Designing B- and T-cell multi-epitope based subunit vaccine using immunoinformatics approach to control Zika virus infection

Delineating multi-epitopes vaccine designing from membrane protein CL5 against all monkeypox strains: a pangenome reverse vaccinology approach

The recently identified monkeypox virus (MPXV or mpox) is a zoonotic orthopox virus that infects humans and causes diseases with traits like smallpox. The world health organization (WHO) estimates that 3-6% of MPXV cases result in death. As it might impact everyone globally, like COVID, and become the next pandemic, the cure for this disease is important for global public health. The high incidence and disease ratio of MPXV necessitates immediate efforts to design a unique vaccine candidate capable of addressing MPXV diseases. Here, we used a computational pan-genome-based vaccine design strategy for all currently reported 19 MPXV strains acquired from different regions of the world. Thus, this study's objective was to develop a new and safe vaccine candidate against MPXV by targeting the membrane CL5 protein; identified after the pangenome analysis. Proteomics and reverse vaccinology have covered up all of the MPXV epitopes that would usually stimulate robust host immune responses. Following this, only two mapped (MHC-I, MHC-II, and B-cell) epitopes were observed to be extremely effective that can be used in the construction of CL5 protein vaccine candidates. The suggested vaccine (V5) candidate from eight vaccine models was shown to be antigenic, non-allergenic, and stable (with 213 amino acids). The vaccine's candidate efficacy was evaluated by using many in silico methods to predict, improve, and validate its 3D structure. Molecular docking and molecular dynamics simulations further reveal that the proposed vaccine candidate ensemble has a high interaction energy with the HLAs and TRL2/4 immunological receptors under study. Later, the vaccine sequence was used to generate an expression vector for the E. coli K12 strain. Further study uncovers that V5 was highly immunogenic because it produced robust primary, secondary, and tertiary immune responses. Eventually, the use of computer-aided vaccine designing may significantly reduce costs and speed up the process of developing vaccines. Although, the results of this research are promising, however, more research (experimental; in vivo, and in vitro studies) is needed to verify the biological efficacy of the proposed vaccine against MPXV.Communicated by Ramaswamy H. Sarma.

Design of multivalent-epitope vaccine models directed toward the world's population against HIV-Gag polyprotein: Reverse vaccinology and immunoinformatics

Significant progress has been made in HIV-1 research; however, researchers have not yet achieved the objective of eradicating HIV-1 infection. Accordingly, in this study, eucaryotic and procaryotic in silico vaccines were developed for HIV-Gag polyproteins from 100 major HIV subtypes and CRFs using immunoinformatic techniques to simulate immune responses in mice and humans. The epitopes located in the conserved domains of the Gag polyprotein were evaluated for allergenicity, antigenicity, immunogenicity, toxicity, homology, topology, and IFN-γ induction. Adjuvants, linkers, CTLs, HTLs, and BCL epitopes were incorporated into the vaccine models. Strong binding affinities were detected between HLA/MHC alleles, TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9, and vaccine models. Immunological simulation showed that innate and adaptive immune cells elicited active and consistent responses. The human vaccine model was matched with approximately 93.91% of the human population. The strong binding of the vaccine to MHC/HLA and TLR molecules was confirmed through molecular dynamic stimulation. Codon optimization ensured the successful translation of the designed constructs into human cells and E. coli hosts. We believe that the HIV-1 Gag vaccine formulated in our research can reduce the challenges faced in developing an HIV-1 vaccine. Nevertheless, experimental verification is necessary to confirm the effectiveness of these vaccines in these models.

Rational design and production of a chimeric antigen targeting Zika virus that induces neutralizing antibodies in mice

The Zika virus (ZIKV) is considered a public health problem worldwide due to its association with the development of microcephaly and the Guillain-Barré syndrome. Currently, there is no specific treatment or vaccine approved to combat this disease, and thus, developing safe and effective vaccines is a relevant goal. In this study, a multi-epitope protein called rpZDIII was designed based on a series of ZIKV antigenic sequences, a bacterial carrier, and linkers. The analysis of the predicted 3D structure of the rpZDIII chimeric antigen was performed on the AlphaFold 2 server, and it was produced in E. coli and purified from inclusion bodies, followed by solubilization and refolding processes. The yield achieved for rpZDIII was 11 mg/L in terms of pure soluble recombinant protein per liter of fermentation. rpZDIII was deemed immunogenic since it induced serum IgG and IgM responses in mice upon subcutaneous immunization in a three-dose scheme. Moreover, sera from mice immunized with rpZDIII showed neutralizing activity against ZIKV. Therefore, this study reveals rpZDIII as a promising immunogen for the development of a rationally designed multi-epitope vaccine against ZIKV, and completion of its preclinical evaluation is guaranteed.

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

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

An immunoinformatics and structural vaccinology approach to design a novel and potent multi-epitope base vaccine targeting Zika virus

Zika virus is an infectious virus, that belongs to Flaviviridae family, which is transferred to humans through mosquito vectors and severely threatens human health; but, apart from available resources, no effective and secure vaccine is present against Zika virus, to prevent such infections. In current study, we employed structural vaccinology approach to design an epitope-based vaccine against Zika virus, which is biocompatible, and secure and might trigger an adaptive and innate immune response by using computational approaches. We first retrieved the protein sequence from National Center for Biotechnology Information (NCBI) database and carried out for BLAST P. After BLAST P, predicted protein sequences were shortlisted and checked for allergic features and antigenic properties. Final sequence of Zika virus, with accession number (APO40588.1) was selected based on high antigenic score and non-allergenicity. Final protein sequence used various computational approaches including antigenicity testing, toxicity evaluation, allergenicity, and conservancy assessment to identify superior B-cell and T-cell epitopes. Two B-cell epitopes, five MHC-six MHC-II epitopes and I were used to construct an immunogenic multi-epitope-based vaccine by using suitable linkers. A 50S ribosomal protein was added at N terminal to improve the immunogenicity of vaccine. In molecular docking, strong interactions were presented between constructed vaccine and Toll-like receptor 9 (- 1100.6 kcal/mol), suggesting their possible relevance in the immunological response to vaccine. The molecular dynamics simulations ensure the dynamic and structural stability of constructed vaccine. The results of C-immune simulation revealed that constructed vaccine activate B and T lymphocytes which induce high level of antibodies and cytokines to combat Zika infection. The constructed vaccine is an effective biomarker with non-sensitization, nontoxicity; nonallergic, good immunogenicity, and antigenicity, however, experimental assays are required to verify the results of present study.

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

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

Immunoinformatics-aided rational design of multiepitope-based peptide vaccine (MEBV) targeting human parainfluenza virus 3 (HPIV-3) stable proteins

BACKGROUND

Human parainfluenza viruses (HPIVs) are common RNA viruses responsible for respiratory tract infections. Human parainfluenza virus 3 (HPIV-3) is particularly pathogenic, causing severe illnesses with no effective vaccine or therapy available.

RESULTS

The current study employed a systematic immunoinformatic/reverse vaccinology approach to design a multiple epitope-based peptide vaccine against HPIV-3 by analyzing the virus proteome. On the basis of a number of therapeutic features, all three stable and antigenic proteins with greater immunological relevance, namely matrix protein, hemagglutinin neuraminidase, and RNA-directed RNA polymerase L, were chosen for predicting and screening suitable T-cell and B-cell epitopes. All of our desired epitopes exhibited no homology with human proteins, greater population coverage (99.26%), and high conservancy among reported HPIV-3 isolates worldwide. All of the T- and B-cell epitopes are then joined by putative ligands, yielding a 478-amino acid-long final construct. Upon computational refinement, validation, and thorough screening, several programs rated our peptide vaccine as biophysically stable, antigenic, allergenic, and non-toxic in humans. The vaccine protein demonstrated sufficiently stable interaction as well as binding affinity with innate immune receptors TLR3, TLR4, and TLR8. Furthermore, codon optimization and virtual cloning of the vaccine sequence in a pET32a ( +) vector showed that it can be readily expressed in the bacterial system.

