Properties of MHC class I presented peptides that enhance immunogenicity

Designing a T cell multi-epitope vaccine against hRSV with reverse vaccinology: An immunoinformatics approach

As an infectious viral pathogen, human respiratory syncytial virus (hRSV) can cause severe respiratory infections and is recognized as one of the highest priority pathogens by the World Health Organization (WHO). Although vaccines play an important role in disease prevention and transmission, the wild-type virus is usually prone to immune escape due to the relatively high mutation rate of biological proteins. Therefore, designing a broad-spectrum hRSV vaccine is essential to provide extensive protection against multiple viral variants. Using a consensus sequence approach, we designed a broad-spectrum T-cell epitope vaccine composed of 385 amino acids, consisting of 12 CTLs and 5 HTLs from the fusion protein and glycoprotein. The designed multi-epitope vaccine was expected to have non-allergenicity, high population coverage, strong antigenicity and immunogenicity, appropriate physical and chemical properties, and high solubility. Meanwhile, the structure of the vaccine had a high similarity to that of the natural virus. In addition, through structural biology analysis, the constructed vaccine achieved robust structural compactness and binding stability. Computer-generated immunological simulations indicated that the vaccine could elicit realistic immune responses in humans. The designed vaccine showed good binding affinity and molecular and immune simulation. In conclusion, the broad-spectrum hRSV vaccine could be an excellent candidate for preventing hRSV infection. The employed prediction pipeline was proved to be an efficient method for screening immunogenic epitopes of additional pathogens.

Comprehensive immunoinformatics and bioinformatics strategies for designing a multi-epitope based vaccine targeting structural proteins of Nipah virus

Background

Nipah virus (NiV) is characterized by recurring outbreaks and causes severe neurological impact, leading to increased mortality rates. Despite the severity of the disease, there is no proven post-exposure treatment available, emphasizing the critical need for the development of an effective vaccine.

Objective

This study was aimed at designing a multi-epitope based vaccine candidate based on an in-silico approach.

Methods

NiV's Structural proteins were screened for B and T-cell epitopes, assessing characteristics like antigenicity, immunogenicity, allergenicity, and toxicity. Two vaccine constructs (NiV_1 & 2) were designed using different adjuvants (Cholera toxin and Beta-defensin 3) and linkers and their predicted 3D structures were evaluated for interaction with Toll-Like Receptor TLR-3 using docking and molecular dynamics (MD) simulation studies. Finally, The potential expression of the vaccine construct in Escherichia coli (E. coli.) was verified by cloning it into the PET28a (+) vector and immune simulations were undertaken.

Results

The study identified 30 conserved, antigenic, immunogenic, non-allergenic, and non-toxic epitopes with a broad population coverage. Based on the stability of vaccine construct in MD simulations results, NiV_1 was considered for further analysis. In-silico immune simulations of NiV_1 indicated a substantial immunogenic response. Moreover, codon optimization and in-silico cloning validated the expressions of designed vaccine construct NiV_1 in E. coli.

Conclusion

The findings indicate that the NiV_1 vaccine construct has the potential to elicit both cellular and humoral immune responses. Additional in vitro and in vivo investigations are required to validate the computational observations.

Identification of three novel B cell epitopes targeting the bovine viral diarrhea virus NS3 protein for use in diagnostics and vaccine development

Bovine viral diarrhea virus (BVDV) is a major pathogen in cattle herds, widely distributed across the globe and causing significant economic losses to the cattle industry. The nonstructural protein NS3 is highly conserved across BVDV subtypes. Identifying and screening epitopes on BVDV NS3 is crucial for developing sensitive, specific diagnostic tools. In this study, we obtained three monoclonal antibodies (mAbs) against the NS3 protein: 2F7, 3E8, and 4D6. Three novel linear B-cell epitope 100EYG102, 384FLDIA388, and 100EYGVK104 were identified through reactions of these mAbs with a series of continuous-truncated peptides and one of which a rare three-amino-acid B-cell epitope 100EYG102. Critical amino acid residues were further characterized through alanine (A)-scanning mutagenesis. Sequence alignment revealed that 100EYG102 and 100EYGVK104 were highly conserved allowing mAbs 2F7 and 4D6 to recognize all BVDV subtypes. In contrast, 384FLDIA388 was specifically conserved in BVDV-1 and BVDV-3 enabling 3E8 mAb to differential diagnosis BVDV-2 from other BVDV subtypes. Additionally, preliminary diagnostic assays for BVDV were established by western blotting and peptide-based blocking ELISA. Moreover, we observed that these mAbs could inhibit the replication of BVDV. These findings provide a theoretical foundation for developing of therapeutic strategies for nonstructural protein and accurate diagnostic procedures.

Design and evaluation of potent multiepitope broad spectrum DNA and protein vaccine candidates against leptospirosis

Leptospirosis is a widespread zoonotic disease that causes severe health complications with no approved vaccine which provide broad range protection. In this study, we have focused on LruC protein from the outer membrane of Leptospira spp. LruC protein has been considered as promising target for vaccine due to its immunogenicity and conservancy. We have identified total 13 conserved B-cell, CTL, and HTL epitopes from 22 different pathogenic Leptospira species and serovars, which were linked with 4 linkers and 3 adjuvants (HBHA, CTB, TLR4) to design 36 multiepitope vaccine constructs to study the effect of different components on vaccine effectiveness. The antigenicity, immunogenicity, and non-allergenicity of the constructs were confirmed through computational analyses. Physico-chemical properties, secondary structure, and tertiary models of the vaccine constructs were predicted and validated. Molecular docking studies were conducted with Toll-like receptors (TLR2, TLR4) to assess binding affinity, identifying three top vaccine candidates (HBHA-construct 6, CTB-construct 9, and TLR4-construct 12) for further investigation. Further, these candidates were successfully cloned into pVAX1 and pET30a vectors to prepare DNA and protein vaccines, respectively. Moreover, these multiepitope vaccines were tested in mice models to assess its immunogenicity. ELISA performed with antisera against vaccine antigen, as well as crude extract of pathogenic Leptospira species showed significant IgG responses, particularly in protein vaccines. Flow cytometry revealed increased IFN-γ producing CD4+ and CD8+ T cells, especially in the TLR4-adjuvanted vaccine groups. The microscopic agglutination test further confirmed the specificity of the antibody response to Leptospira serovars. Overall, this study demonstrates the potential of these multiepitope vaccine constructs in eliciting a robust immune response, laying the foundation for future challenge study and preclinical evaluation.

Targeting immunogenic proteins of Zika virus for the prediction of immunoinformatics-driven circular mRNA vaccine model

Zika virus (ZIKV) is an arbovirus with single-stranded RNA that has become a major health concern worldwide, particularly in tropical and subtropical areas where it is transmitted by Aedes aegypti mosquitoes. Infection can leads to severe neurological complications, including microcephaly in infants and Guillain-Barré syndrome in adults. Due to the absence of a licensed vaccine, the current study was conceived to design a novel circular mRNA vaccine, capable of inducing an effective immune response by targeting the ZIKV proteins. Total 26 top-ranked epitopes (IC50 ≤ 100 nM) were prioritized from the conserved regions of ZIKV proteins. A multi-epitope construct was designed by incorporation of prioritized epitopes and β-defensin III adjuvant sequences to enhance immune activation. Molecular docking analysis revealed significant molecular interactions between the designed vaccine molecule and Toll-like receptors (TLRs), predicting an effective immune capability of the model vaccine. Molecular dynamics simulation validated the molecular and structural stability of the vaccine structure under physiological conditions. Immune simulations analysis predicted that the vaccine molecule could boost antibody production and is capable of achieving a global population coverage of 93.22%. Additionally, the circular mRNA vaccine design exhibited stability, with a minimum free energy (MFE) of - 1515.60 kcal/mol and a secondary centroid structure of - 1181.42 kcal/mol, speculating a resilient vaccine framework for potential ZIKV immunity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00362-0.

Bioinformatics analysis, immunogenicity, and therapeutic efficacy evaluation of a novel multi-stage, multi-epitope DNA vaccine for tuberculosis

BACKGROUND

The global tuberculosis (TB) epidemic remains severe. We aimed to develop a therapeutic DNA vaccine as an adjunct to TB treatment to improve efficacy.

METHODS

The W545 DNA vaccine was constructed using the M. tuberculosis (MTB) antigens Ag85A and Rv1419, integrated with epitopes from the Ag85B, Rv3407, and Rv2628. Bioinformatics tools were used to predict and analyze the physicochemical properties, structure modelling and molecular docking, epitopes (HTL, CTL, and B-cell), safety, population coverage, and simulated immunization of the W545 vaccine protein. Animal studies were then performed to evaluate the vaccine's immunogenicity by measuring Th1-type immune responses (IFN-γ, IL-2) and IgG antibody levels, as well as its therapeutic efficacy in reducing lung inflammation and pathological damage in a murine TB model.

