Epitope Prediction Assays Combined with Validation Assays Strongly Narrows down Putative Cytotoxic T Lymphocyte Epitopes

Structural and Dynamic-Based Characterization of the Recognition Patterns of E7 and TRP-2 Epitopes by MHC Class I Receptors through Computational Approaches

A detailed comprehension of MHC-epitope recognition is essential for the design and development of new antigens that could be effectively used in immunotherapy. Yet, the high variability of the peptide together with the large abundance of MHC variants binding makes the process highly specific and large-scale characterizations extremely challenging by standard experimental techniques. Taking advantage of the striking predictive accuracy of AlphaFold, we report a structural and dynamic-based strategy to gain insights into the molecular basis that drives the recognition and interaction of MHC class I in the immune response triggered by pathogens and/or tumor-derived peptides. Here, we investigated at the atomic level the recognition of E7 and TRP-2 epitopes to their known receptors, thus offering a structural explanation for the different binding preferences of the studied receptors for specific residues in certain positions of the antigen sequences. Moreover, our analysis provides clues on the determinants that dictate the affinity of the same epitope with different receptors. Collectively, the data here presented indicate the reliability of the approach that can be straightforwardly extended to a large number of related systems.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

A Murine CD8+ T Cell Epitope Identified in the Receptor-Binding Domain of the SARS-CoV-2 Spike Protein

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has posed a devastating threat worldwide. The receptor-binding domain (RBD) of the spike protein is one of the most important antigens for SARS-CoV-2 vaccines, while the analysis of CD8 cytotoxic T lymphocyte activity in preclinical studies using mouse models is critical for evaluating vaccine efficacy. Here, we immunized C57BL/6 wild-type mice and transgenic mice expressing human angiotensin-converting enzyme 2 (ACE2) with the SARS-CoV-2 RBD protein to evaluate the IFN-γ-producing T cells in the splenocytes of the immunized mice using an overlapping peptide pool by an enzyme-linked immunospot assay and flow cytometry. We identified SARS-CoV-2 S_395-404 as a major histocompatibility complex (MHC) class I-restricted epitope for the RBD-specific CD8 T cell responses in C57BL/6 mice.

Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus

BACKGROUND

The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity.

METHODS

Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes.

RESULTS

Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability.

CONCLUSION

This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.

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.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.

Exploring HCV genome to construct multi-epitope based subunit vaccine to battle HCV infection: Immunoinformatics based approach

Hepatitis C Virus (HCV) infection is a major cause of chronic liver disease, hepatocellular carcinoma, and the single most common indication for liver transplantation. HCV vaccines eliciting specific T-cell responses, have been considered as potent method to prevent HCV infection. Despite several reports on progress of vaccine, these vaccine failed in mediating clinical relevance activity against HCV in humans. In this study we integrated both immunoinformatic and molecular docking approach to present a multiepitope vaccine against HCV by designating 17 conserved epitopes from eight viral proteins such as Core protein, E1, E2, NS2, NS34A, NS4B, NS5A, and NS5B. The epitopes were prioritized based on conservation among epitopes of T cell, B cell and IFN-γ that were then scanned for non-homologous to host and antigenicity. The prioritized epitopes were then linked together by AAY linker and adjuvant (β-defensin) were attached at N-terminal to enhance immunogenic potential. The construct thus formed were subjected to structural modeling and physiochemical characteristics. The modeled structure were successfully docked to antigenic receptor TLR-3 and In-silico cloning confers the authenticity of its expression efficiency. However, the proposed construct need to be validate experimentally to ensure its safety and immunogenic profile.

Development of multiepitope therapeutic vaccines against the most prevalent high-risk human papillomaviruses

Aim: Our goal was the development of DNA- or peptide-based multiepitope vaccines targeting HPV E7, E6 and E5 oncoproteins in tumor mouse model. Materials & methods: After designing the multiepitope E7, E6 and E5 constructs from four types of high risk HPVs (16, 18, 31 & 45) using bioinformatics tools, mice vaccination was performed by different homologous and heterologous modalities in a prophylactic setting. Then, anti-tumor effects of the best prophylactic strategies were studied in a therapeutic setting. Results: In both prophylactic and therapeutic experiments, groups receiving homologous E7+E6+E5 polypeptide, and heterologous E7+E6+E5 DNA prime/polypeptide boost were successful in complete rejection of tumors. Conclusion: The designed multiepitope constructs can be considered as promising candidates to develop effective therapeutic HPV vaccines.

