World Population Coverage Research Articles

In silico design and evaluation of multi-epitope dengue virus vaccines: a promising approach to combat global dengue burden

Dengue virus, a pervasive mosquito-borne pathogen, imposes a substantial global health burden and is responsible for numerous fatalities annually globally, with tropical and sub-tropical regions particularly susceptible to dengue outbreaks. Despite decades of efforts, there has been no effective treatment or prevention for dengue, which makes it a life-threatening disease. Hence, this study proposes an innovative bioinformatics-driven approach to construct a vaccine targeting the dengue virus. The study involved a comprehensive analysis of conserved regions of dengue virus serotypes 1–4's non-structural proteins (NS1, NS3, and NS5) and structural protein (E) to predict the potential B & T-cell epitopes which were linked with appropriate adjuvants and linkers to generate four distinct vaccine candidates. The constructed vaccine models underwent rigorous evaluation, considering physicochemical attributes, structural integrity, population coverage, and immune system response through simulation. The results confirm that these vaccine candidates are non-allergenic, non-toxic, antigenic, and immunogenic. Additionally, they exhibit 99.70% world population coverage and 100% conservation across all dengue strains, which is crucial for vaccine efficacy. A Ramachandran plot showed that 95.6% of the amino acid residues of the candidates belong to the optimal zone, while around 4% are in additional allowed regions. Further, molecular docking and dynamic simulation of interaction with the human toll-like receptor 4, a fundamental component of innate immunity, was carried out to gain more insight into interaction dynamics. As a result of these analyses, the candidates' binding dynamics and structural stability were revealed. Overall, this study presents promising vaccine candidates for addressing dengue's global health burden. Their robust design and demonstrated immunogenicity make them attractive candidates for further experimental testing and development as potential vaccines against current strains and future variants.

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.

AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations.

The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope-HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope-MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations.

Development of a novel multi‑epitope vaccine against the pathogenic human polyomavirus V6/7 using reverse vaccinology.

Human polyomaviruses contribute to human oncogenesis through persistent infections, but currently there is no effective preventive measure against the malignancies caused by this virus. Therefore, the development of a safe and effective vaccine against HPyV is of high priority. First, the proteomes of 2 polyomavirus species (HPyV6 and HPyV7) were downloaded from the NCBI database for the selection of the target proteins. The epitope identification process focused on selecting proteins that were crucial, associated with virulence, present on the surface, antigenic, non-toxic, and non-homologous with the human proteome. Then, the immunoinformatic methods were used to identify cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes from the target antigens, which could be used to create epitope-based vaccine. The physicochemical features of the designed vaccine were predicted through various online servers. The binding pattern and stability between the vaccine candidate and Toll-like receptors were analyzed through molecular docking and molecular dynamics (MD) simulation, while the immunogenicity of the designed vaccines was assessed using immune simulation. Online tools were utilized to forecast the most optimal epitope from the immunogenic targets, including LTAg, VP1, and VP1 antigens of HPyV6 and HPyV7. A multi-epitope vaccine was developed by combining 10 CTL, 7 HTL, and 6 LBL epitopes with suitable linkers and adjuvant. The vaccine displayed 98.35% of the world's population coverage. The 3D model of the vaccine structure revealed that the majority of residues (87.7%) were located in favored regions of the Ramachandran plot. The evaluation of molecular docking and MD simulation revealed that the constructed vaccine exhibits a strong binding (-1414.0 kcal/mol) towards the host's TLR4. Moreover, the vaccine-TLR complexes remained stable throughout the dynamic conditions present in the natural environment. The immune simulation results demonstrated that the vaccine design had the capacity to elicit robust immune responses in the host. The multi-parametric analysis revealed that the designed vaccine is capable of inducing sustained immunity against the selected polyomaviruses, although further in-vivo investigations are needed to verify its effectiveness.

Advanced vaccinomic, immunoinformatic, and molecular modeling strategies for designing Multi- epitope vaccines against the Enterobacter cloacae complex.