RESULTS

The vaccine protein is a 70 kDa hydrophilic protein with a half-life of 30 h, an instability index of 43.33, and strong affinity to Toll-like receptor (TLR) 2 and TLR4. It contains 397 helper T cell (HTL) epitopes, 248 cytotoxic T cell (CTL) epitopes, and 27 B cell epitopes, with broad population coverage (global: 99.7 %, Chinese: 97.6 %). The W545 vaccine significantly induced a Th1-type immune response, producing high levels of IFN-γ (5.38 pg/ml ± 0.89 pg/ml) and IgG antibodies (OD450: 0.13 ± 0.06). It also reduced the lung weight index, tissue lesions, and severity in the murine TB model.

CONCLUSION

The W545 DNA vaccine effectively induces a Th1-type immune response, alleviates pathological damage, and demonstrates potential as an immunotherapeutic agent. Bioinformatics analysis provides valuable guidance for vaccine design and optimization.

In Silico-Based Investigation of the Immunogenicity and Biochemical Attributes of Toxoplasma gondii Apical Membrane Antigen 1 (TgAMA1)

Background: Apical membrane antigen 1 (AMA1) is a highly conserved microneme protein in apicomplexan parasites. In this study, immunoinformatics tools and in silico protein structure prediction were used to characterize the structure, physicochemical properties, posttranslational modification sites, immunogenic epitopes, allergenicity, and immune simulation of the Toxoplasma gondii AMA1 (TgAMA1) protein. Methods: A comprehensive analysis was performed using multiple bioinformatics web servers to analyze the antigenicity, physicochemical features, secondary and tertiary structures, B and T cell epitopes, and in silico immune simulation of TgAMA1. Results: The analysis revealed that the AMA1 protein consists of 569 amino acid residues and has a molecular weight of approximately 63 kDa. The grand average of hydropathicity (GRAVY) was -0.531 and the aliphatic index was calculated as 64.62. Based on the GOR IV server, TgAMA1 contained 20.21% alpha helices, 58.52% random coils, and 21.27% extended strands. The Ramachandran plot of the refined model revealed that over 97% of the residues were located in the favored region. The AMA1 protein was highly immunogenic and nonallergenic in nature. In silico immune simulation using the C-ImmSim server suggested that three doses of TgAMA1 would elicit potent humoral and cell-mediated immune responses. Conclusion: These findings provide valuable insights for further in vitro and in vivo investigations of TgAMA1's potential as a vaccine candidate against toxoplasmosis.

A modular, Galaxy-based immunopeptidogenomic (iPepGen) analysis pipeline for discovery, verification, and prioritization of candidate cancer neoantigen peptides

Background

Characterizing peptide antigens, processed from tumor-specific proteoforms, and bound to the major histocompatibility complex, is critical for immuno-oncology research. Next-generation sequencing predicts candidate neoantigen peptides derived from DNA mutations and/or RNA transcripts coding proteoform sequences that differ from the reference proteome. Mass spectrometry (MS)-based immunopeptidomics identifies predicted, MHC-bound neoantigen peptides and other tumor antigens. This "immunopeptidogenomic" approach requires multi-omic software integration, challenging researchers with limited bioinformatics expertise and resources. As a solution, we developed the immunopeptidogenomic (iPepGen) pipeline in the Galaxy ecosystem. iPepGen is composed of five core workflow modules, available via publicly accessible, scalable Galaxy instances, accompanied by training resources to empower community adoption.

Findings

Using representative multi-omic data from malignant peripheral nerve sheath tumors, we demonstrate the operation of iPepGen modules with these functions: 1) Predict neoantigen candidates from sequencing data and generate customized protein sequence databases, including reference and non-reference neoantigen candidate sequences; 2) Discover neoantigen peptide candidates by sequence database searching of tandem mass spectrometry (MS/MS) immunopeptidomics data; 3) Verify discovered peptide candidates through a secondary peptide-centric evaluation method against the MS/MS dataset; 4) Visualize and classify the nature of verified neoantigen peptides encoded by the genome and/or transcriptome; 5) Prioritize neoantigens for further exploration and empirical validation.

Conclusions

We demonstrate the effectiveness of the iPepGen pipeline for candidate neoantigen discovery and characterization. With tools, workflows, and training resources available in the open Galaxy ecosystem, iPepGen should provide cancer researchers with a flexible and accessible informatics resource tailored to accelerating immuno-oncology studies.

Immunoinformatics method to design universal multi-epitope nanoparticle vaccine for TGEV S protein

Porcine transmissible enteritis virus (TGEV) is a fatal pathogen affecting newborn piglets, presenting a significant challenge to global intensive pig farming biosecurity due to its ongoing mutation. There is still a lack of effective vaccines to combat this virus, Vaccination has long been considered the most effective way to overcome infectious diseases, however, traditional vaccines cannot be brought to market quickly enough to deal with rapid mutations and emerging viruses. Therefore, this study addresses this gap by using immunoinformatics methods and ferritin nanoparticle delivery system to build a platform for rapid research and development of porcine coronavirus vaccine, designing a candidate nanoparticle vaccine that targets the TGEV S protein. To this end, multiple servers and strict screening criteria were used to analyze the S protein, and 3 CTL dominant epitopes, 3 Th dominant epitopes, and 6 B cell dominant epitopes were obtained. The candidate nanoparticle vaccine was constructed by incorporating ferritin sequences through the C-terminus after they were tandemly linked in a certain order using a flexible linker. Further experimental analyses showed that the designed candidate nanoparticle vaccine possessed relatively high antigenicity, immunogenicity, non-allergenicity, non-transmembrane proteins, suitable physicochemical properties, and high solubility upon overexpression. Tertiary structure modeling and disulfide engineering ensured conformational similarity to natural proteins and high stability. Additionally, the model predicted 6 Linear Epitopes and 6 Discontinuous Epitopes for B-cell conformational epitopes. Docking with TLR-3 and TLR-4 molecules shows a large number of interacting hydrogen-bonded amino acid residues and hydrophobically interacting amino acid residues. Immunomimetic assays show high levels of immunoglobulin, T-lymphocyte and IFN-γ secretion and may elicit specific immune responses. Through computerized cloning, the candidate nanoparticle vaccine can be efficiently expressed in the E. coli K12 expression system, aligning with future large-scale industrial production strategies. Overall, the results indicate that the constructed candidate nanoparticle vaccine can be effectively expressed and may be able to induce a strong immune response, which is expected to be an ideal candidate vaccine against TGEV.

A comprehensive strategy for the development of a multi-epitope vaccine targeting Treponema pallidum, utilizing heat shock proteins, encompassing the entire process from vaccine design to in vitro evaluation of immunogenicity

Background

Treponema pallidum, the causative spirochete of syphilis, is primarily transmitted through sexual contact and has emerged as a significant global health concern. To address this issue, enhancing diagnostic capabilities, strengthening public health interventions, and developing a safe and effective vaccine are critical strategies.

Objective

This study employed an immunoinformatics approach to design a vaccine with high immunogenic potential, targeting the heat shock proteins of T. pallidum.

Methods

Based on heat shock proteins of T. pallidum, we predicted B-cell, CTL, and HTL epitopes and all the selected epitopes were linked to construct a multi-epitope vaccine. Antigenicity, toxicity, and allergenicity of epitopes were checked by VaxiJen 2.0, AllerTOP v2.0, and ToxinPred servers. After constructing the multi-epitope vaccine, we subsequently predicted its secondary and tertiary protein structures. After refining and validating the modeled structure, we utilized advanced computational approaches, including molecular docking and dynamic simulations, to evaluate the binding affinity, compatibility, and stability of the vaccine-adjuvant complexes. Eventually, in silico cloning was conducted to optimize protein expression and production.

Results

The multi-epitope subunit vaccine we developed was constructed by seven cytotoxic T lymphocyte epitopes, five helper T lymphocyte epitopes, four B cell epitopes, and adjuvant β-defensin. An adjuvant was used to enhance immune responses, all of which were linked to one another using GPGPG, AAY, and KK linkers, respectively. The population coverage of the designed vaccine was 94.41% worldwide. Molecular docking and MD simulations indicated strong binding interactions with TLR1/2, TLR-2 and TLR-4 in a stable vaccine-receptor complex. The final designed vaccine, composed of 502 amino acids, theoretically exhibits high antigenicity and immunity, capable of inducing both humoral and cellular immune responses.

Conclusion

The vaccine developed in this study theoretically represents a safe and potent multi-epitope prophylactic strategy against T. pallidum, subject to further experimental validation to ascertain its actual protective efficacy.

