Epitope-based Peptide Vaccine Research Articles

Precision Vaccine Development: Unraveling the Immunoinformatics Code Against Epstein-Barr Virus's T41 Glycoprotein 350/220

Epstein-Barr Virus (EBV) poses a substantial global health threat, with a 90% morbidity rate in adults worldwide. Linked to infectious mononucleosis, autoimmune diseases, and cancers, there is an imperative for effective preventive measures, given the absence of current vaccines or medications. This study employs a bioinformatics strategy to design an epitope vaccine targeting EBV. The primary target for immunity is the EBV glycoprotein 350/220 (gp350), which mediates attachment to B cells through complement receptor 2 (CR2/CD21). The selected protein, EBV glycoprotein 350/220 was chosen based on its role in pathogenicity. In the post-therapeutic approach, three-dimensional structure prediction, validation, and verification processes were conducted, followed by molecular docking studies to identify potential drug-binding sites and suitable therapeutic inhibitors for the target protein. The protein's ability to stimulate humoral and cell-mediated immunity was assessed, leading to the identification of a peptide segment ATNLFLLEL from positions 342 to 356 as the predominant T cell epitope. This epitope demonstrated compatibility with various HLA profiles, enhanced by its interaction with eight HLA-A and HLA-B alleles. Population coverage analysis revealed broad responsiveness across geographies, suggesting its potential as a widely applicable vaccine component. Its non-allergic classification further supports its potential efficacy for diverse individuals. Docking studies with HLA-B*3501 confirmed its significant affinity for MHC-I molecules, establishing it as a promising vaccination candidate. Additionally, a region from amino acid positions 253 to 310 was identified as containing potential B-cell epitopes, with the sequence "LGAGELALTMRSKKLP" showing promise due to its antigenic potential. This computational approach to epitope-based peptide vaccine design and target site prediction against EBV represents a pioneering advancement in EBV virus research, emphasizing the need for validation through <i>in vitro</i> and <i>in vivo</i> trials.

In silico designing of novel epitope-based peptide vaccines against HIV-1.

The HIV-1 virus has been regarded as a catastrophe for human well-being. The global incidence of HIV-1-infected individuals is increasing. Hence, development of effective immunostimulatory molecules has recently attracted an increasing attention in the field of vaccine design against HIV-1 infection. In this study, we explored the impacts of CD40L and IFN-γ as immunostimulatory adjuvants for our candidate HIV-1 Nef vaccine in human and mouse using immunoinformatics analyses. Overall, 18 IFN-γ-based vaccine constructs (9 constructs in human and 9 constructs in mouse), and 18 CD40L-based vaccine constructs (9 constructs in human and 9 constructs in mouse) were designed. To find immunogenic epitopes, important characteristics of each component (e.g., MHC-I and MHC-II binding, and peptide-MHC-I/MHC-II molecular docking) were determined. Then, the selected epitopes were applied to create multiepitope constructs. Finally, the physicochemical properties, linear and discontinuous B cell epitopes, and molecular interaction between the 3D structure of each construct and CD40, IFN-γ receptor or toll-like receptors (TLRs) were predicted. Our data showed that the full-length CD40L and IFN-γ linked to the N-terminal region of Nef were capable of inducing more effective immune response than multiepitope vaccine constructs. Moreover, molecular docking of the non-allergenic full-length- and epitope-based CD40L and IFN-γ constructs to their cognate receptors, CD40 and IFN-γ receptors, and TLRs 4 and 5 in mouse were more potent than in human. Generally, these findings suggest that the full forms of these adjuvants could be more efficient for improvement of HIV-1 Nef vaccine candidate compared to the designed multiepitope-based constructs.

Immunoinformatics, molecular docking and dynamics simulation approaches unveil a multi epitope-based potent peptide vaccine candidate against avian leukosis virus

Lymphoid leukosis is a poultry neoplastic disease caused by avian leukosis virus (ALV) and is characterized by high morbidity and variable mortality rates in chicks. Currently, no effective treatment and vaccination is the only means to control it. This study exploited the immunoinformatics approaches to construct multi-epitope vaccine against ALV. ABCpred and IEDB servers were used to predict B and T lymphocytes epitopes from the viral proteins, respectively. Antigenicity, allergenicity and toxicity of the epitopes were assessed and used to construct the vaccine with suitable adjuvant and linkers. Secondary and tertiary structures of the vaccine were predicted, refined and validated. Structural errors, solubility, stability, immune simulation, dynamic simulation, docking and in silico cloning were also evaluated.The constructed vaccine was hydrophilic, antigenic and non-allergenic. Ramchandran plot showed most of the residues in the favored and additional allowed regions. ProsA server showed no errors in the vaccine structure. Immune simulation showed significant immunoglobulins and cytokines levels. Stability was enhanced by disulfide engineering and molecular dynamic simulation. Docking of the vaccine with chicken’s TLR7 revealed competent binding energies.The vaccine was cloned in pET-30a(+) vector and efficiently expressed in Escherichia coli. This study provided a potent peptide vaccine that could assist in tailoring a rapid and cost-effective vaccine that helps to combat ALV. However, experimental validation is required to assess the vaccine efficiency.

