Properties of MHC class I presented peptides that enhance immunogenicity

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.

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.

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.

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.

In-silico evaluation of the T-cell based immune response against SARS-CoV-2 omicron variants

During of COVID-19 pandemic, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has continuously evolved, resulting in the emergence of several new variants of concerns (VOCs) with numerous mutations. These VOCs dominate in various regions due to increased transmissibility and antibody evasion, potentially reducing vaccine effectiveness. Nonetheless, it remains uncertain whether the recent SARS-CoV-2 VOCs have the ability to circumvent the T cell immunity elicited by either COVID-19 vaccination or natural infection. To address this, we conducted in-silico analysis to examine the impact of VOC-specific mutations at the epitope level and T cell cross-reactivity with the ancestral SARS-CoV-2. According to the in-silico investigation, T cell responses triggered by immunization or prior infections still recognize the variants in spite of mutations. These variants are expected to either maintain their dominant epitope HLA patterns or bind with new HLAs, unlike the epitopes of the ancestral strain. Our findings indicate that a significant proportion of immuno-dominant CD8 + and CD4 + epitopes are conserved across all the variants, implying that existing vaccines might maintain efficacy against new variations. However, further in-vitro and in-vivo studies are needed to validate the in-silico results and fully elucidate immune sensitivities to VOCs.

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.

Differential epitope prediction across diverse circulating variants of SARS-COV-2 in Brazil

COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants.

In Silico Design of a Trans-Amplifying RNA-Based Vaccine against SARS-CoV-2 Structural Proteins

Nucleic acid-based vaccines allow scalable, rapid, and cell-free vaccine production in response to an emerging disease such as the current COVID-19 pandemic. Here, we objected to the design of a multiepitope mRNA vaccine against the structural proteins of SARS-CoV-2. Through an immunoinformatic approach, promising epitopes were predicted for the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins. Fragments rich in overlapping epitopes were selected based on binding affinities with HLA classes I and II for the specific presentation to B and T lymphocytes. Two constructs were designed by fusing the fragments in different arrangements via GG linkers. Construct 1 showed better structural properties and interactions with toll-like receptor 2 (TLR-2), TLR-3, and TLR-4 during molecular docking and dynamic simulation. A 50S ribosomal L7/L12 adjuvant was added to its N-terminus to improve stability and immunogenicity. The final RNA sequence was used to design a trans-amplifying RNA (taRNA) vaccine in a split-vector system. It consists of two molecules: a nonreplicating RNA encoding a trans-acting replicase to amplify the second one, a trans-replicon (TR) RNA encoding the vaccine protein. Overall, the immune response simulation detected that activated B and T lymphocytes and increased memory cell formation. Macrophages and dendritic cells proliferated continuously, and IFN-γ and cytokines like IL-2 were released highly.

Computational Workflow to Design Novel Vaccine Candidates and Small-Molecule Therapeutics for Schistosomiasis

Human schistosomiasis, caused by the Schistosoma trematode, is a neglected parasitic disease affecting over 250 million people worldwide. There is no vaccine, and the single available drug is threatened by drug resistance. This study presents a computational approach to designing multiepitope vaccines (MEVs) targeting the cercarial (CMEV) and schistosomular (SMEV) stages of schistosomes, and identifies potential schistosomicidal compounds from the Medicine for Malaria Ventures (MMV) and SuperNatural Database (SND) libraries. The designed vaccines (CMEV and SMEV) are engineered to provoke robust immune responses by incorporating a blend of T- and B-cell epitopes. Structural and immunoinformatics evaluations predicted robust interactions of CMEV and SMEV with key immune receptors and prolonged immune responses. In addition, molecular docking identified several compounds from the MMV and SND libraries with strong binding affinities to vital Schistosoma cathepsin proteases, indicating their potential as schistosomicidal agents. Our findings contribute to the potential development of effective vaccines and drugs against schistosomiasis.

Advancing one health vaccination: In silico design and evaluation of a multi-epitope subunit vaccine against Nipah virus for cross-species immunization using immunoinformatics and molecular modeling

The resurgence of the Nipah virus (NiV) in 2023 has raised concerns for another potentially severe pandemic, given its history of high mortality from previous outbreaks. Unfortunately, no therapeutics and vaccines have been available for the virus. This study used immunoinformatics and molecular modeling to design and evaluate a multi-epitope subunit vaccine targeting NiV. The designed vaccine construct aims to stimulate immune responses in humans and two other intermediate animal hosts of the virus-swine and equine. Using several epitope prediction tools, ten peptides that induced B-lymphocyte responses, 17 peptides that induced cytotoxic T-lymphocyte (CTL) responses, and 12 peptides that induced helper T-lymphocyte (HTL) responses were mapped from nine NiV protein sequences. However, the CTL and HTL-inducing peptides were reduced to ten and eight, respectively, following molecular docking and dynamics. These screened peptides exhibited stability with 30 common major histocompatibility complex (MHC) receptors found in humans, swine, and equine. All peptides were linked using peptide linkers to form the multi-epitope construct and various adjuvants were tested to enhance its immunogenicity. The vaccine construct with resuscitation-promoting factor E (RpfE) adjuvant was selected as the final design based on its favorable physicochemical properties and superior immune response profile. Molecular docking was used to visualize the interaction of the vaccine to toll-like receptor 4 (TLR4), while molecular dynamics confirmed the structural stability of this interaction. Physicochemical property evaluation and computational simulations showed that the designed vaccine construct exhibited favorable properties and elicited higher antibody titers than the six multi-epitope NiV vaccine designs available in the literature. Further in vivo and in vitro experiments are necessary to validate the immunogenicity conferred by the designed vaccine construct and its epitope components. This study demonstrates the capability of computational methodologies in rational vaccine design and highlights the potential of cross-species vaccination strategies for mitigating potential NiV threats.

