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

Design of a multi-epitope vaccine against Staphylococcus Aureus lukotoxin ED using in silico approaches

Development of a strategy to combat Staphylococcus aureus is a high priority for the World Health Organization. B cell and helper T lymphocyte (HTL) epitopes of leukotoxin ED (LukED) were predicted using computational tools. The predicted epitopes were screened for conservancy, allergenicity, toxicity, autoreactivity, and population coverage. The immunogenic regions of LukED were linked together and to Human β-defensin 3 (hBD3) as adjuvant with appropriate linkers. The predicted 3D structure of the vaccine validated by molecular dynamics (MD) simulations. Subsequently, the 3D structure was docked with the Toll-like receptor (TLR)1/2 to evaluate the binding capacity of the adjuvant. Finally, MD simulation was employed to characterizing the conformational dynamics and stability of this interaction. The predicted epitopes were found to be non-toxic and non-allergenic, with no homology to the human proteome. The vaccine demonstrated a population coverage of 65.15% globally. It was composed of the immunogenic regions of LukED. Molecular docking and MD simulation indicated a stable interaction between hBD3 in the vaccine and TLR1/2 during the simulation period. We have designed vaccine against S. aureus LukED that targets epitope-rich regions, which helps maintain a native-like conformation. This work lays the groundwork for further experimental studies to evaluate the vaccine's neutralizing effects.

Epitope-Based Vaccines: The Next Generation of Promising Vaccines Against Bacterial Infection

The increasing resistance of bacteria to antibiotics has underscored the need for new drugs or vaccines to prevent bacterial infections. Reducing multidrug resistance is a key objective of the WHO's One Health initiative. Epitopes, the key parts of antigen molecules that determine their specificity, directly stimulate the body to produce specific humoral and/or cellular immune responses. Epitope-based vaccines, which combine dominant epitopes in a rational manner, induce a more efficient and specific immune response than the original antigen. While these vaccines face significant challenges, such as epitope escape or low immunogenicity, they offer advantages including minimal adverse reactions, improved efficacy, and optimized protection. As a result, epitope-based vaccines are considered a promising next-generation approach to combating bacterial infections. This review summarizes the latest advancements, challenges, and future prospects of epitope-based vaccines targeting bacteria, with a focus on their development workflow and application in antibiotic-resistant pathogens with high mortality rates, including Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. The goal of this review is to provide insights into next-generation vaccination strategies to combat bacterial infections associated with antibiotic resistance and high mortality rates.

Do Porto D, Custódio FL, Trevizani R, Nicolás MF. PAPreC: A Pipeline for Antigenicity Prediction Comparison Methods across Bacteria

Antigenicity prediction plays a crucial role in vaccine development, antibody-based therapies, and diagnostic assays, as this predictive approach helps assess the potential of molecular structures to induce and recruit immune cells and drive antibody production. Several existing prediction methods, which target complete proteins and epitopes identified through reverse vaccinology, face limitations regarding input data constraints, feature extraction strategies, and insufficient flexibility for model evaluation and interpretation. This work presents PAPreC (Pipeline for Antigenicity Prediction Comparison), an open-source, versatile workflow (available at https://github.com/YasCoMa/paprec_nx_workflow) designed to address these challenges. PAPreC systematically examines three key factors: the selection of training data sets, feature extraction methods (including physicochemical descriptors and ESM-2 encoder-derived embeddings), and diverse classifiers. It provides automated model evaluation, interpretability through SHapley Additive exPlanations (SHAP) analysis, and applicability domain assessments, enabling researchers to identify optimal configurations for their specific data sets. Applying PAPreC to IEDB data as a reference, we demonstrate its effectiveness across the ESKAPE pathogen group. A case study involving Pseudomonas aeruginosa and Staphylococcus aureus shows that specific feature configurations are more suitable for different sequence types, and that ESM-2 embeddings enhance model performance. Moreover, our results indicate that separate models for Gram-positive and Gram-negative bacteria are not required. PAPreC offers a comprehensive, adaptable, and robust framework to streamline and improve antigenicity prediction for diverse bacterial data sets.

