Designing an efficient multi-epitope vaccine displaying interactions with diverse HLA molecules for an efficient humoral and cellular immune response to prevent COVID-19 infection

Immunoinformatic approach for multi-epitope vaccine design against Staphylococcus aureus based on hemolysin proteins

Staphylococcus aureus is a common bacterium that causes a variety of infections in humans. This microorganism produces several virulence factors, including hemolysins, which contribute to its disease-causing ability. The treatment of S. aureus infections typically involves the use of antibiotics. However, the emergence of antibiotic-resistant strains has become a major concern. Therefore, vaccination against S. aureus has gained attention as an alternative approach. Vaccination has the advantage of stimulating the immune system to produce specific antibodies that can neutralize bacteria and prevent infection. However, developing an effective vaccine against S. aureus has proven to be challenging. This study aimed to use in silico methods to design a multi-epitope vaccine against S. aureus infection based on hemolysin proteins. The designed vaccine contained four B-cell epitopes, four CTL epitopes, and four HTL epitopes, as well as the ribosomal protein L7/L12 and pan-HLA DR-binding epitope, included as adjuvants. Furthermore, the vaccine was non-allergenic and non-toxic with the potential to stimulate the TLR2-, TLR-4, and TLR-6 receptors. The predicted vaccine exhibited a high degree of antigenicity and stability, suggesting potential for further development as a viable vaccine candidate. The population coverage of the vaccine was 94.4 %, indicating potential widespread protection against S. aureus. Overall, these findings provide valuable insights into the design of an effective multi-epitope vaccine against S. aureus infection and pave the way for future experimental validations.

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

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

Putative new combination vaccine candidates identified by reverse vaccinology and genomic approaches to control enteric pathogens

OBJECTIVES

The pathogenic microorganisms that cause intestinal diseases can significantly jeopardize people's health. Currently, there are no authorized treatments or vaccinations available to combat the germs responsible for intestinal disease.

METHODS

Using immunoinformatics, we developed a potent multi-epitope Combination (combo) vaccine versus Salmonella and enterohemorrhagic E. coli. The B and T cell epitopes were identified by performing a conservancy assessment, population coverage analysis, physicochemical attributes assessment, and secondary and tertiary structure assessment of the chosen antigenic polypeptide. The selection process for vaccine development included using several bioinformatics tools and approaches to finally choose two linear B-cell epitopes, five CTL epitopes, and two HTL epitopes.

RESULTS

The vaccine had strong immunogenicity, cytokine production, immunological properties, non-toxicity, non-allergenicity, stability, and potential efficacy against infections. Disulfide bonding, codon modification, and computational cloning were also used to enhance the stability and efficacy of expression in the host E. coli. The vaccine's structure has a strong affinity for the TLR4 ligand and is very durable, as shown by molecular docking and molecular modeling. The results of the immunological simulation demonstrated that both B and T cells had a heightened response to the vaccination component.

CONCLUSIONS

The comprehensive in silico analysis reveals that the proposed vaccine will likely elicit a robust immune response against pathogenic bacteria that cause intestinal diseases. Therefore, it is a promising option for further experimental testing.

Insights into structural vaccinology harnessed for universal coronavirus vaccine development

Structural vaccinology is pivotal in expediting vaccine design through high-throughput screening of immunogenic antigens. Leveraging the structural and functional characteristics of antigens and immune cell receptors, this approach employs protein structural comparison to identify conserved patterns in key pathogenic components. Molecular modeling techniques, including homology modeling and molecular docking, analyze specific three-dimensional (3D) structures and protein interactions and offer valuable insights into the 3D interactions and binding affinity between vaccine candidates and target proteins. In this review, we delve into the utilization of various immunoinformatics and molecular modeling tools to streamline the development of broad-protective vaccines against coronavirus disease 2019 variants. Structural vaccinology significantly enhances our understanding of molecular interactions between hosts and pathogens. By accelerating the pace of developing effective and targeted vaccines, particularly against the rapidly mutating severe acute respiratory syndrome coronavirus 2 and other prevalent infectious diseases, this approach stands at the forefront of advancing immunization strategies. The combination of computational techniques and structural insights not only facilitates the identification of potential vaccine candidates but also contributes to the rational design of vaccines, fostering a more efficient and targeted approach to combatting infectious diseases.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Designing of a multi-epitopes based vaccine against Haemophilius parainfluenzae and its validation through integrated computational approaches

Haemophilus parainfluenzae is a Gram-negative opportunist pathogen within the mucus of the nose and mouth without significant symptoms and has an ability to cause various infections ranging from ear, eye, and sinus to pneumonia. A concerning development is the increasing resistance of H. parainfluenzae to beta-lactam antibiotics, with the potential to cause dental infections or abscesses. The principal objective of this investigation is to utilize bioinformatics and immuno-informatic methodologies in the development of a candidate multi-epitope Vaccine. The investigation focuses on identifying potential epitopes for both B cells (B lymphocytes) and T cells (helper T lymphocytes and cytotoxic T lymphocytes) based on high non-toxic and non-allergenic characteristics. The selection process involves identifying human leukocyte antigen alleles demonstrating strong associations with recognized antigenic and overlapping epitopes. Notably, the chosen alleles aim to provide coverage for 90% of the global population. Multi-epitope constructs were designed by using suitable linker sequences. To enhance the immunological potential, an adjuvant sequence was incorporated using the EAAAK linker. The final vaccine construct, comprising 344 amino acids, was achieved after the addition of adjuvants and linkers. This multi-epitope Vaccine demonstrates notable antigenicity and possesses favorable physiochemical characteristics. The three-dimensional conformation underwent modeling and refinement, validated through in-silico methods. Additionally, a protein-protein molecular docking analysis was conducted to predict effective binding poses between the multi-epitope Vaccine and the Toll-like receptor 4 protein. The Molecular Dynamics (MD) investigation of the docked TLR4-vaccine complex demonstrated consistent stability over the simulation period, primarily attributed to electrostatic energy. The docked complex displayed minimal deformation and enhanced rigidity in the motion of residues during the dynamic simulation. Furthermore, codon translational optimization and computational cloning was performed to ensure the reliability and proper expression of the multi-Epitope Vaccine. It is crucial to emphasize that despite these computational validations, experimental research in the laboratory is imperative to demonstrate the immunogenicity and protective efficacy of the developed vaccine. This would involve practical assessments to ascertain the real-world effectiveness of the multi-epitope Vaccine.

In silico discovery of diagnostic/vaccine candidate antigenic epitopes and a multi-epitope peptide vaccine (NaeVac) design for the brain-eating amoeba Naegleria fowleri causing human meningitis

Naegleria fowleri, the brain-eating amoeba, is a free-living amoeboflagellate with three different life cycles (trophozoite, flagellated, and cyst) that lives in a variety of habitats around the world including warm freshwater and soil. It causes a disease called naegleriasis leading meningitis and primary amoebic meningoencephalitis (PAM) in humans. N. fowleri is transmitted through contaminated water sources such as insufficiently chlorinated swimming pool water or contaminated tap water, and swimmers are at risk. N. fowleri is found all over the world, and most infections were reported in both developed and developing countries with high mortality rates and serious clinical findings. Until now, there is no FDA approved vaccine and early diagnosis is urgent against this pathogen. In this study, by analyzing the N. fowleri vaccine candidate proteins (Mp2CL5, Nfa1, Nf314, proNP-A and proNP-B), it was aimed to discover diagnostic/vaccine candidate epitopes and to design a multi-epitope peptide vaccine against this pathogen. After the in silico evaluation, three prominent diagnostic/vaccine candidate epitopes (EAKDSK, LLPHIRILVY, and FYAKLLPHIRILVYS) with the highest antigenicities were discovered and a potentially highly immunogenic/antigenic multi-epitope peptide vaccine (NaeVac) was designed against the brain-eating amoeba N. fowleri causing human meningitis.