Anticipatory in silico vaccine designing based on specific antigenic epitopes from Streptococcus mutans against diabetic pathogenesis

The metabolic disorder Type 2 Diabetes Mellitus (T2DM) is characterized by hyperglycaemia, causing increased mortality and healthcare burden globally. Recent studies emphasize the impact of metabolites in the gut microbiome on T2DM pathogenesis. One such microbial metabolite, imidazole propionate (Imp) derived from histidine metabolism, is shown to interfere with insulin signalling and other key metabolic processes. The key enzyme urocanate reductase (UrdA) is involved in ImP production. Hence, we propose to develop a novel therapeutic vaccine against the gut microbe producing Imp based on UrdA as a target for treating T2DM using immunoinformatics approach. Antigenic, non-allergic, non-toxic, and immunogenic B cell and T cell potential epitopes were predicted using immunoinformatics servers and tools. These epitopes were adjoined using linker sequences, and to increase immunogenicity, adjuvants were added at the N-terminal end of the final vaccine construct. Further, to confirm the vaccine's safety, antigenic and non-allergic characteristics of the developed vaccine construct were assessed. The tertiary structure of the UrdA vaccine sequence was predicted using molecular modelling tools. A molecular docking study was utilized to understand the vaccine construct interaction with immune receptors, followed by molecular dynamics simulation and binding free energy calculations to assess stability of the complex. In silico cloning techniques were employed to evaluate the expression and translation effectiveness of the developed vaccine in pET vector. In conclusion, this study developed an in silico epitope-based vaccine construct as a novel adjunct therapeutic for T2DM.

Graphical Abstract

Identification of highly conserved surface-exposed peptides of spike protein for multiepitope vaccine design against emerging omicron variants: An immunoinformatic approach

The COVID-19 pandemic, originating in Wuhan in 2019, was caused by SARS-CoV-2, leading to significant global fatalities. Despite the development of vaccines, the virus mutates, creating variants that evade vaccine-induced immunity. To address SARS-CoV-2's evolving nature, a multiepitope vaccine was developed using immunoinformatics approach, specifically targeting the Omicron variant's spike protein. This vaccine includes six CD8 + and eleven CD4 + epitopes selected for their immunogenicity, non-toxicity, and significant conservation among former Variants of Concern (VOCs) and Variants of Interest (VOIs), such as Alpha, Beta, Gamma, Delta, Lambda, Mu, R1, and Zeta, as well as current Variants Under Monitoring (VUMs) like XBB.1.5, XBB.1.16, EG.5, BA.2.86, and JN.1. Notably, certain epitopes like ELLHAPATV and PYRVVVLSFELLHAP were fully conserved across all tested variants in the spike protein's receptor binding domain (RBD). Others, such as NATRFASVYAWNRKR, were fully conserved in all former VOCs and VOIs and 93.33 % in current VUMs, while ERDISTEIYQAGNKP was entirely conserved in current VUMs within the RBD region. The study went on to model, refine, and validate the vaccine prototype's tertiary structure. Docking experiments and molecular dynamic simulations revealed robust and stable interactions with Toll-like receptor 4. Cloning and codon optimization confirmed successful expression in E. coli. Subsequently, the immunological reaction of the multiepitope vaccine demonstrated that the three-time administration of the prototype significantly enhanced the antibody response while decreasing the number of antigens. The designed vaccine's epitopes showed significant combined global population coverage of 100 % with 89.75 % for CD8 + and 99.98 % for CD4 + epitopes and conservation across SARS-CoV-2 variants especially in current monitoring omicron subvariants, supporting its broader applicability and potential efficacy. Although, this promising vaccine candidate needs to undergo clinical trials to determine its effectiveness in neutralising SARS-CoV-2.

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.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity

T and B cell activation are equally important in triggering and orchestrating adaptive host responses to design multi-epitope African swine fever virus (ASFV) vaccines. However, few design methods have considered the trade-off between T and B cell immunogenicity when identifying promising ASFV epitopes. This work proposed a novel Pareto front-based ASFV screening method PFAS to identify promising epitopes for designing multi-epitope vaccines utilizing five ASFV Georgia 2007/1 sequences. To accurately predict T cell immunogenicity, four scoring methods were used to estimate the T cell activation in the four stages, including proteasomal cleavage probability, transporter associated with antigen processing transport efficiency, class I binding affinity of the major histocompatibility complex, and CD8 + cytotoxic T cell immunogenicity. PFAS ranked promising epitopes using a Pareto front method considering T and B cell immunogenicity. The coefficient of determination between the Pareto ranks of multi-epitope vaccines and survival days of swine vaccinations was R2 = 0.95. Consequently, PFAS scored complete epitope profiles and identified 72 promising top-ranked epitopes, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes. PFAS is the first method of using the Pareto front approach to identify promising epitopes that considers the objectives of maximizing both T and B cell immunogenicity. The top-ranked promising epitopes can be cost-effectively validated in vitro. The Pareto front approach can be adaptively applied to various epitope predictors for bacterial, viral and cancer vaccine developments. The MATLAB code of the Pareto front method was available at https://github.com/NYCU-ICLAB/PFAS .

An In Silico Design of a Vaccine against All Serotypes of the Dengue Virus Based on Virtual Screening of B-Cell and T-Cell Epitopes

Dengue virus poses a significant global health challenge, particularly in tropical and subtropical regions. Despite the urgent demand for vaccines in the control of the disease, the two approved vaccines, Dengvaxia and TV003/TV005, there are current questions regarding their effectiveness due to an increased risk of antibody-dependent enhancement (ADE) and reduced protection. These challenges have underscored the need for further development of improved vaccines for Dengue Virus. This study presents a new design using an in silico approach to generate a more effective dengue vaccine. Initially, our design process began with the collection of Dengue polyprotein sequences from 10 representative countries worldwide. And then conserved fragments of viral proteins were retrieved as the bases for epitope screening. The selection of epitopes was then carried out with criteria such as antigenicity, immunogenicity, and binding affinity with MHC molecules, while the exclusion criteria were according to their allergenicity, toxicity, and potential for antibody-dependent enhancement. We then constructed a core antigen with the selected epitopes and linked the outcomes with distinct adjuvant proteins, resulting in three candidate vaccines: PSDV-1, PSDV-2, and PSDV-3. Among these, PSDV-2 was selected for further validation due to its superior physicochemical and structural properties. Extensive simulations demonstrated that PSDV-2 exhibited strong binding to pattern recognition receptors, high stability, and robust immune induction, confirming its potential as a high-quality vaccine candidate. For its recombinant expression, a plasmid was subsequently designed. Our new vaccine design offers a promising additional option for Dengue virus protection. Further experimental validations will be conducted to confirm its protective efficacy and safety.

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.