Development of a novel multi-epitope mRNA vaccine candidate to combat SFTSV pandemic

Severe Fever with Thrombocytopenia Syndrome virus (SFTSV) is a novel identified pathogen, despite two decades of research on SFTSV, the potential widespread threats pose a significant challenge for researchers in developing new treatment and prevention methods. In this present, we have developed a multi-epitope mRNA vaccine for SFTSV and valid it with in silico methods. We screened 9 immunodominant epitopes for cytotoxic T cells (CTL), 7 for helper T cells (HTL), and 8 for Linear B-cell (LBL) based on promising candidate protein Gn, Gc, Np, and NSs. All predicted epitopes demonstrated strong antigenicity without any potential harm to humans. Additionally, the high conservancy is required to cover different strains. All epitopes as well as adjuvants were constructed into a final vaccine, which was further assesd by calculating of physicochemical properties. Then, we docked the vaccine protein with immune receptors and analyzed the complexes with dynamic simulations to evaluate its affinity to receptors. Finally, the vaccine sequence was constructed into a mRNA sequence. The constructed vaccine is a potential candidate for combating SFTSV by stimulating protective humoral and cellular immune responses.

Comparative proteomic analysis to annotate the structural association of the hypothetical proteins from the conserved domain of P. aeruginosa as novel vaccine candidates

OBJECTIVES

Pseudomonas aeruginosa, identified as an ESKAPE pathogen, contributes to severe clinical diseases worldwide and despite its prevalence an effective vaccine or treatment remains elusive. Numerous computational methods are being employed to target hypothetical proteins (HPs). Presently, no studies have predicted multi-epitope vaccines for these HPs.

RESULTS

Totally, 877 HPs from P. aeruginosa were included in the study and the data showcased here illustrate a methodical approach to prioritize the proteome by employing diverse comparative proteomics. The study employed physicochemical property assessment and conserved domain analysis to identify stable and immunologically pertinent proteins for epitope prediction. The VaxiJen2.0 antigenicity assessment aided in epitope selection, contributing to the foundational steps in vaccine development by predicting T-cell and B-cell epitopes. Potential T and B cell epitopes with high antigenicity, non-toxic categorization, and robust binding affinities were identified in the investigation. The periplasmic HP WP_132813935.1 was predicted as conserved, stable, and soluble. The T-cell peptide RTSMRALAY and the B-cell peptide MPVYLYLM were predicted to be probable non-allergen and demonstrated strong binding with MHC class I allele HLA-C*03:03.

CONCLUSIONS

This research provides a comprehensive approach to predict T and B cell epitopes for conditions associated with P. aeruginosa, offering a candidate pool for tailored vaccine development. However, the efficacy of these epitopes in vaccine development necessitates clinical validation and testing for confirmation.

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.

VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

Development of a novel multi-epitope vaccine based on capsid and envelope protein against Chikungunya virus

Chikungunya virus (CHIKV), a type A virus borne by mosquitoes that can cause major clinical manifestations including rash, fever and debilitating arthritis, grown into a reemerging serious public health issue. Currently, there is no licensed therapy or vaccine available for CHIKV, although the most promising form of treatment appears to be immunotherapy. Neutralizing antibodies for CHIKV can provide high protection for all CHIKV strains, as well as other alphaviruses. Development of a protective vaccine may be an effective strategy to prevent the outbreak of CHIKV and provide protection for travelers. In this study, we designed a multi-epitope vaccine with a 543-amino-acid structure based on the E1, E2 and capsid proteins of CHIKV, including 6 CTL epitopes, 6 HTL epitopes, 12 linear B epitopes, along with the adjuvant β-defensin III. All T-cell epitopes were docked with their corresponding MHC alleles to validate their effect on inducing immune responses, and the vaccine's sequence was proven to have acceptable physicochemical properties. Further, the developed vaccine was docked with TLR3 and TLR8, both of which play an important role in recognizing RNA viruses. Basic analyses of the docked complexes and molecular dynamic simulations revealed that the vaccine interacted strongly with TLRs. Immunological simulations indicated that the vaccine could induce both cellular and humoral immunity. Hopefully, this proposed vaccine structure can serve as a viable candidate against CHIKV infection.Communicated by Ramaswamy H. Sarma.

Cell-wall-anchored proteins affect invasive host colonization and biofilm formation in Staphylococcus aureus

As a major human and animal pathogen, Staphylococcus aureus can attach to medical implants (abiotic surface) or host tissues (biotic surface), and further establish robust biofilms which enhances resistance and persistence to host immune system and antibiotics. Cell-wall-anchored proteins (CWAPs) covalently link to peptidoglycan, and largely facilitate the colonization of S. aureus on various surfaces (including adhesion and biofilm formation) and invasion into host cells (including adhesion, immune evasion, iron acquisition and biofilm formation). During biofilm formation, CWAPs function in adhesion, aggregation, collagen-like fiber network formation, and consortia formation. In this review, we firstly focus on the structural features of CWAPs, including their intracellular function and interactions with host cells, as well as the functions and ligand binding of CWAPs in different stages of S. aureus biofilm formation. Then, the roles of CWAPs in different biofilm processes with regards in development of therapeutic approaches are clarified, followed by the association between CWAPs genes and clonal lineages. By touching upon these aspects, we hope to provide comprehensive knowledge and clearer understanding on the CWAPs of S. aureus and their roles in biofilm formation, which may further aid in prevention and treatment infection and vaccine development.

