Immunoinformatics and structural vaccinology driven prediction of multi-epitope vaccine against Mayaro virus and validation through in-silico expression

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.

Designing a Multi-Epitope Vaccine against Chlamydia trachomatis by Employing Integrated Core Proteomics, Immuno-Informatics and In Silico Approaches

Chlamydia trachomatis, a Gram-negative bacterium that infects the rectum, urethra, congenital sites, and columnar epithelium of the cervix. It is a major cause of preventable blindness, ectopic pregnancy, and bacterial sexually transmitted infections worldwide. There is currently no licensed multi-epitope vaccination available for this pathogen. This study used core proteomics, immuno-informatics, and subtractive proteomics approaches to identify the best antigenic candidates for the development of a multi-epitope-based vaccine (MEBV). These approaches resulted in six vaccine candidates: Type III secretion system translocon subunit CopD2, SctW family type III secretion system gatekeeper subunit CopN, SycD/LcrH family type III secretion system chaperone Scc2, CT847 family type III secretion system effector, hypothetical protein CTDEC_0668, and CHLPN 76kDa-like protein. A variety of immuno-informatics tools were used to predict B and T cell epitopes from vaccine candidate proteins. An in silico vaccine was developed using carefully selected epitopes (11 CTL, 2 HTL & 10 LBL) and then docked with the MHC molecules (MHC I & MHC II) and human TLR4. The vaccine was coupled with Cholera toxin subunit B (CTB) adjuvant to boost the immune response. Molecular dynamics (MD) simulations, molecular docking, and MMGBSA analysis were carried out to analyze the molecular interactions and binding affinity of MEBV with TLR4 and MHC molecules. To achieve the highest level of vaccine protein expression, the MEBV was cloned and reverse-translated in Escherichia coli. The highest level of expression was achieved, and a CAI score of 0.97 was reported. Further experimental validation of the MEBV is required to prove its efficacy. The vaccine developed will be useful in preventing infections caused by C. trachomatis.

Proteome-Wide Mapping and Reverse Vaccinology Approaches to Design a Multi-Epitope Vaccine against Clostridium perfringens

C. perfringens is a highly versatile bacteria of livestock and humans, causing enteritis (a common food-borne illness in humans), enterotoxaemia (in which toxins are formed in the intestine which damage and destroy organs, i.e., the brain), and gangrene (wound infection). There is no particular cure for the toxins of C. perfringens. Supportive care (medical control of pain, intravenous fluids) is the standard treatment. Therefore, a multiple-epitope vaccine (MEV) should be designed to battle against C. perfringens infection. Furthermore, the main objective of this in silico investigation is to design an MEV that targets C. perfringens. For this purpose, we selected the top three proteins that were highly antigenic using immuno-informatics approaches, including molecular docking. B-cells, IFN-gamma, and T cells for target proteins were predicted and the most conserved epitopes were selected for further investigation. For the development of the final MEV, epitopes of LBL5, CTL17, and HTL13 were linked to GPGPG, AAY, and KK linkers. The vaccine N-end was joined to an adjuvant through an EAAK linker to improve immunogenicity. After the attachment of linkers and adjuvants, the final construct was 415 amino acids. B-cell and IFN-gamma epitopes demonstrate that the model structure is enhanced for humoral and cellular immune responses. To validate the immunogenicity and safety of the final construct, various physicochemical properties, and other properties such as antigenicity and non-allergens, were evaluated. Furthermore, molecular docking was carried out for verification of vaccine compatibility with the receptor, evaluated in silico. Also, in silico cloning was employed for the verification of the proper expression and credibility of the construct.

Subtractive Proteomics and Immuno-informatics Approaches for Multi-peptide Vaccine Prediction Against Klebsiella oxytoca and Validation Through In Silico Expression

Klebsiella oxytoca is a gram-negative bacterium. It is opportunistic in nature and causes hospital acquired infections. Subtractive proteomics and reverse vaccinology approaches were employed to screen out the best proteins for vaccine designing. Whole proteome of K. oxytoca strain ATCC 8724, consisting of 5483 proteins, was used for designing the vaccine. Total 1670 cytotoxic T lymphocyte (CTL) epitope were predicted through NetCTL while 1270 helper T lymphocyte (HTL) epitopes were predicted through IEDB server. The epitopes were screened for non-toxicity, allergenicity, antigenicity and water solubility. After epitope screening 300 CTL and 250 HTL epitopes were submitted to IFN-γ epitope server to predict their Interferon-γ induction response. The selected IFN-γ positive epitopes were tested for their binding affinity with MHCI-DRB1 by MHCPred. The 15 CTL and 13 HTL epitopes were joined by linkers AAY and GPGPG respectively in vaccine construct. Chain C of Pam3CSK4 (PDB ID; 2Z7X) was linked to the vaccine construct as an adjuvant. A 450aa long vaccine construct was submitted to I-TASSER server for 3D structure prediction. Thirteen Linear B cells were predicted by ABCPred server and 10 sets of discontinues epitopes for 3D vaccine structure were predicted by DiscoTope server. The modeled 3D vaccine construct was docked with human Toll-like receptor 2 (PDB ID: 6NIG) by PatchDock. The docked complexes were refined by FireDock. The selected docked complex showed five hydrogen bonds and one salt bridge. The vaccine sequence was reverse transcribed to get nucleotide sequence for In silico cloning. The reverse transcribed sequence strand was cloned in pET28a(+) expression vector. A clone containing 6586 bp was constructed including the 450 bp of query gene sequence.