The increasing and ongoing issue of antibiotic resistance in bacteria is of huge concern globally, mainly to healthcare facilities. It is now crucial to develop a vaccine for therapeutic and preventive purposes against the bacterial species causing hospital-based infections. Among the many antibiotic- resistant bacterial pathogens, the Enterobacter cloacae complex (ECC) including six species, E. Colcae, E. absuriae, E. kobie, E. hormaechei, E. ludwigii, and E. nimipressuralis, are dangerous to public health and may worsen the situation. Vaccination plays a vital role in the prevention of infections and infectious diseases. This research highlighted the construction and design of a multi-epitope vaccine for the E. cloacae complex by retrieving their complete sequenced proteome. The retrieved proteome was assessed to opt for potential vaccine candidates using immunoinformatic tools. Both B and T-cell epitopes were predicted in order to create both humoral and cellular immunity and further scrutinized for antigenicity, allergenicity, water solubility, and toxicity analysis. The final potential epitopes were subjected to population coverage analysis. Major histocompatibility complex (MHC) class combined, and MHC Class I and II world population coverage was obtained as 99.74%, and 98.55% respectively while a combined 81.81% was covered. A multi-epitope peptide-based vaccine construct consisting of the adjuvant, epitopes, and linkers was subjected to the ProtParam tool to calculate its physiochemical properties. The total amino acids were 236, the molecular weight was 27.64kd, and the vaccine construct was stable with an instability index of 27.01. The Grand Average of Hydropathy (GRAVY) (hydrophilicity) value obtained was -0.659, being more negative and depicting the hydrophilic character. It was non-allergen antigenic with an antigenicity of 0.8913. The vaccine construct was further validated for binding efficacy with immune cell receptors MHC-I, MHC-II, and Toll-like receptor (TLR)-4. The molecular docking results depict that the designed vaccine has good binding potency with immune receptors crucial for antigen presentation and processing. Among the Vaccine-MHC-I, Vaccine-MHC-II, and Vaccine-TLR-4 complexes, the best-docked poses were identified based on their lowest binding energy scores of -886.8, -995.6, and -883.6, respectively. Overall, we observed that the designed vaccine construct can evoke a proper immune response and the construct could help experimental researchers in the formulation of a vaccine against the targeted pathogens.

An mRNA-based reverse-vaccinology strategy to stimulate the immune response against Nipah virus in humans using fusion glycoproteins.

The zoonotic pathogen, Nipah virus, is considered a potential healthcare threat due to its high mortality rates and detrimental symptoms like encephalitis. Ribavirin, an antiviral drug helps in overcoming the number of casualties and reducing the mortality rate, but no long-lasting solution has been proposed yet putting global health security in jeopardy. Given the cognizance of mRNA-based vaccines as safe and efficacious preventative strategies against pathogens, the current study has utilized the reverse-vaccinology approach coupled with immunoinformatics to propose an mRNA-based vaccine candidate against the Nipah virus. To ensure the effectiveness of the vaccine candidate against all strains of Nipah and associated viruses, three fusion glycoproteins from Nipah and Hendra viruses were selected. A total of 30 potential epitopes, 10 B-cell-, 10 MHC-I-, and 10 MHC-II-specific, were screened for the construct. The finalized epitopes were highly antigenic with scores ranging from 0.75 to 1.7615 at a threshold of 0.4 for viruses and non-homologous to Homo sapiens eradicating any chance of immune tolerance. The construct, with a World population coverage of 97.2%, was structurally stable, thermostable, and hydrophilic with indices of 32.91, 93.62, and -0.002, respectively. The vaccine candidate's tertiary structure was predicted with a TM score of 0.131 and the refined model displayed superlative RAMA improvement (98.2) and MolProbity score (0.975). A quality factor of 93.5421% further validated the structural quality and stability. A prompt and stable immune response was also simulated, and the vaccine candidate was shown to eliminate from the body within the first five days of injection. Immune complexes count of 7000 mg/mL was predicted against the antigen with a small but nonsignificant danger signal, countered by the cytokines. Lastly, strong molecular interactions of the vaccine candidate with TLR-3 (331.09 kcal/mol) and TLR-4 (-333.31 kcal/mol) and molecular dynamics simulation analysis authenticated the immunogenic potential of the vaccine candidate. This vaccine candidate can serve as a foundation for future in-vitro and in-vivo trials to minimize or eradicate the diseases associated with the Nipah virus or the Henipaviral family.

