared the disease a global health emergency, with 14 500 cases reported by the Centers for Disease Control and Prevention in 60 countries. Therefore, the development of a vaccine based on the current virus genome is paramount in combating new cases. In view of this, we hypothesized the obtainment of rational immunogenic peptides predicted from proteins responsible for entry of the mpox virus into the host (A17L, A26L/A30L, A33R, H2R, L1R), exit (A27L, A35R, A36R, C19L), and both (B5R). To achieve this, we aligned the genome sequencing data of mpox virus isolated from an infected individual in the United States in June 2022 (ON674051.1) with the reference genome dated 2001 (NC_003310.1) for conservation analysis. The Immune Epitope Database server was used for the identification and characterization of the epitopes of each protein related to major histocompatibility complex I or II interaction and recognition by B-cell receptors, resulting in 138 epitopes for A17L, 233 for A28L, 48 for A33R, 77 for H2R, 77 for L1R, 270 for A27L, 72 for A35R, A36R, 148 for C19L, and 276 for B5R. These epitopes were tested in silico for antigenicity, physicochemical properties, and allergenicity, resulting in 51, 40, 10, 34, 38, 57, 25, 7, 47, and 53 epitopes, respectively. Additionally, to select an epitope with the highest promiscuity of binding to major histocompatibility complexes and B-cell receptor simultaneously, all epitopes of each protein were aligned, and the most repetitive and antigenic regions were identified. By classifying the results, we obtained 23 epitopes from the entry proteins, 16 from the exit proteins, and 7 from both. Subsequently, 1 epitope from each protein was selected, and all 3 were fused to construct a chimeric protein that has potential as a multiepitope vaccine. The constructed vaccine was then analyzed for its physicochemical, antigenic, and allergenic properties. Protein modeling, molecular dynamics, and molecular docking were performed on Toll-like receptors 2, 4, and 8, followed by in silico immune simulation of the vaccine. Finally, the results indicate an effective, stable, and safe vaccine that can be further tested, especially in vitro and in vivo, to validate the findings demonstrated in silico.

Proteomics-based vaccine targets annotation and design of multi-epitope vaccine against antibiotic-resistant Streptococcus gallolyticus

Streptococcus gallolyticus is a non-motile, gram-positive bacterium that causes infective endocarditis. S. gallolyticus has developed resistance to existing antibiotics, and no vaccine is currently available. Therefore, it is essential to develop an effective S. gallolyticus vaccine. Core proteomics was used in this study together with subtractive proteomics and reverse vaccinology approach to find antigenic proteins that could be utilized for the design of the S. gallolyticus multi-epitope vaccine. The pipeline identified two antigenic proteins as potential vaccine targets: penicillin-binding protein and the ATP synthase subunit. T and B cell epitopes from the specific proteins were forecasted employing several immunoinformatics and bioinformatics resources. A vaccine (360 amino acids) was created using a combination of seven cytotoxic T cell lymphocyte (CTL), three helper T cell lymphocyte (HTL), and five linear B cell lymphocyte (LBL) epitopes. To increase immune responses, the vaccine was paired with a cholera enterotoxin subunit B (CTB) adjuvant. The developed vaccine was highly antigenic, non-allergenic, and stable for human use. The vaccine's binding affinity and molecular interactions with the human immunological receptor TLR4 were studied using molecular mechanics/generalized Born surface area (MMGBSA), molecular docking, and molecular dynamic (MD) simulation analyses. Escherichia coli (strain K12) plasmid vector pET-28a ( +) was used to examine the ability of the vaccine to be expressed. According to the outcomes of these computer experiments, the vaccine is quite promising in terms of developing a protective immunity against diseases. However, in vitro and animal research are required to validate our findings.

Revolutionizing and identifying novel drug targets in Citrobacter koseri via subtractive proteomics and development of a multi-epitope vaccine using reverse vaccinology and immuno-informatics

Citrobacter koseri is a gram-negative rod that has been linked to infections in people with significant comorbidities and immunocompromised immune systems. It is most commonly known to cause urinary tract infections. Thus, the development of an efficacious C. koseri vaccine is imperative, as the pathogen has acquired resistance to current antibiotics. Subtractive proteomics was employed during this research to identify potential antigenic proteins to design an effective vaccine against C. koseri. The pipeline identified two antigenic proteins as potential vaccine targets: DP-3-O-acyl-N-acetylglucosamine deacetylase and Arabinose 5-phosphate isomerase. B and T cell epitopes from the specific proteins were forecasted employing several immunoinformatic and bioinformatics resources. A vaccine was created using a combination of seven cytotoxic T cell lymphocytes (CTL), five helper T cell lymphocyte (HTL), and seven linear B cell lymphocyte (LBL) epitopes. An adjuvant (β-defensin) was added to the vaccine to enhance immunological responses. The created vaccine was stable for use in humans, highly antigenic, and non-allergenic. The vaccine's molecular and interactions binding affinity with the human immunological receptor TLR3 were studied using MMGBSA, molecular dynamics (MD) simulations, and molecular docking analyses. E. coli (strain-K12) plasmid vector pET-28a (+) was used to examine the ability of the vaccine to be expressed. The vaccine shows great promise in terms of developing protective immunity against diseases, based on the results of these computer experiments. However, in vitro and animal research are required to validate our findings.Communicated by Ramaswamy H. Sarma.

Development of a novel multi‑epitope vaccine against the pathogenic human polyomavirus V6/7 using reverse vaccinology

BACKGROUND

Human polyomaviruses contribute to human oncogenesis through persistent infections, but currently there is no effective preventive measure against the malignancies caused by this virus. Therefore, the development of a safe and effective vaccine against HPyV is of high priority.

METHODS

First, the proteomes of 2 polyomavirus species (HPyV6 and HPyV7) were downloaded from the NCBI database for the selection of the target proteins. The epitope identification process focused on selecting proteins that were crucial, associated with virulence, present on the surface, antigenic, non-toxic, and non-homologous with the human proteome. Then, the immunoinformatic methods were used to identify cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes from the target antigens, which could be used to create epitope-based vaccine. The physicochemical features of the designed vaccine were predicted through various online servers. The binding pattern and stability between the vaccine candidate and Toll-like receptors were analyzed through molecular docking and molecular dynamics (MD) simulation, while the immunogenicity of the designed vaccines was assessed using immune simulation.

