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Dengue is considered as a major health issue which causes a number of deaths worldwide each year; tropical countries are majorly affected by dengue outbreaks. It is considered as life threatening issue because, since many decades not a single effective approach for treatment and prevention of dengue has been developed. Therefore, to find new preventive measure, we used immunoinformatics approaches to develop a multi-epitope based subunit vaccine for dengue which can generate various immune responses inside the host. Different B-cell, T C cell, and T H cell binding epitopes were predicted for structural and non-structural proteins of dengue virus. Final vaccine constructs consisting of T C and T H cell epitopes and an adjuvant (β-defensin) at N-terminal of the construct. Presence of B-cell and IFN-γ inducing epitopes confirms the humoral and cell mediated immune response developed by designed vaccine. Designed vaccine was not found allergic and was potentially antigenic in nature. Modeling of tertiary structure and the refined model was used for molecular docking with TLR-3 (immune receptor). Molecular docking and dynamics simulation confirms the microscopic interactions between ligand and receptor. In silico cloning approach was used to ensure the expression and translation efficiency of vaccine within an expression vector.

Dengue poses a global health threat, which will persist without therapeutic intervention. Immunity induced by exposure to one serotype does not confer long-term protection against secondary infection with other serotypes and is potentially capable of enhancing this infection. Although vaccination is believed to induce durable and protective responses against all the dengue virus (DENV) serotypes in order to reduce the burden posed by this virus, the development of a safe and efficacious vaccine remains a challenge. Immunoinformatics and computational vaccinology have been utilized in studies of infectious diseases to provide insight into the host–pathogen interactions thus justifying their use in vaccine development. Since vaccination is the best bet to reduce the burden posed by DENV, this study is aimed at developing a multi-epitope based vaccines for dengue control. Combined approaches of reverse vaccinology and immunoinformatics were utilized to design multi-epitope based vaccine from the sequence of DENV. Specifically, BCPreds and IEDB servers were used to predict the B-cell and T-cell epitopes, respectively. Molecular docking was carried out using Schrödinger, PATCHDOCK and FIREDOCK. Codon optimization and in silico cloning were done using JCAT and SnapGene respectively. Finally, the efficiency and stability of the designed vaccines were assessed by an in silico immune simulation and molecular dynamic simulation, respectively. The predicted epitopes were prioritized using in-house criteria. Four candidate vaccines (DV-1–4) were designed using suitable adjuvant and linkers in addition to the shortlisted epitopes. The binding interactions of these vaccines against the receptors TLR-2, TLR-4, MHC-1 and MHC-2 show that these candidate vaccines perfectly fit into the binding domains of the receptors. In addition, DV-1 has a better binding energies of − 60.07, − 63.40, − 69.89 kcal/mol against MHC-1, TLR-2, and TLR-4, with respect to the other vaccines. All the designed vaccines were highly antigenic, soluble, non-allergenic, non-toxic, flexible, and topologically assessable. The immune simulation analysis showed that DV-1 may elicit specific immune response against dengue virus. Moreover, codon optimization and in silico cloning validated the expressions of all the designed vaccines in E. coli . Finally, the molecular dynamic study shows that DV-1 is stable with minimum RMSF against TLR4. Immunoinformatics tools are now applied to screen genomes of interest for possible vaccine target. The designed vaccine candidates may be further experimentally investigated as potential vaccines capable of providing definitive preventive measure against dengue virus infection.

Influenza continues to be one of the top public health problems since it creates annual epidemics and can start a worldwide pandemic. The virus’s rapid evolution allows the virus to evade the host defense, and then seasonal vaccines need to be reformulated nearly annually. However, it takes almost half a year for the influenza vaccine to become accessible. This delay is especially concerning in the event of a pandemic breakout. By producing the vaccine through reverse vaccinology and phage display vaccines, this time can be reduced. In this study, epitopes of B lymphocytes, cytotoxic T lymphocytes, and helper T lymphocytes of HA, NA, NP, and M2 proteins from two strains of Influenza A were anticipated. We found two proper epitopes (ASFIYNGRL and LHLILWITDRLFFKC) in Influenza virus proteins for CTL and HTL cells, respectively. Optimal epitopes and linkers in silico were cloned into the N-terminal end of M13 protein III (pIII) to create a multi-epitope-pIII construct, i.e., phage display vaccine. Also, prediction of tertiary structure, molecular docking, molecular dynamics simulation, and immune simulation were performed and showed that the designed multi-epitope vaccine can bind to the receptors and stimulate the immune system response.

