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Background: Bovine leukemia virus (BLV) is an oncogenic delta-retrovirus causing bovine leucosis. Studies on BLV have shown the association with human breast cancer. However, the exact molecular mechanism is neither known nor their appropriate preventative measure to halt the disease initiation and progression. In this study, we designed a multi-epitope vaccine against BLV using a computational analyses., Methods: Following a rigorous assessment, the vaccine was constructed using the T-cell epitopes from each BLV-derived protein with suitable adjuvant and linkers. Both physicochemistry and immunogenic potency as well as the safeness of the vaccine candidate were assessed. Population coverage was done to evaluate the vaccine probable efficiency in eliciting the immune response worldwide. After homology modeling, the three-dimensional structure was refined and validated to determine the quality of the designed vaccine. The vaccine protein was then subjected to molecular docking with Toll-like receptor 3 (TLR3) to evaluate the binding efficiency followed by dynamic simulation for stable interaction., Results: Our vaccine construct has the potential immune response and good physicochemical properties. The vaccine is antigenic and immunogenic, and has no allergenic or toxic effect on the human body. This novel vaccine contains a significant interactions and binding affinity with the TLR3 receptor., Conclusions: The proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat BLV infections. However, experimental evaluations are essential to validate the exact safety and immunogenic profiling of this vaccine., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Chandipura vesiculovirus (CHPV) is a rapidly emerging pathogen responsible for causing acute encephalitis. Due to its widespread occurrence in Asian and African countries, this has become a global threat, and there is an urgent need to design an effective and nonallergenic vaccine against this pathogen. The present study aimed to develop a multi-epitope vaccine using an immunoinformatics approach. The conventional method of vaccine design involves large proteins or whole organism which leads to unnecessary antigenic load with increased chances of allergenic reactions. In addition, the process is also very time-consuming and labor-intensive. These limitations can be overcome by peptide-based vaccines comprising short immunogenic peptide fragments that can elicit highly targeted immune responses, avoiding the chances of allergenic reactions, in a relatively shorter time span. The multi-epitope vaccine constructed using CTL, HTL, and IFN-γ epitopes was able to elicit specific immune responses when exposed to the pathogen, in silico. Not only that, molecular docking and molecular dynamics simulation studies confirmed a stable interaction of the vaccine with the immune receptors. Several physicochemical analyses of the designed vaccine candidate confirmed it to be highly immunogenic and nonallergic. The computer-aided analysis performed in this study suggests that the designed multi-epitope vaccine can elicit specific immune responses and can be a potential candidate against CHPV., (© 2021 Wiley Periodicals LLC.)

Mycobacterium tuberculosis (MTB) is the causative agent of tuberculosis (TB), a prevalent airborne infectious disease. Despite the availability of the Bacille Calmette-Guerin vaccine, its global efficacy remains modest, and tuberculosis persists as a significant global public health threat. Addressing this challenge and advancing towards the End MTB Strategy, we developed a multiepitope vaccine (MEV) based on immuno informatics and computational approaches. Immuno informatics screening of MBT protein identified immune-dominant epitopes based on Major Histocompatibility Complex (MHC) allele binding, immunogenicity, antigenicity, allergenicity, toxicity, and cytokine inducibility. Selected epitopes were integrated into an MEV construct with adjuvant and linkers, forming a fully immunogenic vaccine candidate. Comprehensive analyses encompassed the evaluation of immunological and physicochemical properties, determination of tertiary structure, molecular docking with TollLike Receptors (TLR), molecular dynamics (MD) simulations for all atoms, and immune simulations. Our MEV comprises 534 amino acids, featuring 6 cytotoxic T lymphocyte, 8 helper T lymphocyte, and 7 linear B lymphocyte epitopes, demonstrating high antigenicity and stability. Notably, molecular docking studies and triplicate MD simulations revealed enhanced interactions and stability of MEV with the TLR4 complex compared to TLR2. In addition, the immune simulation indicated the capacity to effectively induce elevated levels of antibodies and cytokines, emphasizing the vaccine’s robust immunogenic response. This study presents a promising MEV against TB, exhibiting favorable immunological and physicochemical attributes. The findings provide theoretical support for TB vaccine development. Our study aligns with the global initiative of the End MTB Strategy, emphasizing its potential impact on addressing persistent challenges in TB control. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) poses a significant global health threat, especially affecting infants and the elderly. Addressing this, the present study proposes an innovative approach to vaccine design, utilizing immunoinformatics and computational strategies. We analyzed RSV's structural proteins across both subtypes A and B, identifying potential helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes. Criteria such as antigenicity, allergenicity, toxicity, and cytokine-inducing potential were rigorously examined. Additionally, we evaluated the conservancy of these epitopes and their population coverage across various RSV strains. The comprehensive analysis identified six major histocompatibility complex class I (MHC-I) binding, five MHC-II binding, and three B-cell epitopes. These were integrated with suitable linkers and adjuvants to form the vaccine. Further, molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors (TLR4 and TLR5), with a notable preference for TLR4. Immune simulation analysis underscored the vaccine's potential to elicit a strong immune response. This study presents a promising RSV vaccine candidate and offers theoretical support, marking a significant advancement in vaccine development efforts. However, the promising in silico findings need to be further validated through additional in vivo studies. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) poses a significant global health threat, especially affecting infants and the elderly. Addressing this, the present study proposes an innovative approach to vaccine design, utilizing immunoinformatics and computational strategies. We analyzed RSV's structural proteins across both subtypes A and B, identifying potential helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes. Criteria such as antigenicity, allergenicity, toxicity, and cytokine-inducing potential were rigorously examined. Additionally, we evaluated the conservancy of these epitopes and their population coverage across various RSV strains. The comprehensive analysis identified six major histocompatibility complex class I (MHC-I) binding, five MHC-II binding, and three B-cell epitopes. These were integrated with suitable linkers and adjuvants to form the vaccine. Further, molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors (TLR4 and TLR5), with a notable preference for TLR4. Immune simulation analysis underscored the vaccine's potential to elicit a strong immune response. This study presents a promising RSV vaccine candidate and offers theoretical support, marking a significant advancement in vaccine development efforts. However, the promising in silico findings need to be further validated through additional in vivo studies.