Epitopes screening and vaccine molecular design of PEDV S protein based on immunoinformatics

Porcine epidemic diarrhea virus (PEDV) is a serious disease that poses a significant threat to the pig industry. This study focused on analyzing the Spike protein of PEDV, which harbors crucial antigenic determinants, in identifying dominant epitopes. Immunoinformatics tools were used to screen for B-cell, CD4+ and CD8+ predominance epitopes. These epitopes were then connected to the N-terminal of ferritin to form a self-assembled nanoparticle vaccine. Various physical and chemical properties of the candidate vaccine were analyzed, including secondary structure prediction, tertiary structure modeling, molecular docking, immune response simulation and computer cloning. The results demonstrated that the candidate vaccine was antigenic, soluble, stable, non-allergic, and formed a stable complex with the target receptor TLR-3. Immune simulation analysis showed that the candidate vaccine effectively stimulated both cellular and humoral reactions, leading to increased related cytokines production. Furthermore, efficient and stable expression of the candidate vaccine was achieved through reverse translation in the Escherichia coli K12 expression system following codon optimization and in silico cloning. The developed nanoparticle candidate vaccine in this study holds promise as an effective PEDV vaccine candidate, offering a new approach for the research, development and improvement of vaccines targeting porcine enteric diarrhea coronavirus.

Humoral and cellular immunity in response to an in silico-designed multi-epitope recombinant protein of Theileria annulata

Tropical theileriosis is a lymphoproliferative disease caused by Theileria annulata and is transmitted by Ixodid ticks of the genus Hyalomma. It causes significant losses in livestock, especially in exotic cattle. The existing methods for controlling it, chemotherapeutic agents and a vaccine based on an attenuated schizont stage parasite, have several limitations. A promising solution to control this disease is the use of molecular vaccines based on potential immunogenic proteins of T. annulata. For this purpose, we selected five antigenic sequences of T. annulata, i.e. SPAG-1, Tams, TaSP, spm2, and Ta9. These were subjected to epitope prediction for cytotoxic T lymphocytes, B-cells, and helper T lymphocytes. CTL and B-cell epitopes with a higher score whereas those of HTL with a lower score, were selected for the construct. A single protein was constructed using specific linkers and evaluated for high antigenicity and low allergenicity. The construct was acidic, hydrophobic, and thermostable in nature. Secondary and tertiary structures of this construct were drawn using the PSIPRED and RaptorX servers, respectively. A Ramachandran plot showed a high percentage of residues in this construct in favorable, allowed, and general regions. Molecular docking studies suggested that the complex was stable and our construct could potentially be a good candidate for immunization trials. Furthermore, we successfully cloned it into the pET-28a plasmid and transformed it into the BL21 strain. A restriction analysis was performed to confirm the transformation of our plasmid. After expression and purification, recombinant protein of 49 kDa was confirmed by western blotting. An ELISA detected increased specific antibody levels in the sera of the immunized animals compared with the control group, and flow cytometric analysis showed a stronger cell-mediated immune response. We believe our multi-epitope recombinant protein has the potential for the large-scale application for disease prevention globally in the bovine population. This study will act as a model for similar parasitic challenges.

Bioinformatic elucidation of conserved epitopes to design a potential vaccine candidate against existing and emerging SARS-CoV-2 variants of concern

The COVID-19 pandemic caused by SARS-CoV-2 poses a significant adverse effects on health and economy globally. Due to mutations in genome, COVID-19 vaccine efficacy decreases. We used immuno-informatics to design a Multi epitope vaccine (MEV) candidate for SARS-CoV-2 variants of concern (VOCs). Hence, we predicted binders/epitopes MHC-I, CD8+, MHC-II, CD4+, and CTLs from spike, membrane and envelope proteins of VOCs. In addition, we assessed the conservation of these binders and epitopes across different VOCs. Subsequently, we designed MEV by combining the predicted CTL and CD4+ epitopes from spike protein, peptide linkers, and an adjuvant. Further, we evaluated the binding of MEV candidate against immune receptors namely HLA class I histocompatibility antigen, HLA class II histocompatibility antigen, and TLR4, achieving binding scores of -1265.3, -1330.7, and -1337.9. Molecular dynamics and normal mode analysis revealed stable docking complexes. Moreover, immune simulation suggested MEV candidate elicits both innate and adaptive immune response. We anticipate that this conserved MEV candidate will provide protection from VOCs and emerging strains.

Artificial intelligence-driven reverse vaccinology for Neisseria gonorrhoeae vaccine: Prioritizing epitope-based candidates

Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhea. The increasing prevalence of this disease worldwide, the rise of antibiotic-resistant strains, and the difficulties in treatment necessitate the development of a vaccine, highlighting the significance of preventative measures to control and eradicate the infection. Currently, there is no widely available vaccine, partly due to the bacterium's ability to evade natural immunity and the limited research investment in gonorrhea compared to other diseases. To identify distinct vaccine candidates, we chose to focus on the uncharacterized, hypothetical proteins (HPs) as our initial approach. Using the in silico method, we first carried out a comprehensive assessment of hypothetical proteins of Neisseria gonorrhoeae, encompassing assessments of physicochemical properties, cellular localization, secretary pathways, transmembrane regions, antigenicity, toxicity, and prediction of B-cell and T-cell epitopes, among other analyses. Detailed analysis of all HPs resulted in the functional annotation of twenty proteins with a great degree of confidence. Further, using the immuno-informatics approach, the prediction pipeline identified one CD8+ restricted T-cell epitope, seven linear B-cell epitopes, and seven conformational B-cell epitopes as putative epitope-based peptide vaccine candidates which certainly require further validation in laboratory settings. The study accentuates the promise of functional annotation and immuno-informatics in the systematic design of epitope-based peptide vaccines targeting Neisseria gonorrhoeae.

Design and characterization of a multi-epitope vaccine targeting Chlamydia abortus using immunoinformatics approach

Chlamydiosis is a widespread ailment affecting humans, livestock, and wildlife, caused by C. abortus, a member of the Chlamydia genus. This disease leads to reproductive disorders in bovines and poses a zoonotic risk, resulting in adverse outcomes such as abortion, stillbirths, weak offspring, endometritis, repeat breeding, and perinatal mortality. However, current chlamydiosis vaccines have limitations in terms of safety, efficacy, and stability, necessitating the development of effective and safe alternatives. In this study, our objective was to design a multi-epitope vaccine (MEV) targeting all strains of C. abortus using bioinformatics and immunoinformatics approaches. We identified highly antigenic and non-allergic proteins (yidC, yajC, secY, CAB503, and CAB746) using VaxiJen and AlgPred tools. Physicochemical analyses and secondary structure predictions confirmed protein stability through ProtParam and SOPMA methods. Furthermore, we employed IEDB-AR, NETMHCpan, and ToxinPred2 tools to predict cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, resulting in the identification of conserved epitopes for further analysis. The MEV construct, consisting of 545 amino acids, incorporated the adjuvant Beta defensin-3, along with 9 CTL epitopes and 21 HTL epitopes linked by EAAAK, KK, and AAY linkers. We assessed the safety and immunogenicity of the vaccine through comprehensive evaluations of antigenicity, toxicity, allergenicity, and physicochemical properties. Structural stability and quality were examined using 3D modeling via the ab initio approach with the Robetta platform. Molecular docking analysis explored the compatibility of the MEV with Toll-like receptor 9 (TLR9) using ClusPro, while molecular dynamics simulation with the DESMOND Maestro software predicted the stability and flexibility of the docked complex. Despite promising in silico findings, further wet lab investigations are crucial to validate the safety and efficacy of the MEV. Successful development and validation of this MEV hold significant potential in combatting chlamydiosis in both animal and human populations.Communicated by Ramaswamy H. Sarma.