RESULTS

Online tools were utilized to forecast the most optimal epitope from the immunogenic targets, including LTAg, VP1, and VP1 antigens of HPyV6 and HPyV7. A multi-epitope vaccine was developed by combining 10 CTL, 7 HTL, and 6 LBL epitopes with suitable linkers and adjuvant. The vaccine displayed 98.35% of the world's population coverage. The 3D model of the vaccine structure revealed that the majority of residues (87.7%) were located in favored regions of the Ramachandran plot. The evaluation of molecular docking and MD simulation revealed that the constructed vaccine exhibits a strong binding (-1414.0 kcal/mol) towards the host's TLR4. Moreover, the vaccine-TLR complexes remained stable throughout the dynamic conditions present in the natural environment. The immune simulation results demonstrated that the vaccine design had the capacity to elicit robust immune responses in the host.

CONCLUSION

The multi-parametric analysis revealed that the designed vaccine is capable of inducing sustained immunity against the selected polyomaviruses, although further in-vivo investigations are needed to verify its effectiveness.

Immunoinformatics-guided approach for designing a pan-proteome multi-epitope subunit vaccine against African swine fever virus

Despite being identified over a hundred years ago, there is still no commercially available vaccine for the highly contagious and deadly African swine fever virus (ASFV). This study used immunoinformatics for the rapid and inexpensive designing of a safe and effective multi-epitope subunit vaccine for ASFV. A total of 18,858 proteins from 100 well-annotated ASFV proteomes were screened using various computational tools to identify potential epitopes, or peptides capable of triggering an immune response in swine. Proteins from genotypes I and II were prioritized for their involvement in the recent global ASFV outbreaks. The screened epitopes exhibited promising qualities that positioned them as effective components of the ASFV vaccine. They demonstrated antigenicity, immunogenicity, and cytokine-inducing properties indicating their ability to induce potent immune responses. They have strong binding affinities to multiple swine allele receptors suggesting a high likelihood of yielding more amplified responses. Moreover, they were non-allergenic and non-toxic, a crucial prerequisite for ensuring safety and minimizing any potential adverse effects when the vaccine is processed within the host. Integrated with an immunogenic 50S ribosomal protein adjuvant and linkers, the epitopes formed a 364-amino acid multi-epitope subunit vaccine. The ASFV vaccine construct exhibited notable immunogenicity in immune simulation and molecular docking analyses, and stable profiles in secondary and tertiary structure assessments. Moreover, this study designed an optimized codon for efficient translation of the ASFV vaccine construct into the Escherichia coli K-12 expression system using the pET28a(+) vector. Overall, both sequence and structural evaluations suggested the potential of the ASFV vaccine construct as a candidate for controlling and eradicating outbreaks caused by the pathogen.

Designing a multi-epitope candidate vaccine by employing immunoinformatics approaches to control African swine fever spread

The African swine fever virus has been circulating for decades and is highly infectious, often fatal to farmed and wild pigs. There is currently no approved vaccine or treatment for the disease, making prevention even more difficult. Therefore, vaccine development is necessary and urgent to limit the consequences of ASF and ensure the food chain and sustainability of the swine industry. This research study was conducted to design a multi-epitope vaccine for controlling veterinary diseases caused by the African swine fever virus. We employed the immunoinformatics approaches to reveal 37 epitopes from different viral proteins of ASFV. These epitopes were linked to adjuvants and linkers to form a full-fledged immunogenic vaccine construct. The tertiary structure of the final vaccine was predicted using a deep-learning approach. The molecular docking and molecular dynamics predicted stable interactions between the vaccine and immune receptor TLR5 of Sus scrofa (Pig). The MD simulation studies reflect that the calculated parameters like RMSD, RMSF, number of hydrogen bonds, and finally, the buried interface surface area for the complex remained stable throughout the simulation time. This analysis suggests the stability of interface interactions between the TLR5 and the multi-epitope vaccine construct. Further, the physiochemical analysis demonstrated that our designed vaccine construct was expected to have high stability and prolonged half-life time in mammalian cells. Traditional vaccine design experiments require significant time and financial input from the development stage to the final product. Studies like this can assist in accelerating vaccine development while minimizing the cost.Communicated by Ramaswamy H. Sarma.

Identification and In Silico Characterization of a Conserved Peptide on Influenza Hemagglutinin Protein: A New Potential Antigen for Universal Influenza Vaccine Development

Antigenic changes in surface proteins of the influenza virus may cause the emergence of new variants that necessitate the reformulation of influenza vaccines every year. Universal influenza vaccine that relies on conserved regions can potentially be effective against all strains regardless of any antigenic changes and as a result, it can bring enormous public health impact and economic benefit worldwide. Here, a conserved peptide (HA288-107) on the stalk domain of hemagglutinin glycoprotein is identified among highly pathogenic influenza viruses. Five top-ranked B-cell and twelve T-cell epitopes were recognized by epitope mapping approaches and the corresponding Human Leukocyte Antigen alleles to T-cell epitopes showed high population coverage (>99%) worldwide. Moreover, molecular docking analysis indicated that VLMENERTL and WTYNAELLV epitopes have high binding affinity to the antigen-binding groove of the HLA-A*02:01 and HLA-A*68:02 molecules, respectively. Theoretical physicochemical properties of the peptide were assessed to ensure its thermostability and hydrophilicity. The results suggest that the HA288-107 peptide can be a promising antigen for universal influenza vaccine design. However, in vitro and in vivo analyses are needed to support and evaluate the effectiveness of the peptide as an immunogen for vaccine development.