Bolivian hemorrhagic fever (BHF) caused by Machupo virus (MACV) is a New World arenavirus having a reported mortality rate of 25–35%. The BHF starts with fever, followed by headache, and nausea which rapidly progresses to severe hemorrhagic phase within 7 days of disease onset. One of the key promoters for MACV viral entry into the cell followed by viral propagation is performed by the viral glycoprotein (GPC). GPC is post-transcriptionally cleaved into GP1, GP2 and a signal peptide. These proteins all take part in the viral infection in host body. Therefore, GPC protein is an ideal target for developing therapeutics against MACV infection. In this study, GPC protein was considered to design a multi-epitope, multivalent vaccine containing antigenic and immunogenic CTL and HTL epitopes. Different structural validations and physicochemical properties were analysed to validate the vaccine. Docking and molecular dynamics simulations were conducted to understand the interactions of the vaccine with various immune receptors. Finally, the vaccine was codon optimised in silico and along with which immune simulation studies was performed in order to evaluate the vaccine's effectiveness in triggering an efficacious immune response against MACV. [ABSTRACT FROM AUTHOR]

Abstract Background Crimean-Congo hemorrhagic fever (CCHF) is a widespread disease transmitted to humans and livestock animals through the bite of infected ticks or close contact with infected persons’ blood, organs, or other bodily fluids. The virus is responsible for severe viral hemorrhagic fever outbreaks, with a case fatality rate of up to 40%. Despite having the highest fatality rate of the virus, a suitable treatment option or vaccination has not been developed yet. Therefore, this study aimed to formulate a multiepitope vaccine against CCHF through computational vaccine design approaches. Methods The glycoprotein, nucleoprotein, and RNA-dependent RNA polymerase of CCHF were utilized to determine immunodominant T- and B-cell epitopes. Subsequently, an integrative computational vaccinology approach was used to formulate a multi-epitopes vaccine candidate against the virus. Results After rigorous assessment, a multiepitope vaccine was constructed, which was antigenic, immunogenic, and non-allergenic with desired physicochemical properties. Molecular dynamics (MD) simulations of the vaccine-receptor complex show strong stability of the vaccine candidates to the targeted immune receptor. Additionally, the immune simulation of the vaccine candidates found that the vaccine could trigger real-life-like immune responses upon administration to humans. Conclusions Finally, we concluded that the formulated multiepitope vaccine candidates would provide excellent prophylactic properties against CCHF.