Designing multi-epitope vaccine against human cytomegalovirus integrating pan-genome and reverse vaccinology pipelines

Human cytomegalovirus (HCMV) is accountable for high morbidity in neonates and immunosuppressed individuals. Due to the high genetic variability of HCMV, current prophylactic measures are insufficient. In this study, we employed a pan-genome and reverse vaccinology approach to screen the target for efficient vaccine candidates. Four proteins, envelope glycoprotein M, UL41A, US23, and US28, were shortlisted based on cellular localization, high solubility, antigenicity, and immunogenicity. A total of 29 B-cell and 44 T-cell highly immunogenic and antigenic epitopes with high global population coverage were finalized using immunoinformatics tools and algorithms. Further, the epitopes that were overlapping among the finalized B-cell and T-cell epitopes were linked with suitable linkers to form various combinations of multi-epitopic vaccine constructs. Among 16 vaccine constructs, Vc12 was selected based on physicochemical and structural properties. The docking and molecular simulations of VC12 were performed, which showed its high binding affinity (-23.35 kcal/mol) towards TLR4 due to intermolecular hydrogen bonds, salt bridges, and hydrophobic interactions, and there were only minimal fluctuations. Furthermore, Vc12 eliciting a good response was checked for its expression in Escherichia coli through in silico cloning and codon optimization, suggesting it to be a potent vaccine candidate.

Multi-epitope peptide vaccines targeting dengue virus serotype 2 created via immunoinformatic analysis

The Middle East has witnessed a greater spread of infectious Dengue viruses, with serotype 2 (DENV-2) being the most prevalent form. Through this work, multi-epitope peptide vaccines against DENV-2 that target E and nonstructural (NS1) proteins were generated through an immunoinformatic approach. MHC class I and II and LBL epitopes among NS1 and envelope E proteins sequences were predicted and their antigenicity, toxicity, and allergenicity were investigated. Studies of the population coverage denoted the high prevalence of NS1 and envelope-E epitopes among different countries where DENV-2 endemic. Further, both the CTL and HTL epitopes retrieved from NS1 epitopes exhibited high conservancies' percentages with other DENV serotypes (1, 3, and 4). Three vaccine constructs were created and the expected immune responses for the constructs were estimated using C-IMMSIM and HADDOCK (against TLR 2,3,4,5, and 7). Molecular dynamics simulation for vaccine construct 2 with TLR4 denoted high binding affinity and stability of the construct with the receptor which might foretell favorable in vivo interaction and immune responses.

Revolutionizing Nipah virus vaccinology: insights into subunit vaccine development strategies and immunological advances

The Nipah virus (NiV), a zoonotic virus in the Henipavirus genus of the Paramyxoviridae family, emerged in Malaysia in 1998 and later spread globally. Diseased patients may have a 40- 70% chance of fatality depending on the severity and early medication. The recent outbreak of NiV was reported in Kerala (India) by a new strain of MCL-19-H-1134 isolate. Currently, no vaccines are available, highlighting the critical need for a conclusive remedy. Our study aims to develop a subunit vaccine against the NiV by analyzing its proteome. NiV genome and proteome sequences were obtained from the NCBI database. A phylogenetic tree was constructed based on genome alignment. T-cell, helper T-cell, and B-cell epitopes were predicted from the protein sequences using NetCTL-1.2, NetMHCIIPan-4.1, and IEDB servers, respectively. High-affinity epitopes for human receptors were selected to construct a multi-epitope vaccine (MEV). These epitopes' antigenicity, toxicity, and allergenicity were evaluated using VaxiJen, AllergenFP-v.1.0, and AllergenFP algorithms. Molecular interactions with specific receptors were analyzed using PyRx and ClusPro. Amino acid interactions were visualized and analyzed using PyMOL and LigPlot. Immuno-simulation was conducted using C-ImmSim to assess the immune response elicited by the MEV. Finally, the vaccine cDNA was inserted into the pET28a(+) expression vector using SnapGene tool for in silico cloning in an E. coli host. The potential for an imminent outbreak cannot be overlooked. A subunit vaccine is more cost-effective and time-efficient. With additional in vitro and in vivo validation, this vaccine could become a superior preventive measure against NiV disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00246-9.

Bioinformatics-Driven mRNA-Based Vaccine Design for Controlling Tinea Cruris Induced by Trichophyton rubrum

Tinea cruris, a dermatophyte fungal infection predominantly caused by Trichophyton rubrum and Epidermophyton floccosum, primarily affects the groin, pubic region, and adjacent thigh. Its recurrence is frequent, attributable to repeated fungal infections in susceptible individuals, especially those with onychomycosis or tinea pedis, which act as reservoirs for dermatophytes. Given the persistent nature of tinea cruris, vaccination emerges as a promising strategy for fungal infection management, offering targeted, durable protection against various fungal species. Vaccines stimulate both humoral and cell-mediated immunity and are administered prophylactically to prevent infections while minimizing the risk of antifungal resistance development. Developing fungal vaccines is challenging due to the thick fungal cell wall, similarities between fungal and human cells, antigenic variation, and evolutionary resemblance to animals, complicating non-toxic target identification and T-cell response variability. No prior research has shown an mRNA vaccine for T. rubrum. Hence, this study proposes a novel mRNA-based vaccine for tinea cruris, potentially offering long-term immunity and reducing reliance on antifungal medications. This study explores the complete proteome of T. rubrum, identifying potential protein candidates for vaccine development through reverse vaccinology. Immunogenic epitopes from these candidates were mapped and integrated into multitope vaccines and reverse translated to construct mRNA vaccines. Then, the mRNA was translated and computationally assessed for physicochemical, chemical, and immunological attributes. Notably, 1,3-beta-glucanosyltransferase, CFEM domain-containing protein, cell wall galactomannoprotein, and LysM domain-containing protein emerged as promising vaccine targets. Antigenic, immunogenic, non-toxic, and non-allergenic cytotoxic T lymphocyte, helper T lymphocyte, and B lymphocyte epitopes were selected and linked with appropriate linkers and Toll-like receptor (TLR) agonist adjuvants to formulate vaccine candidates targeting T. rubrum. The protein-based vaccines underwent reverse translation to construct the mRNA vaccines, which, after inoculation, were translated again by host ribosomes to work as potential components for triggering the immune response. After that, molecular docking, normal mode analysis, and molecular dynamic simulation confirmed strong binding affinities and stable complexes between vaccines and TLR receptors. Furthermore, immune simulations of vaccines with and without adjuvant demonstrated activation of immune responses, evidenced by elevated levels of IgG1, IgG2, IgM antibodies, cytokines, and interleukins. There was no significant change in antibody production between vaccines with and without adjuvants, but adjuvants are crucial for activating the innate immune response via TLRs. Although mRNA vaccines hold promise against fungal infections, further research is essential to assess their safety and efficacy. Experimental validation is crucial for evaluating their immunogenicity, effectiveness, and safety.