CONCLUSION

The in silico designed HPIV-3 vaccine demonstrated potential in evoking an effective immune response. This study paves the way for further preclinical and clinical evaluation of the vaccine, offering hope for a future solution to combat HPIV-3 infections.

Designing a multi-epitope vaccine against Peptostreptococcus anaerobius based on an immunoinformatics approach

Peptostreptococcus anaerobius is an anaerobic bacterium, which has been found selectively en-riched in the fecal and mucosal microbiota of colorectal cancer (CRC) patients. Emerging evidence suggest P. anaerobius may contribute to the development of CRC in human. In this study, we designed a multi-epitope chimeric vaccine against P. anaerobius PCWBR2, a recently identified adhesin that interacts directly with colon cell lines by binding α2/β1 integrin frequently overexpressed in human CRC tumors and cell lines. Immunoinformatics tools predicted six cytotoxic T lymphocyte epitopes, five helper T lymphocyte epitopes, and six linear B lymphocyte epitopes. The predicted epitopes were joined with AAY or GPGPG linkers and a previously reported TLR4 agonist was added to the vaccine construct's N terminal as an adjuvant using EAAAK linkers and the order of epitopes was optimized. Further in silico analysis revealed that the vaccine construct possesses satisfactory antigenicity, allergenicity, solubility, physicochemical properties, adjuvant-TLR4 molecular docking, and immune profile characteristics. Our study provided a promising design for vaccines against P. anaerobius.

Designing a multi-epitope vaccine to control porcine epidemic diarrhea virus infection using immunoinformatics approaches

Porcine epidemic diarrhea virus (PEDV), a continuously evolving pathogen, causes severe diarrhea in piglets with high mortality rates. However, current vaccines cannot provide complete protection against PEDV, so vaccine development is still necessary and urgent. Here, with the help of immunoinformatics approaches, we attempted to design a multi-epitope vaccine named rPMEV to prevent and control PEDV infection. The epitopes of rPMEV were constructed by 9 cytotoxic T lymphocyte epitopes (CTLs), 11 helper T lymphocyte epitopes (HTLs), 6 linear B cell epitopes (LBEs), and 4 conformational B cell epitopes (CBEs) based on the S proteins from the four representative PEDV G2 strains. To enhance immunogenicity, porcine β-defensin-2 (PBD-2) was adjoined to the N-terminal of the vaccine as an adjuvant. All of the epitopes and PBD-2 were joined by corresponding linkers and recombined into the multivalent vaccine, which is stable, antigenic, and non-allergenic. Furthermore, we adopted molecular docking and molecular dynamics simulation methods to analyze the interaction of rPMEV with the Toll-like receptor 4 (TLR4): a stable interaction between them created by 13 hydrogen bonds. In addition, the results of the immune simulation showed that rPMEV could stimulate both cellular and humoral immune responses. Finally, to raise the expression efficiency, the sequence of the vaccine protein was cloned into the pET28a (+) vector after the codon optimization. These studies indicate that the designed multi-epitope vaccine has a potential protective effect, providing a theoretical basis for further confirmation of its protective effect against PEDV infection in vitro and in vivo studies.

Serological Conversion through a Second Exposure to Inactivated Foot-and-Mouth Disease Virus Expressing the JC Epitope on the Viral Surface

Foot-and-mouth disease (FMD) is a fatal contagious viral disease that affects cloven-hoofed animals and causes severe economic damage at the national level. There are seven serotypes of the causative foot-and-mouth disease virus (FMDV), and type O is responsible for serious outbreaks and shows a high incidence. Recently, the Cathay, Southeast Asia (SEA), and ME-SA (Middle East-South Asia) topotypes of type O have been found to frequently occur in Asia. Thus, it is necessary to develop candidate vaccines that afford protection against these three different topotypes. In this study, an experimental FMD vaccine was produced using a recombinant virus (TWN-JC) with the JC epitope (VP1 140-160 sequence of the O/SKR/Jincheon/2014) between amino acid 152 and 153 of VP1 in TWN-R. Immunization with this novel vaccine candidate was found to effectively protect mice against challenge with the three different topotype viruses. Neutralizing antibody titers were considerably higher after a second vaccination. The serological differences between the topotype strains were identified in guinea pigs and swine. In conclusion, a significant serological difference was observed at 56 days post-vaccination between animals that received the TWN-JC vaccine candidate and those that received the positive control virus (TWN-R). The TWN-JC vaccine candidate induced IFNγ and IL-12B.

Identification of two novel T cell epitopes on the E2 protein of classical swine fever virus C-strain

C-strain, also known as the HCLV strain, is a well-known live attenuated vaccine against classical swine fever (CSF), a devastating disease caused by classical swine fever virus (CSFV). Vaccination with C-strain induces a rapid onset of protection, which is associated with virus-specific gamma interferon (IFN-γ)-secreting CD8+ T cell responses. The E2 protein of CSFV is a major protective antigen. However, the T cell epitopes on the E2 protein remain largely unknown. In this study, eight overlapping nonapeptides of the E2 protein were predicted and synthesized to screen for potential T cell epitopes on the CSFV C-strain E2 protein. Molecular docking was performed on the candidate epitopes with the swine leukocyte antigen-1*0401. The analysis obtained two highly conserved T cell epitopes, 90STEEMGDDF98 and 331ATDRHSDYF339, which were further identified by enzyme-linked immunospot assay. Interestingly, the mutants deleting or substituting the epitopes are nonviable. Further analysis demonstrated that 90STEEMGDDF98 is crucial for the E2 homodimerization, while CSFV infection is significantly inhibited by the 331ATDRHSDYF339 peptide treatment. The two novel T cell epitopes can be used to design new vaccines that are able to provide rapid-onset protection.

Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2

In the recent past several vaccines were developed to combat the COVID-19 disease. Unfortunately, the protective efficacy of the current vaccines has been reduced due to the high mutation rate in SARS-CoV-2. Here, we successfully implemented a coevolution based immunoinformatics approach to design an epitope-based peptide vaccine considering variability in spike protein of SARS-CoV-2. The spike glycoprotein was investigated for B- and T-cell epitope prediction. Identified T-cell epitopes were mapped on previously reported coevolving amino acids in the spike protein to introduce mutation. The non-mutated and mutated vaccine components were constructed by selecting epitopes showing overlapping with the predicted B-cell epitopes and highest antigenicity. Selected epitopes were linked with the help of a linker to construct a single vaccine component. Non-mutated and mutated vaccine component sequences were modelled and validated. The in-silico expression level of the vaccine constructs (non-mutated and mutated) in E. coli K12 shows promising results. The molecular docking analysis of vaccine components with toll-like receptor 5 (TLR5) demonstrated strong binding affinity. The time series calculations including root mean square deviation (RMSD), radius of gyration (RGYR), and energy of the system over 100 ns trajectory obtained from all atom molecular dynamics simulation showed stability of the system. The combined coevolutionary and immunoinformatics approach used in this study will certainly help to design an effective peptide vaccine that may work against different strains of SARS-CoV-2. Moreover, the strategy used in this study can be implemented on other pathogens.

Designing a Multi-Epitope Subunit Vaccine against VP1 Major Coat Protein of JC Polyomavirus

The JC polyomavirus virus (JCPyV) affects more than 80% of the human population in their early life stage. It mainly affects immunocompromised individuals where virus replication in oligodendrocytes and astrocytes may lead to fatal progressive multifocal encephalopathy (PML). Virus protein 1 (VP1) is one of the major structural proteins of the viral capsid, responsible for keeping the virus alive in the gastrointestinal and urinary tracts. VP1 is often targeted for antiviral drug and vaccine development. Similarly, this study implied immune-informatics and molecular modeling methods to design a multi-epitope subunit vaccine targeting JCPyV. The VP1 protein epitopic sequences, which are highly conserved, were used to build the vaccine. This designed vaccine includes two adjuvants, five HTL epitopes, five CTL epitopes, and two BCL epitopes to stimulate cellular, humoral, and innate immune responses against the JCPyV. Furthermore, molecular dynamics simulation (100 ns) studies were used to examine the interaction and stability of the vaccine protein with TLR4. Trajectory analysis showed that the vaccine and TLR4 receptor form a stable complex. Overall, this study may contribute to the path of vaccine development against JCPyV.

