An overview of bioinformatics tools for epitope prediction: implications on vaccine development

Expression of Recombinant Stonustoxin Alpha Subunit and Preparation of polyclonal antiserum for Stonustoxin Neutralization Studies

Stonustoxin (SNTX) is a lethal protein found in stonefish venom, responsible for many of the symptoms associated with stonefish envenomation. To counter stonefish venom challenges, antivenom is a well-established and effective solution. In this study, we aimed to produce the recombinant alpha subunit protein of Stonustoxin from Synanceia horrida and prepare antibodies against it The SNTXα gene sequence was optimized for E. coli BL21 (DE3) expression and cloned into the pET17b vector. Following purification, the recombinant protein was subcutaneously injected into rabbits, and antibodies were extracted from rabbit´s serum using a G protein column As a result of codon optimization, the codon adaptation index for the SNTXα cassette increased to 0.94. SDS-PAGE analysis validated the expression of SNTXα, with a band observed at 73.5 kDa with a yield of 60 mg/l. ELISA results demonstrated rabbits antibody titers were detectable up to a 1:256,000 dilution. The isolated antibody from rabbit´s serum exhibited a concentration of 1.5 mg/ml, and its sensitivity allowed the detection of a minimum protein concentration of 9.7 ng. In the neutralization assay the purified antibody against SNTXα protected mice challenged with 2 LD50. In conclusion, our study successfully expressed the alpha subunit of Stonustoxin in a prokaryotic host, enabling the production of antibodies for potential use in developing stonefish antivenom.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

Immunoinformatics Design and In Vivo Immunogenicity Evaluation of a Conserved CTL Multi-Epitope Vaccine Targeting HPV16 E5, E6, and E7 Proteins

Human papillomavirus type 16 (HPV16) infection is responsible for more than 50% of global cervical cancer cases. The development of a vaccine based on cytotoxic T-lymphocyte (CTL) epitopes is a promising strategy for eliminating pre-existing HPV infections and treating patients with cervical cancer. In this study, an immunoinformatics approach was used to predict HLA-I-restricted CTL epitopes in HPV16 E5, E6, and E7 proteins, and a set of conserved CTL epitopes co-restricted by human/murine MHCs was screened and characterized, with the set containing three E5, four E6, and four E7 epitopes. Subsequently, the immunogenicity of the epitope combination was assessed in mice, and the anti-tumor effects of the multi-epitope peptide vaccine E5E6E7pep11 and the recombinant protein vaccine CTB-Epi11E567 were evaluated in the TC-1 mouse tumor model. The results demonstrated that mixed epitope peptides could induce antigen-specific IFN-γ secretion in mice. Prophylactic immunization with E5E6E7pep11 and CTB-Epi11E567 was found to provide 100% protection against tumor growth in mice. Moreover, both types of the multi-epitope vaccine significantly inhibited tumor growth and prolonged mouse survival. In conclusion, in this study, a multi-epitope vaccine targeting HPV16 E5, E6, and E7 proteins was successfully designed and evaluated, demonstrating potential immunogenicity and anti-tumor effects and providing a promising strategy for immunotherapy against HPV-associated tumors.

Immunoinformatics studies and design of a novel multi-epitope peptide vaccine against Toxoplasma gondii based on calcium-dependent protein kinases antigens through an in-silico analysis

Purpose

Infection by the intracellular apicomplexan parasite Toxoplasma gondii has serious clinical consequences in humans and veterinarians around the world. Although about a third of the world's population is infected with T. gondii, there is still no effective vaccine against this disease. The aim of this study was to develop and evaluate a multimeric vaccine against T. gondii using the proteins calcium-dependent protein kinase (CDPK)1, CDPK2, CDPK3, and CDPK5.

Materials and Methods

Top-ranked major histocompatibility complex (MHC)-I and MHC-II binding as well as shared, immunodominant linear B-cell epitopes were predicted and linked using appropriate linkers. Moreover, the 50S ribosomal protein L7/L12 (adjuvant) was mixed with the construct's N-terminal to increase the immunogenicity. Then, the vaccine's physicochemical characteristics, antigenicity, allergenicity, secondary and tertiary structure were predicted.

Results

The finally-engineered chimeric vaccine had a length of 680 amino acids with a molecular weight of 74.66 kDa. Analyses of immunogenicity, allergenicity, and multiple physiochemical parameters indicated that the constructed vaccine candidate was soluble, non-allergenic, and immunogenic, making it compatible with humans and hence, a potentially viable and safe vaccine candidate against T. gondii parasite.

Conclusion

In silico, the vaccine construct was able to trigger primary immune responses. However, further laboratory studies are needed to confirm its effectiveness and safety.

Localization of an IgE epitope of glycinin A2 peptide chain

INTRODUCTION

One of the main allergens in soybeans is glycinin, which seriously impacts the normal lives of allergic people. Previous studies have confirmed that thermal processing and thermal processing combined with ultrahigh-pressure processing could significantly reduce the antigenicity of glycinin. The dominant antigen region of acidic peptide chain A2 of G2 subunit was located by phage display experiment.

METHODS

In this paper, overlapping peptides and alanine substitution techniques were used to explore the key amino acids that significantly affect the antigenicity of A2 peptide chain. The purity of peptide 1, peptide 2 and peptide 3 was identified by mass spectrometry and high-performance liquid chromatography, and the results showed that the purity of the synthesized overlapping peptide was more than 90%. SDS-PAGE showed that the peptide was successfully coupled with bovine serum albumin. The antigenicity of the coupling peptide was tested by ELISA and Dot-Blot, and the allergenicity was detected by reacting with the serum of patients with soybean globulin allergy.

CONCLUSION

The results showed that peptide 3 has stronger antigenicity and sensitization. Alanine substitution technology allowed one to perform site-directed mutagenesis on peptide 3. Dot-Blot and ELISA tests showed that D259, E260, E261, Q263 and C266 may be the key amino acids that significantly affect the antigenicity of peptide 3. The research presented is of great significance for correctly guiding the production of safe food and preventing the occurrence of food allergic diseases. © 2023 Society of Chemical Industry.

Rational design of B-cell and T-cell multi epitope-based vaccine against Zika virus, an in silico study

The Zika virus (ZKV) is a single-stranded positive-sense, enveloped RNA virus. Zika infection during pregnancy can cause congenital microcephaly, Guillain-Barré syndrome, miscarriage, and other CNS abnormalities. The world needs safe and effective vaccinations to fight against ZIKV infection since vaccination is generally regarded as one of the most effective ways to prevent infectious diseases. In the present work, we used immunoinformatics and docking studies to construct a vaccine containing multi-epitopes using the structural and non-structural proteins of ZKV. The structural models of ZKV proteins (PrE, PrM, NS1, and NS2A) were constructed using Pyre2 and RaptorX servers. The epitopes of B-cell, T-cell (HTL and CTL), and IFN-γ were predicted, and each epitope's immunogenic nature and physiochemical properties were confirmed. As an adjuvant, the CPG-Oligodeoxynucleotide, an agonist of Toll-like receptor 9 (TLR9), is associated to cytotoxic T-lymphocytes (CTL) epitopes via PAPAP linker. To assess the binding affinity and the tendency of the designed vaccine to induce an immune response through TLR9, molecular docking was done. In the next step, molecular dynamics (MD) simulation to 100 nanoseconds (ns) was used to evaluate the stability of the interaction of the designed vaccine with TLR9. The designed vaccine is predicted to be highly antigenic, non-toxic, soluble, and stable with low flexibility in MD simulation. MD studies indicated that the finalized vaccine-TLR9 docked complex was stable during simulation time. The vaccine construct is able to stimulate both humoral and cellular immune responses. We suppose that our constructed model of the vaccine may have the ability to induce the host immune response against ZKV. Further studies, including in vitro and in vivo experimental analyses, are needed to prove the constructed vaccine's efficacy with multi-epitopes.Communicated by Ramaswamy H. Sarma.