A computational approach to design a multiepitope vaccine against H5N1 virus

Since 1997, highly pathogenic avian influenza viruses, such as H5N1, have been recognized as a possible pandemic hazard to men and the poultry business. The rapid rate of mutation of H5N1 viruses makes the whole process of designing vaccines extremely challenging. Here, we used an in silico approach to design a multi-epitope vaccine against H5N1 influenza A virus using hemagglutinin (HA) and neuraminidase (NA) antigens. B-cell epitopes, Cytotoxic T lymphocyte (CTL) and Helper T lymphocyte (HTL) were predicted via IEDB, NetMHC-4 and NetMHCII-2.3 respectively. Two adjuvants consisting of Human β-defensin-3 (HβD-3) along with pan HLA DR-binding epitope (PADRE) have been chosen to induce more immune response. Linkers including KK, AAY, HEYGAEALERAG, GPGPGPG and double EAAAK were utilized to link epitopes and adjuvants. This construct encodes a protein having 350 amino acids and 38.46 kDa molecular weight. Antigenicity of ~ 1, the allergenicity of non-allergen, toxicity of negative and solubility of appropriate were confirmed through Vaxigen, AllerTOP, ToxDL and DeepSoluE, respectively. The 3D structure of H5N1 was refined and validated with a Z-Score of - 0.87 and an overall Ramachandran of 99.7%. Docking analysis showed H5N1 could interact with TLR7 (docking score of - 374.08 and by 4 hydrogen bonds) and TLR8 (docking score of - 414.39 and by 3 hydrogen bonds). Molecular dynamics simulations results showed RMSD and RMSF of 0.25 nm and 0.2 for H5N1-TLR7 as well as RMSD and RMSF of 0.45 nm and 0.4 for H5N1-TLR8 complexes, respectively. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) confirmed stability and continuity of interaction between H5N1-TLR7 with the total binding energy of - 29.97 kJ/mol and H5N1-TLR8 with the total binding energy of - 23.9 kJ/mol. Investigating immune response simulation predicted evidence of the ability to stimulate T and B cells of the immunity system that shows the merits of this H5N1 vaccine proposed candidate for clinical trials.

Designing a conjugate vaccine targeting Klebsiella pneumoniae ST258 and ST11

Klebsiella pneumoniae (K. pneumoniae) is a common bacterium that can cause iatrogenic infection. Recently, the rise of antibiotic resistance among K. pneumoniae strains is one key factor associated with antibiotic treatment failure. Hencefore, there is an urgent need for effective K. pneumoniae vaccines. This study aimed to design a multi-epitope vaccine (MEV) candidate against K. pneumonia by utilizing an immunoinformatics method. In this study, we obtained 15 cytotoxic T lymphocyte epitopes, 10 helper T lymphocyte epitopes, 6 linear B-cell epitopes, and 2 conformational B-cell epitopes for further research. Then, we designed a multi-epitope vaccine composed of a total of 743 amino acids, containing the epitopes linked by GPGPG flexible links and an EAAAK linker to the Cholera Toxin Subunit B coadjuvant. The observed properties of the MEV, including non-allergenicity, high antigenicity, and hydrophilicity, are noteworthy. The improvements in the tertiary structure through structural refinement and disulfide bonding, coupled with promising molecular interactions revealed by molecular dynamics simulations with TLR4, position the MEV as a strong candidate for further investigation against K. pneumoniae.

Molecular characterization of Anaplasma ovis Msp4 protein in strains isolated from ticks in Turkey: A multi-epitope synthetic vaccine antigen design against Anaplasma ovis using immunoinformatic tools

Tick-borne pathogens increasingly threaten animal and human health as well as cause great economic loss in the livestock industry. Among these pathogens, Anaplasma ovis causing a decrease in meat and milk yield is frequently detected in sheep in many countries including Turkey. This study aimed to reveal potential vaccine candidate epitopes in Msp4 protein using sequence data from Anaplasma ovis isolates and then to design a multi-epitope protein to be used in vaccine formulations against Anaplasma ovis. For this purpose, Msp4 gene was sequenced from Anaplasma ovis isolates (n:6) detected in ticks collected from sheep in Turkey and the sequence data was compared with previous sequences from different countries in order to detect the variations of Msp4 gene/protein. Potential vaccine candidate and diagnostic epitopes were predicted using various immunoinformatics tools. Among the discovered vaccine candidate epitopes, antigenic and conserved were selected, and then a multi-epitope protein was designed. The designed vaccine protein was tested for the assessment of TLR-2, IgG, and IFN-g responses by molecular docking and immune simulation analyses. Among the discovered epitopes, EVASEGSGVM and YQFTPEISLV epitopes with properties of high antigenicity, non-allergenicity, and non-toxicity were proposed to be used for Anaplasma ovis in further serodiagnostic and vaccine studies.

Promising vaccine models against astrovirus MLB2 using integrated vaccinomics and immunoinformatics approaches

Integrated vaccinomics and immunoinformatics-guided promising vaccine model prioritization against meningitis and disseminated infection-associated astrovirus MLB2.

Promising vaccine models against astrovirus MLB2 using integrated vaccinomics and immunoinformatics approaches

Integrated vaccinomics and immunoinformatics-guided promising vaccine model prioritization against meningitis and disseminated infection-associated astrovirus MLB2.

Computational exploration and design of a multi-epitopes vaccine construct against Chlamydia psittaci

Chlamydia psittaci is an intracellular pathogen and causes variety of deadly infections in humans. Antibiotics are effective against C. psittaci however high percentage of resistant strains have been reported in recent times. As there is no licensed vaccine, we used in-silico techniques to design a multi-epitopes vaccine against C. psittaci. Following a step-wise protocol, the proteome of available 26 strains was retrieved and filtered for subcellular localized proteins. Five proteins were selected (2 extracellular and 3 outer membrane) and were further analyzed for B-cell and T-cell epitopes prediction. Epitopes were further checked for antigenicity, solubility, stability, toxigenicity, allergenicity, and adhesive properties. Filtered epitopes were linked via linkers and the 3D structure of the designed vaccine construct was predicted. Binding of the designed vaccine with immune receptors: MHC-I, MHC-II, and TLR-4 was analyzed, which resulted in docking energy scores of -4.37 kcal/mol, -0.20 kcal/mol and -22.38 kcal/mol, respectively. Further, the docked complexes showed stable dynamics with a maximum value of vaccine-MHC-I complex (7.8 Å), vaccine-MHC-II complex (6.2 Å) and vaccine-TLR4 complex (5.2 Å). As per the results, the designed vaccine construct reported robust immune responses to protect the host against C. psittaci infections. In the study, the C. psittaci proteomes were considered in pan-genome analysis to extract core proteins. The pan-genome analysis was conducted using bacterial pan-genome analysis (BPGA) software. The core proteins were checked further for non-redundant proteins using a CD-Hit server. Surface localized proteins were investigated using PSORTb v 3.0. The surface proteins were BLASTp against Virulence Factor Data Base (VFDB) to predict virulent factors. Antigenicity prediction of the shortlisted proteins was further done using VAXIGEN v 2.0. The epitope mapping was done using the immune epitope database (IEDB). A multi-epitopes vaccine was built and a 3D structure was generated using 3Dprot online server. The docking analysis of the designed vaccine with immune receptors was carried out using PATCHDOCK. Molecular dynamics and post-simulation analyses were carried out using AMBER v20 to decipher the dynamics stability and intermolecular binding energies of the docked complexes.Communicated by Ramaswamy H. Sarma.

