African swine fever virus (ASFV), a highly virulent double-stranded DNA virus, poses a significant threat to global pig farming, with mortality rates in domestic pigs reaching up to 100%. Originating in Kenya in 1921, ASFV has since proliferated to Western Europe, Latin America, Eastern Europe, and most recently China in 2018, resulting in substantial global agricultural losses. Antigenic epitopes, recognized by the immune system\'s T cells and B cells, are pivotal in antiviral immune responses. The identification and characterization of these antigenic epitopes can offer invaluable insights into the immune response against ASFV and aid in the development of innovative immunotherapeutic strategies. Vaccine adjuvants, substances that amplify the body\'s specific immune response to antigens, also play a crucial role. This review provides an overview of the progress in studying T/B-cell epitopes in ASFV proteins and ASFV vaccine adjuvants, highlighting their role in the immune response and potential use in new vaccine development.

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST-BST-XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model\'s reliability and an average accuracy of 97.21% was recorded for the ChST-BST-XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development.

Advances in synthetic peptide synthesis have enabled rapid and cost-effective peptide drug manufacturing. For this reason, peptide drugs that were first produced using recombinant DNA (rDNA) technology are now being produced using solid- and liquid-phase peptide synthesis. While peptide synthesis has some advantages over rDNA expression methods, new peptide-related impurities that differ from the active pharmaceutical ingredient (API) may be generated during synthesis. These impurity byproducts of the original peptide sequence feature amino acid insertions, deletions, and side-chain modifications that may alter the immunogenicity risk profile of the drug product. Impurities resulting from synthesis have become the special focus of regulatory review and approval for human use, as outlined in the FDA\'s Center for Drug Evaluation and Research guidance document, \"ANDAs for Certain Highly Purified Synthetic Peptide Drug Products That Refer to Listed Drugs of rDNA Origin,\" published in 2021. This case study illustrates how in silico and in vitro methods can be applied to assess the immunogenicity risk of impurities that may be present in synthetic generic versions of the salmon calcitonin (SCT) drug product. Sponsors of generic drug abbreviated new drug applications (ANDAs) should consider careful control of these impurities (for example, keeping the concentration of the immunogenic impurities below the cut-off recommended by FDA regulators). Twenty example SCT impurities were analyzed using in silico tools and assessed as having slightly more or less immunogenic risk potential relative to the SCT API peptide. Class II human leukocyte antigen (HLA)-binding assays provided independent confirmation that a 9-mer sequence present in the C-terminus of SCT binds promiscuously to multiple HLA DR alleles, while T-cell assays confirmed the expected T-cell responses to SCT and selected impurities. In silico analysis combined with in vitro assays that directly compare the API to each individual impurity peptide may be a useful approach for assessing the potential immunogenic risk posed by peptide impurities that are present in generic drug products.

The inactivated vaccine is effective in controlling foot-and-mouth disease (FMD), but it has drawbacks such as the need for a biosafety level 3 laboratory facility to handle live foot-and-mouth disease virus (FMDV), high production costs, and biological safety risks. In response to these challenges, we developed a new recombinant protein vaccine (2BT-pIgG-Fc) containing porcine IgG-Fc to enhance protein stability in the body. This vaccine incorporates two-repeat B-cell and one-single T-cell epitope derived from O/Jincheon/SKR/2014. Our study confirmed that 2BT-pIgG-Fc and a commercial FMDV vaccine induced FMDV-specific antibodies in guinea pigs at 28 days post-vaccination. The percentage inhibition (PI) value of 2BT-pIgG-Fc was 90.43%, and the commercial FMDV vaccine was 81.75%. The PI value of 2BT-pIgG-Fc was 8.68% higher than that of commercial FMDV vaccine. In pigs, the primary target animals for FMDV, all five individuals produced FMDV-specific antibodies 42 days after vaccination with 2BT-pIgG-Fc. Furthermore, serum from 2BT-pIgG-Fc-vaccinated pigs exhibited neutralizing ability against FMDV infection. Intriguingly, the 2BT-pIgG-Fc recombinant demonstrated FMDV-specific antibody production rates and neutralization efficiency similar to commercial inactivated vaccines. This study illustrates the potential to enhance vaccine efficacy by strategically combining well-known antigenic domains in the development of recombinant protein-based vaccines.

The highly conserved C129R protein of AFSV was utilized in the development of an ASFV recombinant adenovirus vaccine, demonstrating strong immunogenicity. In this study, we immunized 6-week-old female C57BL/6J mice via subcutaneous injection with 10 μg of purified C129R protein. Humoral and cellular immune effects were assessed using ELISA, flow cytometry, and ELISpot assays. Additionally, 19 peptides of the C129R protein were synthesized and screened for the use of bioinformatics. Positive T-cell epitopes were screened using ELISpot. The results indicated a higher proportion of CD4+ and CD8+ T lymphocytes in immunized mice compared to control mice. ELISA analysis revealed a serum titer of approximately 1:1, 638, 400 in the experimental group of mice. Additionally, peptides C11(53-61aa), C14(81-89aa), C16(97-105aa), and C18(116-124aa) from the C129R protein were able to activate mice spleen lymphocytes to produce IFN-γ. These findings suggest that the C129R protein significantly enhances both humoral and cellular immunity in immunized mice. Moreover, peptides C11, C14, C16, and C18 may serve as potential T-cell epitopes for the C129R protein. These results lay the groundwork for the further exploration of ASFV C129R protein and the identification of novel ASF vaccine antigens.