Prediction of the 3D conformation of a small peptide vaccine targeting Aβ42 oligomers

The original etiology of Alzheimer's disease (AD) is the deposition of amyloid-beta (Aβ) proteins, which starts from the aggregation of the Aβ oligomers. The optimal therapeutic strategy targeting Aβ oligomer aggregation is the development of AD vaccines. Despite the fact that positive progress has been made for experimental attempts at AD vaccines, the physicochemical and even structural properties of these AD vaccines remain unclear. In this study, through immunoinformatic and molecular dynamics (MD) simulations, we first designed and simulated an alternative of vaccine TAPAS and found that the structure of the alternative can reproduce the 3D conformation of TAPAS determined experimentally. Meanwhile, immunoinformatic methods were used to analyze the physicochemical properties of TAPAS, including immunogenicity, antigenicity, thermal stability, and solubility, which confirm well the efficacy and safety of the vaccine, and validate the scheme reliability of immunoinformatic and MD simulations in designing and simulating the TAPAS vaccine. Using the same scheme, we predicted the 3D conformation of the optimized ACI-24 peptide vaccine, an Aβ peptide with the first 15 residues of Aβ42 (Aβ1-15). The vaccine was verified once to be effective against both full-length Aβ1-42 and truncated Aβ4-42 aggregates, but an experimental 3D structure was absent. We have also explored the immune mechanism of the vaccine at the molecular level and found that the optimized ACI-24 and its analogues can block the growth of either full-length Aβ1-42 or truncated Aβ4-42 pentamer by contacting the hydrophobic residues within the N-terminus and β1 region on the contact surface of either pentamer. Additionally, residues (D1, D7, S8, H13, and Q15) were identified as the key residues of the vaccine to contact either of the two Aβ oligomers. This work provides a feasible implementation scheme of immunoinformatic and MD simulations for the development of AD small peptide vaccines, validating the power of the scheme as a parallel tool to the experimental approaches and injecting molecular-level information into the understanding and design of anti-AD vaccines.

Putative new combination vaccine candidates identified by reverse vaccinology and genomic approaches to control enteric pathogens

OBJECTIVES

The pathogenic microorganisms that cause intestinal diseases can significantly jeopardize people's health. Currently, there are no authorized treatments or vaccinations available to combat the germs responsible for intestinal disease.

METHODS

Using immunoinformatics, we developed a potent multi-epitope Combination (combo) vaccine versus Salmonella and enterohemorrhagic E. coli. The B and T cell epitopes were identified by performing a conservancy assessment, population coverage analysis, physicochemical attributes assessment, and secondary and tertiary structure assessment of the chosen antigenic polypeptide. The selection process for vaccine development included using several bioinformatics tools and approaches to finally choose two linear B-cell epitopes, five CTL epitopes, and two HTL epitopes.

RESULTS

The vaccine had strong immunogenicity, cytokine production, immunological properties, non-toxicity, non-allergenicity, stability, and potential efficacy against infections. Disulfide bonding, codon modification, and computational cloning were also used to enhance the stability and efficacy of expression in the host E. coli. The vaccine's structure has a strong affinity for the TLR4 ligand and is very durable, as shown by molecular docking and molecular modeling. The results of the immunological simulation demonstrated that both B and T cells had a heightened response to the vaccination component.

CONCLUSIONS

The comprehensive in silico analysis reveals that the proposed vaccine will likely elicit a robust immune response against pathogenic bacteria that cause intestinal diseases. Therefore, it is a promising option for further experimental testing.

An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Pancreatic ductal adenocarcinoma is the most prevalent pancreatic cancer, which is considered a significant global health concern. Chemotherapy and surgery are the mainstays of current pancreatic cancer treatments; however, a few cases are suitable for surgery, and most of the cases will experience recurrent episodes. Compared to DNA or peptide vaccines, mRNA vaccines for pancreatic cancer have more promise because of their delivery, enhanced immune responses, and lower proneness to mutation. We constructed an mRNA vaccine by analyzing S100 family proteins, which are all major activators of receptors for advanced glycation end products. We applied immunoinformatic approaches, including physicochemical properties analysis, structural prediction and validation, molecular docking study, in silico cloning, and immune simulations. The designed mRNA vaccine was estimated to have a molecular weight of 165023.50 Da and was highly soluble (grand average of hydropathicity of -0.440). In the structural assessment, the vaccine seemed to be a well-stable and functioning protein (Z score of -8.94). Also, the docking analysis suggested that the vaccine had a high affinity for TLR-2 and TLR-4 receptors. Additionally, the molecular mechanics with generalized Born and surface area solvation analysis of the "Vaccine-TLR-2" (-141.07 kcal/mol) and "Vaccine-TLR-4" (-271.72 kcal/mol) complexes also suggests a strong binding affinity for the receptors. Codon optimization also provided a high expression level with a GC content of 47.04% and a codon adaptation index score 1.0. The appearance of memory B-cells and T-cells was also observed over a while, with an increased level of helper T-cells and immunoglobulins (IgM and IgG). Moreover, the minimum free energy of the mRNA vaccine was predicted at -1760.00 kcal/mol, indicating the stability of the vaccine following its entry, transcription, and expression. This hypothetical vaccine offers a groundbreaking tool for future research and therapeutic development of pancreatic cancer.

Next-generation IEDB tools: a platform for epitope prediction and analysis

The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.