The immunopeptidome of colon cancer cells treated with topoisomerase inhibiting drug reveals differential as well as common endogenous protein sampling and display of MHC I-associated peptides

Immunotherapy options for microsatellite stable (MSS) colorectal cancer are currently very limited. The lack of detectably unique or altered immunogens in the tumor microenvironment may be a factor. Radiation and chemotherapy may enhance immunotherapy by increasing cancer cell visibility through Major Histocompatibility Complex I (MHC I) expression. To investigate this, we treated MSS and microsatellite-instable (MSI) colon cancer cells with a topoisomerase inhibitor and analyzed MHC I-associated peptides. Treatment increased peptide numbers by 5% in RKO (MSI) cells and 83% in SW620 (MSS) cells, with 40-50% of peptides being exclusive to treatment. Additionally, clustering analysis revealed a set of peptides with uniquely conserved residues displayed only in treated MSS SW620 cells. Gene Ontology analysis of MHC I-displayed proteins revealed a treatment-induced increase in extracellular vesicle- and nuclear-derived proteins, alongside reduced cytosolic protein sampling. Overall, we present evidence for treatment-inducible differential display of peptides, some of which may affect interactions and functions of immune cells. Given the multitude of factors that modulate the effects of increased MHC I expression and associated peptides, further studies are needed to elucidate the pathophysiological implications of these changes.

Designing a multi-epitope vaccine targeting the HIV-1 subtype CRF01_AE in Indonesia

HIV has markedly affected millions of people globally, with antiretroviral therapy (ART) transforming acquired immunodeficiency syndrome from a fatal disease to a manageable chronic condition. However, global disparities in ART access persist, particularly in low- and middle-income countries, highlighting the urgent need for affordable HIV vaccines. In this study, we investigated the potential development of a multi-epitope vaccine (MEV) targeting the HIV subtype CRF01_AE, which is prevalent in Indonesia. Using likelihood-based evolutionary inference based on site rates to analyze mutation rates, we identified the Pol and Env proteins as optimal targets. Nine T cell epitopes (five cytotoxic and four helper) were selected based on HLA binding affinity, conservation, antigenicity, and predicted immunogenicity, achieving broad population coverage (∼95 % globally and 99.58 % in Indonesia). The MEV construct incorporated epitopes conjugated to a Vibrio cholerae toxin B subunit adjuvant and a B cell epitope known to induce broadly neutralizing antibodies. In silico characterization, including physicochemical analysis, structural modeling (validated using ProSA-web and Ramachandran plot analysis), and protein-protein docking simulations (using HADDOCK and PRODIGY), demonstrated favorable properties, stable conformation, and high-affinity interaction with antibody fragments (ΔGbind = -10.8 kcal/mol). Molecular dynamics simulations confirmed the formation of a stable complex. Immunogenicity tests revealed a strong antibody and cytokine response. These findings suggest that this MEV construct is a promising and affordable HIV-1 vaccine candidate that warrants further validation.

Design of a multi-epitope vaccine (vme-VAC/MST-1) against cholera and vibriosis based on reverse vaccinology and immunoinformatics approaches

Vibriosis and cholera are serious diseases distributed worldwide and caused by six marine bacteria of the Vibrio genus. Thousands of deaths occur each year due to these illnesses, necessitating the development of new preventive measures. Presently, the existing cholera vaccine demonstrates an effectiveness of approximately 60%. Here we describe a new multi-epitope vaccine, 'vme-VAC/MST-1' based on vaccine targets identified by reverse vaccinology and epitopes predicted by immunoinformatics, two currently effective tools for predicting new vaccines for bacterial pathogens. The vaccine was designed to combat vibriosis and cholera by incorporating epitopes predicted for CTL, HTL, and B cells. These epitopes were identified from six vaccine targets revealed through subtractive genomics, combined with reverse vaccinology, and were further filtered using immunoinformatics approaches based on their predicted immunogenicity. To construct the vaccine, 28 epitopes (24 CTL/B and 4 HTL/B) were linked to the sequence of the cholera toxin B subunit adjuvant. In silico analyses indicate that the resulting immunogen is stable, soluble, non-toxic, and non-allergenic. Furthermore, it exhibits no homology to the host and demonstrates a strong capacity to elicit innate, B-cell, and T-cell immune responses. Our analysis suggests that it is likely to elicit immune reactions mediated through the TLR5 pathway, as evidenced by the molecular docking of the vaccine with the receptor, which revealed high affinity and a favorable reaction. Thus, vme-VAC/MST-1 is predicted to be a safe and effective solution against pathogenic Vibrio spp. However, further experimental analyses are required to measure the vaccine's effects In vivo.Communicated by Ramaswamy H. Sarma.

Epitope-Based Vaccines: The Next Generation of Promising Vaccines Against Bacterial Infection

The increasing resistance of bacteria to antibiotics has underscored the need for new drugs or vaccines to prevent bacterial infections. Reducing multidrug resistance is a key objective of the WHO's One Health initiative. Epitopes, the key parts of antigen molecules that determine their specificity, directly stimulate the body to produce specific humoral and/or cellular immune responses. Epitope-based vaccines, which combine dominant epitopes in a rational manner, induce a more efficient and specific immune response than the original antigen. While these vaccines face significant challenges, such as epitope escape or low immunogenicity, they offer advantages including minimal adverse reactions, improved efficacy, and optimized protection. As a result, epitope-based vaccines are considered a promising next-generation approach to combating bacterial infections. This review summarizes the latest advancements, challenges, and future prospects of epitope-based vaccines targeting bacteria, with a focus on their development workflow and application in antibiotic-resistant pathogens with high mortality rates, including Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. The goal of this review is to provide insights into next-generation vaccination strategies to combat bacterial infections associated with antibiotic resistance and high mortality rates.

Vaccine Development T-cell (MHC-I) Epitopes Identification Against the Indian HCV Genotype: An Approach Based on Immunoinformatic

Hepatitis C virus (HCV) infects approximately 58 million individuals worldwide, often progressing to chronic liver disease, cirrhosis, and hepatocellular carcinoma. The viral envelope glycoproteins E1 and E2 are critical for HCV entry and serve as primary targets for neutralizing antibodies. Recent advancements in cryo-electron tomography have provided high-resolution structures (3.5 Å) of the E1E2 heterodimer, revealing interactions between the E1 and E2 ectodomains, as well as neutralizing antibody complexes (e.g., AR4A, AT1209, IGH505). This structural information facilitates the design of a synthetic peptide vaccine targeting conserved E1 and E2 regions. We suggest developing a vaccine tailored to the HLA-A*24:02 allele, the most prevalent in the Indian population. Epitope candidates will be screened using immunoinformatics tools, incorporating epitopes derived from epitope mapping with 7t6x protein structure modeling. Molecular docking studies will identify high-affinity interactions with human MHC-Class I alleles, using tools such as AutoDock and HADDOCK. GROMACS molecular dynamics simulations will assess peptide-HLA binding stability and dynamics. Among ten screened epitopes, KWEYVVLLF and QWQVLPCSF emerged as the most promising based on their toxicity profiles, conservation, and docking scores with HLA-A*24:02 (- 9.3 kcal/mol for KWEYVVLLF and - 225.34 kcal/mol for QWQVLPCSF). Molecular dynamics simulations indicated that the KWEY segment of KWEYVVLLF underwent structural changes, while the VVLLF region maintained stable binding to Chain A, suggesting immunogenic potential. These epitopes represent strong candidates for T-cell-based vaccines, and the reverse vaccinology approach, supported by computational tools, offers a population-specific strategy for HCV vaccine development, advancing precision immunotherapy.

Comprehensive epitope mutational scan database enables accurate T cell receptor cross-reactivity prediction

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.

Computational identification of B and T-cell epitopes for designing a multi-epitope vaccine against SARS-CoV-2 spike glycoprotein

Although the peak of the COVID-19 pandemic has passed, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose a significant global threat and remains a public health concern. Given the ongoing risk and the substantial loss of life caused by the virus, continuous research into vaccine development is essential. This study employs immunoinformatics approaches to identify T-cell and B-cell epitopes for designing a multi-epitope peptide vaccine candidate targeting the Omicron variant. The proposed vaccine construct comprises 1435 amino acids, including eight linear B lymphocyte, seven cytotoxic T lymphocyte, and five helper T lymphocyte epitopes, along with appropriate adjuvants and linkers. The evaluation of the vaccine revealed high antigenicity, non-allergenicity, non-toxicity, and favorable physicochemical properties. To further assess its efficacy, molecular docking studies were performed to investigate interactions between the vaccine and key immune components, including Toll-like receptors and major histocompatibility complex molecules. Stability of these interactions was confirmed using molecular dynamics simulations in triplicate, conducted over 100 ns using GROMACS 2023 to compute key metrics, such as root mean square deviation, root mean square fluctuation, solvent-accessible surface area, and radius of gyration. The results demonstrate that the multi-epitope vaccine has the potential to elicit strong immune responses against the Omicron variant, providing a promising foundation for further experimental validation and clinical development in COVID-19 vaccine research.