Development of multi-epitope based subunit vaccine against Mycobacterium Tuberculosis using immunoinformatics approach

The etiological agent of tuberculosis (TB), Mycobacterium tuberculosis, is a deadly pathogen that adapts to thrive within the host. Since 2020, the COVID-19 pandemic has had colossal health, societal, and economic consequences, which have affected the reporting of new incidences and mortality cases of TB. As per the WHO 2022 report, 10.6 million people were diagnosed with TB, and 1.6 million died worldwide. The increase in resistant strains of tuberculosis is making it more burdensome to reach the End TB strategy. A reliable and efficient TB vaccine that may avert both primary infection and recurrence of latent TB in adults and adolescents is of the utmost importance. In this study, we used computational techniques to predict the ability of HLA molecules to display epitopes for six TB proteins (PPE68, PE_PGRS17, EspC, LDT4, RpfD, and RpfC) to design the multi-epitope subunit vaccine. From the aimed proteins, the potential B-cell, helper T lymphocyte (HTL), and cytotoxic T lymphocyte (CTL) epitopes were predicted and linked together with LPA adjuvant, and the vaccine was designed. The vaccine's physicochemical analysis demonstrates that it is non-allergic, non-toxic, and antigenic. Then, the vaccine structure was predicted, improved, and verified to yield the optimal structure. The developed vaccine's binding mechanism with distinct immunogenic receptors (Tlr2 and MHC-II) was assessed utilizing molecular docking. The molecular dynamic simulation and MMPBSA analysis were performed to comprehend the complexes' dynamics and stability. The immune simulation was utilized to anticipate the vaccine's immunogenic attributes. In silico cloning was employed to demonstrate the efficient expression of the designed vaccine in E. coli as a host. Moreover, in vitro and in vivo animal testing is required to determine the efficacy of the in silico developed vaccine.Communicated by Ramaswamy H. Sarma.

Designing a bioadjuvant candidate vaccine targeting infectious bursal disease virus (IBDV) using viral VP2 fusion and chicken IL-2 antigenic epitope: A bioinformatics approach

Infectious Bursal Disease (IBD) is a common and contagious viral infection that significantly affects the poultry industry. This severely suppresses the immune system in chickens, thereby threating their health and well-being. Vaccination is the most effective strategy for preventing and controlling this infectious agent. The development of VP2-based DNA vaccines combined with biological adjuvants has recently received considerable attention due to their effectiveness in eliciting both humoral and cellular immune responses. In this study, we applied bioinformatics tools to design a fused bioadjuvant candidate vaccine from the full-length sequence of the VP2 protein of IBDV isolated in Iran using the antigenic epitope of chicken IL-2 (chiIL-2). Furthermore, to improve the antigenic epitope presentation and to maintain the three-dimensional structure of the chimeric gene construct, the P2A linker (L) was used to fuse the two fragments. Our in-silico analysis for the design of a candidate vaccine indicates that a continuous sequence of amino acid residues ranging from 105 to 129 in chiIL-2 is proposed as a B cell epitope by epitope prediction servers. The final 3D structure of the VP2-L-chiIL-2105-129 was subjected to physicochemical property determination, molecular dynamic simulation, and antigenic site determination. The results of these analyses led to the development of a stable candidate vaccine that is non-allergenic and has the potential for antigenic surface display potential and adjuvant activity. Finally, it is necessary to investigate the immune response induced by our proposed vaccine in avian hosts. Notably, increasing the immunogenicity of DNA vaccines can be achieved by combining antigenic proteins with molecular adjuvants using the principle of rational vaccine design.

Designing of multi-epitope peptide vaccine against Acinetobacter baumannii through combined immunoinformatics and protein interaction-based approaches

Acinetobacter baumannii is one of the major pathogenic ESKAPE bacterium, which is responsible for about more than 722,000 cases in a year, globally. Despite the alarming increase in multidrug resistance, a safe and effective vaccine for Acinetobacter infections is still not available. Hence in the current study, a multiepitope vaccine construct was developed using linear B cell, cytotoxic T cell, and helper T cell epitopes from the antigenic and well-conserved lipopolysaccharide assembly proteins employing systematic immunoinformatics and structural vaccinology strategies. The multi-peptide vaccine was predicted to be highly antigenic, non-allergenic, non-toxic, and cover maximum population coverage worldwide. Further, the vaccine construct was modeled along with adjuvant and peptide linkers and validated to achieve a high-quality three-dimensional structure which was subsequently utilized for cytokine prediction, disulfide engineering, and docking analyses with Toll-like receptor (TLR4). Ramachandran plot showed 98.3% of the residues were located in the most favorable and permitted regions, thereby corroborating the feasibility of the modeled vaccine construct. Molecular dynamics simulation for a 100 ns timeframe further confirmed the stability of the binding vaccine-receptor complex. Finally, in silico cloning and codon adaptation were also performed with the pET28a (+) plasmid vector to determine the efficiency of expression and translation of the vaccine. Immune simulation studies demonstrated that the vaccine could trigger both B and T cell responses and can elicit strong primary, secondary, and tertiary immune responses. The designed multi-peptide subunit vaccine would certainly expedite the experimental approach for the development of a vaccine against A. baumannii infection.

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.

Designing a Next-Generation Multiepitope-Based Vaccine against Staphylococcus aureus Using Reverse Vaccinology Approaches

Staphylococcus aureus is a human bacterial pathogen that can cause a wide range of symptoms. As virulent and multi-drug-resistant strains of S. aureus have evolved, invasive S. aureus infections in hospitals and the community have become one of the leading causes of mortality and morbidity. The development of novel techniques is therefore necessary to overcome this bacterial infection. Vaccines are an appropriate alternative in this context to control infections. In this study, the collagen-binding protein (CnBP) from S. aureus was chosen as the target antigen, and a series of computational methods were used to find epitopes that may be used in vaccine development in a systematic way. The epitopes were passed through a filtering pipeline that included antigenicity, toxicity, allergenicity, and cytokine inducibility testing, with the objective of identifying epitopes capable of eliciting both T and B cell-mediated immune responses. To improve vaccine immunogenicity, the final epitopes and phenol-soluble modulin α4 adjuvant were fused together using appropriate linkers; as a consequence, a multiepitope vaccine was developed. The chosen T cell epitope ensemble is expected to cover 99.14% of the global human population. Furthermore, docking and dynamics simulations were used to examine the vaccine's interaction with the Toll-like receptor 2 (TLR2), revealing great affinity, consistency, and stability between the two. Overall, the data indicate that the vaccine candidate may be extremely successful, and it will need to be evaluated in experimental systems to confirm its efficiency.

Design of multi-epitope based vaccine against Mycobacterium tuberculosis: a subtractive proteomics and reverse vaccinology based immunoinformatics approach

Tuberculosis is an airborne transmissible disease caused by Mycobacterium tuberculosis that infects millions of lives worldwide. There is still no single comprehensive therapy or preventative available for the lethal illness. Currently, the available vaccine, BCG is ineffectual in preventing the prophylactic adult pulmonary TB and reactivation of latent tuberculosis. Therefore, this investigation was intended to design a new multi-epitope vaccine that can address the existing problems. The subtractive proteomics approach was implemented to prioritize essential, virulence, druggable, and antigenic proteins as suitable vaccine candidates. Furthermore, a reverse vaccinology-based immunoinformatics technique was employed to identify potential B-cell, helper T lymphocytes (HTL), and cytotoxic T lymphocytes (CTL) epitopes from the target proteins. Immune-stimulating adjuvant, linkers, and PADRE (Pan HLA-DR epitopes) amino acid sequences along with the selected epitopes were used to construct a chimeric multi-epitope vaccine. The molecular docking and normal mode analysis (NMA) were carried out to evaluate the binding mode of the designed vaccine with different immunogenic receptors (MHC-I, MHC-II, and Tlr4). In addition, the MD simulation, followed by essential dynamics study and MMPBSA analysis, was carried out to understand the dynamics and stability of the complexes. In-silico cloning was accomplished using E.coli as an expression system to express the designed vaccine successfully. Finally, the immune simulation study has foreseen that our designed vaccine could induce a significant immune response by elevation of different immunoglobulins in the host. However, there is an imperative need for the experimental validation of the designed vaccine in animal models to confer effectiveness and safety.HIGHLIGHTSMulti-epitope based vaccine was designed against Mycobacterium tuberculosis using subtractive proteomics and Immunoinformatics approach.The vaccine was found to be antigenic, non-allergenic, immunogenic, and stable based on in-silico prediction.Population coverage analysis of the proposed vaccine predicts an effective response in the world population.The molecular docking, MD simulation, and MM-PBSA study confirm the stable interaction of the vaccine with immunogenic receptors.In silico cloning and immune simulation of the vaccine demonstrated its successful expression in E.coli and induction of immune response in the host. Communicated by Ramaswamy H. Sarma.