Protein-based nanoparticles for therapeutic nucleic acid delivery

To realize the full potential of emerging nucleic acid therapies, there is a need for effective delivery agents to transport cargo to cells of interest. Protein materials exhibit several unique properties, including biodegradability, biocompatibility, ease of functionalization via recombinant and chemical modifications, among other features, which establish a promising basis for therapeutic nucleic acid delivery systems. In this review, we highlight progress made in the use of non-viral protein-based nanoparticles for nucleic acid delivery in vitro and in vivo, while elaborating on key physicochemical properties that have enabled the use of these materials for nanoparticle formulation and drug delivery. To conclude, we comment on the prospects and unresolved challenges associated with the translation of protein-based nucleic acid delivery systems for therapeutic applications.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Computational structure-based approach to study chimeric antigens using a new protein scaffold displaying foreign epitopes

The identification and recombinant production of functional antigens and/or epitopes of pathogens represent a crucial step for the development of an effective protein-based vaccine. Many vaccine targets are outer membrane proteins anchored into the lipidic bilayer through an extended hydrophobic portion making their recombinant production challenging. Moreover, only the extracellular loops, and not the hydrophobic regions, are naturally exposed to the immune system. In this work, the Domain 3 (D3) from Group B Streptococcus (GBS) pilus 2a backbone protein has been identified and engineered to be used as a scaffold for the display of extracellular loops of two Neisseria gonorrhoeae membrane proteins (PorB.1b and OpaB). A computational structure-based approach has been applied to the design of both the scaffold and the model antigens. Once identified the best D3 engineerable site, several different chimeric D3 displaying PorB.1b and OpaB extracellular loops were produced as soluble proteins. Each molecule has been characterized in terms of solubility, stability, and ability to correctly display the foreign epitope. This antigen dissection strategy allowed the identification of most immunogenic extracellular loops of both PorB.1b and OpaB gonococcal antigens. The crystal structure of chimeric D3 displaying PorB.1b immunodominant loop has been obtained confirming that the engineerization did not alter the predicted native structure of this epitope. Taken together, the reported data suggest that D3 is a novel protein scaffold for epitope insertion and display, and a valid alternative to the production of whole membrane protein antigens. Finally, this work describes a generalized computational structure-based approach for the identification, design, and dissection of epitopes in target antigens through chimeric proteins.

Discovery and development of COVID-19 vaccine from laboratory to clinic

The world has recently experienced one of the biggest and most severe public health disasters with severe acute respiratory syndrome coronavirus (SARS-CoV-2). SARS-CoV-2 is responsible for the coronavirus disease of 2019 (COVID-19) which is one of the most widespread and powerful infections affecting human lungs. Current figures show that the epidemic had reached 216 nations, where it had killed about 6,438,926 individuals and infected 590,405,710. WHO proclaimed the outbreak of the Ebola virus disease (EVD), in 2014 that killed hundreds of people in West Africa. The development of vaccines for SARS-CoV-2 becomes more difficult due to the viral mutation in its non-structural proteins (NSPs) especially NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp). Continuous monitoring of SARS-CoV-2, dynamics of the genomic sequence, and spike protein mutations are very important for the successful development of vaccines with good efficacy. Hence, the vaccine development for SARS-CoV-2 faces specific challenges starting from viral mutation. The requirement of long-term immunity development, safety, efficacy, stability, vaccine allocation, distribution, and finally, its cost is discussed in detail. Currently, 169 vaccines are in the clinical development stage, while 198 vaccines are in the preclinical development stage. The majority of these vaccines belong to the Ps-Protein subunit type which has 54, and the minor BacAg-SPV (Bacterial antigen-spore expression vector) type, at least 1 vaccination. The use of computational methods and models for vaccine development has revolutionized the traditional methods of vaccine development. Further, this updated review highlights the upcoming vaccine development strategies in response to the current pandemic and post-pandemic era, in the field of vaccine development.

Building a Nucleic Acid Nanostructure with DNA-Epitope Conjugates for a Versatile Approach to Electrochemical Protein Detection

The recent surge of effort in nucleic-acid-based electrochemical (EC) sensors has been fruitful, yet there remains a need for more generalizable EC platforms for sensing multiple classes of clinically relevant targets. We recently reported a nucleic acid nanostructure for simple, economical, and more generalizable EC readout of a range of analytes, including small molecules, peptides, proteins, and antibodies. The nanostructure is built through on-electrode enzymatic ligation of three oligonucleotides for attachment, binding, and signaling. However, the generalizable detection of larger proteins remains a challenge. Here, we adapted the sensor to quantify larger proteins in a more generic manner through conjugating the protein's minimized antibody-binding epitope to the central DNA strand. This concept was verified using creatine kinase (CK-MM), a biomarker of muscle damage and several disorders for which rapid clinical sensing is important. DNA-epitope conjugates permitted a competitive immunoassay for the CK protein at the electrode via square-wave voltammetry (SWV). Sensing through a signal-off mechanism, the anti-CK antibody limit of detection (LOD) was 5 nM with a response time as low as 3 min. Antibody displacement by native protein analytes gave a signal-on response with the CK sensing range from the LOD of 14 nM up to 100 nM, overlapping with the normal (nonelevated) human clinical range (3-37 nM), and the sensor was validated in 98% human serum. While a need for improved DNA-epitope conjugate purification was identified, overall, this approach allows the quantification of a generic protein- or peptide-binding antibody and should facilitate future quantitative EC readouts of clinically relevant proteins that were previously inaccessible to EC techniques.

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.

Characteristics of Serum Autoantibody Repertoire and Immune Subgroup Variation of Tuberculosis-Associated Obstructive Pulmonary Disease

Background

Studying the potential etiology and pathogenesis of tuberculosis-associated chronic obstructive pulmonary disease (TOPD) from an autoimmunity perspective may provide insights into peripheral blood autoantibodies and immune cells, as well as their interactions.

Methods

This study examined the serum autoantibody repertoire in healthy individuals, patients with chronic obstructive pulmonary disease (COPD), patients with pulmonary tuberculosis (TB), and TOPD patients using the HuProtTM protein chip. Autoantigens in the peripheral blood of TOPD patients were verified using ELISA assay. Various epitopes and immune simulation were predicted using bioinformatic methods. Flow cytometry was employed to detect macrophages(Mφ), T cells, and innate lymphoid cells (ILCs) in the peripheral blood.