A Peptide Vaccine Design Targeting KIT Mutations in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a leading blood cancer subtype that can be caused by 27 gene mutations. Previous studies have explored potential vaccine and drug treatments against AML, but many were proven immunologically insignificant. Here, we targeted this issue and applied various clinical filters to improve immune response. KIT is an oncogenic gene that can cause AML when mutated and is predicted to be a promising vaccine target because of its immunogenic responses when activated. We designed a multi-epitope vaccine targeting mutations in the KIT oncogene using CD8+ and CD4+ epitopes. We selected the most viable vaccine epitopes based on thresholds for percentile rank, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, allergenicity, and stability. The efficacy of data was observed through world and regional population coverage of our vaccine design. Then, we obtained epitopes for optimized population coverage from PCOptim-CD, a modified version of our original Java-based program code PCOptim. Using 24 mutations on the KIT gene, 12 CD8+ epitopes and 21 CD4+ epitopes were obtained. The CD8+ dataset had a 98.55% world population coverage, while the CD4+ dataset had a 65.14% world population coverage. There were five CD4+ epitopes that overlapped with the top CD8+ epitopes. Strong binding to murine MHC molecules was found in four CD8+ and six CD4+ epitopes, demonstrating the feasibility of our results in preclinical murine vaccine trials. We then created three-dimensional (3D) models to visualize epitope-MHC complexes and TCR interactions. The final candidate is a non-toxic and non-allergenic multi-epitope vaccine against KIT mutations that cause AML. Further research would involve murine trials of the vaccine candidates on tumor cells causing AML.

In silico design and evaluation of a novel mRNA vaccine against BK virus: a reverse vaccinology approach.

Human polyomavirus type 1, or BK virus (BKV), is a ubiquitous pathogen belonging to the polyomaviridae family mostly known for causing BKV-associated nephropathy (BKVN) and allograft rejection in kidney transplant recipients (KTRs) following the immunosuppression regimens recommended in these patients. Reduction of the immunosuppression level and anti-viral agents are the usual approaches for BKV clearance, which have not met a desired outcome yet. There are also debating matters such as the effect of this pathogen on emerging various comorbidities and the related malignancies in the human population. In this study, a reverse vaccinology approach was implemented to design a mRNA vaccine against BKV by identifying the most antigenic proteins of this pathogen. Potential immunogenic T and B lymphocyte epitopes were predicted through various immunoinformatic tools. The final epitopes were selected according to antigenicity, toxicity, allergenicity, and cytokine inducibility scores. According to the obtained results, the designed vaccine was antigenic, neutral at the physiological pH, non-toxic, and non-allergenic with a world population coverage of 93.77%. Since the mRNA codon optimization ensures the efficient expression of the vaccine in a host cell, evaluation of different parameters showed our designed mRNA vaccine has a stable structure. Moreover, it had strong interactions with toll-like receptor 4 (TLR4) according to the molecular dynamic simulation studies. The in silico immune simulation analyses revealed an overall increase in the immune responses following repeated exposure to the designed vaccine. Based on our findings, the vaccine candidate is ready to be tested as a promising novel mRNA therapeutic vaccine against BKV.

SARS-CoV-2 Omicron (BA.1 and BA.2) specific novel CD8+ and CD4+ T cell epitopes targeting spike protein

The Omicron (BA.1/B.1.1.529) variant of SARS-CoV-2 harbors an alarming 37 mutations on its spike protein, reducing the efficacy of current COVID-19 vaccines. In this study, we identified CD8+ and CD4+ T cell epitopes from SARS-CoV-2 S protein mutants. To identify the highest quality CD8 and CD4 epitopes from the Omicron variant, we selected epitopes with a high binding affinity towards both MHC I and MHC II molecules. We applied other clinical checkpoint predictors, including immunogenicity, antigenicity, allergenicity, instability and toxicity. Subsequently, we found eight Omicron (BA.1/B.1.1.529) specific CD8+ and eleven CD4+ T cell epitopes with a world population coverage of 76.16% and 97.46%, respectively. Additionally, we identified common epitopes across Omicron BA.1 and BA.2 lineages that target mutations critical to SARS-CoV-2 virulence. Further, we identified common epitopes across B.1.1.529 and other circulating SARS-CoV-2 variants, such as B.1.617.2 (Delta). We predicted CD8 epitopes’ binding affinity to murine MHC alleles to test the vaccine candidates in preclinical models. The CD8 epitopes were further validated using our previously developed software tool PCOptim. We then modeled the three-dimensional structures of our top CD8 epitopes to investigate the binding interaction between peptide-MHC and peptide-MHC-TCR complexes. Notably, our identified epitopes are targeting the mutations on the RNA-binding domain and the fusion sites of S protein. This could potentially eliminate viral infections and form long-term immune responses compared to relatively short-lived mRNA vaccines and maximize the efficacy of vaccine candidates against the current pandemic and potential future variants.