Acinetobacter baumannii , a notorious nosocomial pathogen, presents a global health challenge due to its high antibiotic resistance and ability to cause various infections. Vaccine development against this pathogen is a pressing need, yet traditional methods have encountered limitations due to the immunogenic complexity and potential autoimmune risks associated with key antigenic components. In this study, we leveraged the reverse vaccinology approach, integrating epitope mapping, immunoinformatics, and structural modeling to design a novel multiepitope vaccine. Our methodology emphasizes the concept of epitope reciprocity, wherein B-cell and T-cell epitopes are co-localized to promote cross-epitope interactions, enhancing immunogenicity and immune response synergy. Additionally, utilizing a trimeric construct, we achieved a threefold increase in epitope density, further amplifying the immune activation potential. Key antigenic proteins implicated in the pathogenicity of A. baumannii were screened, excluding components like OmpA, which is known to induce autoimmune responses. After rigorous silico analyses, including structural validation and molecular docking, the final construct demonstrated promising interaction with Toll-like receptor 1 (TLR-1), suggesting efficient immune recognition. This multiepitope design, grounded in the principles of epitope reciprocity and enhanced by multimeric epitope density, offers a promising platform for developing a safe and effective vaccine against A. baumannii . Further experimental validation is warranted to assess its in vivo efficacy and safety.

Human metapneumovirus (HMPV) is a respiratory viral pathogen classified within the Paramyxoviridae family and is a significant cause of acute respiratory tract infections. The vaccine construct maintains stable binding affinity with targeted receptors anaffects young children, the elderly, and immunocompromised individuals. HMPV was recognized initially in 2001. HMPV exhibits characteristics similar to respiratory syncytial virus (RSV) and generally causes symptoms ranging from mild, cold-like signs to more severe issues such as pneumonia and bronchiolitis. This study employs reverse vaccinology and bioinformatics approaches to screen potential vaccine targets and design epitope vaccine constructs. Following immunoinformatics analysis, six proteins, including >YP_009513265.1 nucleoprotein, >YP_009513266.1 phosphoprotein, >YP_009513268.1 fusion protein, >YP_009513269.1 matrix protein 2–1, >YP_009513272.1 attachment glycoprotein, and >YP_009513273.1 RNA-dependent RNA polymerase, were selected for further analysis. These proteins underwent B-cell epitope prediction, and the identified B-cell epitopes were used in T cell epitope prediction. Both MHC-I and MHC-II epitopes were predicted, with lower percentile scores prioritized. The selected epitopes- SLGKIKNNK, IPQNQRPSA, IPSEPKLAW, FPEDQNVAL, KLKNKNRLR, ELSSIKTRR, and QEKKLVPVY- were further screened through immunoinformatics analysis. These epitopes were connected with a GPGPG linker to form a linear sequence, and an EAAAK linker was used to attach the adjuvant to this sequence, which was then processed for structural modeling. Effective vaccines must bind to receptors on immune cells to activate the immune system. The vaccine-TLR-4 docked complex, predicted using Cluspro 2. 2.0 web server, showed that cluster 4 had the lowest binding energy score of − 1175. 3, indicating potential effectiveness in eliciting an immune response via TLR-4 activation. Similarly, for the vaccine- MHC-I complex, cluster 2 exhibited the lowest energy score of − 1087. 4 kcal/mol, suggesting a stable binding interaction. For the vaccine-MHC-II complex, cluster 2 showed the most stable binding with a lowest energy score of − 1189. 1 kcal/mol. To validate these docking results, molecular dynamics simulations analyze the stability of the complexes. The RMSD from the simulations indicated that the vaccine- MHC-I complex stabilized around 10 to 12 (Å). The vaccine-MHC-II complex stabilized after 60 ns, while the vaccine-TLR-4 complex initially showed a lower RMSD of approximately 5 (Å), which gradually increased to 12–15 (Å), reflecting conformational stability and binding affinity. In addition, in silico immune simulations suggested activation of both humoral and cellular immunity, indicating that the vaccine construct could effectively induce immune responses against HMPV.