Results

COPD patients displayed distinct alterations in their IgG and IgM autoantibodies compared to the other groups. GeneOntology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses revealed that these autoantibodies were associated with regulating macrophages, T cells, and B cells. ELISA results confirmed the upregulation of expression of proliferating cell nuclear antigen (PCNA), Mitogen-Activated Protein Kinase 3 antigen (MAPK3), and threonine protein kinase 1 antigen (AKT1) proteins in the peripheral blood of TOPD patients. Bioinformatic analysis predicted multiple potential epitopes in Th, CTL, and B cells. Immune simulation results demonstrated that PCNA, MAPK3, and AKT1 can activate innate and adaptive immune responses and induce the expression of different cytokines, such as IFN-g and IL-2. Furthermore, data obtained from flow cytometry assay revealed an upregulation in the face of Th1 cells in the peripheral blood of TOPD patients.

Conclusion

Tuberculosis infection can effectively induce autoimmune responses, contributing to increased expression of Th1 cells and associated cytokines, ultimately leading to immune dysregulation. Furthermore, the accumulation of pulmonary inflammatory response facilitates the progression of TOPD and is helpful for the clinical diagnosis and the development of targeted therapeutic drugs for this disease.

VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation

Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew's correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew's correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host's adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.

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

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

Does human homology reduce the potential immunogenicity of non-antibody scaffolds?

Biologics developers are moving beyond antibodies for delivery of a wide range of therapeutic interventions. These non-antibody modalities are often based on 'natural' protein scaffolds that are modified to deliver bioactive sequences. Both human-derived and non-human-sourced scaffold proteins have been developed. New types of "non-antibody" scaffolds are still being discovered, as they offer attractive alternatives to monoclonals due to their smaller size, improved stability, and ease of synthesis. They are believed to have low immunogenic potential. However, while several human-sourced protein scaffolds have not been immunogenic in clinical studies, this may not predict their overall performance in other therapeutic applications. A preliminary evaluation of their potential for immunogenicity is warranted. Immunogenicity risk potential has been clearly linked to the presence of T "helper" epitopes in the sequence of biologic therapeutics. In addition, tolerogenic epitopes are present in some human proteins and may decrease their immunogenic potential. While the detailed sequences of many non-antibody scaffold therapeutic candidates remain unpublished, their backbone sequences are available for review and analysis. We assessed 12 example non-antibody scaffold backbone sequences using our epitope-mapping tools (EpiMatrix) for this perspective. Based on EpiMatrix scoring, their HLA DRB1-restricted T cell epitope content appears to be lower than the average protein, and sequences that may act as tolerogenic epitopes are present in selected human-derived scaffolds. Assessing the potential immunogenicity of scaffold proteins regarding self and non-self T cell epitopes may be of use for drug developers and clinicians, as these exciting new non-antibody molecules begin to emerge from the preclinical pipeline into clinical use.

Multi-epitope vaccine candidates based on mycobacterial membrane protein large (MmpL) proteins against Mycobacterium ulcerans

Buruli ulcer (BU) is a neglected tropical disease. It is caused by the bacterium Mycobacterium ulcerans and is characterized by skin lesions. Several studies were performed testing the Bacillus Calmette-Guérin (BCG) vaccine in human and animal models and M. ulcerans-specific vaccines in animal models. However, there are currently no clinically accepted vaccines to prevent M. ulcerans infection. The aim of this study was to identify T-cell and B-cell epitopes from the mycobacterial membrane protein large (MmpL) proteins of M. ulcerans. These epitopes were analysed for properties including antigenicity, immunogenicity, non-allergenicity, non-toxicity, population coverage and the potential to induce cytokines. The final 8 CD8+, 12 CD4+ T-cell and 5 B-cell epitopes were antigenic, non-allergenic and non-toxic. The estimated global population coverage of the CD8+ and CD4+ epitopes was 97.71%. These epitopes were used to construct five multi-epitope vaccine constructs with different adjuvants and linker combinations. The constructs underwent further structural analyses and refinement. The constructs were then docked with Toll-like receptors. Three of the successfully docked complexes were structurally analysed. Two of the docked complexes successfully underwent molecular dynamics simulations (MDS) and post-MDS analysis. The complexes generated were found to be stable. However, experimental validation of the complexes is required.

Identification and evaluation in-vitro of conserved peptides with high affinity to MHC-I as potential protective epitopes for Newcastle disease virus vaccines

BACKGROUND

Newcastle disease (ND) is a major threat to the poultry industry, leading to significant economic losses. The current ND vaccines, usually based on active or attenuated strains, are only partially effective and can cause adverse effects post-vaccination. Therefore, the development of safer and more efficient vaccines is necessary. Epitopes represent the antigenic portion of the pathogen and their identification and use for immunization could lead to safer and more effective vaccines. However, the prediction of protective epitopes for a pathogen is a major challenge, especially taking into account the immune system of the target species.

RESULTS

In this study, we utilized an artificial intelligence algorithm to predict ND virus (NDV) peptides that exhibit high affinity to the chicken MHC-I complex. We selected the peptides that are conserved across different NDV genotypes and absent in the chicken proteome. From the filtered peptides, we synthesized the five peptides with the highest affinities for the L, HN, and F proteins of NDV. We evaluated these peptides in-vitro for their ability to elicit cell-mediated immunity, which was measured by the lymphocyte proliferation in spleen cells of chickens previously immunized with NDV.

CONCLUSIONS

Our study identified five peptides with high affinity to MHC-I that have the potential to serve as protective epitopes and could be utilized for the development of multi-epitope NDV vaccines. This approach can provide a safer and more efficient method for NDV immunization.

Designing and development of multi-epitope chimeric vaccine against Helicobacter pylori by exploring its entire immunogenic epitopes: an immunoinformatic approach

BACKGROUND

Helicobacter pylori is a prominent causative agent of gastric ulceration, gastric adenocarcinoma and gastric lymphoma and have been categorised as a group 1 carcinogen by WHO. The treatment of H. pylori with proton pump inhibitors and antibiotics is effective but also leads to increased antibiotic resistance, patient dissatisfaction, and chances of reinfection. Therefore, an effective vaccine remains the most suitable prophylactic option for mass administration against this infection.

RESULTS

We modelled a multi-chimera subunit vaccine candidate against H. pylori by screening its secretory/outer membrane proteins. We identified B-cell, MHC-II and IFN-γ-inducing epitopes within these proteins. The population coverage, antigenicity, physiochemical properties and secondary structure were evaluated using different in-silico tools, which showed it can be a good and effective vaccine candidate. The 3-D construct was predicted, refined, validated and docked with TLRs. Finally, we performed the molecular docking/simulation and immune simulation studies to validate the stability of interaction and in-silico cloned the epitope sequences into a pET28b(+) plasmid vector.

CONCLUSION

The multiepitope-constructed vaccine contains T- cells, B-cells along with IFN-γ inducing epitopes that have the property to generate good cell-mediated immunity and humoral response. This vaccine can protect most of the world's population. The docking study and immune simulation revealed a good binding with TLRs and cell-mediated and humoral immune responses, respectively. Overall, we attempted to design a multiepitope vaccine and expect this vaccine will show an encouraging result against H. pylori infection in in-vivo use.

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.