Using a novel structure/function approach to select diverse swine major histocompatibility complex 1 alleles to predict epitopes for vaccine development

MOTIVATION

Swine leukocyte antigens (SLAs) (i.e. swine major histocompatibility complex proteins) conduct a fundamental role in swine immunity. To generate a protective vaccine across an outbred species, such as pigs, it is critical that epitopes that bind to diverse SLA alleles are used in the vaccine development process. We introduced a new strategy for epitope prediction.

RESULTS

We employed molecular dynamics simulation to identify key amino acids for interactions with epitopes. We developed an algorithm wherein each SLA-1 is compared to a crystalized reference allele with unique weighting for non-conserved amino acids based on R group and position. We then performed homology modeling and electrostatic contact mapping to visualize how relatively small changes in sequences impacted the charge distribution in the binding site. We selected eight diverse SLA-1 alleles and performed homology modeling followed, by protein-peptide docking and binding affinity analyses, to identify porcine reproductive and respiratory syndrome virus matrix protein epitopes that bind with high affinity to these alleles. We also performed docking analysis on the epitopes identified as strong binders using NetMHCpan 4.1. Epitopes predicted to bind to our eight SLA-1 alleles had equivalent or higher energetic interactions than those predicted to bind to the NetMHCpan 4.1 allele repertoire. This approach of selecting diverse SLA-1 alleles, followed by homology modeling, and docking simulations, can be used as a novel strategy for epitope prediction that complements other available tools and is especially useful when available tools do not offer a prediction for SLAs/major histocompatibility complex.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the online Supplementary Material.

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.

Cross-reactive MHC class I T cell epitopes may dictate heterologous immune responses between respiratory viruses and food allergens

Respiratory virus infections play a major role in asthma, while there is a close correlation between asthma and food allergy. We hypothesized that T cell-mediated heterologous immunity may induce asthma symptoms among sensitized individuals and used two independent in silico pipelines for the identification of cross-reactive virus- and food allergen- derived T cell epitopes, considering individual peptide sequence similarity, MHC binding affinity and immunogenicity. We assessed the proteomes of human rhinovirus (RV1b), respiratory syncytial virus (RSVA2) and influenza-strains contained in the seasonal quadrivalent influenza vaccine 2019/2020 (QIV 2019/2020), as well as SARS-CoV-2 for human HLA alleles, in addition to more than 200 most common food allergen protein sequences. All resulting allergen-derived peptide candidates were subjected to an elaborate scoring system considering multiple criteria, including clinical relevance. In both bioinformatics approaches, we found that shortlisted peptide pairs that are potentially binding to MHC class II molecules scored up to 10 × lower compared to MHC class I candidate epitopes. For MHC class I food allergen epitopes, several potentially cross-reactive peptides from shrimp, kiwi, apple, soybean and chicken were identified. The shortlisted set of peptide pairs may be implicated in heterologous immune responses and translated to peptide immunization strategies with immunomodulatory properties.

BMX-A and BMX-S: Accessible cell-free methods to estimate peptide-MHC-I affinity and stability

The affinity and stability of peptide binding to Major Histocompatibility Complex Class I (MHC-I) molecules are fundamental parameters that underpin the specificity and magnitude of CD8+ T cell responses. These parameters can be estimated in some cases by computational tools, but experimental validation remains valuable, especially for stability. Methods to measure peptide binding can be broadly categorised into either cell-based assays using TAP-deficient cell lines such as RMA/S, or cell-free strategies, such as peptide competition-binding assays and surface plasmon resonance. Cell-based assays are subject to confounding biological activity, including peptide trimming by peptidases and dilution of peptide-loaded MHC-I on the surface of cells through cell division. Current cell-free methods require in-house production and purification of MHC-I. In this study, we present the development of new cell-free assays to estimate the relative affinity and dissociation kinetics of peptide binding to MHC-I. These assays, which we have called BMX-A (relative affinity) and BMX-S (kinetic stability), are reliable, scalable and accessible, in that they use off-the-shelf commercial reagents and standard flow cytometry techniques.

Computational analysis of immunogenic epitopes in the p30 and p54 proteins of African swine fever virus

African swine fever (ASF) is a highly infectious viral disease of pigs, which causes acute fatal haemorrhage and is a severe concern to the global pork industry. The present study followed computational approaches to identify B- and T-cell epitopes for the p30 and p54 proteins of the African swine fever virus (ASFV) by interacting with the swine leukocyte antigen (SLA) alleles. The amino acid sequences of p30 and p54 were analysed for variability and relative solvent accessibility, and their three-dimensional structures were predicted and validated. Molecular dynamics simulation was performed to study the structural and dynamic properties of the protein. Six and five linear B-cell epitopes have been predicted for p30 and p54, respectively. Four and three discontinuous B-cell epitopes have been predicted for p30 and p54, respectively. Further, the top five T-cell epitopes for SLA-1, 2, and 3 have been listed for both proteins. These results can help us to understand the immunodominant regions in the p30 and p54 proteins of ASFV and potentially assist in designing peptide-based diagnostics and vaccines. Also, the identified T-cell epitopes may be considered for peptide-based vaccine design against ASFV.Communicated by Ramaswamy H. Sarma.

Designing a multi-epitope chimeric protein from different potential targets: A potential vaccine candidate against Plasmodium

Malaria is an infectious disease that has been a continuous threat to mankind since the time immemorial. Owing to the complex multi-staged life cycle of the plasmodium parasite, an effective malaria vaccine which is fully protective against the parasite infection is urgently needed to deal with the challenges. In the present study, essential parasite proteins were identified and a chimeric protein with multivalent epitopes was generated. The designed chimeric protein consists of best potential B and T cell epitopes from five different essential parasite proteins. Physiochemical studies of the chimeric protein showed that the modeled vaccine construct was thermo-stable, hydrophilic and antigenic in nature. And the binding of the vaccine construct with Toll-like receptor-4 (TLR-4) as revealed by the molecular docking suggests the possible interaction and role of the vaccine construct in activating the innate immune response. The constructed vaccine being a chimeric protein containing epitopes from different potential candidates could target different stages or pathways of the parasite. Moreover, the approach used in this study is time and cost effective, and can be applied in the discoveries of new potential vaccine targets for other pathogens.