Synthetic peptides derived from predicted B cell epitopes of nervous necrosis virus (NNV) show antigenicity and elicit immunogenic responses in Asian seabass (Lates calcarifer)

Nervous necrosis virus (NNV) has spread throughout the world, affecting more than 120 freshwater and marine fish species. While vaccination effectively prevents disease outbreaks, the difficulty of producing sufficient viruses using cell lines continues to be a significant disadvantage for producing inactivated vaccines. This study, therefore, explored the application of synthetic peptides as potential vaccine candidates for the prevention of NNV in Asian seabass (Lates calcarifer). Using the epitope prediction tool and molecular docking, three predicted immunogenic B cell epitopes (30-32 aa) derived from NNV coat protein were selected and synthesised, corresponding to amino acid positions 5 to 34 (P1), 133 to 162 (P2) and 181 to 212 (P3). All the predicted peptides interact with Asian sea bass's MHC class II by docking. The antigenicity of these peptides was determined through ELISA and all peptides were able to react with NNV-specific antibodies. Subsequently, the immunogenicity of these synthetic peptides was investigated by immunisation of Asian seabass with individual peptides (30 μg/fish) and a peptide cocktail (P1+P2+P3, 10 μg each/fish) by intraperitoneal injection, followed by a booster dose at day 28 post-primary immunisation. There was a subset of immunised fish that were able to induce upregulation of immune genes (IL-1β, TNFα, MHCI, MHCII β, CD4, CD8, and IgM-like) in the head kidney and spleen post immunization. Importantly, antibodies derived from fish immunised with synthetic peptides reacted with whole NNV virions, and sera from P1 group could neutralise NNV in an in vitro assay. Taken together, these findings indicate that synthetic linear peptides based on predicted B cell epitopes exhibited both antigenic and immunogenic properties, suggesting that they could be potential vaccine candidates for the prevention of NNV in fish.

In silico design and immunoinformatics analysis of a universal multi-epitope vaccine against monkeypox virus

Monkeypox virus (MPXV) outbreaks have been reported in various countries worldwide; however, there is no specific vaccine against MPXV. In this study, therefore, we employed computational approaches to design a multi-epitope vaccine against MPXV. Initially, cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), linear B lymphocytes (LBL) epitopes were predicted from the cell surface-binding protein and envelope protein A28 homolog, both of which play essential roles in MPXV pathogenesis. All of the predicted epitopes were evaluated using key parameters. A total of 7 CTL, 4 HTL, and 5 LBL epitopes were chosen and combined with appropriate linkers and adjuvant to construct a multi-epitope vaccine. The CTL and HTL epitopes of the vaccine construct cover 95.57% of the worldwide population. The designed vaccine construct was found to be highly antigenic, non-allergenic, soluble, and to have acceptable physicochemical properties. The 3D structure of the vaccine and its potential interaction with Toll-Like receptor-4 (TLR4) were predicted. Molecular dynamics (MD) simulation confirmed the vaccine's high stability in complex with TLR4. Finally, codon adaptation and in silico cloning confirmed the high expression rate of the vaccine constructs in strain K12 of Escherichia coli (E. coli). These findings are very encouraging; however, in vitro and animal studies are needed to ensure the potency and safety of this vaccine candidate.

Immunoinformatic prediction of potential immunodominant epitopes from cagW in order to investigate protection against Helicobacter pylori infection based on experimental consequences

Helicobacter pylori is a leading cause of stomach cancer and peptic ulcers. Thus, identifying epitopes in H. pylori antigens is important for disease etiology, immunological surveillance, enhancing early detection tests, and developing optimal epitope-based vaccines. We used immunoinformatic and computational methods to create a potential CagW epitope candidate for H. pylori protection. The cagW gene of H. pylori was amplified and cloned into pcDNA3.1 (+) for injection into the muscles of healthy BALB/c mice to assess the impact of the DNA vaccine on interleukin levels. The results will be compared to a control group of mice that received PBS or cagW-pcDNA3.1 (+) vaccinations. An analysis of CagW protein antigens revealed 8 CTL and 7 HTL epitopes linked with AYY and GPGPG, which were enhanced by adding B-defensins to the N-terminus. The vaccine's immunogenicity, allergenicity, and physiochemistry were validated, and its strong activation of TLRs (1, 2, 3, 4, and 10) suggests it is antigenic. An in-silico cloning and immune response model confirmed the vaccine's expression efficiency and predicted its impact on the immune system. An immunofluorescence experiment showed stable and bioactive cagW gene expression in HDF cells after cloning the whole genome into pcDNA3.1 (+). In vivo vaccination showed that pcDNA3.1 (+)-cagW-immunized mice had stronger immune responses and a longer plasmid DNA release window than control-plasmid-immunized mice. After that, bioinformatics methods predicted, developed, and validated the three-dimensional structure. Many online services docked it with Toll-like receptors. The vaccine was refined using allergenicity, antigenicity, solubility, physicochemical properties, and molecular docking scores. Virtual-reality immune system simulations showed an impressive reaction. Codon optimization and in-silico cloning produced E. coli-expressed vaccines. This study suggests a CagW epitopes-protected H. pylori infection. These studies show that the proposed immunization may elicit particular immune responses against H. pylori, but laboratory confirmation is needed to verify its safety and immunogenicity.

B and T Cell Epitopes of the Incursionary Foot-and-Mouth Disease Virus Serotype SAT2 for Vaccine Development

Failure of cross-protection among interserotypes and intratypes of foot-and-mouth disease virus (FMDV) is a big threat to endemic countries and their prevention and control strategies. However, insights into practices relating to the development of a multi-epitope vaccine appear as a best alternative approach to alleviate the cross-protection-associated problems. In order to facilitate the development of such a vaccine design approach, identification and prediction of the antigenic B and T cell epitopes along with determining the level of immunogenicity are essential bioinformatics steps. These steps are well applied in Eurasian serotypes, but very rare in South African Territories (SAT) Types, particularly in serotype SAT2. For this reason, the available scattered immunogenic information on SAT2 epitopes needs to be organized and clearly understood. Therefore, in this review, we compiled relevant bioinformatic reports about B and T cell epitopes of the incursionary SAT2 FMDV and the promising experimental demonstrations of such designed and developed vaccines against this serotype.

Translational vaccinomics and structural filtration algorithm to device multiepitope vaccine for catastrophic monkeypox virus

Recent outbreak of monkeypox disease commenced in April 2022, and on May 7, the first confirmed case was reported. The world health organization then designated monkeypox disease as a public health emergency of international outrage on July 23, after it spread to 70 non-endemic nations in less than 15 days. This catastrophic viral infection encourages the development of antiviral therapeutics due to the lack of specific treatments with negligible adverse effects. This analysis developed a highly immunogenic multiepitope subunit vaccine against the monkeypox virus using an in silico translational vaccinomics technique. Highly antigenic B cell and T cell (HTL and CTL) epitopes were predicted and conjugated with the help of unique linkers. An adjuvant (β-defensin) and a pan-HLA DR sequence were attached at the vaccine construct's N-terminal to invoke a robust immunological response. Additionally, physiochemical, allergic, toxic, and antigenic properties were anticipated. Interactions between the vaccine candidate and the TLR3 demonstrated that the vaccine candidate triggers a robust immunological response. Finally, the stability is confirmed by the molecular dynamics study. In contrast, the modified vaccine candidate's ability to produce a protective immune response were verified by an immune dynamics simulation study conducted via C-ImmSim server. This study validates the generation of B cell, Th cell, and Tc cell populations as well as the production of IFN-γ.