Computational Modeling of Immune Response Triggering Immunogenic Peptide Vaccine Against the Human Papillomaviruses to Induce Immunity Against Cervical Cancer

Papillomaviruses are placed within the family Papillomaviride, and the members of this family have a double-stranded circular DNA genome. Every year, ∼30% of cancers are reported to be human papillomavirus (HPV) related, which represents 63,000 cancers of all infectious agent-induced cancers. HPV16 and HPV18 are reported to be associated with 70% of cervical cancers. The quest for an effective drug or vaccine candidate still continues. In this study, we aim to design B cell and T cell epitope-based vaccine using the two structural major capsid protein L1 and L2 as well as other three important proteins (E1, E2, and E6) against HPV strain 16 (HPV16). We used a computational pipeline to design a multiepitope subunit vaccine and tested its efficacy using in silico computational modeling approaches. Our analysis revealed that the multiepitope subunit vaccine possesses antigenic properties, and using in silico cloning method revealed proper expression and downstream processing of the vaccine construct. Besides this, we also performed in silico immune simulation to check the immune response upon the injection. Our results strongly suggest that this vaccine candidate should be tested immediately for the immune response against the cervical cancer-causing agent. The safety, efficacy, expression, and immune response profiling makes it the first choice for experimental and in vivo setup.

Pan-vaccinomics approach towards a universal vaccine candidate against WHO priority pathogens to address growing global antibiotic resistance

Antimicrobial resistance (AMR) in bacterial pathogens is a major global distress. Due to the slow progress of antibiotics development and the fast pace of resistance acquisition, there is an urgent need for effective vaccines against such bacterial pathogens. In-silico approaches including pan-genomics, subtractive proteomics, reverse vaccinology, immunoinformatics, molecular docking, and dynamics simulation studies were applied in the current study to identify a universal potential vaccine candidate against the 18 multi-drug resistance (MDRs) bacterial pathogenic species from a WHO priority list. Ten non-redundant, non-homologous, virulent, and antigenic vaccine candidates were filtered against all targeted species. Nine B-cell-derived T-cell antigen epitopes which show a great affinity to the dominant HLA allele (DRB1*0101) in the human population were screened from selected vaccine candidates using immunoinformatics approaches. Screened epitopes were then used to design a multi-epitope peptide vaccine construct (MEPVC) along with β-defensin adjuvant to improve the immunogenic properties of the proposed vaccine construct. Molecular docking and MD simulation were carried out to study the binding affinity and molecular interaction of MEPVC with human immune receptors (TLR2, TLR3, TLR4, and TLR6). The final MEPVC construct was reverse translated and in-silico cloned in the pET28a(+) vector to ensure its effectiveness. This in silico construct is expected to be helpful for vaccinologists to assess its immune protection effectiveness in vivo and in vitro to counter rising antibiotic resistance worldwide.

Proteome Based Approach Defines Candidates for Designing a Multitope Vaccine against the Nipah Virus

Nipah virus is one of the most harmful emerging viruses with deadly effects on both humans and animals. Because of the severe outbreaks, in 2018, the World Health Organization focused on the urgent need for the development of effective solutions against the virus. However, up to date, there is no effective vaccine against the Nipah virus in the market. In the current study, the complete proteome of the Nipah virus (nine proteins) was analyzed for the antigenicity score and the virulence role of each protein, where we came up with fusion glycoprotein (F), glycoprotein (G), protein (V), and protein (W) as the candidates for epitope prediction. Following that, the multitope vaccine was designed based on top-ranking CTL, HTL, and BCL epitopes from the selected proteins. We used suitable linkers, adjuvant, and PADRE peptides to finalize the constructed vaccine, which was analyzed for its physicochemical features, antigenicity, toxicity, allergenicity, and solubility. The designed vaccine passed these assessments through computational analysis and, as a final step, we ran a docking analysis between the designed vaccine and TLR-3 and validated the docked complex through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for Nipah virus. Here, we describe the computational approach for design and analysis of this vaccine.