Next-Generation TB Vaccines: Progress, Challenges, and Prospects

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.

Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine

A vaccine is defined as a biologic preparation that trains the immune system, boosts immunity, and protects against a deadly microbial infection. They have been used for centuries to combat a variety of contagious illnesses by means of subsiding the disease burden as well as eradicating the disease. Since infectious disease pandemics are a recurring global threat, vaccination has emerged as one of the most promising tools to save millions of lives and reduce infection rates. The World Health Organization reports that immunization protects three million individuals annually. Currently, multi-epitope-based peptide vaccines are a unique concept in vaccine formulation. Epitope-based peptide vaccines utilize small fragments of proteins or peptides (parts of the pathogen), called epitopes, that trigger an adequate immune response against a particular pathogen. However, conventional vaccine designing and development techniques are too cumbersome, expensive, and time-consuming. With the recent advancement in bioinformatics, immunoinformatics, and vaccinomics discipline, vaccine science has entered a new era accompanying a modern, impressive, and more realistic paradigm in designing and developing next-generation strong immunogens. In silico designing and developing a safe and novel vaccine construct involves knowledge of reverse vaccinology, various vaccine databases, and high throughput techniques. The computational tools and techniques directly associated with vaccine research are extremely effective, economical, precise, robust, and safe for human use. Many vaccine candidates have entered clinical trials instantly and are available prior to schedule. In light of this, the present article provides researchers with up-to-date information on various approaches, protocols, and databases regarding the computational designing and development of potent multi-epitope-based peptide vaccines that can assist researchers in tailoring vaccines more rapidly and cost-effectively.

Computational and Experimental Evaluation of the Immune Response of Neoantigens for Personalized Vaccine Design

In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Computational design of candidate multi-epitope vaccine against SARS-CoV-2 targeting structural (S and N) and non-structural (NSP3 and NSP12) proteins

The COVID-19 pandemic caused by SARS-CoV-2 virus has created a global damage and has exposed the vulnerable side of scientific research towards novel diseases. The intensity of the pandemic is huge, with mortality rates of more than 6 million people worldwide in a span of 2 years. Considering the gravity of the situation, scientists all across the world are continuously attempting to create successful therapeutic solutions to combat the virus. Various vaccination strategies are being devised to ensure effective immunization against SARS-CoV-2 infection. SARS-CoV-2 spreads very rapidly, and the infection rate is remarkably high than other respiratory tract viruses. The viral entry and recognition of the host cell is facilitated by S protein of the virus. N protein along with NSP3 is majorly responsible for viral genome assembly and NSP12 performs polymerase activity for RNA synthesis. In this study, we have designed a multi-epitope, chimeric vaccine considering the two structural (S and N protein) and two non-structural proteins (NSP3 and NSP12) of SARS-CoV-2 virus. The aim is to induce immune response by generating antibodies against these proteins to target the viral entry and viral replication in the host cell. In this study, computational tools were used, and the reliability of the vaccine was verified using molecular docking, molecular dynamics simulation and immune simulation studies in silico. These studies demonstrate that the vaccine designed shows steady interaction with Toll like receptors with good stability and will be effective in inducing a strong and specific immune response in the body.Communicated by Ramaswamy H. Sarma.

Design of a multi-epitope vaccine against six Nocardia species based on reverse vaccinology combined with immunoinformatics

Background

Nocardia genus, a complex group of species classified to be aerobic actinomycete, can lead to severe concurrent infection as well as disseminated infection, typically in immunocompromised patients. With the expansion of the susceptible population, the incidence of Nocardia has been gradually growing, accompanied by increased resistance of the pathogen to existing therapeutics. However, there is no effective vaccine against this pathogen yet. In this study, a multi-epitope vaccine was designed against the Nocardia infection using reverse vaccinology combined with immunoinformatics approaches.

Methods

First, the proteomes of 6 Nocardia subspecies Nocardia subspecies (Nocardia farcinica, Nocardia cyriacigeorgica, Nocardia abscessus, Nocardia otitidiscaviarum, Nocardia brasiliensis and Nocardia nova) were download NCBI (National Center for Biotechnology Information) database on May 1st, 2022 for the target proteins selection. The essential, virulent-associated or resistant-associated, surface-exposed, antigenic, non-toxic, and non-homologous with the human proteome proteins were selected for epitope identification. The shortlisted T-cell and B-cell epitopes were fused with appropriate adjuvants and linkers to construct vaccines. The physicochemical properties of the designed vaccine were predicted using multiple online servers. The Molecular docking and molecular dynamics (MD) simulation were performed to understand the binding pattern and binding stability between the vaccine candidate and Toll-like receptors (TLRs). The immunogenicity of the designed vaccines was evaluated via immune simulation.

Results

3 proteins that are essential, virulent-associated or resistant-associated, surface-exposed, antigenic, non-toxic, and non-homologous with the human proteome were selected from 218 complete proteome sequences of the 6 Nocardia subspecies epitope identification. After screening, only 4 cytotoxic T lymphocyte (CTL) epitopes, 6 helper T lymphocyte (HTL) epitopes, and 8 B cell epitopes that were antigenic, non-allergenic, and non-toxic were included in the final vaccine construct. The results of molecular docking and MD simulation showed that the vaccine candidate has a strong affinity for TLR2 and TLR4 of the host and the vaccine-TLR complexes were dynamically stable in the natural environment. The results of the immune simulation indicated that the designed vaccine had the potential to induce strong protective immune responses in the host. The codon optimization and cloned analysis showed that the vaccine was available for mass production.

Conclusion

The designed vaccine has the potential to stimulate long-lasting immunity in the host, but further studies are required to validate its safety and efficacy.