Prediction and validation of potent peptides against herpes simplex virus type 1 via immunoinformatic and systems biology approach

The human herpes simplex virus type 1 (HSV-1) is an extremely rampant human pathogen, and its infection could cause life-long diseases, including the central nervous system disorders. The glycoproteins of HSV-1 such as glycoprotein B, glycoprotein C, glycoprotein D, glycoprotein H, and glycoprotein L are highly involved in mediating the viral attachment and infection of the host cell. Therefore, immunoinformatic approaches followed by molecular dynamics simulation and systems biology has been used to analyze these glycoproteins in order to propose effective peptide-based vaccine candidates against the HSV-1 infection. The ElliPro and NetCTL.1.2 online tools were employed to forecast the B- and T-lymphocyte (CTL) epitopes for gB, gC, gD, gH, and gL. The 3D coordinates of these epitopes were modeled and docked against the human major histocompatibility complex molecule-1. The outcomes obtained from postdocking analysis along with TAP (Transporter associated with antigen processing), MHC binding, and C-terminal cleavage score assisted in the selection of potential epitopes. These epitopes were further subjected to molecular dynamics simulation and systems biology approach which showed significant results. On the basis of these substantial outcomes, peptides are proposed that could be used to provoke immunity against the HSV-1 infection.

Broad Spectrum Peptide Vaccine Design Against Hepatitis C Virus

BACKGROUND

Hepatitis C virus (HCV) infection is a global burden. There is no peptide vaccine found as modality to cure the disease is available due to the weak cellular immune response and the limitation to induce humoral immune response.

METHODS

Five predominated HCV subtypes in Indonesia (1a, 1b, 1c, 3a, and 3k) were aligned and the conserved regions were selected. Twenty alleles of class I MHC including HLA-A, HLA-B, and HLAC types were used to predict the potential epitopes by using NetMHCPan and IEDB. Eight alleles of HLA-DRB1, together with a combination of 3 alleles of HLA-DQA1 and 5 alleles of HLA-DQB1 were utilized for Class II MHC epitopes prediction using NetMHCIIPan and IEDB. LBtope and Ig- Pred were used to predict B cells epitopes. Moreover, proteasome analysis was performed by NetCTL and the stability of the epitopes in HLA was calculated using NetMHCStabPan for Class I. All predicted epitopes were analyzed for its antigenicity, toxicity, and stability. Population coverage, molecular docking and molecular dynamics were performed for several best epitopes.

RESULTS

The results showed that two best epitopes from envelop protein, GHRMAWDMMMNWSP (E1) and PALSTGLIHLHQN (E2) were selected as promising B cell and CD8+ T cell inducers. Other two peptides, LGIGTVLDQAETAG and VLVLNPSVAATLGF, taken from NS3 protein were selected as CD4+ T cell inducer.

CONCLUSION

This study suggested the utilization of all four peptides to make a combinational peptide vaccine for in vivo study to prove its ability in inducing secondary response toward HCV.

Epitope-based vaccine as a universal vaccination strategy against Toxoplasma gondii infection: A mini-review

Despite the significant progress in the recent efforts toward developing an effective vaccine against toxoplasmosis, the search for new protective vaccination strategy still remains a challenge and elusive goal because it becomes the appropriate way to prevent the disease. Various experimental approaches in the past few years showed that developing a potential vaccine against the disease can be achievable. The combination of multi-epitopes expressing different stages of the parasite life cycle has become an optimal strategy for acquiring a potent, safe, and effective vaccine. Epitope-based vaccines have gained attention as alternative vaccine candidates due to their ability of inducing protective immune responses. This mini-review highlights the current status and the prospects of Toxoplasma gondii vaccine development along with the application of epitope-based vaccine in the future parasite immunization as a novel under development and evaluation strategy.