Prioritization of potential vaccine candidates and designing a multiepitope-based subunit vaccine against multidrug-resistant Salmonella Typhi str. CT18: A subtractive proteomics and immunoinformatics approach

Salmonella enterica serovar Typhi (S. Typhi), a causative agent of typhoid fever, is a Gram-negative, human-restricted pathogen that causes significant morbidity and mortality, particularly in developing countries. The currently available typhoid vaccines are not recommended to children below six years of age and have poor long-term efficacy. Due to these limitations and the emerging threat of multidrug-resistance (MDR) strains, the development of a new vaccine is urgently needed. The present study aims to design a multiepitope-based subunit vaccine (MESV) against MDR S. Typhi str. CT18 using a computational-based approach comprising subtractive proteomics and immunoinformatics. Firstly, we investigated the proteome of S. Typhi str. CT18 using subtractive proteomics and identified twelve essential, virulent, host non-homologous, and antigenic outer membrane proteins (OMPs) as potential vaccine candidates with low transmembrane helices (≤1) and molecular weight (≤110 kDa). The OMPs were mapped for cytotoxic T lymphocyte(CTL) epitopes, helper T lymphocyte (HTL) epitopes, and linear B lymphocyte (LBL) epitopes using various immunoinformatics tools and servers. A total of 6, 12, and 11 CTL, HTL, and LBL epitopes were shortlisted, respectively, based on their immunogenicity, antigenicity, allergenicity, toxicity, and hydropathicity potential. Four MESV constructs (MESVCs), MESVC-1, MESVC-2, MESVC-3, and MESVC-4, were designed by linking the CTL, HTL, and LBL epitopes with immune-modulating adjuvants, linkers, and PADRE (Pan HLA DR-binding epitope) sequences. The MESVCs were evaluated for their physicochemical properties, allergenicity, antigenicity, toxicity, and solubility potential to ensure their safety and immunogenic behavior. Secondary and tertiary structures of shortlisted MESVCs (MESVC-1, MESVC-3, and MESVC-4) were predicted, modeled, refined, validated, and then docked with various MHC I, MHC II, and TLR4/MD2 complex. Molecular dynamics (MD) simulation of the final selected MESVC-4 with TLR4/MD2 complex confirms its binding affinity and stability. Codon optimization and in silico cloning verified the translation efficiency and successful expression of MESVC-4 in E. coli str. K12. Finally, the efficiency of MESVC-4 to trigger an effective immune response was assessed by an in silico immune simulation. In conclusion, our findings show that the designed MESVC-4 can elicit humoral and cellular immune responses, implying that it may be used for prophylactic or therapeutic purposes. Therefore, it should be subjected to further experimental validations.

Immunoinformatic Approach for the Identification of Potential Epitopes Against Stenotrophomonas maltophilia: A Global Opportunistic Pathogen

Background:

Stenotrophomonas maltophilia is an aerobic, non-fermentative, gram negative, multidrug resistant and opportunistic nosocomial pathogen. It is associated with high morbidity and mortality in severely immunocompromised paediatric patients, including neonates. Immunoinformatic analysis paved a new way to design epitope-based vaccines which resulted in a potential immunogen with advantages such as lower cost, specific immunity, ease of production, devoid of side effects, and less time consumption than conventional vaccines. Till date, there is no development in the vaccines or antibody-based treatments for S. maltophilia-associated infections.

Introduction:

Currently, epitope-based peptide vaccines against pathogenic bacteria have grasped more attention. In our present study, we have utilized various immunoinformatic tools to find a prominent epitope that interacts with the maximum number of HLA alleles and also with the maximum population coverage for developing a vaccine against Stenotrophomonas maltophilia.

Methods:

This study has incorporated an immunoinformatic based screening approach to explore potential epitope-based vaccine candidates in Stenotrophomonas maltophilia proteome. In this study, 4365 proteins of the Stenotrophomonas maltophilia K279a proteome were screened to identify potential antigens that could be used as a good candidate for the vaccine. Various immunoinformatic tools were used to predict the binding of the promiscuous epitopes with Major Histocompatibility Complex (MHC) class I molecules. Other properties such as allergenicity, physiochemical properties, adhesion properties, antigenicity, population coverage, epitope conservancy and toxicity were analysed for the predicted epitope.

Results:

This study helps in finding the prominent epitope in Stenotrophomonas infections. Hence, the main objective in this research was to screen complete Stenotrophomonas maltophilia proteome to recognize putative epitope candidates for vaccine design. Using computational vaccinology and immunoinformatic tools approach, several aspects are obligatory to be fulfilled by an epitope to be considered as a vaccine candidate. Our findings were promising and showed that the predicted epitopes were non-allergenic and fulfilled other parameters required for being a suitable candidate based on certain physio-chemical, antigenic and adhesion properties.