Designing a novel chimeric multi-epitope vaccine against Burkholderia pseudomallei, a causative agent of melioidosis.

Burkholderia pseudomallei, a gram-negative soil-dwelling bacterium, is primarily considered a causative agent of melioidosis infection in both animals and humans. Despite the severity of the disease, there is currently no licensed vaccine on the market. The development of an effective vaccine against B. pseudomallei could help prevent the spread of infection. The purpose of this study was to develop a multi-epitope-based vaccine against B. pseudomallei using advanced bacterial pan-genome analysis. A total of four proteins were prioritized for epitope prediction by using multiple subtractive proteomics filters. Following that, a multi-epitopes based chimeric vaccine construct was modeled and joined with an adjuvant to improve the potency of the designed vaccine construct. The structure of the construct was predicted and analyzed for flexibility. A population coverage analysis was performed to evaluate the broad-spectrum applicability of B. pseudomallei. The computed combined world population coverage was 99.74%. Molecular docking analysis was applied further to evaluate the binding efficacy of the designed vaccine construct with the human toll-like receptors-5 (TLR-5). Furthermore, the dynamic behavior and stability of the docked complexes were investigated using molecular dynamics simulation, and the binding free energy determined for Vaccine-TLR-5 was delta total -168.3588. The docking result revealed that the vaccine construct may elicit a suitable immunological response within the host body. Hence, we believe that the designed in-silico vaccine could be helpful for experimentalists in the formulation of a highly effective vaccine for B. pseudomallei.

Envelope Glycoprotein based multi-epitope vaccine against a co-infection of Human Herpesvirus 5 and Human Herpesvirus 6 using in silico strategies.

The Human Betaherpesviruses HHV-5 and HHV-6 are quite inimical in immunocompromised hosts individually. A co-infection of both has been surmised to be far more disastrous. This can be attributed to a synergetic effect of their combined pathologies. While there have been attempts to develop a vaccine against each virus, no efforts were made to contrive an effective prophylaxis for the highly detrimental co-infection. In this study, an ensemble of viral envelope glycoproteins from both the viruses was utilized to design a multi-epitope vaccine using immunoinformatics tools. A collection of bacterial protein toll-like receptor agonists (BPTAs) was screened to identify a highly immunogenic adjuvant for the vaccine construct. The constructed vaccine was analysed using an array of methodologies ranging from World population coverage analysis to Immune simulation, whose results indicate high vaccine efficacy and stability. Furthermore, codon optimization and in silico cloning analysis were performed to check for efficient expression in a bacterial system. Collectively, these findings demonstrate the potential of the constructed vaccine to elicit an immune response against HHV-5 and HHV-6, thus supporting the viability of in vitro and in vivo studies.

Epitope vaccine design for Toxoplasma gondii based on a genome-wide database of membrane proteins

BackgroundThere is presently no effective and safe vaccine for Toxoplasma gondii for humans. The study described here was designed to search for a novel group of optimal B cell and T cell epitopes from Toxoplasma membrane proteins using genome-wide comprehensive screening.MethodsThe amino acid sequences of membrane proteins of T. gondii were obtained from the UniProt database. The ABCPred and BepiPred servers were employed to predict the linear B cell epitopes. The Immune Epitope Database (IEDB) online service was utilized to forecast T cell epitopes within T. gondii membrane proteins that bind to human leukocyte antigen (HLA) class I (HLA-I) or HLA-II molecules.ResultsFrom the 314 membrane proteins of T. gondii, a total of 14 linear B cell epitopes embedded in 12 membrane proteins were identified. Eight epitopes for major histocompatibility complex (MHC) class I (MHC-I) molecules and 18 epitopes for MHC-II molecules were ultimately selected, for which world population coverage percentiles were 71.94% and 99.76%, respectively. The top rated combinations of linear B cell epitopes and T cell epitopes covering both BALB/c mice and a majority of the human population were identified for the development of a protective vaccine.ConclusionsThe ultimate vaccine construct described here, which comprises B cells, MHC-I and MHC-II epitopes, might protect individuals against T. gondii infection by inducing humoral and cellular immune responses.Graphic abstract

Immunoinformatics- and Bioinformatics-Assisted Computational Designing of a Novel Multiepitopes Vaccine Against Cancer-Causing Merkel Cell Polyomavirus.