A broad-spectrum multiepitope vaccine against seasonal influenza A and B viruses in mice

BACKGROUND

Influenza viruses pose a persistent threat to global public health, necessitating the development of innovative and broadly effective vaccines.

METHODS

This study focuses on a multiepitope vaccine (MEV) designed to provide broad-spectrum protection against different influenza viruses. The MEV, containing 19 B-cell linear epitopes, 7 CD4+ T cells, and 11 CD8+ T cells epitopes identified through enzyme-linked immunospot assay (ELISPOT) in influenza viruses infected mice, was administered through a regimen of two doses of DNA vaccine followed by one dose of a protein vaccine in C57BL/6 female mice.

FINDINGS

Upon lethal challenge with both seasonal circulating strains (H1N1, H3N2, BV, and BY) and historical strains (H1N1-PR8 and H3N2-X31), MEV demonstrated substantial protection against different influenza seasonal strains, with partial efficacy against historical strains. Notably, the increased germinal centre B cells and antibody-secreting cells, along with robust T cell immune responses, highlighted the comprehensive immune defence elicited by MEV. Elevated hemagglutinin inhibition antibody was also observed against seasonal circulating and historical strains. Additionally, mice vaccinated with MEV exhibited significantly lower counts of inflammatory cells in the lungs compared to negative control groups.

INTERPRETATION

Our results demonstrated the efficacy of a broad-spectrum MEV against influenza viruses in mice. Conducting long-term studies to evaluate the durability of MEV-induced immune responses and explore its potential application in diverse populations will offer valuable insights for the continued advancement of this promising vaccine.

FUNDING

Funding bodies are described in the Acknowledgments section.

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.

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.

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.

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.

Tackling suppressive cancer microenvironment by NARF-derived immune modulatory vaccine and its validation using simulation strategies

Introduction: Targeting tumor microenvironment is beneficial and present an ideal setting for the development of futuristic immunotherapy. Here, we make use of Nuclear prelamin A recognition factor (NARF), a protein linked to the coactivation of transcriptional regulators in human breast cancer stem cells (CSC) in our investigation.

Methods: In this study, we initially computed the epitope regions possessing the ability to stimulate both T and B cells within the NARF protein. These identified epitope areas were fused with an adjuvant such as RpfB and RpfE as well as linkers like AAY, GPGPG, KK, and EAAAK. The constructed vaccine was further characterized by assessing its physicochemical properties and population coverage. The potential interactions of the designed vaccine with different toll-like receptors were examined by a sequence of computational studies. Of note, docking study were employed to understand its mechanism of action. Molecular dynamics and immune simulation studies were conducted to comprehend more into their structural stability and immune responses. The resultant vaccine was back-translated, codon-optimised and introduced into pET-28 (+) vector.

Results and discussion: We hypothesize from the results that the designed NARF protein-based vaccine in our analysis could effectively provoke the immune responses in the target organism through TLR-7 binding and promotes MHC class-II mediated antigen presentation. Indeed, comprehensive evaluations conducted in both in vitro and in vivo settings are imperative to substantiate the safety and efficacy of the developed vaccine.

Multi-epitopes vaccine design for surface glycoprotein against SARS-CoV-2 using immunoinformatic approach

Background

The recent COVID vaccinations have successfully reduced death and severity but did not stop the transmission of viruses by the emerging SARS-CoV-2 strain. There is a need for better and long-lasting dynamic vaccines for numerous prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of broad-spectrum strains being used to stop infection by reducing the spread rate and re-infection. The spike (S) glycoprotein is one of the proteins expressed commonly in the early phases of SARS-CoV-2 infection. It has been identified as the most immunogenic protein of SARS-CoV-2.

Methods

In this study, advanced bioinformatics techniques have been exploited to design the novel multi-epitope vaccine using conserved S protein portions from widespread strains of SARS-CoV-2 to predict B cell and T cell epitopes. These epitopes were selected based on toxicity, antigenicity score and immunogenicity. Epitope combinations were used to construct the maximum potent multi-epitope construct with potential immunogenic features. EAAAK, AAY, and GPGPG were used as linkers to construct epitopes.