A Structural View at Vaccine Development against M. tuberculosis

Tuberculosis (TB) is still the leading global cause of death from an infectious bacterial agent. Limiting tuberculosis epidemic spread is therefore an urgent global public health priority. As stated by the WHO, to stop the spread of the disease we need a new vaccine, with better coverage than the current Mycobacterium bovis BCG vaccine. This vaccine was first used in 1921 and, since then, there are still no new licensed tuberculosis vaccines. However, there is extremely active research in the field, with a steep acceleration in the past decades, due to the advance of technologies and more rational vaccine design strategies. This review aims to gather latest updates in vaccine development in the various clinical phases and to underline the contribution of Structural Vaccinology (SV) to the development of safer and effective antigens. In particular, SV and the development of vaccine adjuvants is making the use of subunit vaccines, which are the safest albeit the less antigenic ones, an achievable goal. Indeed, subunit vaccines overcome safety concerns but need to be rationally re-engineered to enhance their immunostimulating effects. The larger availability of antigen structural information as well as a better understanding of the complex host immune response to TB infection is a strong premise for a further acceleration of TB vaccine development.

Challenges in neoantigen-directed therapeutics

A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.

An immunoinformatic approach towards development of a potent and effective multi-epitope vaccine against monkeypox virus (MPXV)

Monkeypox is a viral zoonotic disease, often transmitted to humans from animals. While the whole world is haggling with the COVID-19 pandemic, the emergence of the monkeypox virus (MPXV) arose as a new challenge to mankind. Till date, numerous cases related to the MPXV have been reported in several countries across the globe, but, its momentary distribution in the current time has left everyone in fright with increasing mortality and limited clinically approved treatments. Therefore, it is of immense importance to develop a potent and highly effective vaccine capable of inducing desired immunogenic responses against the highly contagious MPXV. Herein, using various immunoinformatic and computational biology tools, we made an attempt to develop a multi-epitope vaccine construct against the MPXV which is antigenic, non-allergen and non-toxic in nature and capable of exhibiting immunogenic behavior. The sequence of vaccine construct was designed using the proposed 4 MHC-I, 3 MHC-II and 4 B-cell epitopes linked with suitable adjuvant and linkers. The modeled structure of the vaccine construct was used to assess its interaction with the Toll-like Receptor 4 (TLR4) using ClusPro and HADDOCK. All-atoms molecular dynamics simulation of the MPXV vaccine construct-TLR4 complex followed by a high level of gene expression of the construct within the bacterial system affirmed its stability along with induction of immunogenic response within the host cell. Altogether, our immunoinformatic approach aid in the development of a stable chimeric vaccine construct against MPXV and needs further experimental validation for its immunological relevance and usefulness as a vaccine candidate.Communicated by Ramaswamy H. Sarma.

A Sample Guideline for Reverse Vaccinology Approach for the Development of Subunit Vaccine Using Varicella Zoster as a Model Disease

For the development of multi-peptide vaccine, identification of antigenic epitopes is crucial. If it is done using wet lab techniques, the identification process can be time-consuming, laborious, and cost-intensive. In silico tools, on the other hand, enable researchers to predict potential epitopes with little to no cost for further in vivo and in vitro testing. The rapid identification process using in silico tools helps in responding to health emergencies faster. Developing an efficient and high coverage vaccine is one of the ways to reduce morbidity and mortality rates of the diseases and protect the affected populations. In this chapter, we introduce the necessary tools and methodology for the identification and characterization of antigenic epitopes to design a multi-epitope vaccine using varicella-zoster virus as an example vector model.

In-Silico Designing of a Multi-Epitope Vaccine against SARS-CoV2 and Studying the Interaction of the Vaccine with Alpha, Beta, Delta and Omicron Variants of Concern

BACKGROUND

The sudden appearance of the SARS-CoV2 virus has almost changed the future of vaccine development. There have been many different approaches to vaccination; among them, computational vaccinology in the form of multi-epitope vaccines with excellent immunological properties and minimal contamination or other adverse reactions has emerged as a promising strategy with a lot of room for further study in this area.

OBJECTIVE

Designing a multi-epitope vaccine from the spike protein of SARS-CoV2 based on immunoinformatics and in-silico techniques. Evaluating the binding affinity of the constructed vaccine against the major variants of concern (alpha, beta, delta, and omicron) using docking studies.

METHODS

The potential antigenic, immunogenic, and non-allergic T-cell epitopes were thoroughly explored using IEDB, NetCTL1.2, and NetMHCII pan 3.2 servers. The best suitable linker was identified using the ExPASy Protparam tool and VERIFY 3D. The 3D model of the vaccine was developed by RaptorX and the model was validated using ERRAT, Z-score, and Ramachandran Plot. Docking studies of the vaccine with TLR-2, 3, 4, and 7 and alpha, beta, delta, and omicron variants were performed using HADDOCK 2.4.

RESULTS

The vaccine construct showed good antigenic and immunogenic scores and was non-allergic as well. The model was capable of binding to all four selected Toll-like receptors. Docking scores with variants were also promising.

CONCLUSION

All the variants showed good binding ability with the vaccine construct. Interaction with the alpha variant was found to be the most intense, followed by delta, beta, and omicron.

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

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

Immunogenicity of CRISPR therapeutics-Critical considerations for clinical translation

CRISPR offers new hope for many patients and promises to transform the way we think of future therapies. Ensuring safety of CRISPR therapeutics is a top priority for clinical translation and specific recommendations have been recently released by the FDA. Rapid progress in the preclinical and clinical development of CRISPR therapeutics leverages years of experience with gene therapy successes and failures. Adverse events due to immunogenicity have been a major setback that has impacted the field of gene therapy. As several in vivo CRISPR clinical trials make progress, the challenge of immunogenicity remains a significant roadblock to the clinical availability and utility of CRISPR therapeutics. In this review, we examine what is currently known about the immunogenicity of CRISPR therapeutics and discuss several considerations to mitigate immunogenicity for the design of safe and clinically translatable CRISPR therapeutics.

Immunoinformatics Approach for Epitope Mapping of Immunogenic Regions (N, F and H Gene) of Small Ruminant Morbillivirus and Its Comparative Analysis with Standard Vaccinal Strains for Effective Vaccine Development

BACKGROUND

Outbreaks of small ruminant morbillivirus (SRMV) are regularly occurring in Pakistan despite vaccine availability. This study was designed to identify substitutions within the immunogenic structural and functional regions of the nucleocapsid, fusion, and hemagglutinin genes of SRMV and their comparison with vaccinal strains of Nigerian and Indian origin.

