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

Characterization and immunogenicity assessment of MERS-CoV pre-fusion spike trimeric oligomers as vaccine immunogen

Middle East respiratory syndrome coronavirus (MERS-CoV) is a lethal beta-coronavirus that emerged in 2012. The virus is part of the WHO blueprint priority list with a concerning fatality rate of 35%. Scientific efforts are ongoing for the development of vaccines, anti-viral and biotherapeutics, which are majorly directed toward the structural spike protein. However, the ongoing effort is challenging due to conformational instability of the spike protein and the evasion strategy posed by the MERS-CoV. In this study, we have expressed and purified the MERS-CoV pre-fusion spike protein in the Expi293F mammalian expression system. The purified protein was extensively characterized for its biochemical and biophysical properties. Thermal stability analysis showed a melting temperature of 58°C and the protein resisted major structural changes at elevated temperature as revealed by fluorescence spectroscopy and circular dichroism. Immunological assessment of the MERS-CoV spike immunogen in BALB/c mice with AddaVaxTM and Imject alum adjuvants showed elicitation of high titer antibody responses but a more balanced Th1/Th2 response with AddaVaxTM squalene like adjuvant. Together, our results suggest the formation of higher-order trimeric pre-fusion MERS-CoV spike proteins, which were able to induce robust immune responses. The comprehensive characterization of MERS-CoV spike protein warrants a better understanding of MERS spike protein and future vaccine development efforts.

An immunoinformatic approach for developing a multi-epitope subunit vaccine against Monkeypox virus

An in-silico approach was implemented to develop a multi-epitope subunit vaccine construct against the recent outbreak of the Monkeypox virus. The contribution of 10 different antigenic proteins based on their antigenicity led to the selection of 10 HTL, 9 CTL, and 6 BCL epitopes. The construct was further investigated for its allergenicity, antigenicity, and physio-chemical properties using servers such as AllerTOP and Allergen FP, VaxiJen and ANTIGENPro, and ProtParam respectively. The secondary structure of the vaccine was predicted using the SOPMA server followed by I-TASSER for the 3D structure. After refinement and validation of structural stability of the modelled vaccine, a molecular docking assay was implemented to study the interaction of the known TLR4 receptor with that of the constructed vaccine using the ClusPro server. The docked vaccine and TLR4 receptor were studied using the molecular dynamics (MD) simulation to validate the stability of the complex. After codon optimization the cDNA was constructed and in-silico cloning of the vaccine construct was carried out. The vaccine was also subjected to computational immune assay which predicted a powerful immune response against the Monkeypox virus validating that the developed multi-epitope vaccine construct can be a potent vaccine candidate.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00220-5.

Novel toxin-based mRNA vaccine against Clostridium perfringens using in silico approaches

Clostridium perfringens is a bacterium that causes gastrointestinal diseases in humans and animals. The several powerful toxins such as alpha toxin (CPA), beta toxin (CPB), enterotoxin (CPE), Epsilon toxin (ETX), and theta toxin, play a major role in its pathogenesis. Traditional vaccine development methods are time-consuming and costly. In silico approaches offer an alternative strategy for designing vaccines by analyzing biological data and predicting immunogenic peptides. In this study, computational tools were utilized to design a RNA vaccine targeting C. perfringens toxins. Toxin protein sequences were retrieved and their linear B-cell, MHCI, and MHCII binding epitopes were predicted. Allergenicity, toxigenicity, and IFN-γ induction were assessed to select non-allergenic, non-toxic, and IFN-γ-inducing epitopes. Molecular docking was performed to identify epitopes that fit within the binding cleft of MHC alleles. A final peptide vaccine construct was designed with selected epitopes separated by a linker sequence. The antigenicity and physicochemical properties of the vaccine were evaluated. Immune response simulation showed enhanced secondary and tertiary immune responses, increased levels of immunoglobulins, cytotoxic T lymphocytes, helper T lymphocytes, macrophage activity, and elevated levels IFN-γ and interleukin-2. Docking analysis was done to assess interactions between the vaccine structure and Toll-like receptors. Codon optimization was performed, and a final RNA vaccine construct was designed. The secondary structure of the RNA vaccine was predicted and validated. Overall, this study demonstrates the potential of in silico approaches for designing an RNA vaccine against C. perfringens toxins, contributing to improved prevention and control of associated diseases.

Immunoinformatics study to explore dengue (DENV-1) proteome to design multi-epitope vaccine construct by using CD4+ epitopes

BACKGROUND

Immunoinformatics is an emerging interdisciplinary field which integrates immunology, bioinformatics, and computational biology to study the immune system. In this study, we apply immunoinformatics approaches to explore the dengue proteome in order to design a multi-epitope vaccine construct.

METHODS

We used existing databases and algorithms to predict potential epitopes on dengue proteins and used a bioinformatics approach to identify the most promising epitopes. We then used molecular modelling to develop a multi-epitope construct which could be used as a potential vaccine. The results of this study demonstrate that immunoinformatics is a powerful tool for exploring and designing potential vaccines for infectious diseases like dengue.

RESULTS

Here, we found four CD4+ epitopes NLKYSVIVTVHTGDQ, ANPIVTDKEKPVNIE, LDPVVYDAKFEKQL, and VGAIALDFKPGTSGS that were used to design vaccine construct. The vaccine construct docked with TLR5. RMSD values suggest that docked complex of TLR5 and vaccine construct have putative stable interaction to induce immunogenic effects on host.

CONCLUSIONS

Furthermore, our study provides a proof of concept for the use of immunoinformatics approaches in DENV vaccine design. This vaccine can be effective in treating patients infected with DENV virus.

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.