Immunoinformatics Vaccine Design for Zika Virus

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

Prediction of Conformational and Linear B-Cell Epitopes on Envelop Protein of Zika Virus Using Immunoinformatics Approach

The current spread of Zika virus infection in India has become a public health issue due to the virus's possible link to birth abnormalities and neurological disorders. There is a need for enhanced vaccines or drugs as a result of its epidemic outbreak and the lack of potential medication. B-cell mediated adaptive immunity is capable of developing pathogen-specific memory that confers immunological protection. Therefore, in this study, the envelope protein of the Zika virus was retrieved from the NCBI protein database. The ABCpred and BepiPred software were used to discover linear B-cell epitopes on envelope protein. Conformational B-cell epitopes on envelope protein were identified using SEPPA 3.0 and Ellipro tools. Predicted B-cell epitopes were evaluated for allergenicity, toxicity, and antigenicity. Two consensus linear B-cell epitopes, envelope_165-180 (AKVEITPNSPRAEATL) and envelope_224-238 (PWHAGADTGTPHWNN) were identified using ABCpred and BepiPredtools. SEPPA 3.0 and Elliprotools predicted consensus conformational envelope_98-110 (DRGWGNGCGLFGK) and envelope_248-251 (AHAK) epitopes and one residue (⁷⁵PRO) within envelope protein as a component of B-cell epitopes. These predicted linear and conformational B-cell epitopes will help in designing peptide vaccines that will activate the humoral response. However, in-vitro and in-vivo laboratory experimental confirmations are still needed to prove the application's feasibility.

Identification of potential candidate vaccines against Mycobacterium ulcerans based on the major facilitator superfamily transporter protein

Buruli ulcer is a neglected tropical disease that is characterized by non-fatal lesion development. The causative agent is Mycobacterium ulcerans (M. ulcerans). There are no known vectors or transmission methods, preventing the development of control methods. There are effective diagnostic techniques and treatment routines; however, several socioeconomic factors may limit patients' abilities to receive these treatments. The Bacillus Calmette-Guérin vaccine developed against tuberculosis has shown limited efficacy, and no conventionally designed vaccines have passed clinical trials. This study aimed to generate a multi-epitope vaccine against M. ulcerans from the major facilitator superfamily transporter protein using an immunoinformatics approach. Twelve M. ulcerans genome assemblies were analyzed, resulting in the identification of 11 CD8+ and 7 CD4+ T-cell epitopes and 2 B-cell epitopes. These conserved epitopes were computationally predicted to be antigenic, immunogenic, non-allergenic, and non-toxic. The CD4+ T-cell epitopes were capable of inducing interferon-gamma and interleukin-4. They successfully bound to their respective human leukocyte antigens alleles in in silico docking studies. The expected global population coverage of the T-cell epitopes and their restricted human leukocyte antigens alleles was 99.90%. The population coverage of endemic regions ranged from 99.99% (Papua New Guinea) to 21.81% (Liberia). Two vaccine constructs were generated using the Toll-like receptors 2 and 4 agonists, LprG and RpfE, respectively. Both constructs were antigenic, non-allergenic, non-toxic, thermostable, basic, and hydrophilic. The DNA sequences of the vaccine constructs underwent optimization and were successfully in-silico cloned with the pET-28a(+) plasmid. The vaccine constructs were successfully docked to their respective toll-like receptors. Molecular dynamics simulations were carried out to analyze the binding interactions within the complex. The generated binding energies indicate the stability of both complexes. The constructs generated in this study display severable favorable properties, with construct one displaying a greater range of favorable properties. However, further analysis and laboratory validation are required.

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

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

Design of a multi-epitope vaccine against the pathogenic fungi Candida tropicalis using an in silico approach

Background

Candida tropicalis causes tropical invasive fungal infections, with a high mortality. This fungus has been found to be resistant to antifungal classes such as azoles, echinocandins, and polyenes in several studies. As a result, it is vital to identify novel approaches to prevent and treat C. tropicalis infections. In this study, an in silico technique was utilized to deduce and evaluate a powerful multivalent epitope-based vaccine against C. tropicalis, which targets the secreted aspartic protease 2 (SAP2) protein. This protein is implicated in virulence and host invasion.

Results

By focusing on the Sap2 protein, 11 highly antigenic, non-allergic, non-toxic, and conserved epitopes were identified. These were subsequently paired with RS09 and flagellin adjuvants, as well as a pan HLA DR-binding epitope (PADRE) sequence to create a vaccine candidate that elicited both cell-mediated and humoral immune responses. It was projected that the vaccine design would be soluble, stable, antigenic, and non-allergic. Ramachandran plot analysis was applied to validate the vaccine construct’s 3-dimensional model. The vaccine construct was tested (at 100 ns) using molecular docking and molecular dynamics simulations, which demonstrated that it can stably connect with MHC-I and Toll-like receptor molecules. Based on in silico studies, we have shown that the vaccine construct can be expressed in E. coli. We surmise that the vaccine design is unrelated to any human proteins, indicating that it is safe to use.

Conclusions

The vaccine design looks to be an effective option for preventing C. tropicalis infections, based on the outcomes of the studies. A fungal vaccine can be proposed as prophylactic medicine and could provide initial protection as sometimes diagnosis of infection could be challenging. However, more in vitro and in vivo research is needed to prove the efficacy and safety of the proposed vaccine design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00415-3.

Design, construction and in vivo functional assessment of a hinge truncated sFLT01

Gene therapy for the treatment of ocular neovascularization has reached clinical trial phases. The AAV2-sFLT01 construct was already evaluated in a phase 1 open-label trial administered intravitreally to patients with advanced neovascular age-related macular degeneration. SFLT01 protein functions by binding to VEGF and PlGF molecules and inhibiting their activities simultaneously. It consists of human VEGFR1/Flt-1 (hVEGFR1), a polyglycine linker, and the Fc region of human IgG1. The IgG1 upper hinge region of the sFLT01 molecule makes it vulnerable to radical attacks and prone to causing immune reactions. This study pursued two goals: (i) minimizing the immunogenicity and vulnerability of the molecule by designing a truncated molecule called htsFLT01 (hinge truncated sFLT01) that lacked the IgG1 upper hinge and lacked 2 amino acids from the core hinge region; and (ii) investigating the structural and functional properties of the aforesaid chimeric molecule at different levels (in silico, in vitro, and in vivo). Molecular dynamics simulations and molecular mechanics energies combined with Poisson-Boltzmann and surface area continuum solvation calculations revealed comparable free energy of binding and binding affinity for sFLT01 and htsFLT01 to their cognate ligands. Conditioned media from human retinal pigment epithelial (hRPE) cells that expressed htsFLT01 significantly reduced tube formation in HUVECs. The AAV2-htsFLT01 virus suppressed vascular development in the eyes of newborn mice. The htsFLT01 gene construct is a novel anti-angiogenic tool with promising improvements compared to existing treatments.