Immunization of sheep with a recombinant vaccine containing immunogenic nontoxic domains of Clostridium perfringens alpha and beta toxins

Clostridium perfringens (types A and C) can cause several diseases by secreting alpha (CPA) and beta (CPB) exotoxins in the gastrointestinal tract. Although vaccination is the main measure of immunization against C. perfringens, available vaccines have limitations in terms of productivity and safety. Thus, recombinant vaccines are an important, more effective, practical, and safer strategy in the immunization of animals. In this study, we evaluated the immunization of sheep with recombinant Escherichia coli bacterins expressing CPA and CPB complete proteins (co-administered), the immunogenic nontoxic domains rCPA-C247-370 and rCPB-C143-311 co-administered or fused as a bivalent chimera (rCPBcAc). For this, in silico analysis was performed to design rCPBcAc, considering the stability of the mRNA (-278.80 kcal/mol), the degree of antigenicity (0.7557), the epitopes of the B cell ligand, and different physicochemical characteristics. All proteins were expressed in vitro. In vivo, animals vaccinated with the co-administered antigens rCPA + rCPB and rCPA-C+ rCPB-C (200 μg each) had mean CPA and CPB neutralizing antitoxin titers of 4, 10, 4.8, and 14.4 IU/mL, respectively, while those vaccinated with 200 μg of rCPBcAc chimera (approximately 100 μg of each antigen) had titers of <4 and 12 IU/mL of CPA and CPB antitoxins, respectively, 56 days after the administration of the first dose. In addition, the chimera was considered to be immunogenic for inducing antitoxin titers using the half dose. In this study, we presented a new recombinant antigen potentially applicable for vaccines against the CPA and CPB toxins for preventing diseases caused by Clostridium perfringens.

Production of recombinant proteins including the B-cell epitopes of autolysin A of Staphylococcus aureus isolated from clinical sheep mastitis and their potential for vaccine development

Staphylococcus aureus is the most common clinical mastitis-associated pathogen in sheep which contributes to reduced welfare of affected animals and, therefore, compromises the quality and quantity of milk production. To prevent mastitis and its spread, it is essential to guarantee adequate breeding conditions and animal health, through the adoption of good farm management practices and the application of suitable biosecurity measures. Vaccination can play a strategic role in prevention, control, and eradication of diseases. The identification of secreted and cellular antigens of the predominant sheep-CC130/ST700/t1773 lineage would assist in the design of effective vaccine against mammary infections caused by S. aureus. In the current study, we carried out a 3D structural prediction analysis with the identification of the best B cell epitopes of the whole and secreted portion of S. aureus AtlA. Fragments of atlA, containing the main predicted epitopes, were amplified, cloned, and expressed in Escherichia coli for recombinant protein production. Two selected clones produced recombinant proteins (rAtl4 and rAtl8) showing strong reactivity with a hyperimmune serum against the native AtlA and with blood sera collected from sheep with clinical S. aureus mastitis. These may represent potential candidate protein-based vaccines able to elicit a protective immune response to be evaluated by vaccination and subsequent challenge of the vaccinated sheep.

Identification of Potential Novel B-Cell Epitopes of Capsid Protein VP2 in Senecavirus A

Senecavirus A (SVA) is a type of nonenveloped single-stranded, positive-sense RNA virus. The VP2 protein is a structural protein that plays an important role in inducing early and late immune responses of the host. However, its antigenic epitopes have not been fully elucidated. Therefore, defining the B epitopes of the VP2 protein is of great importance to revealing its antigenic characterization. In this study, we analyzed B-cell immunodominant epitopes (IDEs) of the VP2 protein from the SVA strain CH/FJ/2017 using the Pepscan approach and a bioinformatics-based computational prediction method. The following four novel IDEs of VP2 were identified: IDE1, 41TKSDPPSSSTDQPTTT56; IDE2, 145PDGKAKSLQELNEEQW160; IDE3, 161VEMSDDYRTGKNMPF175; and IDE4, 267PYFNGLRNRFTTGT280. Most of the IDEs were highly conserved among the different strains. To our knowledge, the VP2 protein is a major protective antigen of SVA that can induce neutralizing antibodies in animals. Here, we analyzed the immunogenicity and neutralization activity of four IDEs of VP2. Consequently, all four IDEs showed good immunogenicity that could elicit specific antibodies in guinea pigs. A neutralization test in vitro showed that the peptide-specific guinea pig antisera of IDE2 could neutralize SVA strain CH/FJ/2017, and IDE2 was identified as a novel potential neutralizing linear epitope. This is the first time VP2 IDEs have been identified by using the Pepscan method and a bioinformatics-based computational prediction method. These results will help elucidate the antigenic epitopes of VP2 and clarify the basis for immune responses against SVA. IMPORTANCE The clinical symptoms and lesions caused by SVA are indistinguishable from those of other vesicular diseases in pigs. SVA has been associated with recent outbreaks of vesicular disease and epidemic transient neonatal losses in several swine-producing countries. Due to the continuing spread of SVA and the lack of commercial vaccines, the development of improved control strategies is urgently needed. The VP2 protein is a crucial antigen on the capsids of SVA particles. Furthermore, the latest research showed that VP2 could be a promising candidate for the development of novel vaccines and diagnostic tools. Hence, a detailed exploration of epitopes in the VP2 protein is necessary. In this study, four novel B-cell IDEs were identified using two different antisera with two different methods. IDE2 was identified as a new neutralizing linear epitope. Our findings will help in the rational design of epitope vaccines and further understanding of the antigenic structure of VP2.

Impact of Semiochemicals Binding to Fel d 1 on Its 3D Conformation and Predicted B-Cell Epitopes Using Computational Approaches

The major cat allergen Fel d 1 is a tetrameric glycoprotein from the secretoglobin superfamily. Fel d 1's biological role is unknown, but it has been previously shown that it participates in semiochemical binding/transportation. Fel d 1 has linear epitopes, but its conformational epitope sites remain unclear. In this study, we predicted the B-cell epitopes of Fel d 1 and explored semiochemical dynamics with epitopes using bioinformatics tools. The epitope residues were tabulated for chains 1 and 2 and the heterodimers of Fel d 1. The residual interactions of Fel d 1 with IgE were evaluated, and the prominent epitope sites were predicted. The molecular dynamics simulation (MDS) of Fel d 1 was performed with seven reported semiochemicals to evaluate the Fel d 1-ligand complex stability and decipher the semiochemical effect on Fel d 1 conformational epitopes. Fel d 1-lauric acid, Fel d 1-oleic acid, and Fel d 1-progesterone showed more stability and less fluctuation than other compounds. Fel d 1-linoleic acid and Fel d 1-pregnenolone displayed the most unstable complex with fluctuations. The effects of conformational changes on epitopes are discussed. All the ligand complexes drive substantial fluctuation towards the functionally exposed IgE-binding epitopes. Fel d 1 could be examined for its ligand-binding and conformational changes caused by mutations of B-cell epitopes.