Toxoplasma gondii (GRA10): A Closer Glance Into Immunogenic and Biochemical Characteristics Using In Silico Approach

Toxoplasma gondii, an obligate, intracellular, protozoan parasite, is known to infect a wide range of warm-blooded animals, including livestock, marine mammals and humans leading to the development of toxoplasmosis. The dense granule antigens (GRAs) have garnered crucial role in parasite survival, virulence and the formation of the parasitophorous vacuole. The study focusing on the GRA10 protein of T. gondii aims to elucidate its features further to support its potential inclusion in future vaccine formulations. The present study provided an exhaustive elucidation of the key characteristics of the GRA10 protein, encompassing its presence of a transmembrane domain, physico-chemical properties, subcellular localization, potential epitopes recognised by B-cells and T-cells, secondary and tertiary structures, as well as other significant attributes of this protein. The results indicated that the GRA10 protein possesses a total of 192 possible post-translational modification sites, with no transmembrane domains being detected within its structure. In terms of secondary structure, the GRA10 protein is composed of 27.74% alpha-helix, 13.53% extended strand and 58.72% random coil elements. Additionally, various potential B- and T-cell epitopes were pinpointed for the GRA10 protein, suggesting its immunogenic properties. The assessment of antigenicity and allergenicity further confirmed that GRA10 is immunogenic but non-allergenic, making it a promising candidate for further study. Furthermore, the induction of IFN-γ and IL-4 highlighted the ability of related MHC-II molecules to interact with GRA10, indicating its potential role in immune responses. These findings shed light on the multifaceted nature of the GRA10 protein and its significance in immunological processes. The study presented crucial fundamental and theoretical information regarding GRA10 in order to facilitate the creation of a potent vaccine against both acute and chronic toxoplasmosis, warranting additional in vivo examinations.

Development of a novel multi-epitope mRNA vaccine candidate to combat SFTSV pandemic

Severe Fever with Thrombocytopenia Syndrome virus (SFTSV) is a novel identified pathogen, despite two decades of research on SFTSV, the potential widespread threats pose a significant challenge for researchers in developing new treatment and prevention methods. In this present, we have developed a multi-epitope mRNA vaccine for SFTSV and valid it with in silico methods. We screened 9 immunodominant epitopes for cytotoxic T cells (CTL), 7 for helper T cells (HTL), and 8 for Linear B-cell (LBL) based on promising candidate protein Gn, Gc, Np, and NSs. All predicted epitopes demonstrated strong antigenicity without any potential harm to humans. Additionally, the high conservancy is required to cover different strains. All epitopes as well as adjuvants were constructed into a final vaccine, which was further assesd by calculating of physicochemical properties. Then, we docked the vaccine protein with immune receptors and analyzed the complexes with dynamic simulations to evaluate its affinity to receptors. Finally, the vaccine sequence was constructed into a mRNA sequence. The constructed vaccine is a potential candidate for combating SFTSV by stimulating protective humoral and cellular immune responses.

Leveraging immunoinformatics for developing a multi-epitope subunit vaccine against Helicobacter pylori and Fusobacterium nucleatum

Gastric ulcers caused by Helicobacter pylori and Fusobacterium nucleatum remain a significant global health concern without an established vaccine. In this study, we utilized immunoinformatics methods to design a multi-epitope vaccine targeting these pathogens. Outer membrane proteins from H. pylori and F. nucleatum were scrutinized to identify high antigenic T-cell and B-cell epitopes. The resulting vaccine comprised carefully analyzed and evaluated epitopes, including cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocytes epitopes. This vaccine exhibited notable antigenicity, suitable immunogenicity, and demonstrated non-allergenicity and non-toxicity. It displayed favorable physiochemical characteristics and high solubility. In interaction studies, the vaccine exhibited robust binding to toll-like receptor 4 (TLR4). Molecular dynamic simulations revealed cohesive structural integrity and stable attachment. Codon adaptation utilizing Escherichia coli K12 host yielded a vaccine with elevated Codon Adaptation Index (CAI) and optimal GC content. In silico cloning into the pET28+(a) vector demonstrated efficient expression. Immune simulations indicated the vaccine's ability to initiate immune responses in humans, mirroring real-life scenarios. Based on these comprehensive findings, we propose that our developed vaccine has the potential to confer robust immunity against H. pylori and F. nucleatum infections.Communicated by Ramaswamy H. Sarma.

In silico design of multi-epitope vaccine candidate based on structural proteins of porcine reproductive and respiratory syndrome virus

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most common respiratory disease-causing viral agents. Swine infected with PRRSV exhibit severe respiratory symptoms and reproductive failure, leading to significant economic losses. To address this issue, inactivated and live-attenuated vaccines have been developed. However, the current commercially available PRRSV vaccines do not confer sufficient protection or have safety issues. The use of epitope-based subunit vaccines reduce safety risks by including only specific immunogenic portions of the antigens. To enhance immune protection, this study targeted multiple structural proteins of PRRSV, including GP2, GP3, GP4, GP5, membrane (M), envelope (E), GP5a, and nucleocapsid (N), to enable the discovery of novel epitopes. Thus, a reverse vaccinology approach was utilized to design a multi-epitope subunit vaccine construct against PRRSV. Using different tools, seven linear B cell, seven cytotoxic T cell, and three helper T cell epitopes were predicted to be safe, antigenic, and immunogenic. These epitopes were linked together, and a protein adjuvant, heparin-binding hemagglutinin, was added to increase the vaccine's immunogenicity. The construct was then docked to Toll-like receptor 4 (TLR4) to assess its ability to initiate the innate immune response. The final vaccine construct was determined to be antigenic, stable, non-allergenic, and soluble. Furthermore, the vaccine demonstrated stable binding to TLR4 based on coarse-grained and atomistic molecular dynamics simulations. Finally, the immune simulation of the vaccine construct showed a robust immune response against PRRSV. In this study, a candidate vaccine construct was successfully designed as a potential strategy against PRRSV.

HCV immunodominant peptide mapping reveals unique HLA-A*02-restricted signatures: insights for CD8+ T-cell-based vaccines and immunotherapies

Several barriers for the development of an HCV vaccine still exist, including the genetic diversity of the virus, and the shortage of assessable models for in vitro and in vivo assays. Therefore, in this study, HCV epitope mapping was performed for 59 polyprotein sequences from 7 HCV genotypes. Around 2,880 peptides were considered epitopes for CD8+ T cells. The peptide induction of cytokines from Th1 and/or Th2 axes of the cellular immune response was assessed, indicating a tendency for Th2 axis. In vitro evaluation was performed using peptide microarray and a recombinant HLA-A*02:01 molecule. A total of 615 peptides of high reactivity to HLA-A*02:01 were identified, with predominance of leucine and tryptophan residues, highlighting their importance for TCR-epitope binding and CD8+ T activation. Finally, HCV-derived peptide patterns restricted to HLA-A2*02:01 observed in this study provide important information for the development of a multi-epitope-based pan-genotypic vaccine against the virus.

Discovery of conserved peptide-MHC epitopes for directly alloreactive CD8+ T cells

Mass Spectrometry allied with in-vivo generation of activated alloreactive T cell populations and tetramer screening facilitates the identification of endogenous peptides that are directly recognised in complex with allogeneic Major Histocompatibility class I (MHC I) molecules by alloreactive CD8+ T cells. We had previously used this approach for the discovery of immunogenic self-peptides presented by the allomorph H-2Kb (Kb). In this study, we identified 22 highly immunogenic self-peptides presented by H-2Kd (Kd). Peptide abundance across skin, spleen and liver samples (estimated as the product of the spectral intensity obtained for these samples) was the principal factor influencing recognition of peptide-Kd epitopes. Predicted binding affinity (BA score) and overall peptide hydrophobicity were also independently correlated with immunogenicity, while there was no significant correlation between the IEDB immunogenicity score and the proportion of T cells recognising a given epitope. Eight peptide-Kd epitopes were selected for inclusion in a tetramer panel to detect directly alloreactive CD8+ T cells. This panel bound over 30% of activated alloreactive CD8+ T cells after a prime-boost against Kd. Moreover, the panel identified alloreactive CD8+ T cells within the graft infiltrate, spleen and draining lymph node during rejection of a Kd-bearing heart graft. In conclusion, small animal studies have demonstrated the feasibility of high-throughput approaches for the discovery of pMHC epitopes recognised by directly alloreactive T cells. Translating this approach to the human setting is achievable and will yield both critical insights into the fundamental basis of alloreactivity and powerful tools for immune monitoring in transplantation.