Results

The developed vaccine has shown positive results. After the chimeric vaccine construct was cloned into the PET28a (+) vector for expression screening in Escherichia coli, the potential expression of the construct was identified.

Conclusion

The construct vaccine performed well in computer-based immune response simulation and covered a variety of allelic populations. These computational results are more helpful for further analysis of our contract vaccine, which can finally help control and prevent SARS-CoV-2 infections worldwide.

In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Influenza remains a global health concern due to its potential to cause pandemics as a result of rapidly mutating influenza virus strains. Existing vaccines often struggle to keep up with these rapidly mutating flu viruses. Therefore, the development of a broad-spectrum peptide vaccine that can stimulate an optimal antibody response has emerged as an innovative approach to addressing the influenza threat. In this study, an immunoinformatic approach was employed to rapidly predict immunodominant epitopes from different antigens, aiming to develop an effective multiepitope influenza vaccine (MEV). The immunodominant B-cell linear epitopes of seasonal influenza strains hemagglutinin (HA) and neuraminidase (NA) were predicted using an antibody-peptide microarray, involving a human cohort including vaccinees and infected patients. On the other hand, bioinformatics tools were used to predict immunodominant cytotoxic T-cell (CTL) and helper T-cell (HTL) epitopes. Subsequently, these epitopes were evaluated by various immunoinformatic tools. Epitopes with high antigenicity, high immunogenicity, non-allergenicity, non-toxicity, as well as exemplary conservation were then connected in series with appropriate linkers and adjuvants to construct a broad-spectrum MEV. Moreover, the structural analysis revealed that the MEV candidates exhibited good stability, and the docking results demonstrated their strong affinity to Toll-like receptors 4 (TLR4). In addition, molecular dynamics simulation confirmed the stable interaction between TLR4 and MEVs. Three injections with MEVs showed a high level of B-cell and T-cell immune responses according to the immunological simulations in silico. Furthermore, in-silico cloning was performed, and the results indicated that the MEVs could be produced in considerable quantities in Escherichia coli (E. coli). Based on these findings, it is reasonable to create a broad-spectrum MEV against different subtypes of influenza A and B viruses in silico.

Development of a novel multi-epitope mRNA vaccine candidate to combat HMPV virus

Human metapneumovirus (HMPV) is one of the main pathogens causing severe respiratory infections in children, as a common cause of immunodeficiency-related deaths in children and elderly individuals, the prevalence of HMPV has been showing an increasing trend during the last years. However, no vaccines or effective treatment plans are available currently. In this present, based on candidate proteins highly associated with viral virulence and has promising protective potential, we screened for immunodominant cytotoxic T cells, helper T cells, and Linear B-cell epitopes from the most promising candidate Fusion protein, together with G, SH, M, and M2. All epitopes were predicted to have strong antigenicity by Vaxijen and pose no potential toxicity, allergenicity, or hormonology to human proteins by Toxinpred, Allerpred, and Blast analysis, meanwhile, high conservancy is demanded to cover different subtypes. adjuvants β-defensin II and Pam2Cys was attached with EAAAK linkers to improve vaccine's efficiency. Then, calculation of physicochemical properties proved the protein vaccine as a product can stably exist in the human body. Besides, we assessed the docking between the vaccine and immune receptors to evaluate its ability to stimulate immune responses, and the dynamic simulation further confirmed that the vaccine can tightly bind with immune receptors, which approved that the construction has the potential to induce strong humoral and cellular immune response. Finally, the vaccine was constructed into a multi-epitope mRNA vaccine, the immune simulations suggest that this is a vaccine candidate for controlling HMPV infection.

Development of a novel circular mRNA vaccine of six protein combinations against Staphylococcus aureus