In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis

Tuberculosis (TB) is a global health threat, killing approximately 1.5 million people each year. The eradication of Mycobacterium tuberculosis, the main causative agent of TB, is increasingly challenging due to the emergence of extensive drug-resistant strains. Vaccination is considered an effective way to protect the host from pathogens, but the only clinically approved TB vaccine, Bacillus Calmette-Guérin (BCG), has limited protection in adults. Multi-epitope vaccines have been found to enhance immunity to diseases by selectively combining epitopes from several candidate proteins. This study aimed to design a multi-epitope vaccine against TB using an immuno-informatics approach. Through functional enrichment, we identified eight proteins secreted by M. tuberculosis that are either required for pathogenesis, secreted into extracellular space, or both. We then analyzed the epitopes of these proteins and selected 16 helper T lymphocyte epitopes with interferon-γ inducing activity, 15 cytotoxic T lymphocyte epitopes, and 10 linear B-cell epitopes, and conjugated them with adjuvant and Pan HLA DR-binding epitope (PADRE) using appropriate linkers. Moreover, we predicted the tertiary structure of this vaccine, its potential interaction with Toll-Like Receptor-4 (TLR4), and the immune response it might elicit. The results showed that this vaccine had a strong affinity for TLR4, which could significantly stimulate CD4⁺ and CD8⁺ cells to secrete immune factors and B lymphocytes to secrete immunoglobulins, so as to obtain good humoral and cellular immunity. Overall, this multi-epitope protein was predicted to be stable, safe, highly antigenic, and highly immunogenic, which has the potential to serve as a global vaccine against TB.

Characterization and analysis of linear epitopes corresponding to SARS-CoV-2 outbreak in Jilin Province, China

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants have caused hundreds of thousands of deaths and shown serious social influence worldwide. Jilin Province, China, experienced the first wave of the outbreak from December 2020 to February 2021. Here, we analyzed the genomic characteristics of the SARS-CoV-2 outbreak in Jilin province using a phylogeographic tree and found that clinical isolates belonged to the B.1 lineage, which was considered to be the ancestral lineage. Several dominant SARS-CoV-2 specific linear B cell epitopes that reacted with the convalescent sera were also analysed and identified using a peptide microarray composed of S, M, and E proteins. Moreover, the serum of convalescent patients infected with SARS-CoV-2 showed neutralizing activity against four widely spreading SARS-CoV-2 variants; however, significant differences were observed in neutralizing activities against different SARS-CoV-2 variants. These data provide important information on genomic characteristics, linear epitopes, and neutralizing activity of SARS-CoV-2 outbreak in Jilin Province, China, which may aid in understanding disease patterns and regional aspects of the pandemic.

Design of a potential Sema4A-based multi-epitope vaccine to combat triple-negative breast cancer: an immunoinformatic approach

Immunotherapy is revamping the therapeutic strategies for TNBC owing to its higher mutational burden and tumour-associated antigens. One of the most intriguing developments in cancer immunotherapy is the focus on peptide-based cancer vaccines. Thus, the current work aims to develop an efficient peptide-based vaccine against TNBC that targets Sema4A, which has recently been identified as a major regulator of TNBC progression. Initially, the antigenic peptides derived from Sema4A were determined and evaluated based on their capability to provoke immunological responses. The assessed epitopes were then linked with a suitable adjuvant (RpfB and RpfE) and appropriate linkers (AAY, GPGPG, KK and EAAAK) to preclude junctional immunogenicity. Eventually, docking and dynamics simulations are performed against TLR-2, TLR-4, TLR-7 and TLR-9 to assess the interaction between the vaccine construct and TLR receptors, as the TLR signalling pathway is critical in the host immune response. The developed vaccine was then exposed to in silico cloning and immune simulation analysis. The findings suggest that the designed vaccine could potentially evoke significant humoral and cellular immune responses in the intended organism. Considering these outcomes, the final multi-epitope vaccine could be employed to serve as an effective choice for TNBC management and may open new avenues for further studies.

Immunoinformatic approach employing modeling and simulation to design a novel vaccine construct targeting MDR efflux pumps to confer wide protection against typhoidal Salmonella serovars

Overcoming multi drug resistance is one of the crucial challenges to control enteric typhoid fever caused by Salmonella typhi and Salmonella paratyphi. Overexpression of efflux pumps predominantly causes drug resistance in microorganisms. Therefore, immunotherapy targeting the various efflux pumps antigens could be a promising strategy to increase the success of vaccines. An immunoinformatic approach was employed to design a Salmonellosis multi-epitope subunit vaccine peptide consisting of linear B-cell and T-cell epitopes of multidrug resistance protein families including ATP Binding Cassette (ABC), major facilitator superfamily (MFS), resistance nodulation cell division (RND), small multidrug resistance (SMR), and multidrug and toxin extrusion (MATE). The selected epitopes exhibited conservation in both S. typhi and S. paratyphi and thus could be helpful for cross-protection. Further, the final vaccine construct encompassing the peptides, adjuvants and specific linker sequences showed high immunogenicity, solubility, non-allergenic, nontoxic, and wide population coverage due to strong binding affinity to maximum HLA alleles. The three-dimensional structure was predicted, and validated using various structure validation tools. Additionally, protein-protein docking of the chimeric vaccine construct with the TLR-2 protein and molecular dynamics demonstrated stable and efficient binding. Conclusively, the immunoinformatic study showed that the novel multi epitopic vaccine construct can simulate the both T-cell and B-cell immune responses in typhoidal Salmonella serovars and could potentially be used for prophylactic or therapeutic applications.Communicated by Ramaswamy H. Sarma.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

Vaccines against SARS-CoV-2 variants and future pandemics

INTRODUCTION

Vaccination continues to be the most effective method for controlling COVID-19 infectious diseases. Nonetheless, SARS-CoV-2 variants continue to evolve and emerge, resulting in significant public concerns worldwide, even after more than 2 years since the COVID-19 pandemic. It is important to better understand how different COVID-19 vaccine platforms work, why SARS-CoV-2 variants continue to emerge, and what options for improving COVID-19 vaccines can be considered to fight against SARS-CoV-2 variants and future pandemics.

AREA COVERED

Here, we reviewed the innate immune sensors in the recognition of SARS-CoV-2 virus, innate and adaptive immunity including neutralizing antibodies by different COVID-19 vaccines. Efficacy comparison of the several COVID-19 vaccine platforms approved for use in humans, concerns about SARS-CoV-2 variants and breakthrough infections, and the options for developing future COIVD-19 vaccines were also covered.

EXPERT OPINION

Owing to the continuous emergence of novel pathogens and the reemergence of variants, safer and more effective new vaccines are needed. This review also aims to provide the knowledge basis for the development of next-generation COVID-19 and pan-coronavirus vaccines to provide cross-protection against new SARS-CoV-2 variants and future coronavirus pandemics.