Conclusion:

The epitopes LLFVLCWPL and KSGEGKCGA have shown the highest binding score of −103 and −78.1 kcal/mol with HLA-A*0201 and HLA-B*0702 MHC class I allele, respectively. They were also predicted to be immunogenic and non-allergenic. Further various immunological tests, both in vivo and in vitro methods, should be performed for finding the efficiency of the predicted epitope in the development of a targeted vaccine against Stenotrophomonas maltophilia infection.

Design of a novel multiple epitope-based vaccine: An immunoinformatics approach to combat SARS-CoV-2 strains

BACKGROUND

Since the SARS-CoV-2 outbreak in December 2019 in Wuhan, China, the virus has infected more than 153 million individuals across the world due to its human-to-human transmission. The USA is the most affected country having more than 32-million cases till date. Sudden high fever, pneumonia and organ failure have been observed in infected individuals.

OBJECTIVES

In the current situation of emerging viral disease, there is no specific vaccine, or any therapeutics available for SARS-CoV-2, thus there is a dire need to design a potential vaccine to combat the virus by developing immunity in the population. The purpose of present study was to develop a potential vaccine by targeting B and T-cell epitopes using bioinformatics approaches.

METHODS

B- and T-cell epitopes are predicted from novel M protein-SARS-CoV-2 for the development of a unique multiple epitope vaccine by applying bioinformatics approaches. These epitopes were analyzed and selected for their immunogenicity, antigenicity scores, and toxicity in correspondence to their ability to trigger immune response. In combination to epitopes, best multi-epitope of potential immunogenic property was constructed. The epitopes were joined using EAAAK, AAY and GPGPG linkers.

RESULTS

The constructed vaccine showed good results of worldwide population coverage and promising immune response. This constructed vaccine was subjected to in-silico immune simulations by C-ImmSim. Chimeric protein construct was cloned into PET28a (+) vector for expression study in Escherichia coli using snapgene.

CONCLUSION

This vaccine design proved effective in various computer-based immune response analysis as well as showed good population coverage. This study is solely dependent on developing M protein-based vaccine, and these in silico findings would be a breakthrough in the development of an effective vaccine to eradicate SARS-CoV-2 globally.

Integrated Core Proteomics, Subtractive Proteomics, and Immunoinformatics Investigation to Unveil a Potential Multi-Epitope Vaccine against Schistosomiasis

Schistosomiasis is a parasitic infection that causes considerable morbidity and mortality in the world. Infections of parasitic blood flukes, known as schistosomes, cause the disease. No vaccine is available yet and thus there is a need to design an effective vaccine against schistosomiasis. Schistosoma japonicum, Schistosoma mansoni, and Schistosoma haematobium are the main pathogenic species that infect humans. In this research, core proteomics was combined with a subtractive proteomics pipeline to identify suitable antigenic proteins for the construction of a multi-epitope vaccine (MEV) against human-infecting Schistosoma species. The pipeline revealed two antigenic proteins-calcium binding and mycosubtilin synthase subunit C-as promising vaccine targets. T and B cell epitopes from the targeted proteins were predicted using multiple bioinformatics and immunoinformatics databases. Seven cytotoxic T cell lymphocytes (CTL), three helper T cell lymphocytes (HTL), and four linear B cell lymphocytes (LBL) epitopes were fused with a suitable adjuvant and linkers to design a 217 amino-acid-long MEV. The vaccine was coupled with a TLR-4 agonist (RS-09; Sequence: APPHALS) adjuvant to enhance the immune responses. The designed MEV was stable, highly antigenic, and non-allergenic to human use. Molecular docking, molecular dynamics (MD) simulations, and molecular mechanics/generalized Born surface area (MMGBSA) analysis were performed to study the binding affinity and molecular interactions of the MEV with human immune receptors (TLR2 and TLR4) and MHC molecules (MHC I and MHC II). The MEV expression capability was tested in an Escherichia coli (strain-K12) plasmid vector pET-28a(+). Findings of these computer assays proved the MEV as highly promising in establishing protective immunity against the pathogens; nevertheless, additional validation by in vivo and in vitro experiments is required to discuss its real immune-protective efficacy.