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

Background

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

Results

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

Conclusion

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

Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach

Neo-Coronavirus (NeoCoV) is a novel Betacoronavirus (β-CoVs or Beta-CoVs) discovered in bat specimens in South Africa during 2011. The viral sequence is highly similar to Middle East Respiratory Syndrome, particularly that of structural proteins. Thus, scientists have emphasized the threat posed by NeoCoV associated with human angiotensin-converting enzyme 2 (ACE2) usage, which could lead to a high death rate and faster transmission rate in humans. The development of a NeoCoV vaccine could provide a promising option for the future control of the virus in case of human infection. In silico predictions can decrease the number of experiments required, making the immunoinformatics approaches cost-effective and convenient. Herein, with the aid of immunoinformatics and reverse vaccinology, we aimed to formulate a multi-epitope vaccine that may be used to prevent and treat NeoCoV infection. Based on the NeoCoV proteins, B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were shortlisted. Four vaccines (Neo-1-4) were devised by fusing shortlisted epitopes with appropriate adjuvants and linkers. The secondary and three-dimensional structures of final vaccines were then predicted. The binding interactions of these potential vaccines with toll-like immune receptors (TLR-2, TLR-3, and TLR-4) and major histocompatibility complex molecules (MHC-I and II) reveal that they properly fit into the receptors' binding domains. Besides, Neo-1 and Neo-4 vaccines exhibited better docking energies of -101.08 kcal/mol and -114.47 kcal/mol, respectively, with TLR-3 as compared to other vaccine constructs. The constructed vaccines are highly antigenic, non-allergenic, soluble, non-toxic, and topologically assessable with good physiochemical characteristics. Codon optimization and in-silico cloning confirmed efficient expression of the designed vaccines in Escherichia coli strain K12. In-silico immune simulation indicated that Neo-1 and Neo-4 vaccines could induce a strong immune response against NeoCoV. Lastly, the binding stability and strong binding affinity of Neo-1 and Neo-4 with TLR-3 receptor were validated using molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). The final vaccines require experimental validation to establish their safety and effectiveness in preventing NeoCoV infections.

Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates

Zika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen’s selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew’s correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.

Reverse vaccinology approach to design a multi-epitope vaccine construct based on the Mycobacterium tuberculosis biomarker PE_PGRS17

Mycobacterium tuberculosis (Mtb) is responsible for high mortality rates in many low- and middle-income countries. This infectious disease remains accountable for around 1.4 million deaths yearly. Finding effective control measures against Mtb has become imperative. Vaccination has been regarded as the safe and lasting control measure to curtail the impact of Mtb. This study used the Mtb protein biomarker PE_PGRS17 to design a multi-epitope vaccine. A previous study predicted a strong antigenic property of PE_PGRS17. Immunogenic properties such as antigenicity, toxicity, and allergenicity were predicted for the PE_PGRS17 biomarker, specific B- and T-cell epitope sequences, and the final multiple epitope vaccine (MEV) construct. Algorithmic tools predicted the T- and B-cell epitopes and those that met the immunogenic properties were selected to construct the MEV candidate for predicted vaccine development. The epitopes were joined via linkers and an adjuvant was attached to the terminals of the entire vaccine construct. Immunogenic properties, and physicochemical and structural predictions gave insight into the MEV construct. The assembled vaccine candidate was docked with a receptor and validated using web-based tools. An immune simulation was performed to imitate the immune response after exposure to a dosed administrated predicted MEV subunit. An in silico cloning and codon optimisation gave insight into optimal expression conditions regarding the MEV candidate. In conclusion, the generated MEV construct may potentially emit both cellular and humoral responses which are vital in the development of a peptide-based vaccine against Mtb; nonetheless, further experimental validation is still required.

Bioengineering of Novel Non-Replicating mRNA (NRM) and Self-Amplifying mRNA (SAM) Vaccine Candidates Against SARS-CoV-2 Using Immunoinformatics Approach

Presently, the world needs safe and effective vaccines to overcome the COVID-19 pandemic. Our work has focused on formulating two types of mRNA vaccines that differ in capacity to copy themselves inside the cell. These are non-amplifying mRNA (NRM) and self-amplifying mRNA (SAM) vaccines. Both the vaccine candidates encode an engineered viral replicon which can provoke an immune response. Hence we predicted and screened twelve epitopes from the spike glycoprotein of SARS-CoV-2. We used five CTL, four HTL, and three B-cell-activating epitopes to formulate each mRNA vaccine. Molecular docking revealed that these epitopes could combine with HLA molecules that are important for boosting immunogenicity. The B-cell epitopes were adjoined with GPGPG linkers, while CTL and HTL epitopes were linked with KK linkers. The entire protein chain was reverse translated to develop a specific NRM-based vaccine. We incorporate gene encoding replicase in the upstream region of CDS encoding antigen to design the SAM vaccine. Subsequently, signal sequences were added to human mRNA to formulate vaccines. Both vaccine formulations translated to produce the epitopes in host cells, initiate a protective immune cascade, and generate immunogenic memory, which can counter future SARS-CoV-2 viral exposures before the onset of infection.

Evaluation and identification of essential therapeutic proteins and vaccinomics approach towards multi-epitopes vaccine designing against Legionella pneumophila for immune response instigation

The Legionellaceae group comprises the Legionella, containing 58 species with 70 serotypes. For instance, Legionella pneumophila is the deadliest serotype to cause Legionnaires infectious and is responsible for 90% of the infections in humans. The bacterial pathogen is associated with a severe lung infection, known as legionaries' disease. It is resistant to multiple drugs, thus warranting novel vaccine candidates identification to immune the host against infections caused by the said pathogen. For this, we applied the subtractive proteomics and reverse vaccinology approaches to annotate the most essential genes suitable for vaccine designing. From the whole proteome, only five proteins (Q5ZVG4, Q5ZRZ1, Q5ZWE6, Q5ZT09, and Q5ZUZ8) as the best targets for further processing as they fulfill all the standard parameters set for in silico vaccine design. Immuno-informatics approaches were further applied to the selected protein sequences to prioritized antigenic epitopes for design a multi-epitope subunit vaccine. A multi-epitopes vaccine was designed by using suitable linkers to link the CTL (cytotoxic T lymphocytes), HTL (Helper T lymphocytes), B cell epitopes, and adjuvant to strengthen the vaccine's immunogenicity. The MEVC(multi-epitopes vaccine construct) was reported to interact with human immune receptor TLR-2 (toll-like receptor) robustly (docking score = -357.18 kcal/mol), and a higher expression was achieved in the Escherichia coli system (CAI = 0.88, and GC contents = 54.34%). Moreover, immune simulation revealed that on the 3rd day, the neutralization of the antigen started, while on the 5th day, the antigen was completely neutralized by the secreted immune factors. In conclusion, the designed vaccine candidate effectively triggered the immune response against eh pathogen; however, wet lab-based experimentations are highly recommended to prove the protective immunological proficiency of the vaccine against L. pneumophila.

In silico construction of a multiepitope Zika virus vaccine using immunoinformatics tools

Zika virus (ZIKV) is an arbovirus from the Flaviviridae family and Flavivirus genus. Neurological events have been associated with ZIKV-infected individuals, such as Guillain-Barré syndrome, an autoimmune acute neuropathy that causes nerve demyelination and can induce paralysis. With the increase of ZIKV infection incidence in 2015, malformation and microcephaly cases in newborns have grown considerably, which suggested congenital transmission. Therefore, the development of an effective vaccine against ZIKV became an urgent need. Live attenuated vaccines present some theoretical risks for administration in pregnant women. Thus, we developed an in silico multiepitope vaccine against ZIKV. All structural and non-structural proteins were investigated using immunoinformatics tools designed for the prediction of CD4 + and CD8 + T cell epitopes. We selected 13 CD8 + and 12 CD4 + T cell epitopes considering parameters such as binding affinity to HLA class I and II molecules, promiscuity based on the number of different HLA alleles that bind to the epitopes, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the vaccine construct, creating a hybrid protein domain-multiepitope vaccine. Three high scoring continuous and two discontinuous B cell epitopes were found in EDIII. Aiming to increase the candidate vaccine antigenicity even further, we tested secondary and tertiary structures and physicochemical parameters of the vaccine conjugated to four different protein adjuvants: flagellin, 50S ribosomal protein L7/L12, heparin-binding hemagglutinin, or RS09 synthetic peptide. The addition of the flagellin adjuvant increased the vaccine's predicted antigenicity. In silico predictions revealed that the protein is a probable antigen, non-allergenic and predicted to be stable. The vaccine’s average population coverage is estimated to be 87.86%, which indicates it can be administered worldwide. Peripheral Blood Mononuclear Cells (PBMC) of individuals with previous ZIKV infection were tested for cytokine production in response to the pool of CD4 and CD8 ZIKV peptide selected. CD4 + and CD8 + T cells showed significant production of IFN-γ upon stimulation and IL-2 production was also detected by CD8 + T cells, which indicated the potential of our peptides to be recognized by specific T cells and induce immune response. In conclusion, we developed an in silico universal vaccine predicted to induce broad and high-coverage cellular and humoral immune responses against ZIKV, which can be a good candidate for posterior in vivo validation.