Designing multi-epitope-based vaccine targeting surface immunogenic protein of Streptococcus agalactiae using immunoinformatics to control mastitis in dairy cattle

Background

Milk provides energy as well as the basic nutrients required by the body. In particular, milk is beneficial for bone growth and development in children. Based on scientific evidence, cattle milk is an excellent and highly nutritious dietary component that is abundant in vitamins, calcium, potassium, and protein, among other minerals. However, the commercial productivity of cattle milk is markedly affected by mastitis. Mastitis is an economically important disease that is characterized by inflammation of the mammary gland. This disease is frequently caused by microorganisms and is detected as abnormalities in the udder and milk. Streptococcus agalactiae is a prominent cause of mastitis. Antibiotics are rarely used to treat this infection, and other available treatments take a long time to exhibit a therapeutic effect. Vaccination is recommended to protect cattle from mastitis. Accordingly, the present study sought to design a multi-epitope vaccine using immunoinformatics.

Results

The vaccine was designed to be antigenic, immunogenic, non-toxic, and non-allergic, and had a binding affinity with Toll-like receptor 2 (TLR2) and TLR4 based on structural modeling, docking, and molecular dynamics simulation studies. Besides, the designed vaccine was successfully expressed in E. coli. expression vector (pET28a) depicts its easy purification for production on a larger scale, which was determined through in silico cloning. Further, immune simulation analysis revealed the effectiveness of the vaccine with an increase in the population of B and T cells in response to vaccination.

Conclusion

This multi-epitope vaccine is expected to be effective at generating an immune response, thereby paving the way for further experimental studies to combat mastitis.

In-silico design of an immunoinformatics based multi-epitope vaccine against Leishmania donovani

BACKGROUND

Visceral Leishmaniasis (VL) is a fatal vector-borne parasitic disorder occurring mainly in tropical and subtropical regions. VL falls under the category of neglected tropical diseases with growing drug resistance and lacking a licensed vaccine. Conventional vaccine synthesis techniques are often very laborious and challenging. With the advancement of bioinformatics and its application in immunology, it is now more convenient to design multi-epitope vaccines comprising predicted immuno-dominant epitopes of multiple antigenic proteins. We have chosen four antigenic proteins of Leishmania donovani and identified their T-cell and B-cell epitopes, utilizing those for in-silico chimeric vaccine designing. The various physicochemical characteristics of the vaccine have been explored and the tertiary structure of the chimeric construct is predicted to perform docking studies and molecular dynamics simulations.

RESULTS

The vaccine construct is generated by joining the epitopes with specific linkers. The predicted tertiary structure of the vaccine has been found to be valid and docking studies reveal the construct shows a high affinity towards the TLR-4 receptor. Population coverage analysis shows the vaccine can be effective on the majority of the world population. In-silico immune simulation studies confirms the vaccine to raise a pro-inflammatory response with the proliferation of activated T and B cells. In-silico codon optimization and cloning of the vaccine nucleic acid sequence have also been achieved in the pET28a vector.

CONCLUSION

The above bioinformatics data support that the construct may act as a potential vaccine. Further wet lab synthesis of the vaccine and in vivo works has to be undertaken in animal model to confirm vaccine potency.

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.

Immunoinformatics-guided designing of epitope-based subunit vaccine from Pilus assembly protein of Acinetobacter baumannii bacteria

Acinetobacter baumannii, a prominent pathogen responsible for chronic infections in the blood, urinary tract, and lungs, has a high mortality due to its virulence and limited preventive methods. The present study aims to characterize the pilus assembly protein of A. baumannii to offer leads for epitope-based vaccine development. FilF is the putative pilus assembly protein that reportedly plays a supreme character in the virulence of this WHO-listed ESKAPE bacterium. Implementing various bioinformatics tools, led to the recognition of many antigenic B and T cell epitopes. Most promising B and T-cell epitopes were selected based on their binding efficiency with commonly occurring MHC alleles. Finally, we stepped down to fourteen protective antigenic peptides. These epitopes were also revealed to be non-allergenic and non-toxic. As a result, a vaccine chimera was created by linking these epitopes with appropriate linkers and adjuvant such as β-defensins. Furthermore, homology modeling and validation were carried out, with the modeled structure being employed for molecular docking with the immunological receptor (TLR-4) found on lymphocyte cells. As a result of the molecular dynamics simulation, the interaction between human TLR-4 and the multi-epitope vaccine sequence was stable. Finally, in silico cloning and immune simulation were carried out to see the efficacy of the construct vaccine. This is the first study targeting the pilus assembly protein from A. baumannii to identify novel epitopes that hold potential for further experimental design of multi-peptide vaccine construct against the pathogen.

Nonhuman primate models for evaluation of SARS-CoV-2 vaccines

INTRODUCTION

Evaluation of immunogenicity and efficacy in animal models provide critical data in vaccine development. Nonhuman primates (NHPs) have been used extensively in the evaluation of SARS-CoV-2 vaccines.

AREAS COVERED

A critical synthesis of SARS-CoV-2 vaccine development with a focus on challenge studies in NHPs is provided. The benefits and drawbacks of the NHP models are discussed. The citations were selected by the authors based on PubMed searches of the literature, summaries from national public health bodies, and press-release information provided by vaccine developers.

EXPERT OPINION

We identify several aspects of NHP models that limit their usefulness for vaccine-challenge studies and numerous variables that constrain comparisons across vaccine platforms. We propose that studies conducted in NHPs for vaccine development should use a standardized protocol and, where possible, be substituted with smaller animal models. This will ensure continued rapid progression of vaccines to clinical trials without compromising assessments of safety or efficacy.

B-Cell Epitope Mapping from Eight Antigens of Candida albicans to Design a Novel Diagnostic Kit: An Immunoinformatics Approach

Invasive candidiasis is an emerging fungal infection and a leading cause of morbidity in health care facilities. Despite advances in antifungal therapy, increased antifungal drug resistance in Candida albicans has enhanced patient fatality. The most common method for Candida albicans diagnosing is blood culture, which has low sensitivity. Therefore, there is an urgent need to establish a valid diagnostic method. Our study aimed to use the bioinformatics approach to design a diagnostic kit for detecting Candida albicans with high sensitivity and specificity. Eight antigenic proteins of Candida albicans (HYR1, HWP1, ECE1, ALS, EAP1, SAP1, BGL2, and MET6) were selected. Next, a construct containing different immunodominant B-cell epitopes was derived from the antigens and connected using a suitable linker. Different properties of the final construct, such as physicochemical properties, were evaluated. Moreover, the designed construct underwent 3D modeling, reverse translation, and codon optimization. The results confirmed that the designed construct could identify Candida albicans with high sensitivity and specificity in serum samples of patients with invasive candidiasis. However, experimental studies are needed for final confirmation.

Designing a novel multi-epitope vaccine to evoke a robust immune response against pathogenic multidrug-resistant Enterococcus faecium bacterium

Enterococcus faecium is an emerging ESKAPE bacterium that is capable of causing severe public health complications in humans. There are currently no licensed treatments or vaccinations to combat the deadly pathogen. We aimed to design a potent and novel prophylactic chimeric vaccine against E. faecium through an immunoinformatics approach The antigenic Penicillin-binding protein 5 (PBP 5) protein was selected to identify B and T cell epitopes, followed by conservancy analysis, population coverage, physiochemical assessment, secondary and tertiary structural analysis. Using various immunoinformatics methods and tools, two linear B-cell epitopes, five CTL epitopes, and two HTL epitopes were finally selected for vaccine development. The constructed vaccine was determined to be highly immunogenic, cytokine-producing, antigenic, non-toxic, non-allergenic, and stable, as well as potentially effective against E. faecium. In addition, disulfide engineering, codon adaptation, and in silico cloning, were used to improve stability and expression efficiency in the host E. coli. Molecular docking and molecular dynamics simulations indicated that the structure of the vaccine is stable and has a high affinity for the TLR4 receptor. The immune simulation results revealed that both B and T cells had an increased response to the vaccination component. Conclusively, the in-depth in silico analysis suggests, the proposed vaccine to elicit a robust immune response against E. faecium infection and hence a promising target for further experimental trials.