Genome-wide screening of vaccine targets prioritization and reverse vaccinology aided design of peptides vaccine to enforce humoral immune response against Campylobacter jejuni

Campylobacter jejuni, gram-negative bacteria, is an infectious agent of foodborne disease-causing bloody diarrhea, abdominal pain, fever, Guillain-Barré syndrome (GBS) and Miller Fisher syndrome in humans. Campylobacter spp. with multidrug resistance to fluoroquinolones, tetracycline, and erythromycin are reported. Hence, an effective vaccine candidate would provide long-term immunity against C. jejuni infections. Thus, we used a subtractive proteomics pipeline to prioritize essential proteins, which impart a critical role in virulence, replication and survival. Five proteins, i.e. Single-stranded DNA-binding protein, UPF0324 membrane protein Cj0999c, DNA translocase FtsK, 50S ribosomal protein L22, and 50S ribosomal protein L1 were identified as virulent proteins and selected for vaccine designing. We reported that the multi-epitopes subunit vaccine based on CTL, HTL and B-cell epitopes combination possess strong antigenic properties and associates no allergenic reaction. Further investigation revealed that the vaccine interacts with the immune receptor (TLR-4) and triggered the release of primary and secondary immune factors. Moreover, the CAI and GC contents obtained through codon optimization were reported to be 0.93 and 53% that confirmed a high expression in the selected vector. The vaccine designed in this study needs further scientific consensus and will aid in managing C. jejuni infections.

Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches

Staphylococcus aureus is a deadly human bacterial pathogen that causes a wide variety of clinical manifestations. Invasive S. aureus infections in hospitals and the community are one of the main causes of mortality and morbidity, as virulent and multi-drug-resistant strains have evolved. There is an unmet and urgent clinical need for immune-based non-antibiotic approaches to treat these infections as the growing antibiotic resistance poses a significant public health danger. Subtractive proteomics assisted reverse vaccinology-based immunoinformatics pipeline was used in this study to target the suitable antigenic proteins for the development of multi-epitope vaccine (MEV). Three essential virulent and antigenic proteins were identified including Glycosyltransferase, Elastin Binding Protein, and Staphylococcal secretory antigen. A variety of immunoinformatics tools have been used to forecast T-cell and B-cell epitopes from target proteins. Seven CTL, five HTL, and eight LBL epitopes, connected through suitable linkers and adjuvant, were employed to design 444 amino acids long MEV construct. The vaccine was paired with the TLR4 agonist 50S ribosomal protein L7/L12 adjuvant to enhance the immune response towards the vaccine. The predicted MEV structure was assessed to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. Molecular docking simulation of the MEV with the human TLR4 (toll-like receptor 4) and major histocompatibility complex molecules (MHCI and MHCII) was performed to validate the interactions with the receptors. Molecular dynamics (MD) simulation and MMGBSA binding free energy analyses were carried out for the stability evaluation and binding of the MEV docked complexes with TLR4, MHCI and MHCII. To achieve maximal vaccine protein expression with optimal post-translational modifications, MEV was reverse translated, its mRNA structure was analyzed, and finally in silico cloning was performed into E. coli expression host. These rigorous computational analyses supported the effectivity of proposed MEV in protection against infections associated with S. aureus. However, further experimental validations are required to fully evaluate the potential of proposed vaccine candidate.

Immunoinformatics approach for multi-epitope vaccine design against P. falciparum malaria

Plasmodium falciparum (P. falciparum) is a leading causative agent of malaria, an infectious disease that can be fatal. Unfortunately, control measures are becoming less effective over time. A vaccine is needed to effectively control malaria and lead towards the total elimination of the disease. There have been multiple attempts to develop a vaccine, but to date, none have been certified as appropriate for wide-scale use. In this study, an immunoinformatics method is presented to design a multi-epitope vaccine construct predicted to be effective against P. falciparum malaria. This was done through the prediction of 12 CD4+ T-cell, 10 CD8+ T-cell epitopes and, 1 B-cell epitope which were assessed for predicted high antigenicity, immunogenicity, and non-allergenicity through in silico methods. The Human Leukocyte Antigen (HLA) population coverage showed that the alleles associated with the epitopes accounted for 78.48% of the global population. The CD4+ and CD8+ T-cell epitopes were docked to HLA-DRB1*07:01 and HLA-A*32:01 successfully. Therefore, the epitopes were deemed to be suitable as components of a multi-epitope vaccine construct. Adjuvant RS09 was added to the construct to generate a stronger immune response, as confirmed by an immune system simulation. Finally, the structural stability of the predicted multi-epitope vaccine was assessed using molecular dynamics simulations. The results show a promising vaccine design that should be further synthesised and assessed for its efficacy in an experimental laboratory setting.