Identification of novel T-cell epitopes on monkeypox virus and development of multi-epitopes vaccine using immunoinformatics approaches

Monkeypox virus (MPV) is closely related to the smallpox virus, and previous data from Africa suggest that the smallpox vaccine (VARV) is at least 85% effective in preventing MPV. No multi-epitope vaccine has yet been developed to prevent MPV infection. In this work, we used in silico structural biology and advanced immunoinformatic strategies to design a multi-epitope subunit vaccine against MPV infection. The designed vaccine sequence is adjuvanted with CpG-ODN and includes HTL/CTL epitopes for similar proteins between vaccinia virus (VACV) that induced T-cell production in vaccinated volunteers and the first draft sequence of the MPV genome associated with the suspected outbreak in several countries, May 2022. In addition, the specific binding of the modified vaccine and the immune Toll-like receptor 9 (TLR9) was estimated by molecular interaction studies. Strong interaction in the binding groove as well as good docking scores confirmed the stringency of the modified vaccine. The stability of the interaction was confirmed by a classical molecular dynamics simulation and normal mode analysis. Then, the immune simulation also indicated the ability of this vaccine to induce an effective immune response against MPV. Codon optimization and in silico cloning of the vaccine into the pET-28a (+) vector also showed its expression potential in the E. coli K12 system. The promising data obtained from the various in silico studies indicate that this vaccine is effective against MPV. However, additional in vitro and in vivo studies are still needed to confirm its efficacy.Communicated by Ramaswamy H. Sarma.

Integrating pan-genome and reverse vaccinology to design multi-epitope vaccine against Herpes simplex virus type-1

Herpes simplex virus type-1 (HSV-1), the etiological agent of sporadic encephalitis and recurring oral (sometimes genital) infections in humans, affects millions each year. The evolving viral genome reduces susceptibility to existing antivirals and, thus, necessitates new therapeutic strategies. Immunoinformatics strategies have shown promise in designing novel vaccine candidates in the absence of a clinically licensed vaccine to prevent HSV-1. However, to encourage clinical translation, the HSV-1 pan-genome was integrated with the reverse-vaccinology pipeline for rigorous screening of universal vaccine candidates. Viral targets were screened from 104 available complete genomes. Among 364 proteins, envelope glycoprotein D being an outer membrane protein with a high antigenicity score (> 0.4) and solubility (> 0.6) was selected for epitope screening. A total of 17 T-cell and 4 B-cell epitopes with highly antigenic, immunogenic, non-toxic properties and high global population coverage were identified. Furthermore, 8 vaccine constructs were designed using different combinations of epitopes and suitable linkers. VC-8 was identified as the most potential vaccine candidate regarding chemical and structural stability. Molecular docking revealed high interactive affinity (low binding energy: - 56.25 kcal/mol) of VC-8 with the target elicited by firm intermolecular H-bonds, salt-bridges, and hydrophobic interactions, which was validated with simulations. Compatibility of the vaccine candidate to be expressed in pET-29(a) + plasmid was established by in silico cloning studies. Immune simulations confirmed the potential of VC-8 to trigger robust B-cell, T-cell, cytokine, and antibody-mediated responses, thereby suggesting a promising candidate for the future of HSV-1 prevention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04022-6.

Identification of DPP-IV inhibitory peptides derived from buffalo colostrum: Mining through bioinformatics, in silico and in vitro approaches

Bioactive peptides derived from foods provide physiological health benefits beyond nutrition. This study focused on profiling small peptide inhibitors against two key serine proteases, dipeptidyl peptidase-IV (DPP-IV) and prolyl oligopeptidase (POP). DPP-IV is a well-known protein involved in diverse pathways regulating inflammation, renal, cardiovascular physiology, and glucose homeostasis. POP is yet another key target protein for neurodegenerative disorders. The study evaluated peptide libraries of buffalo colostrum whey and fat globule membrane proteins derived from pepsin and pepsin-pancreatin digestion through in silico web tools and structure-based analysis by molecular docking and binding free-energy estimation, followed by in vitro assay for DPP-IV inhibition for the lead peptides. The bioinformatic study indicated 49 peptides presented motifs with DPP-IV inhibition while 5 peptides with sequences for POP inhibition. In the molecular docking interactions study, 22 peptides interacted with active site residues of DPP-IV and 3 peptides with that of POP. The synthesized peptides, SFVSEVPEL and LTFQHNF inhibited DPP-IV in vitro with an IC50 of 193.5 μM and 1.782 mM, respectively. The study revealed the key residues for inhibition of DPP-IV and POP thus affirming the DPP-IV inhibitory potential of milk-derived peptides.

A journey to your self: The vague definition of immune self and its practical implications

The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.

Exploring malaria parasite surface proteins to devise highly immunogenic multi-epitope subunit vaccine for Plasmodium falciparum

BACKGROUND

Malaria has remained a major health concern for decades among people living in tropical and sub-tropical countries. Plasmodium falciparum is one of the critical species that cause severe malaria and is responsible for major mortality. Moreover, the parasite has generated resistance against all WHO recommended drugs and therapies. Therefore, there is an urgent need for preventive measures in the form of reliable vaccines to achieve the target of a malaria-free world. Surface proteins are the preferable choice for subunit vaccine development because they are rapidly detected and engaged by host immune cells and vaccination-induced antibodies. Additionally, abundant surface or membrane proteins may contribute to the opsonization of pathogens by vaccine-induced antibodies.

RESULTS

In our study, we have listed all those surface proteins from the literature that could be functionally important and essential for infection and immune evasion of the malaria parasite. Eight Plasmodium surface and membrane proteins from the pre-erythrocyte and erythrocyte stages were shortlisted. Thirty-seven epitopes (B-cell, CTL, and HTL epitopes) from these proteins were predicted using immune-informatic tools and joined with suitable peptide linkers to design a vaccine construct. A TLR-4 agonist peptide adjuvant was added at the N-terminus of the multi-epitope series, followed by the PADRE sequence and EAAAK linker. The TLR-4 receptor was docked with the construct's anticipated model structure. The complex of vaccine and TLR-4, with the lowest energy -1514, was found to be stable under simulated physiological settings.

CONCLUSION

This study has provided a novel multi-epitope construct that may be exploited further for the development of an efficient vaccine for malaria.