METHODS

Swabs and tissue samples were collected from diseased animals. RT-PCR was used to characterize selected genes encoded by viral RNA. The study's N, F, and H protein sequences and vaccinal strains were analyzed for B and T cell epitope prediction using ABCpred, Bipred, and IEDB, respectively.

RESULTS

Significant substitutions were found on the C terminus of the nucleocapsid, within the fusion motif region of the fusion gene and in the immunoreactive region of the hemagglutinin gene.

CONCLUSION

Our results emphasize the need for the development of effective vaccines that match the existing variants of SRMV strains circulating in Pakistan.

Defining antigen targets to dissect vaccinia virus and monkeypox virus-specific T cell responses in humans

The monkeypox virus (MPXV) outbreak confirmed in May 2022 in non-endemic countries is raising concern about the pandemic potential of novel orthopoxviruses. Little is known regarding MPXV immunity in the context of MPXV infection or vaccination with vaccinia-based vaccines (VACV). As with vaccinia, T cells are likely to provide an important contribution to overall immunity to MPXV. Here, we leveraged the epitope information available in the Immune Epitope Database (IEDB) on VACV to predict potential MPXV targets recognized by CD4⁺ and CD8⁺ T cell responses. We found a high degree of conservation between VACV epitopes and MPXV and defined T cell immunodominant targets. These analyses enabled the design of peptide pools able to experimentally detect VACV-specific T cell responses and MPXV cross-reactive T cells in a cohort of vaccinated individuals. Our findings will facilitate the monitoring of cellular immunity following MPXV infection and vaccination.

mRNA-Based Vaccine Designing against Epstein-Barr Virus to Induce an Immune Response Using Immunoinformatic and Molecular Modelling Approaches

Epstein-Barr Virus (EBV) is a human pathogen that has a morbidity rate of 90% in adults worldwide. Infectious mononucleosis is caused by EBV replication in B cells and epithelial cells of the host. EBV has also been related to autoimmune illnesses, including multiple sclerosis and cancers like nasopharyngeal carcinomas and Burkitt's lymphoma. Currently, no effective medications or vaccinations are available to treat or prevent EBV infection. Thus, the current study focuses on a bioinformatics approach to design an mRNA-based multi-epitope (MEV) vaccine to prevent EBV infections. For this purpose, we selected six antigenic proteins from the EBV proteome based on their role in pathogenicity to predict, extract, and analyze T and B cell epitopes using immunoinformatics tools. The epitopes were directed through filtering parameters including allergenicity, toxicity, antigenicity, solubility, and immunogenicity assessment, and finally, the most potent epitopes able to induce T and B cell immune response were selected. In silico molecular docking of prioritized T cell peptides with respective Human Leukocytes Antigens molecules, were carried out to evaluate the individual peptide's binding affinity. Six CTL, four HTL, and ten linear B cell epitopes fulfilled the set parameters and were selected for MEV-based mRNA vaccine. The prioritized epitopes were joined using suitable linkers to improve epitope presentation. The immune simulation results affirmed the designed vaccine's capacity to elicit a proper immune response. The MEV-based mRNA vaccine constructed in this study offers a promising choice for a potent vaccine against EBV.

Immune epitopes identification and designing of a multi-epitope vaccine against bovine leukemia virus: a molecular dynamics and immune simulation approaches

Background

Bovine leukemia virus (BLV) is an oncogenic delta-retrovirus causing bovine leucosis. Studies on BLV have shown the association with human breast cancer. However, the exact molecular mechanism is neither known nor their appropriate preventative measure to halt the disease initiation and progression. In this study, we designed a multi-epitope vaccine against BLV using a computational analyses.

Methods

Following a rigorous assessment, the vaccine was constructed using the T-cell epitopes from each BLV-derived protein with suitable adjuvant and linkers. Both physicochemistry and immunogenic potency as well as the safeness of the vaccine candidate were assessed. Population coverage was done to evaluate the vaccine probable efficiency in eliciting the immune response worldwide. After homology modeling, the three-dimensional structure was refined and validated to determine the quality of the designed vaccine. The vaccine protein was then subjected to molecular docking with Toll-like receptor 3 (TLR3) to evaluate the binding efficiency followed by dynamic simulation for stable interaction.

Results

Our vaccine construct has the potential immune response and good physicochemical properties. The vaccine is antigenic and immunogenic, and has no allergenic or toxic effect on the human body. This novel vaccine contains a significant interactions and binding affinity with the TLR3 receptor.

Conclusions

The proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat BLV infections. However, experimental evaluations are essential to validate the exact safety and immunogenic profiling of this vaccine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-022-03181-w.

Epitope-based minigene vaccine targeting fibroblast activation protein α induces specific immune responses and anti-tumor effects in 4 T1 murine breast cancer model

Fibroblast activation protein (FAPα) is a tumor stromal antigen expressed by cancer-associated fibroblasts (CAFs) in more than 90 % of malignant epithelial carcinomas. FAPα-based immunotherapy has been reported and showed that FAPα-specific immune response can remold immune microenvironment and contribute to tumor regression. Many FAPα-based vaccines have been investigated in preclinical trials, which can elicit strong and durable cytolytic T lymphocytes (CTL) with good safety. However, epitope-based FAPα vaccines are rarely reported. To break tolerance against self-antigens, analogue epitopes with modified peptides at the anchor residues are typically used to improve epitope immunogenicity. To investigate the feasibility of a FAPα epitope-based vaccine for cancer immunotherapy in vivo, we conducted a preclinical study to identify a homologous CTL epitope of human and mouse FAPα and obtained its analogue epitope in BALB/c mice, and explored the anti-tumor activity of their minigene vaccines in 4 T1 tumor-bearing mice. By using in silico epitope prediction tools and immunogenicity assays, immunodominant epitope FAP.291 (YYFSWLTWV) and its analogue epitope FAP.291I9 (YYFSWLTWI) were identified. The FAP.291-based epitope minigene vaccine successfully stimulated CTLs targeting CAFs and exhibited anti-tumor activity in a 4 T1 murine breast cancer model. Furthermore, although the analogue epitope FAP.291I9 enhanced FAP.291-specific immune responses, improvement of anti-tumor immunity effects was not observed. Check of immunosuppressive factors revealed that the high levels of IL-10, IL-13, myeloid-derived suppressor cells and iNOS induced by FAP.291I9 increased, which considered the main cause of the failure of the analogue epitope-based vaccine. Thus, we demonstrated for the first time that the FAP.291 minigene vaccine could induce mouse CTLs and also function as a tumor regression antigen, providing the basis for future studies of FAPα epitope-based vaccines. This study may also be valuable for further improvement of the immunogenicity of analogue epitope vaccines.