Manufacturing and Financial Evaluation of Peptide-Based Neoantigen Cancer Vaccines for Triple-Negative Breast Cancer in the United Kingdom: Opportunities and Challenges

This review evaluates the financial burden of current treatments for triple-negative breast cancer (TNBC) and projects potential financial scenarios to assess the feasibility of introducing a peptide-based neoantigen cancer vaccine (NCV) targeting the disease, using the UK as a healthcare system model. TNBC, the most aggressive breast cancer subtype, is associated with poor prognosis, worsened by the lack of personalised treatment options. Neoantigen cancer vaccine therapies present a personalised alternative with the potential to enhance T-cell responses independently of genetic factors, unlike approved immunotherapies for TNBC. Through a systematic literature review, the underlying science and manufacturing processes of NCVs are explored, the direct medical costs of existing TNBC treatments are enumerated, and two contrasting pricing scenarios for NCV clinical adoption are evaluated. The findings indicate that limited immunogenicity is the main scientific barrier to NCV clinical advancement, alongside production inefficiencies. Financial analysis shows that the UK spends approximately GBP 230 million annually on TNBC treatments, ranging from GBP 2200 to GBP 54,000 per patient. A best-case pricing model involving government-sponsored NCV therapy appears financially viable, while a worst-case, privately funded model exceeds the National Institute for Health and Care Excellence (NICE) cost thresholds. This study concludes that while NCVs show potential clinical benefits for TNBC, uncertainties about their standalone efficacy make their widespread adoption in the UK unlikely without further clinical research.

Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens

Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein-Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development.

Leveraging mRNA technology for antigen based immuno-oncology therapies

The application of messenger RNA (mRNA) technology in antigen-based immuno-oncology therapies represents a significant advancement in cancer treatment. Cancer vaccines are an effective combinatorial partner to sensitize the host immune system to the tumor and boost the efficacy of immune therapies. Selecting suitable tumor antigens is the key step to devising effective vaccinations and amplifying the immune response. Tumor neoantigens are de novo epitopes derived from somatic mutations, avoiding T-cell central tolerance of self-epitopes and inducing immune responses to tumors. The identification and prioritization of patient-specific tumor neoantigens are based on advanced computational algorithms taking advantage of the profiling with next-generation sequencing considering factors involved in human leukocyte antigen (HLA)-peptide-T-cell receptor (TCR) complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. This review discusses the development and clinical application of mRNA vaccines in oncology, with a particular focus on recent clinical trials and the computational workflows and methodologies for identifying both shared and individual antigens. While this review centers on therapeutic mRNA vaccines targeting existing tumors, it does not cover preventative vaccines. Preclinical experimental validations are crucial in cancer vaccine development, but we emphasize the computational approaches that facilitate neoantigen selection and design, highlighting their role in advancing mRNA vaccine development. The versatility and rapid development potential of mRNA make it an ideal platform for personalized neoantigen immunotherapy. We explore various strategies for antigen target identification, including tumor-associated and tumor-specific antigens and the computational tools used to predict epitopes capable of eliciting strong immune responses. We address key design considerations for enhancing the immunogenicity and stability of mRNA vaccines, as well as emerging trends and challenges in the field. This comprehensive overview highlights the therapeutic potential of mRNA-based cancer vaccines and underscores ongoing research efforts aimed at optimizing these therapies for improved clinical outcomes.

Orthogonalized human protease control of secreted signals

Synthetic circuits that regulate protein secretion in human cells could support cell-based therapies by enabling control over local environments. Although protein-level circuits enable such potential clinical applications, featuring orthogonality and compactness, their non-human origin poses a potential immunogenic risk. In this study, we developed Humanized Drug Induced Regulation of Engineered CyTokines (hDIRECT) as a platform to control cytokine activity exclusively using human-derived proteins. We sourced a specific human protease and its FDA-approved inhibitor. We engineered cytokines (IL-2, IL-6 and IL-10) whose activities can be activated and abrogated by proteolytic cleavage. We used species specificity and re-localization strategies to orthogonalize the cytokines and protease from the human context that they would be deployed in. hDIRECT should enable local cytokine activation to support a variety of cell-based therapies, such as muscle regeneration and cancer immunotherapy. Our work offers a proof of concept for the emerging appreciation of humanization in synthetic biology for human health.

Design of a Multiepitope Pan-Proteomic mRNA Vaccine Construct Against African Swine Fever Virus: A Reverse Vaccinology Approach

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly contagious disease with devastating effects on the global pig industry. This warrants the development of effective control strategies, such as vaccines. However, previously developed inactivated vaccines have proven ineffective, while live-attenuated vaccines carry inherent safety risks. The use of mRNA vaccines eliminates these risks offering a safe, cost-effective, and efficient vaccine strategy against ASFV. In this study, a reverse vaccinology approach was used to design a multiepitope pan-proteomic mRNA vaccine against ASFV. Various bioinformatics tools were employed to predict epitopes for cytotoxic T lymphocytes, helper T lymphocytes, and B lymphocytes. A 50S ribosomal L7/L12 protein adjuvant, 5' cap, poly(A) tail, signal peptide, and MHC-I-targeting domain were incorporated into the design using appropriate linkers to increase immunogenicity, stability, and recognition efficiency. The physicochemical properties of the final construct were evaluated, and docking analyses were done with Toll-like receptors (TLRs) 3, 4, and 7 to evaluate binding affinity. A molecular dynamics simulation was then performed to determine binding stability, while immune simulations evaluated host's immune response. Based on 100 ASFV proteomes, six epitopes that induce cytotoxic T-cell responses, five epitopes that induce helper T-cell responses, and four epitopes that induce antibody production were predicted. The designed vaccine construct was found to be nonallergenic, antigenic, and stable when bound to TLR4 while the binding pocket analyses of the vaccine construct to TLR3 and TLR7 indicate high translation efficiency. Immune simulations demonstrated successful induction of immune responses and generation of antigen-specific memory cells. In conclusion, this study introduces an mRNA vaccine construct as a potential disease control strategy against ASF for in vitro confirmation.

Engineering synthetic phosphorylation signaling networks in human cells

Protein phosphorylation signaling networks have a central role in how cells sense and respond to their environment. We engineered artificial phosphorylation networks in which reversible enzymatic phosphorylation cycles were assembled from modular protein domain parts and wired together to create synthetic phosphorylation circuits in human cells. Our design scheme enabled model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, and downstream connections can regulate gene expression. We engineered cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach that allows the design of signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Molecular dynamics simulation based prediction of T-cell epitopes for the production of effector molecules for liver cancer immunotherapy

Liver cancer is the sixth most frequent malignancy and the fourth major cause of deaths worldwide. The current treatments are only effective in early stages of cancer. To overcome the therapeutic challenges and exploration of immunotherapeutic options, broad spectral therapeutic vaccines could have significant impact. Based on immunoinformatic and integrated machine learning tools, we predicted the potential therapeutic vaccine candidates of liver cancer. In this study, machine learning and MD simulation-based approach are effectively used to design T-cell epitopes that aid the immune system against liver cancer. Antigenicity, molecular weight, subcellular localization and expression site predictions were used to shortlist liver cancer associated proteins including AMBP, CFB, CDHR5, VTN, APOBR, AFP, SERPINA1 and APOE. We predicted CD8+ T-cell epitopes of these proteins containing LGEGATEAE, LLYIGKDRK, EDIGTEADV, QVDAAMAGR, HLEARKKSK, HLCIRHEMT, LKLSKAVHK, EQGRVRAAT and CD4+ T-cell epitopes of VLGEGATEA, WVTKQLNEI, VEEDTKVNS, FTRINCQGK, WGILGREEA, LQDGEKIMS, VKFNKPFVF, VRAATVGSL. We observed the substantial physicochemical properties of these epitopes with a significant binding affinity with MHC molecules. A polyvalent construct of these epitopes was designed using suitable linkers and adjuvant indicated significant binding energy (>-10.5 kcal/mol) with MHC class-I and II molecule. Based on in silico cloning, we found the considerable compatibility of this polyvalent construct with the E. coli expression system and the efficiency of its translation in host. The system-level and machine learning based cross validations showed the possible effect of these T-cell epitopes as potential vaccine candidates for the treatment of liver cancer.

Analysis of Wilms' tumor protein 1 specific TCR repertoire in AML patients uncovers higher diversity in patients in remission than in relapsed

The Wilms' tumor protein 1 (WT1) is a well-known and prioritized tumor-associated antigen expressed in numerous solid and blood tumors. Its abundance and immunogenicity have led to the development of different WT1-specific immune therapies. The driving player in these therapies, the WT1-specific T-cell receptor (TCR) repertoire, has received much less attention. Importantly, T cells with high affinity against the WT1 self-antigen are normally eliminated after negative selection in the thymus and are thus rare in peripheral blood. Here, we developed computational models for the robust and fast identification of WT1-specific TCRs from TCR repertoire data. To this end, WT137-45 (WT1-37) and WT1126-134 (WT1-126)-specific T cells were isolated from WT1 peptide-stimulated blood of healthy individuals. The TCR repertoire from these WT1-specific T cells was sequenced and used to train a pattern recognition model for the identification of WT1-specific TCR patterns for the WT1-37 or WT1-126 epitopes. The resulting computational models were applied on an independent published dataset from acute myeloid leukemia (AML) patients, treated with hematopoietic stem cell transplantation, to track WT1-specific TCRs in silico. Several WT1-specific TCRs were found in AML patients. Subsequent clustering analysis of all repertoires indicated the presence of more diverse TCR patterns within the WT1-specific TCR repertoires of AML patients in complete remission in contrast to relapsing patients. We demonstrate the possibility of tracking WT1-37 and WT1-126-specific TCRs directly from TCR repertoire data using computational methods, eliminating the need for additional blood samples and experiments for the two studied WT1 epitopes.