In silico prediction of CD8+ and CD4+ T cell epitopes in Leishmania major proteome: Using immunoinformatics

The leishmaniases are NDTs (neglected tropical diseases) that affect people all over the world. They are brought on by protozoans from the genus Leishmania and disseminated by phlebotomine flies that are afflicted with the disease. The best option to manage and lower the incidence of these diseases has been thought by the creation of a safe and effective vaccination. This research used an in silico based mining approach to look for high potential epitopes that might bind to MHC Class I and MHC Class II molecules (mainly; HLA-A*02:01 & HLA-DRB1*03:01) from human population in order to promote vaccine development. Based on the presence of signal peptides, GPI anchors, antigenicity predictions, and a subtractive proteomic technique, we have screened 17 putative antigenic proteins from the 8083 total proteins of L. major. After that thorough immunogenic epitope prediction were done using IEDB-AR tools. We isolated five immunogenic epitopes (three 9-mer & two 15-mer) from five antigenic proteins through docking and MD simulation analysis. Finally, these five anticipated epitopes, viz., TLPEIPVNV, ELMAPVFGL, TLAAAVALL, NSINIRLDGVTSAGF and NVPLVVDASSLFRVA have considerably stronger binding potential with their respective alleles and may trigger immunological responses. The goal of this work was to identify MHC restricted epitopes for CD8+ and CD4+ T cells activation using immunoinformatics in order to identify potential vaccine candidates against L. major parasites.

Immunoinformatics-Based Design of Multi-epitope DNA and mRNA Vaccines Against Zika Virus

In this study, we used an immunoinformatics approach to predict antigenic epitopes of Zika virus (ZIKV) proteins to assist in designing a vaccine antigen against ZIKV. We performed the prediction of CD8+ T-lymphocyte and antigenic B-cell epitopes of ZIKV proteins. The binding interactions of T-cell epitopes with major histocompatibility complex class I (MHC-I) proteins were assessed. We selected the antigenic, conserved, nontoxic, and immunogenic epitopes, which indicated significant interactions with the human leucocyte antigen (HLA-A and HLA-B) alleles and worldwide population coverage of 76.35%. The predicted epitopes were joined with the help of linkers and an adjuvant. The vaccine antigen was then analyzed through molecular docking with TLR3 and TLR8, and it was in silico cloned in the pVAX1 vector to be used as a DNA vaccine and designed as a mRNA vaccine.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Advancing molecular modeling and reverse vaccinology in broad-spectrum yellow fever virus vaccine development

Yellow fever outbreaks are prevalent, particularly in endemic regions. Given the lack of an established treatment for this disease, significant attention has been directed toward managing this arbovirus. In response, we developed a multiepitope vaccine designed to elicit an immune response, utilizing advanced immunoinformatic and molecular modeling techniques. To achieve this, we predicted B- and T-cell epitopes using the sequences from all structural (E, prM, and C) and nonstructural proteins of 196 YFV strains. Through comprehensive analysis, we identified 10 cytotoxic T-lymphocyte (CTL) and 5T-helper (Th) epitopes that exhibited overlap with B-lymphocyte epitopes. These epitopes were further evaluated for their affinity to a wide range of human leukocyte antigen system alleles and were rigorously tested for antigenicity, immunogenicity, allergenicity, toxicity, and conservation. These epitopes were linked to an adjuvant ( β -defensin) and to each other using ligands, resulting in a vaccine sequence with appropriate physicochemical properties. The 3D structure of this sequence was created, improved, and quality checked; then it was anchored to the Toll-like receptor. Molecular Dynamics and Quantum Mechanics/Molecular Mechanics simulations were employed to enhance the accuracy of docking calculations, with the QM portion of the simulations carried out utilizing the density functional theory formalism. Moreover, the inoculation model was able to provide an optimal codon sequence that was inserted into the pET-28a( +) vector for in silico cloning and could even stimulate highly relevant humoral and cellular immunological responses. Overall, these results suggest that the designed multi-epitope vaccine can serve as prophylaxis against the yellow fever virus.

Multi-epitope vaccine design using in silico analysis of glycoprotein and nucleocapsid of NIPAH virus

According to the 2018 WHO R&D Blueprint, Nipah virus (NiV) is a priority disease, and the development of a vaccine against NiV is strongly encouraged. According to criteria used to categorize zoonotic diseases, NiV is a stage III disease that can spread to people and cause unpredictable outbreaks. Since 2001, the NiV virus has caused annual outbreaks in Bangladesh, while in India it has caused occasional outbreaks. According to estimates, the mortality rate for infected individuals ranges from 70 to 91%. Using immunoinformatic approaches to anticipate the epitopes of the MHC-I, MHC-II, and B-cells, they were predicted using the NiV glycoprotein and nucleocapsid protein. The selected epitopes were used to develop a multi-epitope vaccine construct connected with linkers and adjuvants in order to improve immune responses to the vaccine construct. The 3D structure of the engineered vaccine was anticipated, optimized, and confirmed using a variety of computer simulation techniques so that its stability could be assessed. According to the immunological simulation tests, it was found that the vaccination elicits a targeted immune response against the NiV. Docking with TLR-3, 7, and 8 revealed that vaccine candidates had high binding affinities and low binding energies. Finally, molecular dynamic analysis confirms the stability of the new vaccine. Codon optimization and in silico cloning showed that the proposed vaccine was expressed to a high degree in Escherichia coli. The study will help in identifying a potential epitope for a vaccine candidate against NiV. The developed multi-epitope vaccine construct has a lot of potential, but they still need to be verified by in vitro & in vivo studies.

Immunoinformatics approach for designing a multiepitope subunit vaccine against porcine epidemic diarrhea virus genotype IIA spike protein

Background

Porcine epidemic diarrhea (PED), caused by the porcine epidemic diarrhea virus (PEDV), is associated with high mortality and morbidity rates, especially in neonatal pigs. This has resulted in significant economic losses for the pig industry. PEDV genotype II-based vaccines were found to confer better immunity against both heterologous and homologous challenges; specifically, spike (S) proteins, which are known to play a significant role during infection, are ideal for vaccine development.

Aim

This study aims to design a multi-epitope subunit vaccine targeting the S protein of the PEDV GIIa strain using an immunoinformatics approach.

Methods

Various bioinformatics tools were used to predict HTL, CTL, and B-cell epitopes. The epitopes were connected using appropriate linkers and conjugated with the CTB adjuvant and M-ligand. The final multiepitope vaccine construct (fMEVc) was then docked to toll-like receptor 4 (TLR4). The stability of the fMEVc-TLR4 complex was then simulated using GROMACS. C-immsim was then used to predict the in vitro immune response of the fMEVc.