SILVI, an open-source pipeline for T-cell epitope selection

High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909.

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.

A comprehensive analysis of the IEDB MHC class-I automated benchmark

In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.

In silico design of multi-epitope-based peptide vaccine against SARS-CoV-2 using its spike protein

SARS-CoV-2 has been efficient in ensuring that many countries are brought to a standstill. With repercussions ranging from rampant mortality, fear, paranoia, and economic recession, the virus has brought together countries to look at possible therapeutic countermeasures. With prophylactic interventions possibly months away from being particularly effective, a slew of measures and possibilities concerning the design of vaccines are being worked upon. We attempted a structure-based approach utilizing a combination of epitope prediction servers and Molecular dynamic (MD) simulations to develop a multi-epitope-based subunit vaccine that involves the two subunits of the spike glycoprotein of SARS-CoV-2 (S1 and S2) coupled with a substantially effective chimeric adjuvant to create stable vaccine constructs. The designed constructs were evaluated based on their docking with Toll-Like Receptor (TLR) 4. Our findings provide an epitope-based peptide fragment that can be a potential candidate for the development of a vaccine against SARS-CoV-2. Recent experimental studies based on determining immunodominant regions across the spike glycoprotein of SARS-CoV-2 indicate the presence of the predicted epitopes included in this study.Communicated by Ramaswamy H. Sarma.

Redirecting anti-Vaccinia virus T cell immunity for cancer treatment by AAV-mediated delivery of the VV B8R gene

Immunotherapies, such as immune checkpoint inhibitors (ICIs) and chimeric antigen receptor-T (CAR-T) cells, are only efficient in a small proportion of tumor patients. One of the major reasons for this is the lack of immune cell infiltration and activation in the tumor microenvironment (TME). Recent research reported that abundant bystander CD8+ T cells targeting viral antigens exist in tumor infiltrates and that virus-specific memory T cells could be recalled to kill tumor cells. Therefore, virus-specific memory T cells may be effective candidates for tumor immunotherapy. In this study, we established subcutaneous tumor mice models that were pre-immunized with Vaccinia virus (VV) and confirmed that tumor cells with ectopic expression of the viral B8R protein could be recognized and killed by memory T cells. To create a therapeutic delivery system, we designed a recombinant adeno-associated virus (rAAV) with a modified tumor-specific promoter and used it to deliver VV B8R to tumor cells. We observed that rAAV gene therapy can retard tumor growth in VV pre-immunized mice. In summary, our study demonstrates that rAAV containing a tumor-specific promoter to restrict VV B8R gene expression to tumor cells is a potential therapeutic agent for cancer treatment in VV pre-immunized or VV-treated mice bearing tumors.

Genetic variability in minor capsid protein (L2 gene) of human papillomavirus type 16 among Indian women

Human papillomavirus type 16 (HPV-16) is the predominant genotype worldwide associated with invasive cervical cancer and hence remains as the focus for diagnostic development and vaccine research. L2, the minor capsid protein forms the packaging unit for the HPV genome along with the L1 protein and is primarily associated with transport of genomic DNA to the nucleus. Unlike L1, L2 is known to elicit cross-neutralizing antibodies and thus becomes a suitable candidate for pan-HPV prophylactic vaccine development. In the present study, a total of 148 cervical HPV-16 isolates from Indian women were analyzed by PCR-directed sequencing, phylogenetic analysis and in silico immunoinformatics tools to determine the L2 variations that may impact the immune response and oncogenesis. Ninety-one SNPs translating to 35 non-synonymous amino acid substitutions were observed, of these 16 substitutions are reported in the Indian isolates for the first time. T245A, L266F, S378V and S384A substitutions were significantly associated with high-grade cervical neoplastic status. Multiple substitutions were observed in samples from high-grade cervical neoplastic status as compared to those from normal cervical status (p = 0.027), specifically from the D3 sub-lineage. It was observed that substitution T85A was part of both, B and T cell epitopes recognized by MHC-I molecules; T245A was common to B and T cell epitopes recognized by MHC-II molecules and S122P/A was common to the region recognized by both MHC-I and MHC-II molecules. These findings reporting L2 protein substitutions have implications on cervical oncogenesis and design of next-generation L2-based HPV vaccines.

Reduction of Pre-Existing Adaptive Immune Responses Against SaCas9 in Humans Using Epitope Mapping and Identification

The CRISPR-Cas9 system is increasingly being used as a gene editing therapeutic technique in complex diseases but concerns remain regarding the clinical risks of Cas9 immunogenicity. In this study, we detected antibodies against Staphylococcus aureus Cas9 (SaCas9) and anti-SaCas9 T cells in 4.8% and 70% of Chinese donors, respectively. We predicted 135 SaCas9-derived B cell epitopes and 50 SaCas9-derived CD8+ T cell epitopes for HLA-A*24:02, HLA-A*11:01, and HLA-A*02:01. We identified R338 as an immunodominant SaCas9 B cell epitope and SaCas9_200-208 as an immunodominant CD8+ T cell epitope for the three human leukocyte antigen allotypes through immunological assays using sera positive for SaCas9-specific antibodies and peripheral blood mononuclear cells positive for SaCas9-reactive T cells, respectively. We also demonstrated that an SaCas9 variant bearing an R338G substitution reduces B cell immunogenicity and retains its gene-editing function. Our study highlights the immunological risks of the CRISPR-Cas9 system and provides a solution to mitigate pre-existing adaptive immune responses against Cas9 in humans.