Immunoinformatic and systems biology approaches to predict and validate peptide vaccines against Epstein-Barr virus (EBV)

Epstein-Barr virus (EBV), also known as human herpesvirus 4 (HHV-4), is a member of the Herpesviridae family and causes infectious mononucleosis, Burkitt's lymphoma, and nasopharyngeal carcinoma. Even in the United States of America, the situation is alarming, as EBV affects 95% of the young population between 35 and 40 years of age. In this study, both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted by using the ElliPro and NetCTL.1.2 webservers for EBV proteins (GH, GL, GB, GN, GM, GP42 and GP350). Molecular modelling tools were used to predict the 3D coordinates of peptides, and these peptides were then docked against the MHC molecules to obtain peptide-MHC complexes. Studies of their post-docking interactions helped to select potential candidates for the development of peptide vaccines. Our results predicted a total of 58 T-cell epitopes of EBV;  where the most potential were selected based on their TAP, MHC binding and C-terminal Cleavage score. The top most peptides were subjected to MD simulation and stability analysis. Validation of our predicted epitopes using a 0.45 µM concentration was carried out by using a systems biology approach. Our results suggest a panel of epitopes that could be used to immunize populations to protect against multiple diseases caused by EBV.

Immunoinformatics and molecular docking studies reveal potential epitope-based peptide vaccine against DENV-NS3 protein

Dengue, still a "Neglected Tropical Disease" is somehow injustice and remains uncontrolled globally. World Health Organization (2012-2020) reported that the world's half population is living in dengue-affected regions. Therefore, effective drug candidates or promising vaccines are urgently needed to control the dengue. It is an acute febrile disease caused by mosquito borne dengue viruses (DENVs) which belong to the genus Flavivirus with four serotypes. In present work, immunoinformatics approach was utilized to predict the antigenic epitopes of dengue proteins for the development of DENV vaccine. B-cell and cytotoxic T-lymphocyte epitopes were predicted for NS3 dengue protein. Docking complexes of 17 antigenic B-cell epitopes of various lengths and 4 CTL epitopes with antigenic sites were investigated followed by binding interaction analyses of top predicted peptides with MHC-I HLA-A2 molecule. These predicted epitopes with antigenic amino acids might present a preliminary set of peptides for future vaccine development against DENV.

Recombinant Mip-PilE-FlaA dominant epitopes vaccine candidate against Legionella pneumophila

Legionella pneumophila is the main causative agent of Legionnaires' disease, which is a severe multi-system disease with pneumonia as the primary manifestation. We designed a recombinant Mip-PilE-FlaA dominant epitopes vaccine against Legionella pneumophila to prevent the disease and evaluated its immunogenicity and protective immunity. The protein structures of Mip, PilE and FlaA were analyzed using a computer, and the gene sequences of the dominant epitopes of the three proteins were selected to construct and optimize the vaccine. The optimized mip, pilE, flaA and recombinant mip-pilE-flaA gene sequences were cloned, expressed and purified. The purified proteins were used as dominant epitopes vaccines to immunize BALB/c mice and determine the protective immunity and immunogenicity of these purified proteins. The identification confirmed that the recombinant mip-pilE-flaA was successfully cloned and expressed. ELISA revealed that the Mip-PilE-FlaA group produced the highest IgG response, and this protein may considerably improve the production of some cytokines in BALB/c mice. Histopathology analyses of lungs from mice immunized with Mip-PilE-FlaA revealed a certain protective effect. Our work demonstrated that the recombinant dominant epitopes of Mip-PilE-FlaA exhibited strong immunogenicity and immune protection, and this protein may be an efficient epitopes vaccine candidate against Legionella pneumophila.