Rickettsia is a genus of bacteria that are obligate intracellular parasites and are responsible for the febrile diseases known collectively as Rickettsioses. The emergence of antibiotic resistance is an escalating concern and thus developing a vaccine against Rickettsia is of paramount importance due to the significant public health threat posed by these bacteria. Thus, we employed structural vaccinology guided by machine learning algorithms to explore the virulence landscape of Rickettsia prowazekii to design a multi-epitopes-based vaccine (MEVC) that is immunogenic and safe. From a pool of virulence factors, we shortlisted five targets including sca0, sca1, sca4, sca5 and tlyA that were classified as non-allergenic as well as antigenic. The immune epitopes mapping results shortlisted five CTL epitopes, five HTL (IFN+) epitopes and five B cell epitopes as the best choice to design a vaccine construct of 475 amino acids. Various parameters were used to validate the designed MEVC which involved prediction of physiochemical properties, modeling and validation of the 3D structure, interaction with the immune receptors such as TLR2 (Toll-like receptor) and TLR4. Moreover, all-atoms simulation and binding free energy (BFE) results revealed a stable and favorable dynamic properties determined by these complexes. Jcat revealed that the improved sequence exhibits a GC content of 48.14% and a CAI (Codon Adaptation Index) value of 1.0. We used a multi-dose criterion at different time intervals i.e., 1st, 84th and 170th day to understand the immune potential of our constructed vaccine. The results provide a comprehensive overview of immune factors that ensure effective antigen memory cells generation after each injection, as predicted by the in silico pipeline. However, limitations in current algorithms particularly their inability to fully account for HLA polymorphism and the lack of experimental and clinical validation remain major shortcomings of the study. These issues should be addressed in future research to support the development of a robust immune response against Rickettsia infections.

Context Multi-epitope vaccine (MEV) construction is a technique which combines multiple epitopes, both B cell epitopes and T cell epitopes which have the potential to elicit a much stronger immune response compared to a subunit vaccine. Therefore, recently, a lot of research has been focused on development and improvement of multiepitope vaccines. The strategy of designing a MEV in silico lies in a few basic steps, including procuring the amino acid sequence of the B cell and T cell epitopes from literature search, bioinformatics approach, to construct a potent immunogen capable of eliciting both humoral and cell-mediated response and finally joining these epitopes by linkers. However, a vaccine constructed by merely joining the epitopes may not always result in a stable globular structured protein. In this study, we have focused on developing a strategy where a potential vaccine candidate of Mycobacterium tuberculosis has been used as a scaffold and the low complexity regions of this scaffold have been replaced by the predicated epitopes. Essentially, instead of joining the epitopes by linkers, they have been carefully positioned on a scaffold of a protein that is itself a vaccine candidate to derive a MEV against Mycobacterium tuberculosis. Method In this study, a methodology has been detailed to tackle this great challenge using a simple approach of protein engineering. A scaffold-based MEV has been designed against Mtb by converting a vaccine candidate protein, Ag85A, into a scaffold by truncating its low complexity non-immunogenic regions, and the gaps were supplemented by the highly immunogenic epitopes. Replicated 500 ns molecular dynamics simulation at different temperatures (300 K and 310 K) and principal component analysis proved that MEV built on the scaffold is more stable than the conventional one.