Merkel cell polyomavirus (MCV) contains double-stranded DNA as its genome and is the fifth polyomavirus that infects humans. The virus causes Merkel cell carcinoma (aggressive skin cancer). Till present, no proper drug or vaccines are available to treat/prevent the virus infection and stop the emergence of Merkel cell carcinoma. In this study, computational vaccine design strategies were applied to design a chimeric-epitopes vaccine against the virus. The complete proteome comprised of four proteins was filtered through various vaccine candidacy parameters and as such two proteins, namely, capsid protein VP1 and capsid protein VP2, were considered as good vaccine targets. Furthermore, they harbor safe and potential B and T cell epitopes, which can be used in a chimeric multiepitopes-based vaccine design. The epitopes of the vaccine have maximum world population coverage of 95.04%. The designed vaccine structure was modeled in 3D that reported maximum residues in favored regions (95.7%) of the Ramachandran plot. The interactions analysis with different human immune receptors like TLR3, MHC-I, and MHC-II illustrated vaccine's good binding affinity and stable dynamics. The structural deviations of the vaccine receptor(s) complexes are within 5 Å, where majority of the receptors residues remain in good equilibrium in the simulation time. Also, the vaccine was found to form between 60 and 100 hydrogen bonds to receptors. The vaccine stimulated strong immune responses in addition to interferon and cytokines. The strength of vaccine-receptor(s) binding was further affirmed by binding energies estimation that concluded <-150.32 kcal/mol of net binding energy. All these findings suggest the vaccine as a promising candidate that needs further experimental testing to disclose its real immune protective efficacy. Furthermore, the designed vaccine might accelerate vaccine development against the MCV and could save time and expenses.

Development of Multi-epitope Based Subunit Vaccine Against Crimean-Congo Hemorrhagic Fever Virus Using Reverse Vaccinology Approach.

Crimean-Congo hemorrhagic fever (CCHF) is a viral disease caused by the Crimean-Congo hemorrhagic fever virus (CCHFV) of the Nairovirus genus. CCHF has occurred endemically in several regions of Africa, Southern Europe, and Central and Southeast Asia, with a case fatality rate of 5 to 80%. The World health organization enlisted CCHF as one of the top prioritized diseases for research and development in emergency contexts that making it a public health concern as no effective vaccine is available till date. Therefore, the present study aims to develop an effective multi-epitope subunit vaccine using immunoinformatics and reverse vaccinology approach against this virus. The B-cell and T-cell epitopes were predicted from structural and non-structural proteins, and filtered by immunogenicity, allergenicity, toxicity, conservancy, and cross-reactivity. The computational analysis revealed that the epitopes could induce an adequate immune response and had strong associations with their respective human leukocyte antigen (HLA) alleles with 98.94% of total world population coverage. Finally, the vaccine with 427 amino acids was constructed by connecting 8 cytotoxic T-lymphocytes, 4 helper T-lymphocytes, and 10 B-cell epitopes with appropriate linkers and β-defensin as an adjuvant. The antigenicity, allergenicity, solubility, and physiochemical properties of the vaccine were evaluated, followed by structural modelling, refinement, and validation. In addition, molecular docking and molecular dynamic simulations revealed a robust binding affinity and stability of the vaccine-immune receptor complex. Moreover, the codons were optimized for its higher expression in Escherichia coli (E. coli) K12 strain followed by in silico cloning. The proposed subunit vaccine developed in this study could be a potential candidate against CCHFV. However, further experimental validation is required to ensure the immunogenicity and safety profile of the proposed vaccine for combating and eradicating CCHFV.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10989-022-10430-0.

A New Epitope Selection Method: Application to Design a Multi-Valent Epitope Vaccine Targeting HRAS Oncogene in Squamous Cell Carcinoma