The Evolving T Cell Receptor Recognition Code: The Rules Are More Like Guidelines

αβ T cell receptor (TCR) recognition of peptide-MHC complexes lies at the core of adaptive immunity, balancing specificity and cross-reactivity to facilitate effective antigen discrimination. Early structural studies established basic frameworks helpful for understanding and contextualizing TCR recognition and features such as peptide specificity and MHC restriction. However, the growing TCR structural database and studies launched from structural work continue to reveal exceptions to common assumptions and simplifications derived from earlier work. Here we explore our evolving understanding of TCR recognition, illustrating how structural and biophysical investigations regularly uncover complex phenomena that push against paradigms and expand our understanding of how TCRs bind to and discriminate between peptide/MHC complexes. We discuss the implications of these findings for basic, translational, and predictive immunology, including the challenges in accounting for the inherent adaptability, flexibility, and occasional biophysical sloppiness that characterize TCR recognition.

In silico analysis and structural vaccinology prediction of Toxoplasma gondii ROP41 gene via immunoinformatics methods as a vaccine candidate

INTRODUCTION

Toxoplasma gondii (T. gondii) infects all warm-blooded animals, including humans. Currently, no effective treatments exist to prevent the generation of chronic tissue cysts in infected hosts. Therefore, developing a vaccine to protect to deal with toxoplasmosis is a promising strategy, as a single immunization could provide lifelong protective immunity. Rhoptry proteins (ROPs) play a vital role for the parasite's survival within host cells and perform critical functions during different phases of parasite invasion. Little is known about ROP41 gene. Nevertheless, Understanding the characteristics of ROP41 will enhance diagnostic and vaccine research.

MATERIALS AND METHODS

The current article provides a comprehensive analysis of the essential components of the ROP41 protein, including its transmembrane domain, physico-chemical properties, subcellular location, tertiary and secondary structures, and potential T- and B-cell epitopes. These features were determined by many bioinformatics approaches to identify possible epitopes for developing a highly effective vaccine.

RESULTS

ROP41 protein showed 36 possible post-translational modification regions. The ROP41 protein secondary structure contains 17.35 % extended strand, 33.47 % alpha-helix, and 49.18 % random coil. Also, ROP41 showed many possible B- and T-cell epitopes. According to the Ramachandran plot, 90.78 % of amino acid residues had been placed in favored, 3.28 % in outlier, and 5.94 % in allowed areas. Also, the allergenicity and antigenicity evaluation indicated that ROP41 is non-allergenic and immunogenic.

CONCLUSION

The current study offered critical basic and conceptual information on ROP41 to increase a successful vaccine in opposition to continual and acute toxoplasmosis for in addition in vivo assessments. Further research is necessary for the development of vaccines utilizing ROP41 alone or combined with various antigens.

Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design

The personalized neoantigen nanovaccine (PNVAC) platform for patients with gastric cancer we established previously exhibited promising anti-tumor immunoreaction. However, limited by the ability of traditional neoantigen prediction tools, a portion of epitopes failed to induce specific immune response. In order to filter out more neoantigens to optimize our PNVAC platform, we develop a novel neoantigen prediction model, NUCC. This prediction tool trained through a deep learning approach exhibits better neoantigen prediction performance than other prediction tools, not only in two independent epitope datasets, but also in a totally new epitope dataset we construct from scratch, including 25 patients with advance gastric cancer and 150 candidate mutant peptides, 13 of which prove to be neoantigen by immunogenicity test in vitro. Our work lay the foundation for the improvement of our PNVAC platform for gastric cancer in the future.

In silico identification and ex vivo evaluation of Toxoplasma gondii peptides restricted to HLA-A*02, HLA-A*24 and HLA-B*35 alleles in human PBMC from a Colombian population

Toxoplasma gondii infects approximately 30% of the population, and there is currently no approved vaccine. Identifying immunogenic peptides with high affinity to different HLA molecules is a promising vaccine strategy. This study used an in silico approach using artificial neural networks to identify T. gondii peptides restricted to HLA-A*02, HLA-A*24, and HLA-B*35 alleles. Proteomes from seven T. gondii strains and transcriptomic data of overexpressed genes from T. gondii-RH in human PBMC were also used. Parasite protein sequences were analyzed with R 'Epitope Prediction' library. Peptide candidates were evaluated in the artificial neural networks based on the probabilities of output neurons (p > 0.5). The IFN-γ responses in PBMC from T. gondii seronegative and seropositive individuals were evaluated by ELISpot. Peptides with higher IFN-γ induction were evaluated to identify cytotoxic response in CD8+ T cells (CD107a). In silico analysis identified 36 peptides from T. gondii proteins with predicted affinity to HLA-A*02, A*24, and B*35 alleles. Experiments with PBMCs revealed that a peptide restricted to HLA-A02 (P1: FLFAWITYV) induced a significant increase in IFN-γ-producing cells (p = 0.004). For HLA-A24, a peptide (P8: VFAFAFAFFLI) also induced a significant IFN-γ response (p = 0.004), while for the HLA-B*35 allele, the P6 peptide (YPIAPSFAM) induced a response that differed significantly from the control (p = 0.05). These peptides induced also a significant percentage of central memory CD8 + T cells expressing the degranulation marker CD107a (p < 0.05). Finally, we identified three T. gondii peptides that induced IFN-γ response, and a cytotoxic response measured by CD107a expression on CD45RAneg-CD8 cells. These peptides could be considered part of a multi-epitope vaccine against toxoplasmosis in humans.

Design and in silico analysis of a novel peptide-based multiepitope vaccine against glioblastoma multiforme by targeting tumor-associated macrophage

CD204 is a distinct indicator for tumor-associated macrophages (TAMs) in glioma. Evidence indicates that CD204-positive TAMs are involved in the aggressive behavior of various types of cancers. This study was conducted to develop a new and effective peptide-based vaccine for GBM, specifically targeting CD204. Epitopes of the target protein were identified using NetMHCpan 4.1a, NetMHCIIpan-4.0, and ABCpred tools. Subsequently, the predicted epitopes were evaluated using bioinformatics tools to assess their antigenicity, non-allergenicity, immunogenicity, non-toxicity, and potential to stimulate the production of IL-4 and IFN-γ in HTL epitopes. Selected T-cell epitopes demonstrated a robust binding affinity with the particular HLA alleles. Finally, four HTL epitopes, three CTL epitopes, and two B-cell epitopes, jointed via linkers and adjuvant, were used for the final vaccine construct design. Analysis disclosed that the developed vaccine demonstrated robust antigenic properties while proving soluble, stable, non-toxic, and non-allergenic. Additionally, molecular docking studies and molecular dynamics simulations confirmed a robust correlation between the designed vaccine and TLR-2 and TLR-4 immune receptors. The molecular docking results demonstrated a strong interaction between the newly developed vaccine and TLR2 (-895.1 kcal/mol) and TLR4 (-881.0 kcal/mol) receptors. During the simulation, the vaccine-TLR2 and vaccine-TLR4 complexes exhibited binding energies of -113.41 and -106.61 kcal/mol, respectively. Analysis by different bioinformatic tools indicated the potential of the designed vaccine in immune stimulation and a significant elevation in IgG and IgM antibodies, T-helper cells, T-cytotoxic cells, INF-γ, IL-2, and IL-4. Research findings show that the newly designed multi-epitope vaccine is promising in providing long-term immunity against GBM and offers a promising therapeutic alternative.