Staphylococcus aureus is an extraordinarily versatile pathogen, which is currently the most common cause of nosocomial and community infections. Considering that increased antibiotic resistance may hasten the spread of S. aureus, developing an effective vaccine can possibly aid in its control. The RNA vaccine coding immunodominance epitopes from bacteria provide a potential method to induce T and B cell immune responses by translating them into cells. Furthermore, using bioinformatics to create circular RNA vaccines can ensure that the translation of the vaccine is potent and durable. In this study, 7 cytotoxic T lymphocyte (CTL) epitopes, 4 helper T lymphocyte (HTL) epitopes, and 15 B cell epitopes from 6 proteins that are closely associated with the S. aureus virulence and invasion and critical to natural immune responses were mapped. To verify their interactions, all epitopes were docked with the corresponding MHC alleles. The final vaccine was composed of 26 epitopes and the adjuvant β-defencin, and a disulfide bond was also introduced to improve its stability. After the prediction of structure and characteristics, the developed vaccine was docked with TLR2 and TLR4, which induce immunological responses in S. aureus infection. According to the molecular dynamic simulation, the vaccine might interact strongly with TLRs. Meanwhile, it performed well in immunological simulation and population coverage prediction. Finally, the vaccine was converted into a circular RNA using a series of helper sequences to aid in vaccine circulation translation. Hopefully, this proposed structure will be proven to serve a viable vaccine against S. aureus.Communicated by Ramaswamy H. Sarma.

An integrative reverse vaccinology, immunoinformatic, docking and simulation approaches towards designing of multi-epitopes based vaccine against monkeypox virus

Monkeypox is a viral zoonotic disease that is caused by the monkeypox virus (MPXV) and is mainly transmitted to human through close contact with an infected person, animal, or fomites which is contaminated by the virus. In the present research work, reverse vaccinology and several other bioinformatics and immunoinformatics tools were utilized to design multi-epitopes-based vaccine against MPXV by exploring three probable antigenic extracellular proteins: cupin domain-containing protein, ABC transporter ATP-binding protein and DUF192 domain-containing protein. Both cellular and humoral immunity induction were the main concerning qualities of the vaccine construct, hence from selected proteins both B and T-cells epitopes were predicted. Antigenicity, allergenicity, toxicity, and water solubility of the predicted epitopes were assessed and only probable antigenic, non-allergic, non-toxic and good water-soluble epitopes were used in the multi-epitopes vaccine construct. The developed vaccine was found to be potentially effective against MPXV and to be highly immunogenic, cytokine-producing, antigenic, non-toxic, non-allergenic, and stable. Additionally, to increase stability and expression efficiency in the host E. coli, disulfide engineering, codon adaptation, and in silico cloning were employed. Molecular docking and other biophysical approaches were utilized to evaluate the binding mode and dynamic behavior of the vaccine construct with TLR-2, TLR-4, and TLR-8. The outcomes of the immune simulation demonstrated that both B and T cells responded more strongly to the vaccination component. The detailed in silico analysis concludes that the proposed vaccine will induce a strong immune response against MPXV infection, making it a promising target for additional experimental trials.Communicated by Ramaswamy H. Sarma.

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.

Designing a novel SOX9 based multi-epitope vaccine to combat metastatic triple-negative breast cancer using immunoinformatics approach

Immunotherapies are a promising treatment option especially for the management of TNBC owing to its higher levels of tumour-associated antigens together with higher mutational load. Of note, the administration of preventive vaccines in the early stage of the cancer holds promise for effective disease management. Therefore, the present study aimed to develop a novel multi-epitope peptide-based vaccination against TNBC employing SOX9, which has recently been recognized as a key regulator of TNBC metastasis. The immunodominant regions from the SOX9 protein were computed and assessed based on their ability to elicit both T and B lymphocyte mediated responses. The resultant epitopes were fused using appropriate linkers (EAAAK, KK, AAY and GPGPG) and adjuvant (50S ribosomal protein L7/L12) to enhance the vaccine's immunogenicity. The physicochemical properties and population coverage were also anticipated for the constructed vaccine. Adding together, docking and dynamics simulation studies were performed on the modelled vaccine against TLR-4 to provide insight into the stability. Finally, the designed vaccine was cloned into the pET28 (+) vector and immunological simulation studies were carried out. These results demonstrate that our designed vaccine had the potency to trigger humoral and cellular immune responses. Based on these collective evidences, the final proposed vaccine could be an interesting therapeutics for the management of TNBC in the near future. Schematic representation of an efficient vaccine design framework by combining the range of immunoinformatics strategies.