We developed an epitope selection method for the design of MHC targeting peptide vaccines. The method utilizes predictions for several clinical checkpoint filters, including binding affinity, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, and instability. The accuracy of the prediction tools for these filter variables was confirmed using experimental data obtained from the Immune Epitope Database (IEDB). We also developed a graphical user interface computational tool called ‘PCOptim’ to assess the success of an epitope filtration method. To validate the filtration methods, we used a large data set of experimentally determined, immunogenic SARS-CoV-2 epitopes, which were obtained from a meta-analysis. The validation process proved that placing filters on individual parameters was the most effective method to select top epitopes. For a proof-of-concept, we designed epitope-based vaccine candidates for squamous cell carcinoma, selected from the top mutated epitopes of the HRAS gene. By comparing the filtered epitopes to PCOptim’s output, we assessed the success of the epitope selection method. The top 15 mutations in squamous cell carcinoma resulted in 16 CD8 epitopes which passed the clinical checkpoints filters. Notably, the identified HRAS epitopes are the same as the clinical immunogenic HRAS epitope-based vaccine candidates identified by the previous studies. This indicates further validation of our filtration method. We expect a similar turn-around for the other designed HRAS epitopes as a vaccine candidate for squamous cell carcinoma. Furthermore, we obtained a world population coverage of 89.45% for the top MHC Class I epitopes and 98.55% population coverage in the absence of the IFNγ release clinical checkpoint filter. We also identified some of the predicted human epitopes to be strong binders to murine MHC molecules, which provides insight into studying their immunogenicity in preclinical models. Further investigation in murine models could warrant the application of these epitopes for treatment or prevention of squamous cell carcinoma.

In silico CD4 + T-cell multiepitope prediction and HLA distribution analysis for Marburg Virus—A strategy for vaccine designing

Marburg, a RNA virus (MRV), is responsible for causing hemorrhagic fever that affects humans and non-human primates. World Health Organization (WHO), National Institutes of Health (NIH) and Centre of Disease Control and Prevention (CDC) considered this as an extremely dangerous virus, thus categorised as risk group 4, category A priority pathogen and category “A” bioterrorism agent, respectively. Despite of all these alarming concerns, no prophylaxis arrangements are available against this virus till date. In fact, the construction of immunogenic vaccine candidates by traditional molecular immunology methods is time consuming and very expensive. Considering these concerns, herein, we have designed CD4 + T Cell multiepitopes against MRV using in silico approach. The pin-point criteria of the screening and selection of potential epitopes are, non-mutagenic, antigenic, large HLAs coverage, non-toxic and high world population coverage. This kind of methodology and investigations can precisely reduce the expenditure and valuable time for experimental planning in development of vaccines in laboratories. In current scenario, researchers are frequently using in silico approaches to speed up their vaccine-based lab studies. The computational studies are highly valuable for the screening of large epitope dataset into smaller one prior to in vitro and in vivo confirmatory analyses.

Immunoinformatic identification of the epitope-based vaccine candidates from Maltoporin, FepA and OmpW of Shigella Spp, with molecular docking confirmation.

Shigella is a bacterial pathogen that causes shigellosis, fatal bacillary dysentery, responsible for a higher level of mortality worldwide. We adopted a number of computational approaches to predict potential epitope-based vaccine candidates of immunogenic proteins of Shigella spp. We selected three cell surface proteins of the bacterium according to their antigenicity using the VaxiJen server, including, FepA, Maltoporin, and OmpW. The sequence analyses by the IEDB server resulted in three 15-mer peptides of the core epitope, FTAEHTQSV, FLVNQTLTL, and MRAGSATVR from FepA, Maltoporin, and OmpW, respectively, as the most potential epitopes that have an affinity with both cytotoxic and helper T-cells. Moreover, the epitopes showed 73.76%, 99.0%, and 93.07% world population coverage, along with 100% conservancy among the Shigella subspecies. The molecular docking simulation studies were performed to verify the interactions between the peptides and the respective HLAs. Docking analyses showed that the Epitope-MHC complexes had a higher level of global energy score dictating strong binding. We have also predicted B-cell epitopes from the sequences of these three proteins. In vivo study of the proposed epitope might contribute to the development of a functional and efficient vaccine, which might be an effective way to elude dysentery from the world.

Epitope-Based Vaccine Design for Tuberculosis HIV Infection Through in silico Approach