Identification of promiscuous T cell epitopes on Mayaro virus structural proteins using immunoinformatics, molecular modeling, and QM:MM approaches

The Mayaro virus (MAYV) belongs to genus Alphavirus (family Togaviridae) and has been reported in several countries, especially in tropical regions of America. Due to its outbreaks and potential lack of medication, an effective vaccine formulation is strongly required. This study aimed to predict promiscuous T cell epitopes from structural polyproteins of MAYV using an immunoinformatics approach. For this purpose, consensus sequences were used to identify short protein sequences capable of binding to MHC class I and class II alleles. Our analysis pointed out 4 MHC-I/TCD8+ and 21 MHC-II/TCD4+ epitopes on capside (1;3), E1 (2;5), E2 (1;10), E3 (0;2), and 6 K (0;1) proteins. These predicted epitopes were characterized by high antigenicity, immunogenicity, conservancy, non-allergenic, non-toxic, and good population coverage rate values for North and South American geographical areas. Afterwards, we used the crystal structure of human toll-like receptor 3 (TLR3) ectodomain as a template to predict, through docking essays, the placement of a vaccine prototype at the TLR3 receptor binding site. Finally, classical and quantum mechanics/molecular mechanics (QM:MM) computations were employed to improve the quality of docking calculations, with the QM part of the simulations being accomplished by using the density functional theory (DFT) formalism. These results provide important insights into the advancement of diagnostic platforms, the development of vaccines, and immunotherapeutic interventions.

Proteome wide vaccine targets prioritization and designing of antigenic vaccine candidate to trigger the host immune response against the Mycoplasma genitalium infection

Mycoplasma genitalium is a small size, sexually transmitted bacterial pathogen that causes urethritis in males and cervicitis in females. Being resistant to antibiotics, difficulty in diagnosis, treatment, and control of this cosmopolitan infection, vaccination is the alternating method for its effective management. Herein, this study was conducted to computationally design a multi-epitope vaccine to boost host immune responses against M. genitalium. To achieve the study aim, immunoinformatics approaches were applied to the said pathogen's proteomics sequence data. B and T cell epitopes were projected from the three shortlisted vaccine proteins; MG014, MG015, Hmw3MG317. The final vaccine ensemble comprises cytotoxic and helper T cell epitopes fused through appropriate linkers. The epitopes peptide is then liked to an adjuvant for efficient recognition and processing by the host immune system. The various physicochemical parameters such as allergenicity, antigenicity, theoretical pI, GRAVY, and molecular weight of the vaccine were checked and found safe and effective to be used in post-experimental studies. The stability and binding affinity of the vaccine with the TLR1/2 heterodimer were ensured by performing molecular docking. The best-docked complex was considered, ranked top having the lowest binding energy and strong intermolecular binding and stability. Finally, the vaccine constructs better expression was obtained by in silico cloning into the pET28a (+) vector in Escherichia coli K-12 strain, and immune simulation validated the immune response. In a nutshell, all these approaches lead to developing a multi-epitope vaccine that possessed the ability to induce cellular and antibody-mediated immune responses against the pathogen used.

Development of multi-epitope subunit vaccine for protection against the norovirus' infections based on computational vaccinology

Human Norovirus belongs to a family Calciviridae, and was identified in the outbreak of gastroenteritis in Norwalk, due to its seasonal prevalence known as "winter vomiting disease." Treatment of Norovirus infection is still mysterious because there is no effective antiviral drugs or vaccine developed to protect against the infection, to eradicate the infection an effective vaccine should be developed. In this study, capsid protein (A7YK10), small protein (A7YK11), and polyprotein (A7YK09) were utilized. These proteins were subjected to B and T cell epitopes prediction by using reliable immunoinformatics tools. The antigenic and non-allergenic epitopes were selected for the subunit vaccine, which can activate cellular and humoral immune responses. Linkers joined these epitopes together. The vaccine structure was modelled and validated by using Errat, ProSA, and rampage servers. The modelled vaccine was docked with TLR-7. The stability of the docked complex was evaluated by MD simulation. To apply the concept in a wet lab, the reverse translated vaccine sequence was cloned in pET28a (+). The vaccine developed in this study requires experimental validation to ensure its effectiveness against the disease.Communicated by Ramaswamy H. Sarma.

Design and optimization of a subunit vaccine targeting COVID-19 molecular shreds using an immunoinformatics framework

COVID-19 has been declared as a global health emergency and exposed the world to a deadly virus, which has dramatically changed the lives of humans for an unknown period of time. In the battleground with the virus, we have employed an immunoinformatics framework to design a robust vaccine as an insurance plan for the future. The pathogenic sequence with cryptic epitope taken from patients in Wuhan, China, was harnessed to design a promiscuous cytotoxic T-lymphocyte, helper T-lymphocyte, and B-cell epitope based subunit vaccine, engineered with adjuvants and conformational linkers. The reported vaccine has high antigenicity and immunogenicity profiles with potential TAP affinity, which ensures elevated antigen processing capability. It has strong binding with major histocompatibility complex (MHC) receptors (MHC-1 and MHC-2) and virus-specific membrane receptor TLR-2, with scores of -1010.7, -1035.7, and -1076.3 kcal mol-1, respectively. Molecular dynamics simulation analysis was used to assess the stable binding with TLR-2 with minimal atomic motions through a deformation plot, covariance matrix, and elastic network. Importantly, an in silico immunization assay showed the reliable elicitation of key players in terms of immune cells together with memory cells to evoke adaptive immune responses upon administration of the construct. In view of favorable outcomes, we also propose a plausible vaccine mechanism to elicit an immune response to fight coronavirus.