MERS virus spike protein HTL-epitopes selection and multi-epitope vaccine design using computational biology

MERS-CoV, a zoonotic virus, poses a serious threat to public health globally. Thus, it is imperative to develop an effective vaccination strategy for protection against MERS-CoV. Immunoinformatics and computational biology tools provide a faster and more cost-effective strategy to design potential vaccine candidates. In this work, the spike proteins from different strains of MERS-CoV were selected to predict HTL-epitopes that show affinity for T-helper MHC-class II HTL allelic determinant (HLA-DRB1:0101). The antigenicity and conservation of these epitopes among the selected spike protein variants in different MERS-CoV strains were analyzed. The analysis identified five epitopes with high antigenicity: QSIFYRLNGVGITQQ, DTIKYYSIIPHSIRS, PEPITSLNTKYVAPQ, INGRLTTLNAFVAQQ and GDMYVYSAGHATGTT. Then, a multi-epitope vaccine candidate was designed using linkers and adjuvant molecules. Finally, the vaccine construct was subjected to molecular docking with TLR5 (Toll-like receptor-5). The proposed vaccine construct had strong binding energy of -32.3 kcal/mol when interacting with TLR5.Molecular dynamics simulation analysis showed that the complex of the vaccine construct and TLR5 is stable. Analysis using in silico immune simulation also showed that the prospective multi-epitope vaccine design had the potential to elicit a response within 70 days, with the immune system producing cytokines and immunoglobulins. Finally, codon adaptation and in silico cloning analysis showed that the candidate vaccine could be expressed in the Escherichia coli K12 strain. Here we also designed support vaccine construct MEV-2 by using B-cell and CD8+ CTL epitopes to generate the complete immunogenic effect. This study opens new avenues for the extension of research on MERS vaccine development.Communicated by Ramaswamy H. Sarma.

Immunoinformatics aided approach for predicting potent cytotoxic T cell epitopes of respiratory syncytial virus

Respiratory syncytial virus (RSV) is an infectious viral pathogen that causing serious respiratory infection in adults and neonates. The only approved therapies for RSV are the monoclonal antibodies palivizumab and its derivative motavizumab. Both treatments are expensive and require a hospital setting for administration. A vaccine represents a safe, effective and cheaper alternative for preventing RSV infection. In silico prediction methods have proven to be valuable in speeding up the process of vaccine design. In this study, reverse vaccinology methods were used to predict the cytotoxic T lymphocytes (CTL) epitopes from the entire proteome of RSV strain A. From amongst 3402 predicted binders to 12 high frequency alleles from the Immune Epitope Database (IEDB), 567 had positive processing scores while 327 epitopes were predicted to be immunogenic. A thorough examination of the 327 epitopes for possible antigenicity, allergenicity and toxicity resulted in 95 epitopes with desirable properties. A BLASTp analysis revealed 94 unique and non-homologous epitopes that were subjected to molecular docking across the 12 high frequency alleles. The final dataset of 70 epitopes contained 13 experimentally proven and 57 unique epitopes from a total of 11 RSV proteins. From our findings on selected T-cell-specific RSV antigen epitopes, notably the four epitopes confirmed to exhibit stable binding by molecular dynamics. The prediction pipeline used in this study represents an effective way to screen the immunogenic epitopes from other pathogens.Communicated by Ramaswamy H. Sarma.

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.

HPV and molecular mimicry in systemic lupus erythematosus and an impact of compiling B-cell epitopes and MHC-class II binding profiles with in silico evidence

Epidemiological link between HPV and SLE is evolving. The possibility of HPV infection-induced molecular mimicry and systemic lupus erythematosus (SLE) was elucidated through detailed in silico analyses. Conserved regions in the structural protein sequences of high-risk HPV types were inferred, and sequence homologies between viral and human peptides were identified to delineate proteins implicated in SLE. B-cell epitopes and MHC-class II binding were compiled using Immune Epitope Database and ProPred II analysis tool. Molecular modeling and molecular dynamics/simulation (MDS) were performed using AutoDock Vina and GROMACS, respectively. Sequence alignment revealed 32 conserved regions, and 27/32 viral peptides showed varying similarities to human peptides, rich in B-cell epitopes with superior accessibility, high hydrophilicity, antigenicity and disposition to bind many class-II HLA alleles. Molecular docking of 13 viral peptides homologous (100%) to human peptides implicated in SLE showed that VIR-PEP1 (QLFNKPYWL) and VIR-PEP2 (DTYRFVTS) exhibited higher binding affinities than corresponding human peptides to SLE predisposing HLA-DRB1 allele. MDS of these peptides showed that the viral peptides had superior folding, compactness, and a higher number of hydrogen bonds than human peptides throughout the simulation period. SASA analysis revealed that the VIR-PEP1&2 fluctuated less frequently than corresponding human peptides. MM-PBSA revealed that the VIR-PEP2 complex exhibited higher binding energy than the human peptide complex. This suggests that highly conserved structural peptides of high-risk HPV types homologous to human peptides could compete and bind avidly to the HLA allele associated with SLE and predispose HPV-infected individuals to SLE through molecular mimicry.Communicated by Ramaswamy H. Sarma.

Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking

Tea (Camellia sinensis L.) is considered as to be one of the most consumed beverages globally and a reservoir of phytochemicals with immense health benefits. Despite numerous advantages, tea compounds lack a robust multi-disease target study. In this work, we presented a unique in silico approach consisting of molecular docking, multivariate statistics, pharmacophore analysis, and network pharmacology approaches. Eight tea phytochemicals were identified through literature mining, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate (ECG), quercetin, kaempferol, and ellagic acid, based on their richness in tea leaves. Further, exploration of databases revealed 30 target proteins related to the pharmacological properties of tea compounds and multiple associated diseases. Molecular docking experiment with eight tea compounds and all 30 proteins revealed that except gallic acid all other seven phytochemicals had potential inhibitory activities against these targets. The docking experiment was validated by comparing the binding affinities (Kcal mol^-1) of the compounds with known drug molecules for the respective proteins. Further, with the aid of the application of statistical tools (principal component analysis and clustering), we identified two major clusters of phytochemicals based on their chemical properties and docking scores (Kcal mol^-1). Pharmacophore analysis of these clusters revealed the functional descriptors of phytochemicals, related to the ligand-protein docking interactions. Tripartite network was constructed based on the docking scores, and it consisted of seven tea phytochemicals (gallic acid was excluded) targeting five proteins and ten associated diseases. Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex was found to be highest in docking performance (10 kcal mol^-1). Finally, molecular dynamic simulation showed that ECG-1FYR could make a stable complex in the near-native physiological condition.

Six Pivotal Lessons Learned in South Korea for Whole-of-Government Approach to Successful COVID-19 Vaccine Rollout in Planetary Health

The COVID-19 pandemic has underscored that systems medicine depends on health systems and services that can respond to planetary health threats in scale and with speed. After 2 years of the pandemic, there are lessons learned in South Korea, from governance to risk communication for planetary health. The learning is timely because COVID-19 likely signals future ecological crises and emerging pathogens amid the climate emergency in the 21st century. By the end of March 2022, South Korea, with a population of 51.6 million, administered more COVID-19 vaccine doses per 100 people than other developed countries, including United Kingdom, Italy, Australia, Israel, Germany, and United States. In South Korea, 233.47 doses of COVID-19 vaccine were administered per 100 people, while Italy (229.48), China (229.39), Australia (217.44), the United Kingdom (211.39), Israel (194.82), and the United States (170.59) fell behind in vaccine rollout. Korea's whole-of-government approach to robust COVID-19 vaccination rollout prompted broad interest in planetary health. Notwithstanding that vaccine rollout is a complex multifactorial outcome, six salient, and often interdependent, factors are identified in this study of South Korea: (1) governance system; (2) logistic capability; (3) delivery strategy and accessibility; (4) established public health infrastructure; (5) risk communication; and (6) public cooperation and engagement. Integration of these factors can create multisectoral synergy to achieve better outcomes in vaccine rollout. The lessons from South Korea can help cultivate planetary health action in other parts of the world during the current COVID-19 pandemic, and in ecological crises in the future.

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.

A designed peptide-based vaccine to combat Brucella melitensis, B. suis and B. abortus: Harnessing an epitope mapping and immunoinformatics approach

Vaccines against Brucella abortus, B. melitensis and B. suis have been based on weakened or killed bacteria, however there is no recombinant vaccine for disease prevention or therapy. This study attempted to predict IFN-γ epitopes, T cell cytotoxicity, and T lymphocytes in order to produce a multiepitope vaccine based on BtpA, Omp16, Omp28, virB10, Omp25, and Omp31 antigens against B. melitensis, B. abortus, and B. suis. AAY, GPGPG, and EAAAK peptides were used as epitope linkers, while the PADRE sequence was used as a Toll-like receptor 2 (TLR2) and TLR4 agonist. The final construct included 389 amino acids, and was a soluble protein with a molecular weight of 41.3 kDa, and nonallergenic and antigenic properties. Based on molecular docking studies, molecular dynamics simulations such as Gyration, RMSF, and RMSD, as well as tertiary structure validation methods, the modeled protein had a stable structure capable of interacting with TLR2/4. As a result, this novel vaccine may stimulate immune responses in B and T cells, and could prevent infection by B. suis, B. abortus, and B. melitensis.

