Harnessing reverse vaccinology for the design and validation of mRNA vaccine targeting the glycoprotein of human metapneumovirus (HMPV)

Integrative reverse vaccinology and computational modeling for the rational design of a broadly immunogenic multi-epitope vaccine against Marburg virus infection.

Structure-Guided Immunoinformatics for the Rational Design of a Multi-Epitope Vaccine Against Batai Orthobunyavirus

Background/Objectives: Batai orthobunyavirus (BATV) is an emerging mosquito-borne zoonotic pathogen for which no licensed vaccine is currently available. The viral envelope glycoprotein plays an important role in viral attachment and host immune recognition, making it a potential target for rational vaccine design. Methods: In this study, an immunoinformatics-based framework was used to design and evaluate a multi-epitope vaccine candidate targeting the BATV envelope glycoprotein. Selected B-cell, cytotoxic T-lymphocyte (CTL), and helper T-lymphocyte (HTL) epitopes were assembled using appropriate linkers and a human β-defensin adjuvant. Population coverage and in silico immune simulations were conducted to evaluate the potential breadth and magnitude of immune response. Results: The final vaccine construct demonstrated favorable physicochemical characteristics, high predicted antigenicity (0.7959), and non-allergenic properties while maintaining favorable predicted structural characteristics and broad predicted population coverage (99.92%). Structural docking revealed a stable interaction between the vaccine construct and human TLR4, with a weighted docking score of −1194.8, suggesting favorable molecular recognition and receptor engagement. Normal Mode Analysis further supported the structural stability and conformational integrity of the vaccine receptor complex. Immune simulation predicted robust primary and secondary immune responses characterized by elevated IgM and IgG antibody production, sustained memory cell formation, and strong IFN-γ and IL-2 responses, indicating the potential to elicit balanced humoral and cellular immunity. Conclusions: This study presents a structurally optimized and validated multiepitope vaccine candidate against the emerging Batai orthobunyavirus. These computational findings identified a promising vaccine candidate for further investigation; however, its immunogenicity, safety, and protective efficacy before further vaccine development can be considered.

Integrative immunoinformatics and structural modeling for the rational design of a multi-epitope vaccine candidate against human cytomegalovirus

Human cytomegalovirus (CMV) is a globally widespread pathogen associated with significant morbidity in immunocompromised individuals. Despite its clinical importance, no licensed vaccine is currently available. This study aimed to design a rational multi-epitope vaccine candidate targeting CMV using an integrative approach combining immunoinformatics and structural biology. Viral proteins were screened to identify epitopes with high affinity for B cells, cytotoxic T cells (CTLs), and helper T cells (HTLs) using the Immune Epitope Database (IEDB). Selected epitopes were filtered according to their antigenicity and toxicity and then assembled into a chimeric construct incorporating an immunostimulatory adjuvant. The designed vaccine was evaluated for its physicochemical properties, validated by Ramchandran and ERRAT analyses. Molecular modeling demonstrated strong and stable interactions with key innate immunity receptors, including TLR7 and TLR9, interactions confirmed by molecular dynamics simulations. In silico immune simulation predicted a robust and durable immune response, characterized by high levels of IgM and IgG, as well as significant activation of CD4 + and CD8 + lymphocytes and innate immunity components. These results highlight the potential of the proposed multi-epitope construct as a promising vaccine candidate against HCMV. However, experimental validation is essential to confirm its immunogenicity, safety, and translational applicability.

Immunoinformatics-Based Multi-Epitope Vaccine Design Against Influenza A (H1N1) Hemagglutinin

Background: Influenza A (H1N1) remains a significant global health threat due to its high mutation rate and antigenic variability, which limit the long-term efficacy of conventional strain-specific vaccines. This study employed an immunoinformatics approach to design a broadly protective multi-epitope vaccine targeting the hemagglutinin (HA) protein. Methods: The HA protein sequence was analyzed for physicochemical properties, antigenicity, and epitope prediction. Promising B-cell and T-cell epitopes were selected and assembled into a multi-epitope vaccine construct. Structural modeling, molecular docking with Toll-like receptor 3 (TLR3), molecular dynamics simulation, population coverage analysis, codon optimization, and in silico cloning were performed to evaluate the vaccine candidate. Results: The HA protein exhibited favorable physicochemical characteristics and strong antigenicity. The final vaccine construct was predicted to be highly antigenic, non-allergenic, and non-toxic, with a global population coverage of 81.68%. Structural validation confirmed model quality, while docking and molecular dynamics analyses demonstrated stable interactions with TLR3, indicating its potential to induce robust immune responses. Codon optimization and in silico cloning suggested efficient expression in the host system. Conclusion: The designed HA-based multi-epitope vaccine demonstrated promising immunogenicity, safety, structural stability, and broad population coverage in silico. These findings support its potential as a vaccine candidate against Influenza A (H1N1), warranting further experimental validation through in vitro and in vivo studies.

In silico pharmacological evaluation of a multi-epitope vaccine candidate targeting the G1 and G2 glycoproteins of Oropouche virus via TLR3 interaction.

Immunoinformatics-guided design of a conserved multi-epitope vaccine candidate against pathogenic human coronaviruses.