Despite significant strides in vaccine research and the availability of vaccines for many infectious diseases, the threat posed by both known and emerging infectious diseases persists. Moreover, breakthrough infections following vaccination remain a concern. Therefore, the development of novel vaccines is imperative. These vaccines must exhibit robust protective efficacy, broad-spectrum coverage, and long-lasting immunity. One promising avenue in vaccine development lies in leveraging T-cells, which play a crucial role in adaptive immunity and regulate immune responses during viral infections. T-cell recognition can target highly variable or conserved viral proteins, and memory T-cells offer the potential for durable immunity. Consequently, T-cell-based vaccines hold promise for advancing vaccine development efforts. This review delves into the latest research advancements in T-cell-based vaccines across various platforms and discusses the associated challenges.

B2 haplotype major histocompatibility complex (MHC) has been extensively reported to confer resistance to various avian diseases. But its peptide-binding motif is unknown, and the presenting peptide is rarely identified. Here, we identified its peptide-binding motif (X-A/V/I/L/P/S/G-X-X-X-X-X-X-V/I/L) in vitro using Random Peptide Library-based MHC I LC-MS/MS analysis. To further clarify the structure basis of motif, we determined the crystal structure of the BF2∗02:01-PB2552-560 complex at 1.9 Å resolution. We found that BF2∗02:01 had a relatively wide antigen-binding groove, and the structural characterization of pockets was consistent with the characterization of peptide-binding motif. The wider features of the peptide-binding motif and increased number of peptides bound by BF2∗02:01 than BF2∗04:01 might resolve the puzzles for the presence of potential H9N2 resistance in B2 chickens. Afterward, we explored the H9N2 avian influenza virus (AIV)-induced cellular immune response in B2 haplotype chickens in vivo. We found that ratio of CD8+ T cell and kinetic expression of cytotoxicity genes including Granzyme K, interferon-γ, NK lysin, and poly-(ADP-ribose) polymerase in peripheral blood mononuclear cells were significantly increased in defending against H9N2 AIV infection. Especially, we selected 425 epitopes as candidate epitopes based on the peptide-binding motif and further identified four CD8+ T-cell epitopes on H9N2 AIV including NS198-106, PB2552-560, NP182-190, and NP455-463 via ELI-spot interferon-γ detections after stimulating memory lymphocytes with peptides. More importantly, these epitopes were found to be conserved in H7N9 AIV and H9N2 AIV. These findings provide direction for developing effective T cell epitope vaccines using well-conserved internal viral antigens in chickens.

Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen. However PBVs have received less attention despite holding huge untapped potential for boosting vaccine safety and immunogenicity. To identify these TCEs for designing PBV, wet-lab experiments are difficult, expensive, and time-consuming. Machine learning (ML) techniques can accurately predict TCEs, saving time and cost for speedy vaccine development. This work proposes novel hybrid ML techniques based on the physicochemical properties of peptides to predict SARS-CoV-2 TCEs. The proposed hybrid ML technique was evaluated using various ML model evaluation metrics and demonstrated promising results. The hybrid technique of decision tree classifier with chi-squared feature weighting technique and forward search optimal feature searching algorithm has been identified as the best model with an accuracy of 98.19%. Furthermore, K-fold cross-validation (KFCV) was performed to ensure that the model is reliable and the results indicate that the hybrid random forest model performs consistently well in terms of accuracy with respect to other hybrid approaches. The predicted TCEs are highly likely to serve as promising vaccine targets, subject to evaluations both in-vivo and in-vitro. This development could potentially save countless lives globally, prevent future epidemic-scale outbreaks, and reduce the risk of mutation escape.

Tropomyosin (TM) is a pan-allergen with cross-reactivity to arthropods, insects, and nematodes in tropical regions. While IgE epitopes of TM contribute to sensitization, T-cell (MHC-II) epitopes polarize the Th2 immune response. This study aimed to identify linear B and T consensus epitopes among house dust mites, cockroaches, Ascaris lumbricoides, shrimp, and mosquitoes, exploring the molecular basis of cross-reactivity in allergic diseases. Amino acid sequences of Der p 10, Der f 10, Blo t 10, Lit v 1, Pen a 1, Pen m 1, rAsc l 3, Per a 7, Bla g 7, and Aed a 10 were collected from Allergen Nomenclature and UniProt. B epitopes were predicted using AlgPred 2.0 and BepiPred 3.0. T epitopes were predicted with NetMHCIIpan 4.1 against 10 HLA-II alleles. Consensus epitopes were obtained through analysis and Epitope Cluster Analysis in the Immune Epitope Database. We found 7 B-cell epitopes and 28 linear T-cell epitopes binding to MHC II. A unique peptide (residues 160-174) exhibited overlap between linear B-cell and T-cell epitopes, highly conserved across tropomyosin sequences. These findings shed light on IgE cross-reactivity among the tested species. The described immuno-informatics pipeline and epitopes can inform in vitro research and guide synthetic multi-epitope proteins\' design for potential allergology immunotherapies. Further in silico studies are warranted to confirm epitope accuracy and guide future experimental protocols.

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.