Core-Proteomics-Based Annotation of Antigenic Targets and Reverse-Vaccinology-Assisted Design of Ensemble Immunogen against the Emerging Nosocomial Infection-Causing Bacterium Elizabethkingia meningoseptica

Elizabethkingia meningoseptica is a ubiquitous Gram-negative emerging pathogen that causes hospital-acquired infection in both immunocompromised and immunocompetent patients. It is a multi-drug-resistant bacterium; therefore, an effective subunit immunogenic candidate is of great interest to encounter the pathogenesis of this pathogen. A protein-wide annotation of immunogenic targets was performed to fast-track the vaccine development against this pathogen, and structural-vaccinology-assisted epitopes were predicted. Among the total proteins, only three, A0A1T3FLU2, A0A1T3INK9, and A0A1V3U124, were shortlisted, which are the essential vaccine targets and were subjected to immune epitope mapping. The linkers EAAK, AAY, and GPGPG were used to link CTL, HTL, and B-cell epitopes and an adjuvant was also added at the N-terminal to design a multi-epitope immunogenic construct (MEIC). The computationally predicted physiochemical properties of the ensemble immunogen reported a highly antigenic nature and produced multiple interactions with immune receptors. In addition, the molecular dynamics simulation confirmed stable binding and good dynamic properties. Furthermore, the computationally modeled immune response proposed that the immunogen triggered a strong immune response after several doses at different intervals. Neutralization of the antigen was observed on the 3rd day of injection. Conclusively, the immunogenic construct produces protection against Elizabethkingia meningoseptica; however, further immunological testing is needed to unveil its real efficacy.

In-Silico Vaccine Design Based on a Novel Vaccine Candidate Against Infections Caused by Acinetobacter baumannii

Acinetobacter baumannii is notorious for causing serious infections of the skin, lungs, soft tissues, bloodstream, and urinary tract. Despite the overwhelming information available so far, there has still been no approved vaccine in the market to prevent these infections. Therefore, this study focuses on developing a rational vaccine design using the technique of epitope mapping to curb the infections caused by A. baumannii. An outer membrane protein with immunogenic potential as well as all the properties of a good vaccine candidate was selected and used to calculate epitopes for selection on the basis of a low percentile rank, high binding scores, good immunological properties, and non-allergenicity. Thus, a 240 amino-acid vaccine sequence was obtained by manually joining all the epitopes in sequence-wise manner with the appropriate linkers, namely AAY, GPGPG, and EAAAK. Additionally, a 50S ribosomal protein L7/L12, agonist to the human innate immune receptors was attached to the N-terminus to increase the overall immune response towards the vaccine. As a result, enhanced overall protein stability, expression, immunostimulatory capabilities, and solubility of the designed construct were observed. Molecular dynamic simulations revealed the compactness and stability of the polypeptide construct. Moreover, molecular docking exhibited strong binding of the designed vaccine with TLR-4 and TLR-9. In-silico immune simulations indicated an immense increment in T-cell and B-cell populations. Bioinformatic tools also significantly assisted with optimizing codons which allowed for successful cloning of constructs into desired host vectors. Using in-silico tools to design a vaccine against A. baumannii demonstrated that this construct could pave the way for successfully combating infections caused by multidrug-resistant bacteria.

Rational design of chimeric Multiepitope Based Vaccine (MEBV) against human T-cell lymphotropic virus type 1: An integrated vaccine informatics and molecular docking based approach

Human T-cell lymphotropic virus type 1 (HTLV-1) is an infectious virus that has been linked to adult T cell leukemia /lymphoma, aggressive CD4-T cell malignancy and many other immune-related medical illnesses. So far, no effective vaccine is known to combat HTLV-1, hence, the current research work was performed to design a potential multi-epitope-based subunit vaccine (MEBV) by adopting the latest methodology of reverse vaccinology. Briefly, three highly antigenic proteins (Glycoprotein, Accessory protein, and Tax protein) with no or minimal (<37%) similarity with human proteome were sorted out and potential B- and T-cell epitopes were forecasted from them. Highly antigenic, immunogenic, non-toxic, non-allergenic and overlapping epitopes were short-listed for vaccine development. The chosen T-cell epitopes displayed a strong binding affinity with their corresponding Human Leukocyte Antigen alleles and demonstrated 95.8% coverage of the world's population. Finally, nine Cytotoxic T Lymphocytes, six Helper T Lymphocytes and five Linear B Lymphocytes epitopes, joint through linkers and adjuvant, were exploited to design the final MEBV construct, comprising of 382 amino acids. The developed MEBV structure showed highly antigenic properties while being non-toxic, soluble, non-allergenic, and stable in nature. Moreover, disulphide engineering further enhanced the stability of the final vaccine protein. Additionally, Molecular docking analysis and Molecular Dynamics (MD) simulations confirmed the strong association between MEBV construct and human pathogenic immune receptor TLR-3. Repeated-exposure simulations and Immune simulations ensured the rapid antigen clearance and higher levels of cell-mediated immunity, respectively. Furthermore, MEBV codon optimization and in-silico cloning was carried out to confirm its augmented expression. Results of our experiments suggested that the proposed MEBV could be a potential immunogenic against HTLV-1; nevertheless, additional wet lab experiments are needed to elucidate our conclusion.

Identification of immunodominant epitopes in allelic variants VK210 and VK247 of Plasmodium Vivax Circumsporozoite immunogen

Plasmodium vivax-induced malaria is among the leading causes of morbidity and mortality in sub-tropical and tropical regions and infect 2.85 billion people globally. The continual rise and propagation of resistance against anti-malarial drugs is a prerequisite to develop a potent vaccine candidate for Plasmodium vivax (P. vivax). Circumsporozoite protein (CSP) is an important immunogen of malaria parasite that has the conserved CSP structure as an immune dominant B-cell epitope. In current study, we focused on designing multi-epitope vaccines (MEVs) using various immunoinformatics tools against Pakistani based allelic variants VK210 and VK247 of P. vivax CSP (PvCSP) gene. Antigenicity, allergic potential and physicochemical parameters of both PvCSP variants were assessed for the designed MEVs and they were within acceptable range suitable for post experimental investigations. The three-dimensional structures of both MEVs have been predicted ab initio, optimized, and validated by using different online servers. The both MEVs candidates were stable and free from aggregation-prone regions. The stability of both MEVs had been improved by a disulfide engineering approach. To estimate the binding energy and stability of the MEVs, molecular docking simulation and binding free energy calculations with TLR-4 immune receptor have been conducted. The docking score of PvCSP210 and PvCSP247 for TLR-4 was -6.34 kJ/mol and - 2.3 kJ/mol, respectively. For PvCSP210-TLR4 system, mean RMSD was 4.96 Å while PvCSP247-TLR4 system, average RMSD was 4.49 Å. The binding free energy of PvCSP210-TLR4 complex and PvCSP247-TLR4 complex was -50.49/-117.15 kcal/mol (MMGBSA/MMPSA) and -52.94/-96.26 kcal/mol (MMGBSA/MMPSA), respectively. The expression of both MEVs produced in Escherichia coli K12 expression system by in silico cloning was significant. Immune simulation revealed that the proposed MEVs induce strong humoral and cellular immunological responses, in addition to significant production of interleukins and cytokines. In conclusions, we believed that the MEVs proposed in current research, using combine approach of immunoinformatics, structural biology and biophysical approaches, could induce protective and effective immune responses against P. vivax and the experimental validation of our findings could contribute to the development of potential malaria vaccine.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Design of Monovalent and Chimeric Tetravalent Dengue Vaccine Using an Immunoinformatics Approach

An immunoinformatics technique was used to predict a monovalent amide immunogen candidate capable of producing therapeutic antibodies as well as a potent immunogen candidate capable of acting as a universal vaccination against all dengue fever virus serotypes. The capsid protein is an attractive goal for anti-DENV due to its position in the dengue existence cycle. The widely accessible immunological data, advances in antigenic peptide prediction using reverse vaccinology, and the introduction of molecular docking in immunoinformatics have directed vaccine manufacturing. The C-proteins of DENV-1-4 serotypes were known as antigens to assist with logical design. Binding epitopes for TC cells, TH cells, and B cells is predicted from structural dengue virus capsid proteins. Each T cell epitope of C-protein integrated with a B cell as a templet was used as a vaccine and produce antibodies in contrast to serotype of the dengue virus. A chimeric tetravalent vaccine was created by combining four vaccines, each representing four dengue serotypes, to serve as a standard vaccine candidate for all four Sero groups. The LKRARNRVS, RGFRKEIGR, KNGAIKVLR, and KAINVLRGF from dengue 1, dengue 2, dengue 3, and dengue 4 epitopes may be essential immunotherapeutic representatives for controlling outbreaks.