Multivalent vaccines demonstrate immunogenicity and protect against Coxiella burnetii aerosol challenge

Vaccines are among the most cost-effective public health measures for controlling infectious diseases. Coxiella burnetii is the etiological agent of Q fever, a disease with a wide clinical spectrum that ranges from mild symptoms, such as fever and fatigue, to more severe disease, such as pneumonia and endocarditis. The formalin-inactivated whole-cell vaccine Q-VAX® contains hundreds of antigens and confers lifelong protection in humans, but prior sensitization from infection or vaccination can result in deleterious reactogenic responses to vaccination. Consequently, there is great interest in developing non-reactogenic alternatives based on adjuvanted recombinant proteins. In this study, we aimed to develop a multivalent vaccine that conferred protection with reduced reactogenicity. We hypothesized that a multivalent vaccine consisting of multiple antigens would be more immunogenic and protective than a monovalent vaccine owing to the large number of potential protective antigens in the C. burnetii proteome. To address this, we identified immunogenic T and B cell antigens, and selected proteins were purified to evaluate with a combination adjuvant (IVAX-1), with or without C. burnetii lipopolysaccharide (LPS) in immunogenicity studies in vivo in mice and in a Hartley guinea pig intratracheal aerosol challenge model using C. burnetii strain NMI RSA 493. The data showed that multivalent vaccines are more immunogenic than monovalent vaccines and more closely emulate the protection achieved by Q-VAX. Although six antigens were the most immunogenic, we also discovered that multiplexing beyond four antigens introduces detectable reactogenicity, indicating that there is an upper limit to the number of antigens that can be safely included in a multivalent Q-fever vaccine. C. burnetii LPS also demonstrates efficacy as a vaccine antigen in conferring protection in an otherwise monovalent vaccine formulation, suggesting that its addition in multivalent vaccines, as demonstrated by a quadrivalent formulation, would improve protective responses.

Multidimensional futuristic approaches to address the pandemics beyond COVID-19

Globally, the impact of the coronavirus disease 2019 (COVID-19) pandemic has been enormous and unrelenting with ∼6.9 million deaths and ∼765 million infections. This review mainly focuses on the recent advances and potentially novel molecular tools for viral diagnostics and therapeutics with far-reaching implications in managing the future pandemics. In addition to briefly highlighting the existing and recent methods of viral diagnostics, we propose a couple of potentially novel non-PCR-based methods for rapid, cost-effective, and single-step detection of nucleic acids of viruses using RNA mimics of green fluorescent protein (GFP) and nuclease-based approaches. We also highlight key innovations in miniaturized Lab-on-Chip (LoC) devices, which in combination with cyber-physical systems, could serve as ideal futuristic platforms for viral diagnosis and disease management. We also discuss underexplored and underutilized antiviral strategies, including ribozyme-mediated RNA-cleaving tools for targeting viral RNA, and recent advances in plant-based platforms for rapid, low-cost, and large-scale production and oral delivery of antiviral agents/vaccines. Lastly, we propose repurposing of the existing vaccines for newer applications with a major emphasis on Bacillus Calmette-Guérin (BCG)-based vaccine engineering.

Allergen-specific immunotherapy by recombinant Der P1 allergen-derived peptide-based vaccine in an allergic mouse model

AIM

Increased IgE levels have made house dust mite allergens one of the most frequent causes of allergies worldwide. Treatment reduces the IgE antibodies and types two cytokines, namely interleukin-4 (IL-4) and IL-13. Although existing treatments significantly reduce IgE or IL-4/IL-13, they are very costly. This study aimed to construct a recombinant protein derived from rDer p1 peptides in the form of an immunotherapy approach and to measure the response of IgE and IgG antibodies.

METHODS

The proteins were isolated, purified, and evaluated using the SDS-PAGE and Bradford test and confirmed by using Western blot. To evaluate immunotherapy efficiency, 24 BALB/C mice were sensitized intraperitoneally with house dust mites (HDM) adsorbed to Aluminum hydroxide (Alum) and randomly divided into four groups of six: control sensitized, HDM extract, rDer p1, and DpTTDp vaccine. To immunization, four groups of random mice were each treated with phosphate-buffered saline, 100 μg of rDer p1 protein, DpTTDp, or HDM extract, every 3 days. Direct ELISA determined HDM-specific IgG and IgE subclasses. Data were analyzed in SPSS and Graph pad prism software. Values of p < .05 were considered significant.

RESULTS

After immunization of mice, the rDer P1 and recombinant vaccine like HDM extract increased IgG antibody titer and decreased IgE-dependent reactivity in allergic mice to rDer P1. Also, the levels of inflammatory IL-4 and IL-13 cytokines as allergic stimulants decreased.

CONCLUSION

The use of present available recombinant proteins is considered a viable, cost-effective, and long-term option for providing effective HDM allergy immunotherapy vaccines without side effects.

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.

In silico design and immunoinformatics analysis of a chimeric vaccine construct based on Salmonella pathogenesis factors

Currently, there are two vaccines based on killed and/or weakened Salmonella bacteria, but no recombinant vaccine is available for preventing or treating the disease. We used an in silico approach to design a multi-epitope vaccine against Salmonella using OmpA, OmpS, SopB, SseB, SthA and FilC antigens. We predicted helper T lymphocyte, cytotoxic T lymphocyte, and IFN-γ epitopes. The FilC sequence was used as a bovine TLR5 agonist, and the linkers KK, AAY, GPGPG and EAAAK were used to connect epitopes. The final sequence consisted of 747 amino acid residues, and the expressed soluble protein (∼79.6 kDa) was predicted to be both non-allergenic and antigenic. The tertiary structure of modeled protein was refined and validated, and the interactions of vaccine 3D structure were evaluated using molecular docking, and molecular dynamics simulation (RMSD, RMSF and Gyration). This structurally stable protein could interact with human TLR5. The C-ImmSim server predicted that this proposed vaccine likely induces an immune response by stimulating T and B cells, making it a potential candidate for further evaluation for the prevention and treatment of Salmonella infection.

An immunoinformatics and structural vaccinology study to design a multi-epitope vaccine against Staphylococcus aureus infection

Staphylococcus aureus has been widely reported to be majorly responsible for causing nosocomial infections worldwide. Due to an increase in antibiotic-resistant strains, the development of an effective vaccine against the bacteria is the most viable alternative. Therefore, in the current work, an effort has been undertaken to develop a novel peptide-based vaccine construct against S aureus that can potentially evoke the B and T cell immune responses. The fibronectin-binding proteins are an attractive target as they play a prominent role in bacterial adherence and host cell invasion and are also well conserved among rapidly mutating pathogens. Therefore, highly immunogenic linear B lymphocytes (LBL), cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL) epitopes were identified from the antigenic fibronectin-binding proteins A and B (FnBPA and FnBPB) of S aureus using immunoinformatics approaches. The selected peptides were confirmed to be non-allergenic, non-toxic, and with a high binding affinity to the majority of human leukocyte antigens (HLA) alleles. Consequently, the multi-peptide vaccine construct was developed by fusing the screened epitopes (three LBL, five CTL, and two HTL) together with the suitable adjuvant and linkers. In addition, the tertiary conformation of the peptide construct was modeled and later docked to the Toll-like receptor 2. Subsequently, a molecular dynamics simulation of 100 ns was employed to corroborate the stability of the designed vaccine-receptor complex. Besides exhibiting high immunogenicity and conformational stability, the developed vaccine was observed to possess wide population coverage of 99.51% worldwide. Additional in vivo and in vitro validation studies would certainly corroborate the designed vaccine construct to have improved prophylactic efficacy against S aureus.