Immunoinformatic paradigm predicts macrophage and T-cells epitope responses against globally conserved spike fragments of SARS CoV-2 for universal vaccination

Background

Different quickly-developed vaccines are introduced against COVID-19 with inconclusive results especially against some recent variants. Eventually, somewhere COVID-19 cases decline and in some countries it revived with some new mutant-variants (i.e. D614G, Delta and Omicron).

Objectives

Proposing a universal vaccination strategy by screening globally-conserved SARS-CoV-2 spike-epitopes.

Methods

Presently, several conserved (186-countries) sequences including multiple-variants (ClustalX2) epitopic-regions (SVMTriP and IEDB) and in-silico mutants of SARS-CoV-2 spike-protein-fragments (Cut1-4) were screened for their stability against proteases, antigenicity (VaxiJen V2.0 and for glycosylation effects NetOGlyc-NetNGlyc), MHCI/II reactivity (IEDB-TOOLS) and CD4+ responses by molecular-docking (Haddock2.4/PatchDock). We also examined Molecular-Dynamic-Simulation (myPresto verson-5) of MHC-II 3LQZ with 3-Cuts and T-cell 2-molecules (1KGC/4JRX) with SM3-Cut. The MD-simulation was run with 5000-cycles after 300 k-heating/1-atm pressure adjustment for the system-equilibration. Finally, 1000 fs production was run.

Results

The cut4-mutant (SRLFRKSNLKPFERD) showed the highest combined-score 48.23548 and Immunogenicity-Score of 92.0887. The core-sequence SRLFRKSNL showed the highest Median-Percentile-Rank (7-HLA-allele) of 19. CD4+ immunogenicity also confirms the representation of the CUT4TM2 epitope SRLFRKSNL by MHC Class II. The epitope YNYKYRLFR from CUT4 showed an IC50 of ∼30 nM with allele HLA-DRB1*11:01 and HLA-DRB5*01:01 with plenty H-bonding. Cut4 double-mutants strongly interact with the exposed T-cell surface and are facilitated by its receptors. The MD-simulation data suggest that TM2 has a maximum RMSD value of 1.7 Å, DM2 is at 1.55 Å and SM3 is at 1.5 Å. These variations correspond to structural adjustments and involve binding/unbinding chemical interactions. The RMSD plot shows that 1KGC T-cell molecule is at 2.2 Å and the 4JRX is at 1.2 Å, which increases with the simulation time.

Conclusions

Screening of conserved SARS-CoV-2 spike fragments helps to find the most stable antigenic-determinant which with some mutations showed better antigenicity. Further studies are necessary to develop global vaccination strategies against COVID-19.

Immune escape facilitation by mutations of epitope residues in RdRp of SARS-CoV-2

Mutations drive viral evolution and genome variability that causes viruses to escape host immunity and to develop drug resistance. SARS-CoV-2 has considerably higher mutation rate. SARS-CoV-2 possesses a RNA dependent RNA polymerase (RdRp) which helps to replicate its genome. The mutation P323L in RdRp is associated with the loss of a particular epitope (321-327) from this protein. We consider the effects of mutations in some of the epitope region including the naturally occurring mutation P323L on the structure of the epitope and their interface with paratope using all-atom molecular dynamics (MD) simulation studies. We observe that the mutations cause conformational changes in the epitope region by opening up the region associated with increase in the radius of gyration and intramolecular hydrogen bonds, making the region less accessible. Moreover, we study the conformational stability of the epitope region and epitope:paratope interface under the mutation from the fluctuations in the dihedral angles. We observe that the mutation renders the epitope and the epitope:paratope interface unstable compared to the corresponding wild type ones. Thus, the mutations may help in escaping antibody mediated immunity of the hostCommunicated by Ramaswamy H. Sarma.

Exploring Klebsiella pneumoniae capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia

BACKGROUND

Klebsiella pneumoniae is an emerging human pathogen causing neonatal lung disease, catheter-associated infections, and nosocomial outbreaks with high fatality rates. Capsular polysaccharide (CPS) protein plays a major determinant in virulence and is considered as a promising target for vaccine development.

RESEARCH DESIGN AND METHODS

In this study, we used immunoinformatic approaches to design a multi-peptide vaccine against K. pneumonia. The epitopes were selected through several immune filters, such as antigenicity, conservancy, nontoxicity, non-allergenicity, binding affinity to HLA alleles, overlapping epitopes, and peptides having common epitopes.

RESULTS

Finally, a construct comprising 2 B-Cell, 8 CTL, 2 HTL epitopes, along with adjuvant, linkers was designed. Peptide-HLA interaction analysis showed strong binding of these epitopes with several common HLA molecules. The in silico immune simulation and population coverage analysis of the vaccine showed its potential to evoke strong immune responses.. Further, the interaction between vaccine and immune was evaluated by docking and simulation, revealing high affinity and complex stability. Codon adaptation and in silico cloning revealed higher expression of vaccine in E. coli K12 expression system.

CONCLUSIONS

Conclusively, the findings of the present study suggest that the designed novel multi-epitopic vaccine holds potential for further experimental validation against the pathogen.

Molecular Characterization and Designing of a Novel Multiepitope Vaccine Construct Against Pseudomonas aeruginosa

ABSTRACT

Pseudomonas aeruginosa, an ESKAPE pathogen causes many fatal clinical diseases in humans across the globe. Despite an increase in clinical instances of Pseudomonas infection, there is currently no effective vaccine or treatment available. The major membrane protein candidate of the P. aeruginosa bacterial cell is known to be a critical component for cellular bacterial susceptibility to antimicrobial peptides and survival inside the host organisms. Therefore, the current computational study aims to examine P. aeruginosa's major membrane protein, OprF, and OprI, in order to design linear B-cell, cytotoxic T-cell, and helper T-cell peptide-based vaccine constructs. Utilizing various immune-informatics tools and databases, a total of two B-cells and twelve T-cells peptides were predicted. The final vaccine design was simulated to generate a high-quality three-dimensional structure, which included epitopes, adjuvant, and linkers. The vaccine was shown to be nonallergenic, antigenic, soluble, and had the best biophysical properties. The vaccine and Toll-like receptor 4 have a strong and stable interaction, according to protein-protein docking and molecular dynamics simulations. Additionally, in silico cloning was employed to see how the developed vaccine expressed in the pET28a (+) vector. Ultimately, an immune simulation was performed to see the vaccine efficacy. In conclusion, the newly developed vaccine appears to be a promising option for a vaccine against P. aeruginosa infection.

GRAPHICAL ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10989-021-10356-z.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Genome-based identification and comparative analysis of enzymes for carotenoid biosynthesis in microalgae

Microalgae are potential feedstocks for the commercial production of carotenoids, however, the metabolic pathways for carotenoid biosynthesis across algal lineage are largely unexplored. This work is the first to provide a comprehensive survey of genes and enzymes associated with the less studied methylerythritol 4-phosphate/1-deoxy-D-xylulose 5-phosphate pathway as well as the carotenoid biosynthetic pathway in microalgae through bioinformatics and comparative genomics approach. Candidate genes/enzymes were subsequently analyzed across 22 microalgae species of lineages Chlorophyta, Rhodophyta, Heterokonta, Haptophyta, Cryptophyta, and known Arabidopsis homologs in order to study the evolutional divergence in terms of sequence-structure properties. A total of 403 enzymes playing a vital role in carotene, lutein, zeaxanthin, violaxanthin, canthaxanthin, and astaxanthin were unraveled. Of these, 85 were hypothetical proteins whose biological roles are not yet experimentally characterized. Putative functions to these hypothetical proteins were successfully assigned through a comprehensive investigation of the protein family, motifs, intrinsic physicochemical features, subcellular localization, pathway analysis, etc. Furthermore, these enzymes were categorized into major classes as per the conserved domain and gene ontology. Functional signature sequences were also identified which were observed conserved across microalgal genomes. Additionally, the structural modeling and active site architecture of three vital enzymes, DXR, PSY, and ZDS catalyzing the vital rate-limiting steps in Dunaliella salina were achieved. The enzymes were confirmed to be stereochemically reliable and stable as revealed during molecular dynamics simulation of 100 ns. The detailed functional information about individual vital enzymes will certainly help to design genetically modified algal strains with enhanced carotenoid contents.