Immunoinformatics approach for predicting epitopes in HN and F proteins of Porcine rubulavirus

Porcine rubulavirus (PRV), which belongs to the family Paramyxoviridae, causes blue eye disease in pigs, characterized by encephalitis and reproductive failure in newborn and adult pigs, respectively. There is no effective treatment against PRV and no information on the effectiveness of the available vaccines. Continuous outbreaks have occurred in Mexico since the early 1980s, which have caused serious economic losses to pig producers. Vaccination can be used to control this disease. Searching for effective antigen candidates against PRV, we first sequenced the PAC1 F protein, then we used various immunoinformatics tools to predict antigenic determinants of B-cells and T-cells against the two glycoproteins of the virus (HN and F proteins). Finally, we used AutoDock Vina to determine the binding energies. We obtained the F gene sequence of a PRV strain collected in the early 1990s in Mexico and compared its amino acid profile with previous and more recent strains, obtaining an identity similarity of 97.78 to 99.26%. For the F proteins, seven linear B-cell epitopes, six conformational B-cell epitopes and twenty-nine T-cell MHC class I epitopes were predicted. For the HN proteins, sixteen linear B-cell epitopes, seven conformational B-cell epitopes and thirty-four T-cell MHC class I epitopes were predicted. The ATRSETDYY and AAYTTTTCF epitopes of the HN protein might be important for neutralizing the viral infection. We determined the in silico binding energy between the predicted epitopes on the F and HN proteins and swine MHC-I molecules. The binding energy of these epitopes ranged from -5.8 to -7.8 kcal/mol. The present study aimed to assess the use of HN and F proteins as antigens, either as recombinant proteins or as a series of peptides that could activate different responses of the immune system. This may help identify relevant immunogens, saving time and costs in the development of new vaccines or diagnostic tools.

Proteome-wide mapping and reverse vaccinology-based B and T cell multi-epitope subunit vaccine designing for immune response reinforcement against Porphyromonas gingivalis

Porphyromonas gingivalis, a prominent pathogen responsible for acute periodontal diseases, is widely studied by the scientific community for its successful evasion of the host immune system. P. gingivalis is associated with rheumatoid arthritis, dementia, and Alzheimer's. The pathogen successfully survives itself against the heavy load of conventional antibiotics because of its ability to evade the host immune system. Subtractive proteomics and reverse vaccinology approaches were employed in order to prioritize the best proteins for vaccine designing. Three vaccine candidates with Uniprot ID: Q7MWZ2 (histidine Kinase), Q7MVL1 (Fe (2+) transporter), and Q7MWZ2 (Capsular polysaccharide transport protein) were identified for vaccine designing. These proteins are antigenic and essential for pathogen survival. A wide range of immunoinformatics tools was applied for the prediction of epitopes, B, and T cells, for the vaccine candidate proteins. Molecular docking of the predicted epitopes against the MHC molecules were carried out. In-silico vaccine was constructed using carefully evaluated epitopes and consequently modeled for docking with human Toll-like receptor 2. Chain C of Pam3CSK4 (PDB ID; 2Z7X) was linked to the vaccine as an adjuvant to boost immune response towards the vaccine. For stability evaluation of the vaccine-TLR-2 docked complex, Molecular Dynamics simulations were performed. The reverse-translated nucleotide sequence cloned in Eschericia coli to attain the maximal expression of the vaccine protein. The maximal expression was ensured by CAI score of 0.96. The current vaccine requires future experimental validation to confirm its effectiveness. The vaccine developed will be helpful to protect against P. gingivalis associated infections.Communicated by Ramaswamy H. Sarma.

A SARS-CoV-2 vaccine candidate: In-silico cloning and validation

SARS-CoV-2 is spreading globally at a rapid pace. To contain its spread and prevent further fatalities, the development of a vaccine against SARS-CoV-2 is an urgent prerequisite. Thus, in this article, by utilizing the in-silico approach, a vaccine candidate for SARS-CoV-2 has been proposed. Moreover, the effectiveness and safety measures of our proposed epitopic vaccine candidate have been evaluated by in-silico tools and servers (AllerTOP and AllergenFP servers). We observed that the vaccine candidate has no allergenicity and successfully combined with Toll-like receptor (TLR) protein to elicit an inflammatory immune response. Stable, functional mobility of the vaccine-TLR protein binding interface was confirmed by the Normal Mode Analysis. The in-silico cloning model demonstrated the efficacy of the construct vaccine along with the identified epitopes against SARS-CoV-2. Taken together, our proposed in-silico vaccine candidate has potent efficacy against COVID-19 infection, and successive research work might validate its effectiveness in in vitro and in vivo models.