In silico design of a novel peptide-based vaccine against the ubiquitous apicomplexan Toxoplasma gondii using surface antigens

Human toxoplasmosis is a global public health concern and a commercial vaccine is still lacking. The present in silico study was done to design a novel vaccine candidate using tachyzoite-specific SAG1-realted sequence (SRS) proteins. Overlapping B-cell and strictly-chosen human MHC-I binding epitopes were predicted and connected together using appropriate spacers. Moreover, a TLR4 agonist, human high mobility group box protein 1 (HMGB1), and His-tag were added to the N- and C-terminus of the vaccine sequence. The final vaccine had 442 residues and a molecular weight of 47.71 kDa. Physico-chemical evaluation showed a soluble, highly antigenic and non-allergen protein, with coils and helices as secondary structures. The vaccine 3D model was predicted by ITASSER server, subsequently refined and was shown to possess significant interactions with human TLR4. As well, potent stimulation of cellular and humoral immunity was demonstrated upon chimeric vaccine injection. Finally, the outputs showed that this vaccine model possesses top antigenicity, which could provoke significant cell-mediated immune profile including IFN-γ, and can be utilized towards prophylactic purposes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00140-w.

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

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.

Immuno-informatics guided designing of a multi-epitope vaccine against Dengue and Zika

Dengue and zika are amongst the most prevalent mosquito-borne diseases caused by closely related members Dengue virus (DENV) and Zika virus (ZIKV), respectively, of the Flaviviridae family. DENV and ZIKV have been reported to co-infect several people, resulting in fatalities across the world. A vaccine that can safeguard against both these pathogens concurrently, can offer several advantages. This study has employed immuno-informatics for devising a multi-epitope, multi-pathogenic vaccine against both these viruses. Since, the two viruses share a common vector source, whose salivary components are reported to aid viral pathogenesis; antigenic salivary proteins from Aedes aegypti were also incorporated into the design of the vaccine along with conserved structural and non-structural viral proteins. Conserved B- and T-cell epitopes were identified for all the selected antigenic proteins. These epitopes were merged and further supplemented with β-defensin as an adjuvant, to yield an immunogenic vaccine construct. In-silico 3D modeling and structural validation of the vaccine construct was conducted, followed by its molecular docking and molecular dynamics simulation studies with human TLR2. Immune simulation study was also performed, and it further provided support that the designed vaccine can mount an effective immune response and hence provide protection against both DENV and ZIKV. Communicated by Ramaswamy H. Sarma.

Immunoinformatics Vaccine Design for Zika Virus

Zika virus (ZIKV) is an emerging virus from the Flaviviridae family and Flavivirus genus that has caused important outbreaks around the world. ZIKV infection is associated with severe neuropathology in newborns and adults. Until now, there is no licensed vaccine available for ZIKV infection. Therefore, the development of a safe and effective vaccine against ZIKV is an urgent need. Recently, we designed an in silico multi-epitope vaccine for ZIKV based on immunoinformatics tools. To construct this in silico ZIKV vaccine, we used a consensus sequence generated from ZIKV sequences available in databank. Then, we selected CD4+ and CD8+ T cell epitopes from all ZIKV proteins based on the binding prediction to class II and class I human leukocyte antigen (HLA) molecules, promiscuity, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the construct and B cell epitopes were identified. Adjuvants were associated to increase immunogenicity. Distinct linkers were used for connecting the CD4+ and CD8+ T cell epitopes, EDIII, and adjuvants. Several analyses, such as antigenicity, population coverage, allergenicity, autoimmunity, and secondary and tertiary structures of the vaccine, were evaluated using various immunoinformatics tools and online web servers. In this chapter, we present the protocols with the rationale and detailed steps needed for this in silico multi-epitope ZIKV vaccine design.

Design of a chimeric protein composed of FimH, FyuA and CNF-1 virulence factors from uropathogenic Escherichia coli and evaluation its biological activity and immunogenicity in vitro and in vivo

Urinary tract infections (UTIs) caused by Uropathogenic Escherichia coli (UPEC) are among the most prevalent bacterial infections in humans. Antibiotic resistance among UPEC isolates is increasing, and designing an effective vaccine can prevent or reduce these infections. FimH adhesin, iron scavenger receptor FyuA, and cytotoxic necrotizing factor -1 (CNF-1) are among the most important virulence factors of UPEC strains. Thus, a novel multi-epitope protein composed of FimH, FyuA, and CNF-1 was designed to evaluate its biological activity and immunogenicity in vitro and in vivo, respectively. The final vaccine design had seven domains, including the N-terminal domain of FimH, four domains of FyuA, and two domains of CNF-1, as determined by immunoinformatics analysis. The results of tertiary structure prediction showed that the chimeric protein had a C-score of -0.25 and Z-score of -1.94. Molecular docking indicated that thirty six ligand residues of the chimeric protein interacted with 53 receptor residues of TLR-4 by hydrogen bonds and hydrophobic interactions. Analysis of protein expression by SDS-PAGE showed an approximately 44 kDa band with different concentrations of IPTG which were confirmed by Western blot. According to ELISA results, the level of IL-8 produced by stimulated Ht29 cells with the chimeric protein was significantly higher than the stimulated Ht29 cells with CNF-1 alone and un-stimulated Ht29 cells. Rabbits subcutaneously immunized with the chimeric protein admixed with Freund adjuvant induced higher level of serum IgG on day 14 after the first vaccination than control rabbits. Furthermore, the booster dose of the chimeric protein significantly enhanced the IgG levels as compared to day 14 and also controls. As, the chimeric protein has suitable B-cell epitopes and MHC-I and MHC-II binding epitopes to stimulate humoral and cellular immunity, it could be a promising vaccine candidate against UTIs caused by UPEC. Evaluating the multi-epitope protein in inducing humoral and cellular immune responses, as well as protection, is ongoing in the mice models.

Recent Advances in Genomics-Based Approaches for the Development of Intracellular Bacterial Pathogen Vaccines

Infectious diseases continue to be a leading cause of morbidity and mortality worldwide. The majority of infectious diseases are caused by intracellular pathogenic bacteria (IPB). Historically, conventional vaccination drives have helped control the pathogenesis of intracellular bacteria and the emergence of antimicrobial resistance, saving millions of lives. However, in light of various limitations, many diseases that involve IPB still do not have adequate vaccines. In response to increasing demand for novel vaccine development strategies, a new area of vaccine research emerged following the advent of genomics technology, which changed the paradigm of vaccine development by utilizing the complete genomic data of microorganisms against them. It became possible to identify genes related to disease virulence, genetic patterns linked to disease virulence, as well as the genetic components that supported immunity and favorable vaccine responses. Complete genomic databases, and advancements in transcriptomics, metabolomics, structural genomics, proteomics, immunomics, pan-genomics, synthetic genomics, and population biology have allowed researchers to identify potential vaccine candidates and predict their effects in patients. New vaccines have been created against diseases for which previously there were no vaccines available, and existing vaccines have been improved. This review highlights the key issues and explores the evolution of vaccines. The increasing volume of IPB genomic data, and their application in novel genome-based techniques for vaccine development, were also examined, along with their characteristics, and the opportunities and obstacles involved. Critically, the application of genomics technology has helped researchers rapidly select and evaluate candidate antigens. Novel vaccines capable of addressing the limitations associated with conventional vaccines have been developed and pressing healthcare issues are being addressed.

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.