Role of nanotechnology in facing SARS-CoV-2 pandemic: Solving crux of the matter with a hopeful arrow in the quiver

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense single-stranded RNA virus species with a zoonotic origin and responsible for the coronavirus disease 2019(COVID-19). This novel virus has an extremely high infectious rate, which occurs through the contact of contaminated surfaces and also by cough, sneeze, hand-to-mouth-to-eye contact with an affected person. The progression of infection, which goes beyond complications of pneumonia to affecting other physiological functions which cause gastrointestinal, Renal, and neurological complication makes this a life threatening condition. Intense efforts are going across the scientific community in elucidating various aspects of this virus, such as understanding the pathophysiology of the disease, molecular biology, and cellular pathways of viral replication. We hope that nanotechnology and material science can provide a significant contribution to tackle this problem through both diagnostic and therapeutic strategies. But the area is still in the budding phase, which needs urgent and significant attention. This review provides a brief idea regarding the various nanotechnological approaches reported for managing COVID-19 infection. The nanomaterials recently said to have good antiviral activities like Carbon nanotubes (CNTs) and quantum dots (QDs) were also discussed since they are also in the emerging stage of attaining research interest regarding antiviral applications.

Towards A Novel Multi-Epitopes Chimeric Vaccine for Simulating Strong Immune Responses and Protection against Morganella morganii

Morganella morganii is one of the main etiological agents of hospital-acquired infections and no licensed vaccine is available against the pathogen. Herein, we designed a multi-epitope-based vaccine against M. morganii. Predicted proteins from fully sequenced genomes of the pathogen were subjected to a core sequences analysis, followed by the prioritization of non-redundant, host non-homologous and extracellular, outer membrane and periplasmic membrane virulent proteins as vaccine targets. Five proteins (TonB-dependent siderophore receptor, serralysin family metalloprotease, type 1 fimbrial protein, flagellar hook protein (FlgE), and pilus periplasmic chaperone) were shortlisted for the epitope prediction. The predicted epitopes were checked for antigenicity, toxicity, solubility, and binding affinity with the DRB*0101 allele. The selected epitopes were linked with each other through GPGPG linkers and were joined with the cholera toxin B subunit (CTBS) to boost immune responses. The tertiary structure of the vaccine was modeled and blindly docked with MHC-I, MHC-II, and Toll-like receptors 4 (TLR4). Molecular dynamic simulations of 250 nanoseconds affirmed that the designed vaccine showed stable conformation with the receptors. Further, intermolecular binding free energies demonstrated the domination of both the van der Waals and electrostatic energies. Overall, the results of the current study might help experimentalists to develop a novel vaccine against M. morganii.

Immunoinformatics and molecular docking studies reveal a novel Multi-Epitope peptide vaccine against pneumonia infection

Pneumonia is a major endemic disease around the world, and an effective vaccine is the need of the hour to fight against the disease. When there are no appropriate antiviral and associated therapies available, vaccine development becomes even more essential. Therefore, in the present study, a variety of immunoinformatics techniques was utilized to develop a novel multi-epitope vaccine that targets the highly immunodominant type 3 fimbrial protein of Klebsiella pneumoniae, the causal organism for pneumonia. The putative B and T cell epitopes were predicted from the protein and screened for antigenicity, toxicity, allergenicity, and cross-reactivity with human proteomes. Subsequently, the selected epitopes were joined with the help of linkers to form a robust vaccine construct. In addition, an adjuvant was applied to the N-terminal of the construct to improve the immunogenicity of the vaccine. The physicochemical properties, solubility, the secondary and tertiary structure of the final vaccine were also established. MD simulations for 100 ns were employed to assess the stability of the vaccine-TLR-2 docked complex. The final vaccine was optimized and cloned in pET28a (+) vector with His-tag to achieve maximum vaccine protein expression for ease of purification. Immune simulation results indicated the potency of this vaccine candidate as a probable therapeutic agent. In conclusion, the overall results of various immunoinformatics tools and methods employed revealed that the constructed multi-epitope vaccine exhibits a high potential for stimulating both B and T-cells immune responses against pneumonia infection. However, experimental immunological studies are required to corroborate the viability of the novel multi-epitope construct as a commercial vaccine.

Development of a Conserved Chimeric Vaccine for Induction of Strong Immune Response against Staphylococcus aureus Using Immunoinformatics Approaches

Staphylococcus aureus is one of the most notorious Gram-positive bacteria with a very high mortality rate. The WHO has listed S. aureus as one of the ESKAPE pathogens requiring urgent research and development efforts to fight against it. Yet there is a major layback in the advancement of effective vaccines against this multidrug-resistant pathogen. SdrD and SdrE proteins are attractive immunogen candidates as they are conserved among all the strains and contribute specifically to bacterial adherence to the host cells. Furthermore, these proteins are predicted to be highly antigenic and essential for pathogen survival. Therefore, in this study, using the immunoinformatics approach, a novel vaccine candidate was constructed using highly immunogenic conserved T-cell and B-cell epitopes along with specific linkers, adjuvants, and consequently modeled for docking with human Toll-like receptor 2. Additionally, physicochemical properties, secondary structure, disulphide engineering, and population coverage analysis were also analyzed for the vaccine. The constructed vaccine showed good results of worldwide population coverage and a promising immune response. For evaluation of the stability of the vaccine-TLR-2 docked complex, a molecular dynamics simulation was performed. The constructed vaccine was subjected to in silico immune simulations by C-ImmSim and Immune simulation significantly provided high levels of immunoglobulins, T-helper cells, T-cytotoxic cells, and INF-γ. Lastly, upon cloning, the vaccine protein was reverse transcribed into a DNA sequence and cloned into a pET28a (+) vector to ensure translational potency and microbial expression. The overall results of the study showed that the designed novel chimeric vaccine can simultaneously elicit humoral and cell-mediated immune responses and is a reliable construct for subsequent in vivo and in vitro studies against the pathogen.

B and T cell epitope-based peptides predicted from clumping factor protein of Staphylococcus aureus as vaccine targets

Staphylococcus aureus infection is emerging as a global threat because of the highly debilitating nature of the associated disease's unprecedented magnitude of its spread and growing global resistance to antimicrobial medicines. Recently WHO has categorized these bacteria under the high global priority pathogen list and is one of the six nosocomial pathogens termed as ESKAPE pathogens which have emerged as a serious threat to public health worldwide. The development of a specific vaccine can stimulate an optimal antibody response, thus providing immunity against it. Therefore, in the present study efforts have been made to identify potential vaccine candidates from the Clumping factor surface proteins (ClfA and ClfB) of S. aureus. Employing the immunoinformatics approach, fourteen antigenic peptides including T-cell, B-cell epitopes were identified which were non-toxic, non-allergenic, high antigenicity, strong binding efficiency with commonly occurring MHC alleles. Consequently, a multi-epitope vaccine chimera was designed by connecting these epitopes with suitable linkers an adjuvant to enhance immunogenicity. Further, homology modeling and molecular docking were performed to construct the three-dimensional structure of the vaccine and study the interaction between the modeled structure and immune receptor (TLR-2) present on lymphocyte cells. Consequently, molecular dynamics simulation for 100 ns period confirmed the stability of the interaction and reliability of the structure for further analysis. Finally, codon optimization and in silico cloning were employed to ensure the successful expression of the vaccine candidate. As the targeted protein is highly antigenic and conserved, hence the designed novel vaccine construct holds potential against emerging multi-drug-resistant organisms.