Lassa virus (LASV), a member of the Arenaviridae , is an ambisense RNA virus that causes severe hemorrhagic fever with a high fatality rate in humans in West and Central Africa. Currently, no FDA approved drugs or vaccines are available for the treatment of LASV fever. The LASV glycoprotein complex (GP) is a promising target for vaccine or drug development. It is situated on the virion envelope and plays key roles in LASV growth, cell tropism, host range, and pathogenicity. In an effort to discover new LASV vaccines, we employ several sequence-based computational prediction tools to identify LASV GP major histocompatibility complex (MHC) class I and II T-cell epitopes. In addition, many sequence- and structure-based computational prediction tools were used to identify LASV GP B-cell epitopes. The predicted T- and B-cell epitopes were further filtered based on the consensus approach that resulted in the identification of thirty new epitopes that have not been previously tested experimentally. Epitope-allele complexes were obtained for selected strongly binding alleles to the MHC-I T-cell epitopes using molecular docking and the complexes were relaxed with molecular dynamics simulations to investigate the interaction and dynamics of the epitope-allele complexes. These predictions provide guidance to the experimental investigations and validation of the epitopes with the potential for stimulating T-cell responses and B-cell antibodies against LASV and allow the design and development of LASV vaccines.

The disappointing efficacy of blood-stage malaria vaccines may be explained in part by allele-specific immune responses that are directed against polymorphic epitopes on blood-stage antigens. FMP2.1/AS02 A , a bloodstage candidate vaccine based on apical membrane antigen 1 (AMA1) from the 3D7 strain of Plasmodium falciparum had allele-specific efficacy against clinical malaria in a phase II trial in Malian children. We assessed the cross-protective efficacy of the malaria vaccine and inferred which polymorphic amino acid positions in AMA1 were the targets of protective allele-specific immune responses. FMP2.1/AS02 A had the highest efficacy against AMA1 alíeles that were identical to the 3D7 vaccine-type alíele at 8 highly polymorphic amino acid positions in the cluster 1 loop (c1L) but differed from 3D7 elsewhere in the molecule. Comparison of the incidence of vaccine-type alíeles before and after vaccination in the malaria vaccine and control groups and examination of the patterns of alíele change at polymorphic positions in consecutive malaria episodes suggest that the highly polymorphic amino acid position 197 in c1L was the most critical determinant of allele-specific efficacy. These results indicate that a multivalent AMA1 vaccine with broad efficacy could include only a limited set of key alíeles of this extremely polymorphic antigen.

Cystic echinococcosis (CE) is a worldwide zoonotic public health issue. The reasons for this include a lack of specific therapy options, increasing antiparasitic drug resistance, a lack of control strategies, and the absence of an approved vaccine. The aim of the current study is to develop a multiepitope vaccine against CE by in-silico identification and using different Antigen B subunits. The five Echinococcus granulosus antigen B (EgAgB) subunits were examined for eminent antigenic epitopes, and then the best B-cell and Major Histocompatibility Complex MHC-binding epitopes were predicted. Most significant epitopes were combined to create an effective multi-epitope vaccine, which was then validated by testing its secondary and tertiary structures, physicochemical properties, and molecular dynamics (MD) modelling. A multi-epitope vaccine construct of 483 amino acid sequences was designed. It contains B-cell, Helper T Lymphocyte (HTL), and Cytotoxic T Lymphocyte (CTL) epitopes as well as the appropriate adjuvant and linker molecules. The resultant vaccinal construct had a GDT-HA value of 0.9725, RMSD of 0.299, MolProbity of 1.891, Clash score of 13.1, Poor rotamers of 0.9, and qualifying features with Rama favoured of 89.9. It was also highly immunogenic and less allergic. The majority of the amino acids were positioned in the Ramachandran plot’s favourable area, and during the molecular dynamic simulation at 100 ns, no notable structural abnormalities were noticed. The resultant construct was significantly expressed and received good endorsement in the pIB2-SEC13-mEGFP expressional vector. In conclusion, the current in-silico multi-epitope vaccine may be evaluated in-vitro, in-vivo, and in clinical trials as an immunogenic vaccine model. It can also play a vital role in preventing this zoonotic parasite infection.