Bioinformatics Designing and Molecular Modelling of a Universal mRNA Vaccine for SARS-CoV-2 Infection

At this present stage of COVID-19 re-emergence, designing an effective candidate vaccine for different variants of SARS-CoV-2 is a study worthy of consideration. This research used bioinformatics tools to design an mRNA vaccine that captures all the circulating variants and lineages of the virus in its construct. Sequences of these viruses were retrieved across the six continents and analyzed using different tools to screen for the preferable CD8⁺ T lymphocytes (CTL), CD4⁺ T lymphocytes (HTL), and B-cell epitopes. These epitopes were used to design the vaccine. In addition, several other co-translational residues were added to the construct of an mRNA vaccine whose molecular weight is 285.29686 kDa with an estimated pI of 9.2 and has no cross affinity with the human genome with an estimated over 68% to cover the world population. It is relatively stable, with minimal deformability in its interaction with the human innate immune receptor, which includes TLR 3 and TLR 9. The overall result has proven that the designed candidate vaccine is capable of modulating cell-mediated immune responses by activating the actions of CD4⁺ T cells, natural killer cells, and macrophages, and displayed an increased memory T cell and B cell activities, which may further be validated via in vivo and in vitro techniques.

Exploiting reverse vaccinology approach for the design of a multiepitope subunit vaccine against the major SARS-CoV-2 variants

The current COVID-19 pandemic, an infectious disease caused by the novel coronavirus (SARS-CoV-2), poses a threat to global health because of its high rate of spread and death. Currently, vaccination is the most effective method to prevent the spread of this disease. In the present study, we developed a novel multiepitope vaccine against SARS-CoV-2 containing Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (BA.1) variants. To this end, we performed a robust immunoinformatics approach based on multiple epitopes of the four structural proteins of SARS-CoV-2 (S, M, N, and E) from 475 SARS-CoV-2 genomes sequenced from the regions with the highest number of registered cases, namely the United States, India, Brazil, France, Germany, and the United Kingdom. To investigate the best immunogenic epitopes for linear B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL), we evaluated antigenicity, allergenicity, conservation, immunogenicity, toxicity, human population coverage, IFN-inducing, post-translational modifications, and physicochemical properties. The tertiary structure of a vaccine prototype was predicted, refined, and validated. Through docking experiments, we evaluated its molecular coupling to the key immune receptor Toll-Like Receptor 3 (TLR3). To improve the quality of docking calculations, quantum mechanics/molecular mechanics calculations (QM/MM) were used, with the QM part of the simulations performed using the density functional theory formalism (DFT). Cloning and codon optimization were performed for the successful expression of the vaccine in E. coli. Finally, we investigated the immunogenic properties and immune response of our SARS-CoV-2 multiepitope vaccine. The results of the simulations show that administering our prototype three times significantly increases the antibody response and decreases the amount of antigens. The proposed vaccine candidate should therefore be tested in clinical trials for its efficacy in neutralizing SARS-CoV-2.

An Attention Based Bidirectional LSTM Method to Predict the Binding of TCR and Epitope

The T-cell epitope prediction has always been a long-term challenge in immunoinformatics and bioinformatics. Studying the specific recognition between T-cell receptor (TCR) and peptide-major histocompatibility complex (p-MHC) complexes can help us better understand the immune mechanism, it's also make a signification contribution in developing vaccines and targeted drugs. Meanwhile, more advanced methods are needed for distinguishing TCRs binding from different epitopes. In this paper, we introduce a hybrid model composed of bidirectional long short-term memory networks (BiLSTM), attention and convolutional neural networks (CNN) that can identified the binding of TCRs to epitopes. The BiLSTM can more completely extract amino acid forward and backward information in the sequence, and attention mechanism can focus on amino acids at certain positions from complex sequences to capture the most important feature, then CNN was used to further extract salient features to predict the binding of TCR-epitope. In McPAS dataset, the AUC value (the area under ROC curve) of naive TCR-epitope binding is 0.974 and specific TCR-epitope binding is 0.887. The model has achieved better prediction results than other existing models (TCRGP, ERGO, NetTCR), and some experiments are used to analyze the advantages of our model. The algorithm is available at https://github.com/bijingshu/BiAttCNN.git.

The Advantage of Using Immunoinformatic Tools on Vaccine Design and Development for Coronavirus

After the outbreak of SARS-CoV-2 by the end of 2019, the vaccine development strategies became a worldwide priority. Furthermore, the appearances of novel SARS-CoV-2 variants challenge researchers to develop new pharmacological or preventive strategies. However, vaccines still represent an efficient way to control the SARS-CoV-2 pandemic worldwide. This review describes the importance of bioinformatic and immunoinformatic tools (in silico) for guide vaccine design. In silico strategies permit the identification of epitopes (immunogenic peptides) which could be used as potential vaccines, as well as nonacarriers such as: vector viral based vaccines, RNA-based vaccines and dendrimers through immunoinformatics. Currently, nucleic acid and protein sequential as well structural analyses through bioinformatic tools allow us to get immunogenic epitopes which can induce immune response alone or in complex with nanocarriers. One of the advantages of in silico techniques is that they facilitate the identification of epitopes, while accelerating the process and helping to economize some stages of the development of safe vaccines.

A specific Leishmania infantum polyepitope vaccine triggers Th1-type immune response and protects against experimental visceral leishmaniasis

The development of an immunogenic, effective, and safe vaccine is essential as an alternative for disease control. The present study aimed to evaluate the immunogenicity and efficacy potential of a polyepitope T-cell antigen candidate against visceral leishmaniasis in a murine model. BALB/c mice were immunized with three doses subcutaneously with Poly-T Leish alone or adjuvanted with Saponin plus Monophosphoryl lipid A, with 15-day intervals between doses, and challenged with 10⁷ stationary-phase Leishmania infantum promastigotes via tail vein. Immunogenicity and parasitism in spleen and liver of immunized mice were evaluated 45 days post-challenge. Our results revealed that the immunization with Poly-T Leish and Poly-T Leish/SM increases the percentage of specific T (CD4⁺ and CD8⁺) lymphocytes proliferation in vitro after antigen-specific stimulation. Also, Poly-T Leish and Poly-T Leish/SM groups showed a high percentage of IFN-γ and TNF-α-producing T cells, meanwhile, the Poly-T Leish/SM group also showed an increased percentage of multifunctional T cells producing double and triple-positive (IFN-γ⁺TNF-α⁺IL-2⁺) cytokines. The immunization with Poly-T Leish or Poly-T Leish/SM stimulated a decreased IL-4 and IL-10 compared to the Saline and adjuvant group. Poly-T Leish/SM immunized mice exhibit a noteworthy reduction in the parasite burden (spleen and liver) through real-time PCR (96%). Moreover, we observed higher nitrite secretion in 120-hour stimulated-culture supernatant using Griess method. We demonstrated that the Poly-T Leish/SM candidate was potentially immunogenic, providing enhancement of protective immune mechanisms, and conferred protection reducing parasitism. Our candidate was considered potential against visceral leishmaniasis, and eventually, could be tested in phase I and II clinical trials in dogs.