Proteome based mapping and reverse vaccinology techniques to contrive multi-epitope based subunit vaccine (MEBSV) against Streptococcus pyogenes

Streptococcus pyogenes is a root cause of human infection like pharyngitis, tonsillitis, scarlet fever, impetigo, and respiratory tract infections. About 11 million individuals in the US suffer from pharyngitis every year. Unfortunately, no vaccine against S. pyogenes is available yet. The purpose of this study is to create a multiepitope-based subunit vaccine (MEBSV) targeting S. pyogenes top four highly antigenic proteins by using a combination of immunological techniques and molecular docking to tackle term group A streptococcal (GAS) infections. T-cell (HTL & CTL), B-cell, and IFN-γ of target proteins were forecasted and epitopes having high antigenic properties being selected for subsequent research. For designing of final vaccine, 5LBL, 9CTL, and 4HTL epitopes were joined by the KK, AAY, and GPGPG linkers. To enhance the immune response, the N-end of the vaccine was linked by adjuvant (Cholera enterotoxin subunit B) with a linker named EAAAK. With the addition of adjuvants and linkers, the construct size was 421 amino acids. IFN-γ and B-cell epitopes illustrate that the modeled construct is optimized for cell-mediated immune or humoral responses. The developed MEBSV structure was assessed to be highly antigenic, non-toxic, and non-allergenic. Moreover, disulphide engineering further enhanced the stability of the final vaccine protein. Molecular docking of the MEBSV with toll-like receptor 4 (TLR4) was conducted to check the vaccine's compatibility with the receptor. Besides, in-silico cloning has been carried out for credibility validation and proper expression of vaccine construct. These findings suggested that the multi-epitope vaccine produced might be a potential immunogenic against Group A streptococcus infections but further experimental testing is required to validate this study.

Development of epitope-based chimeric protein as a vaccine against Lujo virus by utilizing immunoinformatic tools

Aim: Lujo is a modern zoonotic virus that is potentially fatal and spreads by bodily fluids. In this research, immunoinformatic tools are used to build a vaccine. Methodology: The epitopes of cytotoxic T-lymphocytes, helper T-lymphocytes and linear B-lymphocytes were predicted from the most antigenic protein. The designed vaccine's physiochemical properties and 3D structure have been forecasted. Low free energy and strong binding affinity estimated in molecular docking against toll-like receptor 4 (TLR4) and dynamic simulation. Furthermore, in silico cloning in the Escherichia coli K12 host system was performed for high level of expression. Conclusion: Finally, immune simulation was used to determine immune responses to the vaccine that was formulated confirming the developed vaccine as a good candidate against Lujo virus.

Immune profile and responses of a novel dengue DNA vaccine encoding an EDIII-NS1 consensus design based on Indo-African sequences

The ongoing COVID-19 pandemic highlights the need to tackle viral variants, expand the number of antigens, and assess diverse delivery systems for vaccines against emerging viruses. In the present study, a DNA vaccine candidate was generated by combining in tandem envelope protein domain III (EDIII) of dengue virus serotypes 1-4 and a dengue virus (DENV)-2 non-structural protein 1 (NS1) protein-coding region. Each domain was designed as a serotype-specific consensus coding sequence derived from different genotypes based on the whole genome sequencing of clinical isolates in India and complemented with data from Africa. This sequence was further optimized for protein expression. In silico structural analysis of the EDIII consensus sequence revealed that epitopes are structurally conserved and immunogenic. The vaccination of mice with this construct induced pan-serotype neutralizing antibodies and antigen-specific T cell responses. Assaying intracellular interferon (IFN)-γ staining, immunoglobulin IgG2(a/c)/IgG1 ratios, and immune gene profiling suggests a strong Th1-dominant immune response. Finally, the passive transfer of immune sera protected AG129 mice challenged with a virulent, non-mouse-adapted DENV-2 strain. Our findings collectively suggest an alternative strategy for dengue vaccine design by offering a novel vaccine candidate with a possible broad-spectrum protection and a successful clinical translation either as a stand alone or in a mix and match strategy.

MHCRoBERTa: pan-specific peptide-MHC class I binding prediction through transfer learning with label-agnostic protein sequences

Predicting the binding of peptide and major histocompatibility complex (MHC) plays a vital role in immunotherapy for cancer. The success of Alphafold of applying natural language processing (NLP) algorithms in protein secondary struction prediction has inspired us to explore the possibility of NLP methods in predicting peptide-MHC class I binding. Based on the above motivations, we propose the MHCRoBERTa method, RoBERTa pre-training approach, for predicting the binding affinity between type I MHC and peptides. Analysis of the results on benchmark dataset demonstrates that MHCRoBERTa can outperform other state-of-art prediction methods with an increase of the Spearman rank correlation coefficient (SRCC) value. Notably, our model gave a significant improvement on IC50 value. Our method has achieved SRCC value and AUC value as 0.785 and 0.817, respectively. Our SRCC value is 14.3% higher than NetMHCpan3.0 (the second highest SRCC value on pan-specific) and is 3% higher than MHCflurry (the second highest SRCC value on all methods). The AUC value is also better than any other pan-specific methods. Moreover, we visualize the multi-head self-attention for the token representation across the layers and heads by this method. Through the analysis of the representation of each layer and head, we can show whether the model has learned the syntax and semantics necessary to perform the prediction task well. All these results demonstrate that our model can accurately predict the peptide-MHC class I binding affinity and that MHCRoBERTa is a powerful tool for screening potential neoantigens for cancer immunotherapy. MHCRoBERTa is available as an open source software at github (https://github.com/FuxuWang/MHCRoBERTa).