&lt;b&gt;Background and Objective:&lt;/b&gt; Tuberculosis (TB) is one of the leading causes of HIV-related death among people living with it. TB occurs more often severe in a weakened immune system, particularly when a patient is infected with HIV. People infected with HIV are 15-22 times more likely to fall ill with TB. In this research, an epitope-based vaccine has been specially designed for people living with HIV, since the current tuberculosis vaccine Bacillus Calmette-Guerin (BCG) has been proven to cause more harm than good in treating patients suffering from poor immune systems. &lt;b&gt;Materials and Methods:&lt;/b&gt; The epitopes were selected from polysaccharide-protein of Mycobacterium tuberculosis and protein envelope of the Human immunodeficiency virus. B cell epitopes have been predicted using BepiPred 2.0, while T cell epitopes predicted using SMM, both are provided by Immune Epitope Database (IEDB). &lt;b&gt;Results:&lt;/b&gt; This research had designed vaccine combinations for each type of epitopes and types of the pathogen with world population coverage of >85% for MHC class I epitopes and >99% for MHC class II epitopes. &lt;b&gt;Conclusion:&lt;/b&gt; With each epitope were selected based on how strong its bond with HLA and how many HLA can bind with it. As this research was done through &lt;i&gt;in silico&lt;/i&gt; approach, &lt;i&gt;in vivo&lt;/i&gt; test is still needed to guarantee the result of the designed vaccine.

Mining the Mycobacterium tuberculosis proteome for identification of potential T-cell epitope based vaccine candidates

Identification of protective antigens for designing a high-efficacy tuberculosis vaccine is the need of the hour. Till date only 7% of the Mycobacterium tuberculosis proteome has been explored for discovering antigens capable of activating T-cell responses. Therefore, it becomes crucial to screen the remaining Mycobacterium tuberculosis proteome for more immunodominant T-cell epitopes. An extensive knowledge of the epitopes recognized by our immune system can aid this process of finding potential T cell antigens for development of a better TB vaccine. In the present in-silico study, 237 proteins belonging to the ‘virulence, detoxification, and adaptation’ category of Mycobacterium tuberculosis proteome were targeted for T-cell epitope screening. 50825 MHC Class I and 49357 MHC Class II epitopes were generated using NetMHC3.4 and IEDB servers respectively and tested for their antigenicity and cytokine stimulation. The highest antigenic epitopes were analyzed for their world population coverage and epitope conservancy. Molecular docking and molecular dynamics simulation studies were performed to corroborate the binding affinities and structural stability of the peptide-MHC complexes. We predicted a total of 3 MHC Class I (ILLKMCWPA, FAVGMNVYV, and SLAGNSAKV) and 7 MHC Class II (DLTIGFFLHIPFPPV, RPDLTIGFFLHIPFP, LTIGFFLHIPFPPVE, VLVFALVVALVYLQF, LVFALVVALVYLQFR, PNLVAARFIQLTPVY, and LVLVFALVVALVYLQ) epitopes that can be promising vaccine candidates. These predicted epitopes belong to 6 distinct proteins: Rv0169 (mce1a), Rv3490 (ostA), Rv3496 (mce4D), Rv1085c, Rv0563 (HtpX), Rv3497c (mce4C). All these proteins are expressed at different stages in the life cycle of Mycobacterium tuberculosis and thus, the predicted epitopes could be employed as candidates for designing a multistage-multiepitopic vaccine.

Exploring the cancer-testis antigen BORIS to design a novel multi-epitope vaccine against breast cancer based on immunoinformatics approaches

Recently, cancer immunotherapy has gained lots of attention to replace the current chemoradiation approaches and multi-epitope cancer vaccines are manifesting as the next generation of cancer immunotherapy. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a novel multi-epitope vaccine against breast cancer. The most immunogenic regions of the BORIS cancer-testis antigen were selected according to the binding affinity to MHC-I and II molecules as well as containing multiple cytotoxic T lymphocyte (CTL) epitopes by multiple immunoinformatics servers. The selected regions were linked together by GPGPG linker. Also, a T helper epitope (PADRE) and the TLR-4/MD-2 agonist (L7/L12 ribosomal protein from mycobacterium) were incorporated by A(EAAAK)3A linker to form the final vaccine construct. Then, its physicochemical properties, cleavage sites, TAP transport efficiency, B cell epitopes, IFN-γ inducing epitopes and population coverage were predicted. The final vaccine construct was reverse translated, codon-optimized and inserted into pcDNA3.1 to form the DNA vaccine. The final vaccine construct was a stable, immunogenic and non-allergenic protein that contained numerous CTL epitopes, IFN-γ inducing epitopes and several linear and conformational B cell epitopes. Also, the final vaccine construct formed stable and significant interactions with TLR-4/MD-2 complex according to molecular docking and dynamics simulations. Moreover, its world population coverage for HLA-I and HLA-II were about 93% and 96%, respectively. Taking together, these preliminary results can be used as an appropriate platform for further experimental investigations. Communicated by Ramaswamy H. Sarma