A T-Cell Epitope-Based Multi-Epitope Vaccine Designed Using Human HLA Specific T Cell Epitopes Induces a Near-Sterile Immunity against Experimental Visceral Leishmaniasis in Hamsters

Visceral leishmaniasis is a neglected tropical disease affecting 12 million people annually. Even in the second decade of the 21st century, it has remained without an effective vaccine for human use. In the current study, we designed three multiepitope vaccine candidates by the selection of multiple IFN-γ inducing MHC-I and MHC-II binder T-cell specific epitopes from three previously identified antigen genes of Leishmania donovani from our lab by an immuno-informatic approach using IFNepitope, the Immune Epitope Database (IEDB) T cell epitope identification tools, NET-MHC-1, and NET MHC-2 webservers. We tested the protective potential of these three multiepitope proteins as a vaccine in a hamster model of visceral leishmaniasis. The immunization data revealed that the vaccine candidates induced a very high level of Th1 biased protective immune response in-vivo in a hamster model of experimental visceral leishmaniasis, with one of the candidates inducing a near-sterile immunity. The vaccinated animals displayed highly activated monocyte macrophages with the capability of clearing intracellular parasites due to increased respiratory burst. Additionally, these proteins induced activation of polyfunctional T cells secreting INF-γ, TNF-α, and IL-2 in an ex-vivo stimulation of human peripheral blood mononuclear cells, further supporting the protective nature of the designed candidates.

Computational Design of a Multi-epitope Vaccine Against Clostridium chauvoei: An Immunoinformatics Approach

Blackleg is an infectious disease of animals that is commonly caused by Clostridium chauvoei and characterized by localized muscle necrosis. In this study, proteome-mining and immunoinformatics approaches were applied to identify novel antigenic proteins and to construct a multi-epitope vaccine against C. chauvoei. All proteins of C. chauvoei strains were retrieved from the NCBI Microbial Genome Database containing both genomic and proteomic data of prokaryotes. The proteins were analyzed to exclude non-redundant sequences and to determine antigenic, virulent, and non-allergenic vaccine candidates through several online tools, resulting in seven protein candidates. Cytotoxic T and B cell epitopes of these proteins were evaluated through the tools present in the immune epitope database and the prioritized antigenic epitopes were then conjugated via appropriate linkers to construct the vaccine candidate. After the evaluation of physicochemical properties of the construct, the tertiary structure was modeled and refined through trRosetta and GalaxyRefine, respectively. The quality of the 3D structure was validated by ERRAT score, z-score, and Ramachandran plot and the construct was then docked with bovine Toll-like receptor 4 (TLR 4) using ClusPro. The docked complex was subjected to Molecular Mechanics/Generalized Born Surface Area in the HawkDock server and normal mode analysis in the iMODS simulation suite to assess the binding energy and stability of the complex, respectively. Overall, the vaccine construct was found stable and energetically feasible for bovine TLR 4 binding. Therefore, it can be used as a multi-epitope vaccine construct in clostridial vaccines to control the blackleg disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10989-021-10279-9.

Immunoinformatics Approach to Design Multi-Epitope- Subunit Vaccine against Bovine Ephemeral Fever Disease

Bovine ephemeral fever virus (BEFV) is an overlooked pathogen, recently gaining widespread attention owing to its associated enormous economic impacts affecting the global livestock industries. High endemicity with rapid spread and morbidity greatly impacts bovine species, demanding adequate attention towards BEFV prophylaxis. Currently, a few suboptimum vaccines are prevailing, but were confined to local strains with limited protection. Therefore, we designed a highly efficacious multi-epitope vaccine candidate targeted against the geographically distributed BEFV population. By utilizing immunoinformatics technology, all structural proteins were targeted for B- and T-cell epitope prediction against the entire allele population of BoLA molecules. Prioritized epitopes were adjoined by linkers and adjuvants to effectively induce both cellular and humoral immune responses in bovine. Subsequently, the in silico construct was characterized for its physicochemical parameters, high immunogenicity, least allergenicity, and non-toxicity. The 3D modeling, refinement, and validation of ligand (vaccine construct) and receptor (bovine TLR7) then followed molecular docking and molecular dynamic simulation to validate their stable interactions. Moreover, in silico cloning of codon-optimized vaccine construct in the prokaryotic expression vector (pET28a) was explored. This is the first time HTL epitopes have been predicted using bovine datasets. We anticipate that the designed construct could be an effective prophylactic remedy for the BEF disease that may pave the way for future laboratory experiments.

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.

In-silico design of a multivalent epitope-based vaccine against Candida auris

Candida auris is a rapidly emerging human pathogenic fungus with a high mortality rate. Recent report suggests that the new clinical isolates are showing resistance to the major classes of antifungal drugs. Due to the emergence of drug resistance, it becomes imperative to seek novel therapies for the treatment of C. auris. The potent vaccine could be one of the promising strategies for recalcitrant and multidrug-resistant pathogens. Using in silico approach we designed a novel multivalent vaccine against C. auris. We have selected the agglutinin-like sequence-3 (Als3) an adhesion protein, involved in virulence. The Als3p protein of C. auris was targeted to predict T cell and B cell epitopes. Epitopes which were found to be non-toxic, non-allergenic, highly conserved, and antigenic and could induce interferon-γ synthesis were selected for vaccine design. The selected epitopes were linked with suitable adjuvants to construct the final vaccine. The vaccine construct was predicted to be stable, soluble, antigenic, non-allergic with desirable physicochemical properties. We also constructed the 3D model of the vaccine and validated it with the Ramachandran plot. The ability of the vaccine construct to interact with Toll-like receptor (TLR) and major histocompatibility complex (MHC) was determined by molecular docking experiments. The binding energy of the vaccine construct with the TLR and MHC were found to be stable as predicted by molecular dynamics simulation. Further, in-silico cloning analysis showed that the vaccine construct can be successfully cloned and expressed in E. coli. Based on the results, we surmise that our candidate vaccine can be used as an alternative therapy for the treatment of C. auris. However, the efficacy and the safety of the vaccine model need to be determined by performing in vivo studies.