Results

Six epitopes were predicted to induce antibody production, ten epitopes were predicted to induce CTL responses, and four epitopes were predicted to induce HTL responses. The assembled epitopes conjugated with the CTB adjuvant and M-ligand, fMEVc, is antigenic, non-allergenic, stable, and soluble. The construct showed a favorable binding affinity for TLR4, and the protein complex was shown to be stable through molecular dynamics simulations. A robust immune response was induced after immunization, as demonstrated through immune stimulation.

Conclusion

In conclusion, the multi-epitope subunit vaccine construct for PEDV designed in this study exhibits promising antigenicity, stability, and immunogenicity, eliciting robust immune responses and suggesting its potential as a candidate for further vaccine development.

A novel enzyme-linked ligand-sorbent assay (ELLSA) to screening pulmonary tuberculosis: a retrospective cross-sectional study

BACKGROUND

Little knowledge of antigen existence in the pulmonary tuberculosis (PTB) patient serum impeded its development in antigen detection technology, despite its considerable potential.

METHODS

Human ligand proteins and their adsorbent Mycobacterium tuberculosis (M.tb) proteins in the serum of PTB patients were identified using human protein chip (HuProt™) and LC-MS/MS, successively. The monoclonal antibody of ligand proteins, C5orf24, and polyclonal antibody of 9 M.tb proteins were prepared on mice and rabbits which were used to develop a novel enzyme-linked ligand-sorbent assay (ELLSA). The 412 volunteers were divided into the PTB group (n = 250) and the healthy control (n = 162). The PTB group was further divided into ATB (n = 131), LTBI (n = 18), Clinical diagnosis (n = 18), and Suspected (n = 73). All samples were tested by ELLSA to evaluate the diagnostic performance of ELLSA in PTB patients.

RESULTS

Nine ligand proteins specific to PTB patients were identified on chips, with Chromosome 5 Open Reading Frame 24 (C5orf24) and kinocilin (KNCN) showing significantly higher signals. Proteomic analysis of the C5orf24-and KNCN-adsorbent protein complexes revealed 10 and 10 of the M.tb proteins, respectively. According to the composition reference of standard, the ELLSA based on C5orf24 ligand demonstrated a higher sensitivity of 69.6% and specificity of 90.18% in ATB patients and had a sensitivity of 64.22% in bacterial negative pulmonary tuberculosis, whereas the sensitivity of MGIT 960 and Xpert M.tb/RIF were 0%, respectively.

CONCLUSIONS

M.tb proteins in serum can be enriched by ligand proteins and detected by ELLSA which proved to have excellent diagnostic performance for PTB.

Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection

Tuberculosis (TB) is an infectious disease that significantly threatens human health. However, the differential diagnosis of latent tuberculosis infection (LTBI) and active tuberculosis (ATB) remains a challenge for clinicians in early detection and preventive intervention. In this study, we developed a novel biomarker named HP16118P, utilizing 16 helper T lymphocyte (HTL) epitopes, 11 cytotoxic T lymphocyte (CTL) epitopes, and 8 B cell epitopes identified from 15 antigens associated with LTBI-RD using the IEDB database. We analyzed the physicochemical properties, spatial structure, and immunological characteristics of HP16118P using various tools, which indicated that it is a hydrophilic and relatively stable alkaline protein. Furthermore, HP16118P exhibited good antigenicity and immunogenicity, while being non-toxic and non-allergenic, with the potential to induce immune responses. We observed that HP16118P can stimulate the production of high levels of IFN-γ+ T lymphocytes in individuals with ATB, LTBI, and health controls. IL-5 induced by HP16118P demonstrated potential in distinguishing LTBI individuals and ATB patients (p=0.0372, AUC=0.8214, 95% CI [0.5843 to 1.000]) with a sensitivity of 100% and specificity of 71.43%. Furthermore, we incorporated the GM-CSF, IL-23, IL-5, and MCP-3 induced by HP16118P into 15 machine learning algorithms to construct a model. It was found that the Quadratic discriminant analysis model exhibited the best diagnostic performance for discriminating between LTBI and ATB, with a sensitivity of 1.00, specificity of 0.86, and accuracy of 0.93. In summary, HP16118P has demonstrated strong antigenicity and immunogenicity, with the induction of GM-CSF, IL-23, IL-5, and MCP-3, suggesting their potential for the differential diagnosis of LTBI and ATB.

Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum

Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Background

Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.

Method

To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.

Results

We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.

Conclusion

Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

An integrated database of experimentally validated major histocompatibility complex epitopes for antigen-specific cancer therapy

Cancer immunotherapy represents a paradigm shift in oncology, offering a superior anti-tumor efficacy and the potential for durable remission. The success of personalized vaccines and cell therapies hinges on the identification of immunogenic epitopes capable of eliciting an effective immune response. Current limitations in the availability of immunogenic epitopes restrict the broader application of such therapies. A critical criterion for serving as potential cancer antigens is their ability to stably bind to the major histocompatibility complex (MHC) for presentation on the surface of tumor cells. To address this, we have developed a comprehensive database of MHC epitopes, experimentally validated for their MHC binding and cell surface presentation. Our database catalogs 451 065 MHC peptide epitopes, each with experimental evidence for MHC binding, along with detailed information on human leukocyte antigen allele specificity, source peptides, and references to original studies. We also provide the grand average of hydropathy scores and predicted immunogenicity for the epitopes. The database (MHCepitopes) has been made available on the web and can be accessed at https://github.com/jcm1201/MHCepitopes.git. By consolidating empirical data from various sources coupled with calculated immunogenicity and hydropathy values, our database offers a robust resource for selecting actionable tumor antigens and advancing the design of antigen-specific cancer immunotherapies. It streamlines the process of identifying promising immunotherapeutic targets, potentially expediting the development of effective antigen-based cancer immunotherapies.

A Multi-epitope Vaccine Candidate Against Bolivian Hemorrhagic fever Caused by Machupo Virus

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

Synthetic transcription factor engineering for cell and gene therapy

Synthetic transcription factors (synTFs) that control beneficial transgene expression are an important method to increase the safety and efficacy of cell and gene therapy. Reliance on synTF components from non-human sources has slowed progress in the field because of concerns about immunogenicity and inducer drug properties. Recent advances in human-derived DNA-binding domains (DBDs) and transcriptional activation domains (TADs) paired with novel control modules responsive to clinically approved small molecules have poised the synTF field to overcome these hurdles. Advances include controllers inducible by autonomous signaling inputs and more complex, multi-input synTF circuits. Demonstrations of advanced control strategies with human-derived transcription factor components in clinically relevant vectors and in vivo models will facilitate progression into the clinic.

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect's vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.