Nanotechnology and COVID-19 Convergence: Toward New Planetary Health Interventions Against the Pandemic

COVID-19 is a systemic disease affecting multiple organ systems and caused by infection with the SARS-CoV-2 virus. Two years into the COVID-19 pandemic and after the introduction of several vaccines, the pandemic continues to evolve in part owing to global inequities in access to preventive and therapeutic measures. We are also witnessing the introduction of antivirals against COVID-19. Against this current background, we review the progress made with nanotechnology-based approaches such as nanoformulations to combat the multiorgan effects of SARS-CoV-2 infection from a systems medicine lens. While nanotechnology has previously been widely utilized in the antiviral research domain, it has not yet received the commensurate interest in the case of COVID-19 pandemic response strategies. Notably, SARS-CoV-2 and nanomaterials are similar in size ranging from 50 to 200 nm. Nanomaterials offer the promise to reduce the side effects of antiviral drugs, codeliver multiple drugs while maintaining stability in the biological milieu, and sustain the release of entrapped drug(s) for a predetermined time period, to name but a few conceivable scenarios, wherein nanotechnology can enable and empower preventive medicine and therapeutic innovations against SARS-CoV-2. We conclude the article by underlining that nanotechnology-based interventions warrant further consideration to enable precision planetary health responses against the COVID-19 pandemic.

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.

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.

Development of peptide vaccine candidate using highly antigenic PE-PGRS family proteins to stimulate the host immune response against Mycobacterium tuberculosis H37Rv: an immuno-informatics approach

Tuberculosis (TB) is a fast spreading; transmissible disease caused by the Mycobacterium tuberculosis (M. tuberculosis). M. tuberculosis has a high death rate in its endemic regions due to a lack of appropriate treatment and preventative measures. We have used a vaccinomics strategy to create an effective multi-epitope vaccine against M. tuberculosis. The antigenic proteins with the highest antigenicity were utilised to predict cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes. CTL and HTL epitopes were covered in 99.97% of the population. Seven epitopes each of CTL, HTL, and LBL were ultimately selected and utilised to develop a multi-epitope vaccine. A vaccine design was developed by combining these epitopes with suitable linkers and LprG adjuvant. The vaccine chimera was revealed to be highly immunogenic, non-allergenic, and non-toxic. To ensure a better expression within the Escherichia coli K12 (E. coli K12) host system, codon adaptation and in silico cloning were accomplished. Following that, various validation studies were conducted, including molecular docking, molecular dynamics simulation, and immunological simulation, all of which indicated that the designed vaccine would be stable in the biological environment and effective against M. tuberculosis infection. The immune simulation revealed higher levels of T-cell and B-cell activity, which corresponded to the actual immune response. Exposure simulations were repeated several times, resulting in increased clonal selection and faster antigen clearance. These results suggest that, if proposed vaccine chimera would test both in-vitro and in-vivo, it could be a viable treatment and preventive strategy for TB.Communicated by Ramaswamy H. Sarma.

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.

Deciphering the immunogenic T-cell epitopes from spike protein of SARS-CoV-2 concerning the diverse population of India

Scientists are rigorously looking for an efficient vaccine against the current pandemic due to the SARS-CoV-2 virus. The reverse vaccinology approach may provide us with significant therapeutic leads in this direction and further determination of T-cell/B-cell response to antigen. In the present study, we conducted a population coverage analysis referring to the diverse Indian population. From the Immune epitope database (IEDB), HLA- distribution analysis was performed to find the most promiscuous T-cell epitope out of In silico determined epitope of Spike protein from SARS-CoV-2. Epitopes were selected based on their binding affinity with the maximum number of HLA alleles belonging to the highest population coverage rate values for the chosen geographical area in India. 404 cleavage sites within the 1288 amino acids sequence of spike glycoprotein were determined by NetChop proteasomal cleavage prediction suggesting the presence of adequate sites in the protein sequence for cleaving into appropriate epitopes. For population coverage analysis, 179 selected epitopes present the projected population coverage up to 97.45% with 56.16 average hit and 15.07 pc90. 54 epitopes are found with the highest coverage among the Indian population and highly conserved within the given spike RBD domain sequence. Among all the predicted epitopes, 9-mer TRFASVYAW and RFDNPVLPF along with 12-mer LLAGTITSGWTF and VSQPFLMDLEGK epitopes are observed as the best due to their decent docking score and best binding affinity to corresponding HLA alleles during MD simulations. Outcomes from this study could be critical to design a vaccine against SARS-CoV-2 for a different set of populations within the country.Communicated 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.

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.

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.