An in-depth in silico and immunoinformatics approach for designing a potential multi-epitope construct for the effective development of vaccine to combat against SARS-CoV-2 encompassing variants of concern and interest

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the latest of the several viral pathogens that have acted as a threat to human health around the world. Thus, to prevent COVID-19 and control the outbreak, the development of vaccines against SARS-CoV-2 is one of the most important strategies at present. The study aimed to design a multi-epitope vaccine (MEV) against SARS-CoV-2. For the development of a more effective vaccine, 1549 nucleotide sequences were taken into consideration, including the variants of concern (B.1.1.7, B.1.351, P.1 and, B.1.617.2) and variants of interest (B.1.427, B.1.429, B.1.526, B.1.617.1 and P.2). A total of 11 SARS-CoV-2 proteins (S, N, E, M, ORF1ab polyprotein, ORF3a, ORF6, ORF7a, ORF7b, ORF8, ORF10) were targeted for T-cell epitope prediction and S protein was targeted for B-cell epitope prediction. MEV was constructed using linkers and adjuvant beta-defensin. The vaccine construct was verified, based on its antigenicity, physicochemical properties, and its binding potential, with toll-like receptors (TLR2, TLR4), ACE2 receptor and B cell receptor. The selected vaccine construct showed considerable binding with all the receptors and a significant immune response, including elevated antibody titer and B cell population along with augmented activity of TH cells, Tc cells and NK cells. Thus, immunoinformatics and in silico-based approaches were used for constructing MEV which is capable of eliciting both innate and adaptive immunity. In conclusion, the vaccine construct developed in this study has all the potential for the development of a next-generation vaccine which may in turn effectively combat the new variants of SARS-CoV-2 identified so far. However, in vitro and animal studies are warranted to justify our findings for its utility as probable preventive measure.

Graphene, Carbon Nanotube and Plasmonic Nanosensors for Detection of Viral Pathogens: Opportunities for Rapid Testing in Pandemics like COVID-19

With the emergence of global pandemics such as the Black Death (Plague), 1918 influenza, smallpox, tuberculosis, HIV/AIDS, and currently the COVID-19 outbreak caused by the SARS-CoV-2 virus, there is an urgent, pressing medical need to devise methods of rapid testing and diagnostics to screen a large population of the planet. The important considerations for any such diagnostic test include: 1) high sensitivity (to maximize true positive rate of detection); 2) high specificity (to minimize false positives); 3) low cost of testing (to enable widespread adoption, even in resource-constrained settings); 4) rapid turnaround time from sample collection to test result; and 5) test assay without the need for specialized equipment. While existing testing methods for COVID-19 such as RT-PCR (real-time reverse transcriptase polymerase chain reaction) offer high sensitivity and specificity, they are quite expensive – in terms of the reagents and equipment required, the laboratory expertise needed to run and interpret the test data, and the turnaround time. In this review, we summarize the recent advances made using carbon nanotubes for sensors; as a nanotechnology-based approach for diagnostic testing of viral pathogens; to improve the performance of the detection assays with respect to sensitivity, specificity and cost. Carbon nanomaterials are an attractive platform for designing biosensors due to their scalability, tunable functionality, photostability, and unique opto-electronic properties. Two possible approaches for pathogen detection using carbon nanomaterials are discussed here: 1) optical sensing, and 2) electrochemical sensing. We explore the chemical modifications performed to add functionality to the carbon nanotubes, and the physical, optical and/or electronic considerations used for testing devices or sensors fabricated using these carbon nanomaterials. Given this progress, it is reason to be cautiously optimistic that nanosensors based on carbon nanotubes, graphene technology and plasmonic resonance effects can play an important role towards the development of accurate, cost-effective, widespread testing capacity for the world’s population, to help detect, monitor and mitigate the spread of disease outbreaks.

Lessons Learned from Cutting-Edge Immunoinformatics on Next-Generation COVID-19 Vaccine Research

Presently, immunoinformatics and bioinformatics approaches are contributing actively to COVID-19 vaccine research. The first immunoinformatics-based vaccine construct against SARS-CoV-2 was published in February 2020. Following this, immunoinformatics and bioinformatics approaches have created a new direction in COVID-19 vaccine research. Several researchers have designed the next-generation COVID-19 vaccines using these approaches. Presently, immunoinformatics has accelerated immunology research immensely in the area of COVID-19. Hence, we have tried to depict the current scenario of immunoinformatics and bioinformatics in COVID-19 vaccine research.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Perspectives of Manipulative and High-Performance Nanosystems to Manage Consequences of Emerging New Severe Acute Respiratory Syndrome Coronavirus 2 Variants

The emergence of new SARS-CoV-2 variants made the COVID-19 infection pandemic and/or endemic more severe and life-threatening due to ease of transmission, rapid infection, high mortality, and capacity to neutralize the therapeutic ability of developed vaccines. These consequences raise questions on established COVID-19 infection management strategies based on nano-assisted approaches, including rapid diagnostics, therapeutics, and efficient trapping and virus eradication through stimuli-assisted masks and filters composed of nanosystems. Considering these concerns as motivation, this perspective article highlights the role and aspects of nano-enabled approaches to manage the consequences of the COVID-19 infection pandemic associated with newer SARS-CoV-2 variants of concern and significance generated due to mutations. The controlled high-performance of a nanosystem seems capable of effectively detecting new variables for rapid diagnostics, performing site-specific delivery of a therapeutic agent needed for effective treatment, and developing technologies to purify the air and sanitizing premises. The outcomes of this report project manipulative, multifunctional nanosystems for developing high-performance technologies needed to manage consequences of newer SARS-CoV-2 variants efficiently and effectively through an overall targeted, smart approach.

Designing of a Novel Fusion Protein Vaccine Candidate Against Human Visceral Leishmaniasis (VL) Using Immunoinformatics and Structural Approaches

Leishmaniasis is caused by an obligate intracellular protozoan parasite. The clinical forms of leishmaniasis differ from cutaneous leishmaniasis, mucocutaneous leishmaniasis and visceral leishmaniasis (VL) which depend on the parasite species and the host's immune responses. There are significant challenges to the available anti-leishmanial drug therapy, particularly in severe forms of disease, and the rise of drug resistance has made it more difficult. Currently, no licensed vaccines have been introduced to the market for the control and elimination of VL. A potential target for use in candidate vaccines against leishmaniasis has been shown to be leishmania Kinetoplastid membrane protein-11 (KMP-11) antigen. In this study, we chose KMP-11 antigen as target antigen in our vaccine construct. In addition, B-type flagellin (fliC) was used as an adjuvant for enhancing vaccine immunogenicity. The GSGSGSGSGSG linker was applied to link the KMP-11 antigen and fliC (KMP-11-fliC) to construct our fusion protein. Bioinformatics approaches such as; 3D homology modeling, CTL, B-cell, MHC class I and II epitopes prediction, allergenicity, antigenicity evaluations, molecular docking, fast simulations of flexibility of docked complex and in silico cloning were employed to analysis and evaluation of various properties of the designed fusion construct. Computational results showed that our engineered structure has the potential for proper stimulation of cellular and humoral immune responses against VL. Consequently, it could be proposed as a candidate vaccine against VL according to these data and after verifying the efficacy of the candidate vaccine through in vivo and in vitro immunological tests.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.