B.1.1.7 (Alpha) variant is the most antigenic compared to Wuhan strain, B.1.351, B.1.1.28/triple mutant and B.1.429 variants

The rapid spread of the SARS-CoV-2 virus and its variants has created a catastrophic impact worldwide. Several variants have emerged, including B.1.351 (Beta), B.1.1.28/triple mutant (P.1), B.1.1.7 (Alpha), and B.1.429 (Epsilon). We performed comparative and comprehensive antigenicity mapping of the total S-glycoprotein using the Wuhan strain and the other variants and identified 9-mer, 15-mer, and 20-mer CTL epitopes through in silico analysis. The study found that 9-mer CTL epitope regions in the B.1.1.7 variant had the highest antigenicity and an average of the three epitope types. Cluster analysis of the 9-mer CTL epitopes depicted one significant cluster at the 70% level with two nodes (KGFNCYFPL and EGFNCYFPL). The phage-displayed peptides showed mimic 9-mer CTL epitopes with three clusters. CD spectra analysis showed the same band pattern of S-glycoprotein of Wuhan strain and all variants other than B.1.429. The developed 3D model of the superantigen (SAg)-like regions found an interaction pattern with the human TCR, indicating that the SAg-like component might interact with the TCR beta chain. The present study identified another partial SAg-like region (ANQFNSAIGKI) from the S-glycoprotein. Future research should examine the molecular mechanism of antigen processing for CD8⁺ T cells, especially all the variants’ antigens of S-glycoprotein.

Multi Epitope-Based Vaccine Design for Protection Against Mycobacterium tuberculosis and SARS-CoV-2 Coinfection

Background

A prophylactic and immunotherapeutic vaccine for Mycobacterium tuberculosis (MTB) and SARS-CoV-2 coinfection needs to be developed for a proactive and effective therapeutic approach. Therefore, this study aims to use immunoinformatics to design a multi-epitope vaccine for protection against MTB and SARS-CoV-2 coinfection.

Methods

The bioinformatic techniques were used to screen and construct potential epitopes from outer membrane protein A Rv0899 of MTB and spike glycoprotein of SARS-CoV-2 for B and T cells. The antigenicity, allergenicity, and several physiochemical properties of the developed multi-epitope vaccination were then evaluated. Additionally, molecular docking and normal mode analysis (NMA) were utilized in evaluating the vaccine’s immunogenicity and complex stability.

Results

Selected proteins and predicted epitopes suggest that the vaccine prediction can be helpful in the protection against both SARS-CoV-2 and MTB coinfection. Through docking molecular and NMA, the vaccine-TLR4 protein interaction was predicted to be efficient with a high level of IgG, T-helper cells, T-cytotoxic cells, andIFN-γ.

Conclusion

This epitope-based vaccine is a potentially attractive tool for SARS-CoV-2 and MTB coinfection vaccine development.

Engineering a novel immunogenic chimera protein utilizing bacterial infections associated with atherosclerosis to induce a deviation in adaptive immune responses via Immunoinformatics approaches

Recent studies have established the role of bacteria including Streptococcus pneumoniae, Helicobacter pylori, Chlamydia pneumonia, Mycobacterium tuberculosis, and Porphyromonas gingivalis in the development of atherosclerosis. These bacteria contribute to plaque formation via promoting Th1 immune responses and speeding up ox-LDL formation. Hence, we employed computational reverse vaccinology (RV) approaches to deviate immune response toward Th2 via engineering a novel immunogenic chimera protein. Prominent atherogenic antigens from related bacteria were identified. Then, machine learning-based servers were employed for predicting CTL and HTL epitopes. We selected epitopes from a wide variety of HLAs. Then, a chimeric protein sequence containing TAT peptide, adjuvant, IL-10 inducer, and linker-separated epitopes was designed. The conformational structure of the vaccine was built via multiple-template homology modelling using MODELLER. The initial structure was refined and validated by Ramachandran plot. The vaccine was also docked with TLR4. After that, molecular dynamics (MD) simulation of the docked vaccine-TLR4 was conducted. Finally, the immune simulation of the vaccine was conducted via the C-ImmSim server. A chimera protein with 629 amino acids was built and, classified as a non-allergenic probable antigen. An improved ERRAT score of 80.95 for the refined structure verified its stability. Additionally, validation via the Ramachandran plot showed 98.09% of the residues were located in the most favorable and permitted regions. MD simulations showed the vaccine-TLR4 complex reached a stable conformation. Also, RMS fluctuations analysis revealed no sign of protein denaturation or unfolding. Finally, immune response simulations indicated a promising response by innate and adaptive immunity. In summary, we built an immunogenic vaccine against atherosclerosis and demonstrated its favorable properties via advanced Immunoinformatics analyses. This study may pave the path for combat against atherosclerosis.

Detection of cell markers from single cell RNA-seq with sc2marker

Background

Single-cell RNA sequencing (scRNA-seq) allows the detection of rare cell types in complex tissues. The detection of markers for rare cell types is useful for further biological analysis of, for example, flow cytometry and imaging data sets for either physical isolation or spatial characterization of these cells. However, only a few computational approaches consider the problem of selecting specific marker genes from scRNA-seq data.

Results

Here, we propose sc2marker, which is based on the maximum margin index and a database of proteins with antibodies, to select markers for flow cytometry or imaging. We evaluated the performances of sc2marker and competing methods in ranking known markers in scRNA-seq data of immune and stromal cells. The results showed that sc2marker performed better than the competing methods in accuracy, while having a competitive running time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04817-5.

Genomic surveillance reveals antibiotic resistance gene transmission via phage recombinases within sheep mastitis-associated Streptococcus uberis

Background

Streptococcus uberis is one of the main causative agents of ovine mastitis, however little is known about this global, environmental pathogen and its genomic mechanisms of disease. In this study, we performed genomic analysis on 46 S. uberis isolates collected from mastitis-infected sheep in Sardinia (Italy).

Results

Genomes were assigned into lineage clusters using PopPUNK, which found 27 distinct isolate clusters, indicating considerable genetic variability consistent with environmental isolates. Geographic trends were identified including regional linkage of several isolate clusters. Multi-locus Sequence Typing (MLST) performed poorly and provided no new insights.

Genomes were then screened for antimicrobial resistance genes, which were compared to phenotypic resistance profiles. Isolates showed consistent phenotypic resistance to aminoglycosides with variable resistance to novobiocin and tetracycline. In general, identification of antimicrobial resistance genes did not correlate with phenotypic resistance profiles, indicating unknown genetic determinants. A multi-antimicrobial resistance cassette (aminoglycoside, lincosamide and streptogramin) was identified in the chromosome of three genomes, flanked by vestigial phage recombinases. This locus appears to have spread horizontally within discrete S. uberis populations within a 40 km radius (Sassari region).

Genomes were screened for putative virulence factors, which identified 16 genes conserved between sheep and cow isolates, with no host-specific genes shared uniformly across all host-specific isolates.

Pangenomic analysis was then performed to identify core genes which were putatively surface-exposed, for identification of potential vaccine targets. As all genomes encoded sortase, core genes were screened for the sortase cleavage motif. Of the 1445 core S. uberis genes, 64 were putative sortase substrates and were predominantly adhesins, permeases and peptidases, consistent with compounds found within ruminant milk such as xanthine, fibronectin and lactoferrin.

Conclusions

This study demonstrated the importance of whole genome sequencing for surveillance of S. uberis and tracking horizontal acquisition of antimicrobial resistance genes, as well as providing insight into genetic determinants of disease, which cannot be inferred from the MLST schemes. Future mastitis surveillance should be informed by genomic analysis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-022-03341-1.

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.