Novel Potent IFN-γ-Inducing CD8+ T Cell Epitopes Conserved among Diverse Bovine Viral Diarrhea Virus Strains

Studies of immune responses elicited by bovine viral diarrhea virus (BVDV) vaccines have primarily focused on the characterization of neutralizing B cell and CD4⁺ T cell epitopes. Despite the availability of commercial vaccines for decades, BVDV prevalence in cattle has remained largely unaffected. There is limited knowledge regarding the role of BVDV-specific CD8⁺ T cells in immune protection, and indirect evidence suggests that they play a crucial role during BVDV infection. In this study, the presence of BVDV-specific CD8⁺ T cells that are highly cross-reactive in cattle was demonstrated. Most importantly, novel potent IFN-γ-inducing CD8⁺ T cell epitopes were identified from different regions of BVDV polyprotein. Eight CD8⁺ T cell epitopes were identified from the following structural BVDV Ags: E^rns, E1, and E2 glycoproteins. In addition, from nonstructural BVDV Ags N^pro, NS2-3, NS4A-B, and NS5A-B, 20 CD8⁺ T cell epitopes were identified. The majority of these IFN-γ-inducing CD8⁺ T cell epitopes were found to be highly conserved among more than 200 strains from BVDV-1 and -2 genotypes. These conserved epitopes were also validated as cross-reactive because they induced high recall IFN-γ⁺CD8⁺ T cell responses ex vivo in purified bovine CD8⁺ T cells isolated from BVDV-1- and -2-immunized cattle. Altogether, 28 bovine MHC class I-binding epitopes were identified from key BVDV Ags that can elicit broadly reactive CD8⁺ T cells against diverse BVDV strains. The data presented in this study will lay the groundwork for the development of a contemporary CD8⁺ T cell-based BVDV vaccine capable of addressing BVDV heterogeneity more effectively than current vaccines.

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

Rational design of multi epitope-based subunit vaccine by exploring MERS-COV proteome: Reverse vaccinology and molecular docking approach

Middle East respiratory syndrome (MERS-COV), first identified in Saudi Arabia, was caused by a novel strain of coronavirus. Outbreaks were recorded from different regions of the world, especially South Korea and the Middle East, and were correlated with a 35% mortality rate. MERS-COV is a single-stranded, positive RNA virus that reaches the host by binding to the receptor of dipeptidyl-peptides. Because of the unavailability of the vaccine available for the protection from MERS-COV infection, the rapid case detection, isolation, infection prevention has been recommended to combat MERS-COV infection. So, vaccines for the treatment of MERS-COV infection need to be developed urgently. A possible antiviral mechanism for preventing MERS-CoV infection has been considered to be MERS-CoV vaccines that elicit unique T-cell responses. In the present study, we incorporated both molecular docking and immunoinformatic approach to introduce a multiepitope vaccine (MEP) against MERS-CoV by selecting 15 conserved epitopes from seven viral proteins such as three structural proteins (envelope, membrane, and nucleoprotein) and four non-structural proteins (ORF1a, ORF8, ORF3, ORF4a). The epitopes, which were examined for non-homologous to host and antigenicity, were selected on the basis of conservation between T-cell, B-cell, and IFN-γ epitopes. The selected epitopes were then connected to the adjuvant (β-defensin) at the N-terminal through an AAY linker to increase the immunogenic potential. Structural modelling and physiochemical characteristic were applied to the vaccine construct developed. Afterwards the structure has been successfully docked with antigenic receptor, Toll-like receptor 3 (TLR-3) and in-silico cloning ensures that its expression efficiency is legitimate. Nonetheless the MEP presented needs tests to verify its safety and immunogenic profile.

Designing a next generation multi-epitope based peptide vaccine candidate against SARS-CoV-2 using computational approaches

COVID-19 caused by SARS-CoV-2 was declared a global pandemic by WHO (World Health Organization) in March, 2020. Within 6 months, nearly 750,000 deaths are claimed by COVID-19 across the globe. This called for immediate social, scientific, technological, public and community interventions. Considering the severity of infection and the associated mortalities, global efforts are underway to develop preventive measures against SARS-CoV-2. Among the SARS-CoV-2 target proteins, Spike (S) glycoprotein (a.k.a S Protein) is the most studied target known to trigger strong host immune response. A detailed analysis of S protein-based epitopes enabled us to design a novel B-cell-derived T-cell Multi-epitope-based peptide (MEBP) vaccine candidate. This involved a systematic and comprehensive computational protocol consisting of prediction of dual-purpose epitopes and designing an MEBP vaccine construct. This was followed by 3D structure validation, MEBP complex interaction studies, in silico cloning and vaccine dose-based immune response simulation to evaluate the immunogenic potency of the vaccine construct. The dual-purpose epitope prediction protocol was designed such that the same epitope elicits both humoral and cellular immune response unlike the earlier designs. Further, the epitopes predicted were screened against stringent criteria to ensure selection of a potent candidate with maximum antigen coverage and best immune response. The vaccine dose-based immune response simulation studies revealed a rapid antigen clearance through antibody generation and elevated levels of cell-mediated immunity during repeated exposure of the vaccine. The favourable results of the analysis strongly indicate that the vaccine construct is indeed a potent vaccine candidate and ready to proceed to the next steps of experimental validation and efficacy studies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-020-02574-x.

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.

In Silico and in Vivo Analysis of HIV-1 Rev Regulatory Protein for Evaluation of a Multiepitope-based Vaccine Candidate

In silico-designed multiepitope conserved regions of human immunodeficiency virus 1 (HIV-1) proteins would be a beneficial strategy for antigen design which induces effective anti-HIV-1 T-cell responses. The conserved multiple HLA-DR-binding epitopes of Rev protein were identified using IEDB MHC-I prediction tools and SYFPEITHI webserver to screen potential T-cell epitopes. We analyzed toxicity, allergenicity, immunogenicity, hemolytic activity, cross-reactivity, cell-penetrating peptide (CPP) potency, and molecular docking of the candidate epitopes using several immune-informatics tools. Afterward, we designed a novel multiepitope construct based on non-toxic and non-allergenic Rev, Nef, Gp160 and P24-derived cytotoxic T cell (CTL) and T-helper cell (HTL) epitopes. Next, the designed construct (Nef-Rev-Gp160-P24) was subjected to three B-cell epitope prediction webservers, ProtParam and Protein-Sol to obtain the physicochemical features. Then, the recombinant multiepitope DNA and polypeptide constructs were complexed with different CPPs for nanoparticle formation and pass them via the cell membranes. Finally, the immunogenicity of multiepitope constructs in a variety of modalities was evaluated in mice. The results demonstrated that groups immunized with heterologous DNA+ MPG or HR9 CPP prime/rNef-Rev-Gp160-P24 polypeptide + LDP-NLS CPP boost regimens could significantly produce higher levels of IFN-γ and Granzyme B, and lower amounts of IL-10 than other groups. Moreover, higher levels of IgG2a and IgG2b were observed in all heterologous prime-boost regimens than homologous DNA or polypeptide regimens. Altogether, the present findings indicated that the Nef-Rev-Gp160-P24 polypeptide meets the criteria to be potentially useful as a multiepitope-based vaccine candidate against HIV-1 infection.

Exploring rotavirus proteome to identify potential B- and T-cell epitope using computational immunoinformatics

Rotavirus is the most common cause of acute gastroenteritis in infants and children worldwide. The functional correlation of B- and T-cells to long-lasting immunity against rotavirus infection in the literature is limited. In this work, a series of computational immuno-informatics approaches were applied and identified 28 linear B-cells, 26 conformational B-cell, 44 T_C cell and 40 T_H cell binding epitopes for structural and non-structural proteins of rotavirus. Further selection of putative B and T cell epitopes in the multi-epitope vaccine construct was carried out based on immunogenicity, conservancy, allergenicity and the helical content of predicted epitopes. An in-silico vaccine constructs was developed using an N-terminal adjuvant (RGD motif) followed by T_C and T_H cell epitopes and B-cell epitope with an appropriate linker. Multi-threading models of multi-epitope vaccine construct with B- and T-cell epitopes were generated and molecular dynamics simulation was performed to determine the stability of designed vaccine. Codon optimized multi-epitope vaccine antigens was expressed and affinity purified using the E. coli expression system. Further the T cell epitope presentation assay using the recombinant multi-epitope constructs and the T cell epitope predicted and identified in this study have not been investigated. Multi-epitope vaccine construct encompassing predicted B- and T-cell epitopes may help to generate long-term immune responses against rotavirus. The computational findings reported in this study may provide information in developing epitope-based vaccine and diagnostic assay for rotavirus-led diarrhea in children's.