Driver mutation landscape of acute myeloid leukemia provides insights for neoantigen-based immunotherapy

Acute myeloid leukemia (AML) has lagged in benefiting from immunotherapies, primarily due to the scarcity of actionable AML-specific antigens. Driver mutations represent promising immunogenic targets, but a comprehensive characterization of the AML neoantigen landscape and their impact on patient outcomes and the AML immune microenvironment remain unclear. Herein, we conducted matched DNA and RNA sequencing on 304 AML patients and extensively integrated data from additional ∼2500 AML cases, identifying 49 driver genes, notably characterized by a significant proportion of insertions and deletions (indels). Neoantigen analysis showed that indels triggered a higher abundance of neoantigens both in quantity and quality compared to single nucleotide variants (SNVs) and gene fusions. By integrating peptide features pertinent to neoantigen presentation and T cell recognition, we developed two robust models of epitope immunogenicity that significantly enriched immunogenic neoepitopes. We validated 30 neoantigens through in vitro direct binding assays of predicted peptides to MHC proteins and confirmed the immunogenicity of 20 neoantigens using interferon-γ ELISpot and tetramer assays. Moreover, we demonstrated that patients with higher neoantigen loads, derived from driver mutations, exhibited poor clinical outcomes and an IFN-driven adaptive immune response, which was associated with immune suppression and tumor evasion. Through deconvolution of large-scale bulk transcriptomes, integration of single-cell RNA sequencing and multiparametric flow cytometry, we confirmed a strong association between neoantigen load and CD8+ T cell exhaustion. This study provides a comprehensive landscape of AML neoantigens derived from driver mutations, offering putative immunogenic targets and emphasizing the need for strategies to revitalize the immunosuppressive milieu.

Design of multi-epitope-based therapeutic vaccine candidates from HBc and HBx proteins of hepatitis B virus using reverse vaccinology and immunoinformatics approaches

The major problem in cases of chronic hepatitis B (CHB) is the failure of the patient's immune response to eliminate the covalently closed circular DNA (cccDNA) minichromosome of hepatitis B virus (HBV). Epigenetic regulation involving the HBV core protein (HBc) and HBV X protein (HBx) influences the transcription and stability of the cccDNA minichromosome. The HBc and/or HBx-based therapeutic vaccines that have been developed cannot accommodate differences between HBV genotypes. This research aims to design a therapeutic vaccine candidate based on the multi-epitope of HBc and HBx using reverse vaccinology (RV) and immunoinformatics approach. HBc and HBx sequences from 10 HBV genotypes were obtained from the NCBI Entrez Protein database. Epitopes were predicted from consensus sequences, which consisted of 13,610 HBc sequences and 12,333 HBx sequences. The study identified four cytotoxic T lymphocyte epitopes, two helper T lymphocyte epitopes, and five linear B lymphocyte that met the inclusion criteria. The vaccine candidate designed using cholera toxin subunit B and pan HLA DR-binding epitope adjuvants was predicted to be safe, antigenic, stable, and has a global population coverage of 99.43%. Molecular docking and molecular dynamics simulations demonstrated that the vaccine candidate could stably bind to B cell receptor, cytotoxic T cell receptor, and TLR4 for 100 ns. Immune response simulation indicated that it can induce antibody production and the proliferation of B and T cells. It can be concluded that RV and immunoinformatics successfully facilitated the design of a multi-epitope therapeutic vaccine candidate for CHB.

Peptide vaccine design against glioblastoma by applying immunoinformatics approach

Brain tumors are considered to be one of the most fatal forms of cancer owing to their highly aggressive attributes, diverse characteristics, and notably low rate of survival. Among these tumors, glioblastoma stands out as the prevalent and perilous variant Despite the present advancements in surgical procedures, pharmacological treatment, and radiation therapy, the overall prognosis remains notably unfavorable, as merely 4.3 % of individuals manage to attain a five-year survival rate; For this reason, it has emerged as a challenge for cancer researchers. Therefore, among several immunotherapy methods, using peptide-based vaccines for cancer treatment is considered promising due to their ability to generate a focused immune response with minimal damage. This study endeavors to devise a multi-epitope vaccine utilizing an immunoinformatics methodology to address the challenge posed by glioblastoma disease. Through this approach, it is anticipated that the duration and expenses associated with vaccine manufacturing can be diminished, while simultaneously enhancing the characteristics of the vaccine. The target gene in this research is ITGA5, which was achieved through TCGA analysis by targeting the PI3K-Akt pathway as a significant association with patient survival. Subsequently, the suitable epitopes of T and B cells were selected through various immunoinformatics tools by analyzing their sequence. Then, nine epitopes were merged with GM-CSF as an adjuvant to enhance immunogenicity. The outcomes encompass molecular docking, molecular dynamics (MD) simulation, simulation of the immune response, prognosis and confirmation of the secondary and tertiary structure, Chemical and physical characteristics, toxicity, as well as antigenicity and allergenicity of the potential vaccine candidate against glioblastoma.

Altered epitopes enhance macrophage-mediated anti-tumour immunity to low-immunogenic tumour mutations

Personalized neoantigen therapy has shown long-term and stable efficacy in specific patient populations. However, not all patients have sufficient levels of neoantigens for treatment. Although somatic mutations are commonly found in tumours, a significant portion of these mutations do not trigger an immune response. Patients with low mutation burdens continue to exhibit unresponsiveness to this treatment. We propose a design paradigm for neoantigen vaccines by utilizing the highly immunogenic unnatural amino acid p-nitrophenylalanine (pNO2Phe) for sequence alteration of somatic mutations that failed to generate neoepitopes. This enhances the immunogenicity of the mutations and transforms it into a suitable candidate for immunotherapy. The nitrated altered epitope vaccines designed according to this paradigm is capable of activating circulating CD8+ T cells and inducing immune cross-reactivity against autologous mutated epitopes in different MHC backgrounds (H-2Kb, H-2Kd, and human HLA-A02:01), leading to the elimination of tumour cells carrying the mutation. After immunization with the altered epitopes, tumour growth was significantly inhibited. It is noteworthy that nitrated epitopes induce tumour-infiltrating macrophages to differentiate into the M1 phenotype, surprisingly enhancing the MHC II molecule presenting pathway of macrophages. Nitrated epitope-treated macrophages have the potential to cross-activate CD4+ and CD8+ T cells, which may explain why pNO2Phe can enhance the immunogenicity of epitopes. Meanwhile, the immunosuppressive microenvironment of the tumour is altered due to the activation of macrophages. The nitrated neoantigen vaccine strategy enables the design of vaccines targeting non-immunogenic tumour mutations, expanding the pool of potential peptides for personalized and shared novel antigen therapy. This approach provides treatment opportunities for patients previously ineligible for new antigen vaccine therapy.

Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate

BACKGROUND

Tuberculosis (TB) remains a global public health challenge. The existing Bacillus Calmette-Guérin vaccine has limited efficacy in preventing adult pulmonary TB, necessitating the development of new vaccines with improved protective effects.

METHODS

Computer-aided design and artificial intelligence technologies, combined with bioinformatics and immunoinformatics approaches, were used to design a multi-epitope vaccine (MEV) against TB. Comprehensive bioinformatics analyses were conducted to evaluate the physicochemical properties, spatial structure, immunogenicity, molecular dynamics (MD), and immunological characteristics of the MEV.

RESULTS

We constructed a MEV, designated ZL12138L, containing 13 helper T lymphocyte epitopes, 12 cytotoxic T lymphocyte epitopes, 8 B-cell epitopes, as well as Toll-like receptor (TLR) agonists and helper peptides. Bioinformatics analyses revealed that ZL12138L should exhibit excellent immunogenicity and antigenicity, with no toxicity or allergenicity, and had potential to induce robust immune responses and high solubility, the immunogenicity score was 4.14449, the antigenicity score was 0.8843, and the immunological score was 0.470. Moreover, ZL12138L showed high population coverage for human leukocyte antigen class I and II alleles, reaching 92.41% and 90.17%, respectively, globally. Molecular docking analysis indicated favorable binding affinity of ZL12138L with TLR-2 and TLR-4, with binding energies of -1173.4 and -1360.5 kcal/mol, respectively. Normal mode analysis and MD simulations indicated the stability and dynamic properties of the vaccine construct. Immune simulation predictions suggested that ZL12138L could effectively activate innate and adaptive immune cells, inducing high levels of Type 1 T helper cell cytokines.

CONCLUSIONS

This study provides compelling evidence for ZL12138L as a promising TB vaccine candidate. Future research will focus on experimental validation and further optimization of the vaccine design.

Exploring immunogenic CD8 + T-cell epitopes for peptide-based vaccine development against evolving SARS-CoV-2 variants: An immunoinformatics approach

The COVID-19 pandemic originated in Wuhan in 2019 due to a novel SARS-COV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) responsible for the massive number of deaths across the globe. So far, several vaccines have been developed using highly antigenic Spike protein and authorized for emergency use, reducing the severity of the infection. Nonetheless, the virus continues to evolve through multiple mutations, resulting in numerous variants with enhanced transmission that evade the vaccine-induced immune response. Given the persistently mutating nature of the SARS-COV-2 virus, peptide-based vaccines with highly conserved epitopes may offer lasting protection against evolving variants. This study presents an immunoinformatics-based identification of potentially immunogenic CD8 + T-cell epitopes (CTLs) of Spike (S), Membrane (M), Nucleocapsid (N) and Envelope (E) proteins of SARS-COV-2. By utilizing the immunoinformatic approach, 21 epitopes have successfully been evaluated, where 15, 3, 2, and 1 epitopes are respectively from Spike, Membrane, Envelope and Nucleocapsid proteins. Out of these, 20 are found to be identical with experimentally verified immunogenic epitopes, except for the novel NTQEVFAQV epitope from spike protein. These epitopes show a high degree of conservation in both former variants of concerns (VOCs), variants of interest (VOIs) and current variants under monitoring (VUMs), are non-toxic, non-homologous to humans and have a wide range of global population coverage. Furthermore, utilizing molecular docking analysis followed by molecular dynamics simulation, these epitopes have been verified as having stable interactions with their respective HLA molecules. The described framework and projected immunogenic epitopes could significantly impact the development of SARS-COV-2 vaccines based on peptides.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00894-7.