In silico integrative analysis predicts relevant properties of exotoxin-derived peptides for the design of vaccines against Pseudomonas aeruginosa

Pseudomonas aeruginosa (PA) is an opportunistic human pathogen responsible for causing serious infections in patients with cystic fibrosis. Infections caused by PA are difficult to treat and eradicate due to intrinsic and added resistance to antibiotic therapy. Therefore, it is necessary to establish effective prevention strategies against this infectious agent. In this study, a combination of immunoinformatic tools was applied to predict immunogenic and immunodominant regions in the structure of exotoxins commonly secreted as virulence factors in PA infection (ExoA, ExoS, ExoT, ExoU and ExoY). The peptides derived from exotoxins were evaluated for the potential affinity for human leukocyte antigen (HLA) I and HLA-II molecules, antigenicity score and toxicity profile. From an initial screening of 941 peptides, 13 (1.38%) were successful in all analyzes. The peptides with relevant immunogenic properties were mainly those derived from Exo A (10 / 76.9%). All peptides selected in the last analysis present a high population coverage rate based on the interaction of HLA alleles (95.36 ± 7.83%). Therefore, the peptides characterized in this study are recommended for in vitro and in vivo studies and can provide the basis for the rational design of a vaccine against PA.

Designing a vaccine for fascioliasis using immunogenic 24 kDa mu-class glutathione s-transferase

Fascioliasis, caused by the liver fluke Fasciola gigantica, is a significant zoonotic disease of the livestock and human, causing substantial economic loss worldwide. Triclabendazole (TCBZ) is the only drug available for the management of the disease against which there is an alarming increase in drug resistance. No vaccine is available commercially for the protection against this disease. Increasing resistance to TCBZ and the lack of a successful vaccine against fascioliasis demands the development of vaccines. In the present study, a structural immunoinformatics approach was used to design a multi-epitope subunit vaccine using the glutathione S-transferase (GST) protein of Fasciola gigantica. The GST antigen is a safe, non-allergic, highly antigenic, and effective vaccine candidate against various parasitic flukes and worms. The cytotoxic T lymphocytes, helper T lymphocytes, and B-cell epitopes were selected for constructing the vaccine based on their immunogenic behavior and binding affinity. The physicochemical properties, allergenicity, and antigenicity of the designed vaccine were analyzed. To elucidate the tertiary structure of the vaccine, homology modeling was performed, followed by structure refinement and docking against the TLR2 immune receptor. Molecular dynamics simulations showed a stable interaction between the vaccine and the receptor complex. Finally, in silico cloning was performed to evaluate the expression and translation of the vaccine construct in the E. coli expression system. Further studies require experimental validation for the safety and immunogenic behavior of the designed vaccine.

Multiepitope Subunit Vaccine to Evoke Immune Response against Acute Encephalitis

Acute encephalitis syndrome outbreak has emerged as a major health concern on both national and international scales. Brain inflammation/infections caused by Japanese encephalitis virus (JEV) can lead to death. The cases are growing in numbers globally, and this emergent health concern requires an effective and viable vaccine to strengthen the body's immune system against this deadly virus. Proteomic analyses of JEV revealed the envelope protein as a potential target for vaccine development by patient samples analysis. Hence, in this study, we aimed to design a multiepitope subunit vaccine for acute encephalitis using the advanced structural biology and immunoinformatics approaches. We report the multiepitope subunit vaccine consisted of the putative T-cell epitope (MHC-1 and MHC-2 restricted) and B-cell epitope and with high antigenicity and immunogenicity. The TAP affinity epitopes along with adjuvants were engineered to the vaccine, to ensure the ease transportation inside the host and elicitation of a strong immune response. The specificity of vaccine construct was evaluated by molecular docking with major histocompatibility complex (MHC) receptors and host membrane receptor TLR2. High docking scores and a close interaction to the binding groove of receptors confirmed the potency and specificity of the vaccine. Also, molecular dynamics simulation studies confirmed the stable interaction of vaccine with TLR2 for a long run (100 ns), which showed the prolonged elicitation of the strong immune response. Peptide dynamics studies showed the flexible, strong, and stable binding of vaccine with minimal deviation in root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and secondary structure estimation (SSE) plots till 100 ns simulation run. The in silico immune simulation approach based on the position-specific scoring matrix and machine learning methods resulted in the strong immune response reinforcement statistics of immune cells (T-cells, B-cells population, and memory cells) in response to vaccine candidate. The favorable results and well-correlated data of varied in silico techniques paved for a potent multiepitope vaccine and helped us to propose the mechanism of action of designed vaccine and generation of the immune response against acute encephalitis syndrome.