Breast cancer (BC) continues to be the malignancy with the highest diagnosis rate worldwide. Between 15 % and 30 % of BC patients show overexpressed human epidermal growth factor receptor 2 (HER2), which is linked to poor clinical results in terms of invasiveness and recurrence risk. Passive immunity-based therapeutic approaches for treating HER2-enriched BC, are not effective and significant problems need to be tackled. Constructing multi-epitope vaccines is favored over single-epitope vaccines due to its ability to induce immunity against a variety of antigenic targets which will improve the efficacy of the vaccine. The current study describes a multi-epitope vaccine from HER2 protein against HER2-positive BC using several immunoinformatic techniques to achieve a potent and durable immune response. Nine Cytotoxic T lymphocytes (CTL) and five Helper T lymphocytes (HTL) epitopes were predicted and validated from HER2 protein using in silico tools. The expressed protein of the designed vaccine is predicted to be highly thermostable with better solubility. The predicted vaccine 3D structure was validated by ProSA servers and by the ERRAT server. Molecular docking analysis revealed a high binding affinity and stability of the designed vaccine with MHCI and TLR-2, 4, 7, and 9 receptors. The analysis of the C-ImmSim server revealed that the novel vaccine construct had the ability to elicit robust anti-cancerous innate, humoral, and cell-mediated immune responses. The vaccine can be a suitable option for HER2-positive BC patients and other patients with HER2-positive cancers to evoke immune responses. However, in vitro and in vivo experiments are needed to assess its effectiveness and safety. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is an aggressive, metastatic/invasive sub-class of breast cancer (BCa). Cell surface protein-derived multi-epitope vaccine-mediated targeting of TNBC cells could be a better strategy against the disease. Literature-based identified potential cell surface markers for TNBC cells were subjected to expression pattern and survival analysis in BCa patient sample using TCGA database. The cytotoxic and helper T-lymphocytes antigenic epitopes in the test proteins were identified, selected and fused together with the appropriate linkers and an adjuvant, to construct the multi-epitope vaccine (MEV). The immune profile, physiochemical property (PP) and world population coverage of the MEV was studied. Immune simulation, cloning in a suitable vector, molecular docking (against Toll-like receptors, MHC (I/II) molecules), and molecular dynamics simulations of the MEV was performed. Cell surface markers were differentially expressed in TNBC samples and showed poor survival in TNBC patients. Satisfactory PP and WPC (up to 89 and 99%) was observed. MEV significant stable binding with the immune molecules and induced the immune cells in silico. The designed vaccine has capability to elicit immune response which could be utilized to target TNBC alone/combination with other therapy. The experimental studies are required to check the efficacy of the vaccine. [ABSTRACT FROM AUTHOR]

Hantaviruses are an emerging zoonotic group of rodent-borne viruses that are having serious implications on global public health due to the increase in outbreaks. Since there is no permanent cure, there is increasing interest in developing a vaccine against the hantavirus. This research aimed to design a robust cross-protective subunit vaccine using a novel immunoinformatics approach. After careful evaluation, the best predicted cytotoxic & helper T-cell and B-cell epitopes from nucleocapsid proteins, glycoproteins, RdRp proteins, and non-structural proteins were considered as potential vaccine candidates. Among the four generated vaccine models with different adjuvant, the model with toll-like receptor-4 (TLR-4) agonist adjuvant was selected because of its high antigenicity, non-allergenicity, and structural quality. The selected model was 654 amino acids long and had a molecular weight of 70.5 kDa, which characterizes the construct as a good antigenic vaccine candidate. The prediction of the conformational B-lymphocyte (CBL) epitope secured its ability to induce the humoral response. Thereafter, disulfide engineering improved vaccine stability. Afterwards, the molecular docking confirmed a good binding affinity of −1292 kj/mol with considered immune receptor TLR-4 and the dynamics simulation showed high stability of the vaccine-receptor complex. Later, the in silico cloning confirmed the better expression of the constructed vaccine protein in E. coli K12. Finally, in in silico immune simulation, significantly high levels of immunoglobulin M (IgM), immunoglobulin G1 (IgG1), cytotoxic & helper T lymphocyte (CTL & HTL) populations, and numerous cytokines such as interferon-γ (IFN-γ), interleukin-2 (IL-2) etc. were found as coherence with actual immune response and also showed faster antigen clearance for repeated exposures. Nonetheless, experimental validation can demonstrate the safety and cross-protective ability of the proposed vaccine to fight against hantavirus infection. Image 1 • The high antigenic proteins were selected by studying whole proteome of hantavirus. • Robust T & B cell vaccine candidates were predicted from high antigenic proteins. • The best vaccine model has highest antigenic, hydrophilic, and non-allergic scores. • The vaccine-receptor interaction & stability were ensured by docking & MDS. • Immune simulation ensured the consistent immune response. [ABSTRACT FROM AUTHOR]