Development of multi-epitope mRNA vaccine against Clostridioides difficile using reverse vaccinology and immunoinformatics approaches

Clostridioides difficile (C. difficile), as the major pathogen of diarrhea in healthcare settings, has become increasingly prevalent within community populations, resulting in significant morbidity and mortality. However, the therapeutic options for Clostridioides difficile infection (CDI) remain limited, and as of now, no authorized vaccine is available to combat this disease. Therefore, the development of a novel vaccine against C. difficile is of paramount importance. In our study, the complete proteome sequences of 118 strains of C. difficile were downloaded and analyzed. We found four antigenic proteins that were highly conserved and can be used for epitope identification. We designed two vaccines, WLcd1 and WLcd2, that contain the ideal T-cell and B-cell epitopes, adjuvants, and the pan HLA DR-binding epitope (PADRE) sequences. The biophysical and chemical assessments of these vaccine candidates indicated that they were suitable for immunogenic applications. Molecular docking analyses revealed that WLcd1 bonded with higher affinity to Toll-like receptors (TLRs) than WLcd2. Furthermore, molecular dynamics (MD) simulations, performed using Gmx_MMPBSA v1.56, confirmed the binding stability of WLcd1 with TLR2 and TLR4. The preliminary findings suggested that this multi-epitope vaccine could be a promising candidate for protection against CDI; however, experimental studies are necessary to confirm these predictions.

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

Emergence of Omicron FN.1 a descendent of BQ.1.1 in Botswana

Botswana, like the rest of the world, has been significantly impacted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In December 2022, we detected a monophyletic cluster of genomes comprising a sublineage of the Omicron variant of concern (VOC) designated as B.1.1.529.5.3.1.1.1.1.1.1.74.1 (alias FN.1, clade 22E). These genomes were sourced from both epidemiologically linked and unlinked samples collected in three close locations within the district of Greater Gaborone. In this study, we assessed the worldwide prevalence of the FN.1 lineage, evaluated its mutational profile, and conducted a phylogeographic analysis to reveal its global dispersal dynamics. Among approximately 16 million publicly available SARS-CoV-2 sequences generated by 30 September 2023, only 87 were of the FN.1 lineage, including 22 from Botswana, 6 from South Africa, and 59 from the UK. The estimated time to the most recent common ancestor of the 87 FN.1 sequences was 22 October 2022 [95% highest posterior density: 2 September 2022-24 November 2022], with the earliest of the 22 Botswana sequences having been sampled on 7 December 2022. Discrete trait reconstruction of FN.1 identified Botswana as the most probable place of origin. The FN.1 lineage is derived from the BQ.1.1 lineage and carries two missense variants in the spike protein, S:K182E in NTD and S:T478R in RDB. Among the over 90 SARS-CoV-2 lineages circulating in Botswana between September 2020 and July 2023, FN.1 was most closely related to BQ.1.1.74 based on maximum likelihood phylogenetic inference, differing only by the S:K182E mutation found in FN.1. Given the early detection of numerous novel variants from Botswana and its neighbouring countries, our study underscores the necessity of continuous surveillance to monitor the emergence of potential VOCs, integrating molecular and spatial data to identify dissemination patterns enhancing preparedness efforts.

Bioinformatics analysis to design a multi-epitope mRNA vaccine against S. agalactiae exploiting pathogenic proteins

Antibiotic resistance in bacterial pathogen infections is a growing global issue that occurs due to their adaptation to changing environmental conditions. Therefore, producing an efficient vaccine as an alternative approach can improve the immune system, eradicate related pathogens, and overcome this growing problem. Streptococcus agalactiae belongs to group B Streptococcus (GBS). Colonization of GBS during pregnancy is a significant risk factor for infants and young children. S. agalactiae infected population exhibits resistance to beta-lactams, including penicillin and the second-line antibiotics erythromycin and clindamycin. On the other hand, there are currently no commercial vaccines against this pathogen. Vaccination of pregnant women is a highly effective method to protect newborns and infants from S. agalactiae infection, and it has been identified as an urgent demand by the World Health Organization. This study employed various immunoinformatic tools to develop an effective vaccine that could trigger both humoral and cell-mediated immunity and prevent disease. For this purpose, three conserved antigenic proteins of the main pathogenic strains of S. agalactiae were utilized to predict CTL, HTL, and B-cell epitopes for producing an mRNA vaccine against different strains of S. agalactiae. The selected epitopes were fused using proper linkers. The Resuscitation promoting factor E (RpfE) sequence was incorporated in the designed vaccine construct as an adjuvant to boost its immune response. Different physicochemical characteristics of the final designed vaccine, modeling of the three-dimensional structure, molecular docking, molecular dynamics simulation, and immunological response simulation were screened following vaccine administration in an in vivo model. Computational immune simulation data identified that IgG1, IgM, INF γ, IL-2, T helper, and B-cell populations increased significantly after vaccination. These findings suggested that the vaccine candidate may provide good protection against S. agalactiae infection. However, experimental and animal model studies are required for additional validation and implementation in human vaccination programs.

Non-RBD peptides of SARS-CoV-2 spike protein exhibit immunodominance as they elicit both innate and adaptive immune responses

Severe acute respiratory coronavirus-2 (SARS-CoV-2) emerged in 2019 as a new virus and caused worldwide outbreaks, quickly turning into a pandemic disease called coronavirus disease-19 (COVID-19). All the existing methodologies were used for developing vaccines for this virus. But sporadic infections of this virus and the emergence of new strains to date suggest the incomplete protection offered by the developed vaccines and the need for new research. In this direction, we identified five epitopes present in the non-RBD region and on the surface of the spike protein by in silico analysis. They are epitope I (aa 80-90), epitope II (aa 262-270), and a small protein with three epitopes (aa 1059-1124). Antigenicity scores of these epitopes were found to be higher than the full length spike protein and its RBD region. These epitopes showed high conserveness across the emerging strains, high immunogenicity, non-toxicity, no homology with human sequences and high affinity for MHC class I & II molecules. Antibodies raised against these epitopes interacted with the bacterially expressed spike protein in western blotting. The antiserum of COVID-19 recovered participants reacted with the developed epitopes (small protein). Furthermore, in the presence of the respective antiserum and COVID-19 convalescent serum, these epitopes successfully fixed the complement, implying a possible role in innate immunity. The epitopes were also found to activate the peripheral blood mononuclear cells (PBMCs) isolated from the blood samples of COVID-19 recovered/vaccinated participants, suggesting a possible role in adaptive immunity. The need for the new SARS-CoV-2 vaccines is further highlighted in light of current reports about the side effects of a developed vaccine (AstraZeneca) and the circulating new strains. The epitopes presented in this study represent the potential immunogens and expect certain pitfalls of the existing vaccines would be sealed.

Vaccination with OprB porin, and its epitopes offers protection against A. baumannii infections in mice

A. baumannii is a deadly antimicrobial resistance pathogen that acquires drug resistance through different mechanisms. Therefore, it is necessary to investigate all its virulence factors and design effective vaccines against it. For this purpose, OprB, an outer membrane porin, was investigated in this study, and its secondary and tertiary structures, physicochemical properties, and B-T epitopes were determined. The vaccine potential of this protein and its linear, non-continuous, and chimeric epitopes were also in-vivo analyzed. Based on the results, two surface epitopes and one non-continuous epitope were identified. Surface contiguous epitopes were produced recombinantly and non-continuous epitope sequences were synthesized and then produced. The chimeric epitope was also produced via the SOE-PCR technique. Active and passive immunization of mice with the whole OprB protein, non-continuous epitope, contiguous epitopes, two epitopes in chimeric form, as well as the mixture of two purified epitopes showed that the survival level and total IgG titer of the mice compared to non-vaccinated mice or mice that were vaccinated with an internal fragment increased significantly. The bacterial load in the immunized mice's lung, liver, kidney, and spleen was much lower than in the control groups, and the TNF-α, IFN-γ, and IL-6 cytokines levels were also lower in these groups and were similar to the naive mice. On the other hand, subunit vaccines showed acceptable safety and due to their minimal cross-activity, their use is much safer.

Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data

Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens based on features of known antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of only the unlabeled proteins from the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.