T-cell epitopes predicted from the Nucleocapsid protein of Sin Nombre virus restricted to 30 HLA alleles common to the North American population

Hantavirus cardiopulmonary syndrome in North America is caused by Sin Nombre virus (SNV) and poses a public health problem. We identified T-cell epitopes restricted to HLA alleles commonly seen in the N. American population. Nucleocapsid (N) protein is 428 aminoacid in length and binds to RNA and functions also as a key molecule between virus and host cell processes. The predicted epitopes from N protein that bind to class I MHC were analyzed for human proteasomes cleavage, TAP efficiency, immunogenicity and antigenicity. We identified 8 epitopes through MHC binding prediction, proteasomal cleavage prediction and TAP efficiency. Epitope VMGVIGFSF had highest Vaxijen score and the epitope, TNRAYFITR had highest immunogenicity score. Epitope AAVSALETK and TIACGLFPA had 100% homology to many HCPS causing viruses. Our study focused on T-cell epitope prediction specific to restricted HLA haplotypes of racial groups in North America for the potential vaccine development. Among the candidate epitopes, FLAARCPFL was conserved in SNV, which is suitable for vaccine specific to the virus genotype. Peptide-based vaccines can be designed to include multiple determinants from several hantavirus genotypes, or multiple epitopes from the same genotype. Thereby, immune response will focus solely on relevant epitopes, avoiding non-protective responses or immune evasion. The other advantages include absence of infectious material unlike in live or attenuated vaccines. There is no risk of reversion or formation of adverse reassortants leading to virulence and no risk of genetic integration or recombination forming a rationale for vaccine design including for distinct geographical regions.

Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins

The recent outbreak of Zika virus (ZIKV) infection in Brazil has developed to a global health concern due to its likely association with birth defects (primary microcephaly) and neurological complications. Consequently, there is an urgent need to develop a vaccine to prevent or a medicine to treat the infection. In this study, immunoinformatics approach was employed to predict antigenic epitopes of Zika viral proteins to aid in development of a peptide vaccine against ZIKV. Both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted for ZIKV Envelope (E), NS3 and NS5 proteins. We further investigated the binding interactions of altogether 15 antigenic CTL epitopes with three class I major histocompatibility complex (MHC I) proteins after docking the peptides to the binding groove of the MHC I proteins. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlight the limits of rigid-body docking methods. Some of the antigenic epitopes predicted and analyzed in this work might present a preliminary set of peptides for future vaccine development against ZIKV.

Computer-aided vaccine designing approach against fish pathogens Edwardsiella tarda and Flavobacterium columnare using bioinformatics softwares

Edwardsiella tarda and Flavobacterium columnare are two important intracellular pathogenic bacteria that cause the infectious diseases edwardsiellosis and columnaris in wild and cultured fish. Prediction of major histocompatibility complex (MHC) binding is an important issue in T-cell epitope prediction. In a healthy immune system, the T-cells must recognize epitopes and induce the immune response. In this study, T-cell epitopes were predicted by using in silico immunoinformatics approach with the help of bioinformatics tools that are less expensive and are not time consuming. Such identification of binding interaction between peptides and MHC alleles aids in the discovery of new peptide vaccines. We have reported the potential peptides chosen from the outer membrane proteins (OMPs) of E. tarda and F. columnare, which interact well with MHC class I alleles. OMPs from E. tarda and F. columnare were selected and analyzed based on their antigenic and immunogenic properties. The OMPs of the genes TolC and FCOL_04620, respectively, from E. tarda and F. columnare were taken for study. Finally, two epitopes from the OMP of E. tarda exhibited excellent protein-peptide interaction when docked with MHC class I alleles. Five epitopes from the OMP of F. columnare had good protein-peptide interaction when docked with MHC class I alleles. Further in vitro studies can aid in the development of potential peptide vaccines using the predicted peptides.

Post-Genomics and Vaccine Improvement for Leishmania

Leishmaniasis is a parasitic disease that primarily affects Asia, Africa, South America, and the Mediterranean basin. Despite extensive efforts to develop an effective prophylactic vaccine, no promising vaccine is available yet. However, recent advancements in computational vaccinology on the one hand and genome sequencing approaches on the other have generated new hopes in vaccine development. Computational genome mining for new vaccine candidates is known as reverse vaccinology and is believed to further extend the current list of Leishmania vaccine candidates. Reverse vaccinology can also reduce the intrinsic risks associated with live attenuated vaccines. Individual epitopes arranged in tandem as polytopes are also a possible outcome of reverse genome mining. Here, we will briefly compare reverse vaccinology with conventional vaccinology in respect to Leishmania vaccine, and we will discuss how it influences the aforementioned topics. We will also introduce new in vivo models that will bridge the gap between human and laboratory animal models in future studies.