Construction of Peptide Vaccine Candidate Based on β-Cell Epitopes of Indonesian Monkeypox Virus (MPXV) Virulence Protein:A Reverse Vaccinology

Infection with a DNA virus called monkeypox virus (MPXV) in humans has been identified in the Congo since 1970. Antiviral drugs are not effective for preventing MPXV infection. MPXV infection cases in Indonesia are very low but MPXV has the potential to become a global pandemic so it is very important to do prevention such as vaccine development. This study aims to construct a B cell epitope-based peptide vaccine candidate in Indonesian MPXV through an in silico approach.The development of the MPXV vaccine can be performed through a computational approach for preliminary studies. In silico-based construction of vaccines using B cell epitopes, antigenicity, allergenicity, docking, and molecular dynamics analysis have been used by researchers and scientists in solving viral infection cases. We recommend Pep A and Pep D as vaccine candidates because they allow recognition by B cells, antigenic peptides, non-allergenic and non-toxin. Peptide vaccine candidate can trigger B-cell activation to produce IgM isotype-specific antibodies through BCR interaction. In summary, the results of this study can be used for an initial study of MPXV vaccine development in Indonesia.

Exploring dengue genome to design effective multi epitope-based peptide vaccine by immunoinformatics approach against all serotypes of dengue virus

Dengue fever is an acute tropical disease that is spread by Aedes species mosquitoes. It is a virus-mediated illness that causes headaches, fever, severe joint and muscle pain, swollen glands (lymphadenopathy), and rashes. Every year, 390 million people around the world suffer from it and ∼20 % die from cases that are left untreated. The virus is transmitted to humans by mosquito bites of infected Aedes species. When it gets into the body, its envelope protein attaches to the host cell surface receptor and initiates the viral infection cycle by the formation of a smooth coat with icosahedral symmetry. Due to its high mutation-prone nature and differences in protein sequences among serotypes, it is difficult to develop a stable vaccine suitable against all serotypes. In this study, a software-based vaccinology approach was used to design a vaccine construct against the envelope protein of all four subtypes of the dengue virus. This construct was designed by joining two epitopes of the protein. One epitope was taken from the envelope protein of DEN-2 which was conserved for DEN-1, DEN-2, and DEN-3, and another epitope from the same protein of DEN-4 serotype as the sequence composition of the protein is slightly different in these two groups of serotypes. Here, firstly 18 epitopes for dengue virus-1, 2, 3, and 12 epitopes for dengue virus-4 were retrieved based on the highest combined score of NetCTL server analysis of human leukocyte antigen (HLA) supertypes. Among them, the epitope MAILGDTAW has shown 100 % conservancy in strains of dengue virus 1, 2, 3, and KLRIKGMSY, TTAKEVALL, TPRSPSVEV, FRKGSSIGK, KTWLVHKQW have shown 100 % conservancy in dengue virus 4. In population coverage analysis, MAILGDTAW has shown 88.63 % and FRKGSSIGK has shown 90.72 % (highest among other DEN-4 epitopes) population coverage of the entire world population. These two epitopes along with their 15-mer extensions (MHCII binding peptides) were found as non-allergen and non-toxic. Finally, the two different 15-mer extensions (to get 100 % sequence conservancy) of the epitope MAILGDTAW and the 15-mer extension of the epitope FRKGSSIGK were put together using an EAAAK linker to construct the multi-epitope vaccine. In-vivo analysis of the proposed vaccine construct would aid in the development of a functional immune response that would eradicate dengue complications worldwide.

Immunoinformatics-aided rational design of multiepitope-based peptide vaccine (MEBV) targeting human parainfluenza virus 3 (HPIV-3) stable proteins

BackgroundHuman parainfluenza viruses (HPIVs) are common RNA viruses responsible for respiratory tract infections. Human parainfluenza virus 3 (HPIV-3) is particularly pathogenic, causing severe illnesses with no effective vaccine or therapy available. ResultsThe current study employed a systematic immunoinformatic/reverse vaccinology approach to design a multiple epitope-based peptide vaccine against HPIV-3 by analyzing the virus proteome. On the basis of a number of therapeutic features, all three stable and antigenic proteins with greater immunological relevance, namely matrix protein, hemagglutinin neuraminidase, and RNA-directed RNA polymerase L, were chosen for predicting and screening suitable T-cell and B-cell epitopes. All of our desired epitopes exhibited no homology with human proteins, greater population coverage (99.26%), and high conservancy among reported HPIV-3 isolates worldwide. All of the T- and B-cell epitopes are then joined by putative ligands, yielding a 478-amino acid-long final construct. Upon computational refinement, validation, and thorough screening, several programs rated our peptide vaccine as biophysically stable, antigenic, allergenic, and non-toxic in humans. The vaccine protein demonstrated sufficiently stable interaction as well as binding affinity with innate immune receptors TLR3, TLR4, and TLR8. Furthermore, codon optimization and virtual cloning of the vaccine sequence in a pET32a (+) vector showed that it can be readily expressed in the bacterial system. ConclusionThe in silico designed HPIV-3 vaccine demonstrated potential in evoking an effective immune response. This study paves the way for further preclinical and clinical evaluation of the vaccine, offering hope for a future solution to combat HPIV-3 infections.

Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines.

The online version contains supplementary material available at 10.1007/s13337-023-00852-9.

In silico designing of an epitope-based peptide vaccine cocktail against Nipah virus: an Indian population-based epidemiological study.

Nipah virus, a zoonotic virus from the family Paramyxoviridae has led to significant loss of lives till date with the most recent outbreak in India reported in Kerala. The virus has a considerably high mortality rate along with lack of characteristic symptoms which results in the delay of the virus detection. No specific vaccine is available for the virus although monoclonal antibody treatment has been seen to be effective along with favipiravir. The high mortality and complications caused by the virus underscores the necessity to develop alternative modes of vaccination. One such method has been designed in this study using peptide cocktail consisting of the immunologically important epitopes for use as vaccine. The human leucocytic antigens that are used for the study were analyzed for their presence in various ethnic Indian populations. This study may serve as a new avenue for development of more efficient peptide cocktail vaccines in recent future based on the population genetics and ethnicity.

Coarse-grained molecular dynamics-guided immunoinformatics to explain the binder and non-binder classification of Cytotoxic T-cell epitope for SARS-CoV-2 peptide-based vaccine discovery.

Epitope-based peptide vaccine can elicit T-cell immunity against SARS-CoV-2 to clear the infection. However, finding the best epitope from the whole antigen is challenging. A peptide screening using immunoinformatics usually starts from MHC-binding peptide, immunogenicity, cross-reactivity with the human proteome, to toxicity analysis. This pipeline classified the peptides into three categories, i.e., strong-, weak-, and non-binder, without incorporating the structural aspect. For this reason, the molecular detail that discriminates the binders from non-binder is interesting to be investigated. In this study, five CTL epitopes against HLA-A*02:01 were identified from the coarse-grained molecular dynamics-guided immunoinformatics screening. The strong binder showed distinctive activities from the non-binder in terms of structural and energetic properties. Furthermore, the second residue from the nonameric peptide was most important in the interaction with HLA-A*02:01. By understanding the nature of MHC-peptide interaction, we hoped to improve the chance of finding the best epitope for a peptide vaccine candidate.

Reverse Vaccinology Analysis of B-cell Epitope against Nipah Virus using Fusion Protein

Nipah virus (NiV) is an RNA virus, a pathogenic paramyxovirus that causes nonlethal respiratory illness in pigs. It was originally reported in Malaysia in 1998. NiV is considered a potential outbreak threat because it is zoonotic. However, no vaccines or antiviral drugs have been found against NiV. Therefore, the main objective is to develop effective vaccines by characterizing the fusion protein of NiV. We used a reference sequence retrieved from the National Center for Biotechnology Information (NCBI), then 3D modeled it to obtain the conserved region of the fusion protein. The interaction between the conserved region and B-cell receptors has been evaluated through a molecular docking approach. The B-cell epitope was identified using the Immune Epitope Database (IEDB) web server. As a result, we recommend Pep_D FANCISVTCQCQ as an epitope-based peptide vaccine candidate against Nipah virus. Pep D is highly immunogenic and does not cause autoimmune reactions. Pep D has the lowest binding energy for BCR molecular complexes, which can activate the transduction signal and direct B-cell immune response. However, further studies are required for confirmation (in vitro and in vivo).

Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2

In the recent past several vaccines were developed to combat the COVID-19 disease. Unfortunately, the protective efficacy of the current vaccines has been reduced due to the high mutation rate in SARS-CoV-2. Here, we successfully implemented a coevolution based immunoinformatics approach to design an epitope-based peptide vaccine considering variability in spike protein of SARS-CoV-2. The spike glycoprotein was investigated for B- and T-cell epitope prediction. Identified T-cell epitopes were mapped on previously reported coevolving amino acids in the spike protein to introduce mutation. The non-mutated and mutated vaccine components were constructed by selecting epitopes showing overlapping with the predicted B-cell epitopes and highest antigenicity. Selected epitopes were linked with the help of a linker to construct a single vaccine component. Non-mutated and mutated vaccine component sequences were modelled and validated. The in-silico expression level of the vaccine constructs (non-mutated and mutated) in E. coli K12 shows promising results. The molecular docking analysis of vaccine components with toll-like receptor 5 (TLR5) demonstrated strong binding affinity. The time series calculations including root mean square deviation (RMSD), radius of gyration (RGYR), and energy of the system over 100 ns trajectory obtained from all atom molecular dynamics simulation showed stability of the system. The combined coevolutionary and immunoinformatics approach used in this study will certainly help to design an effective peptide vaccine that may work against different strains of SARS-CoV-2. Moreover, the strategy used in this study can be implemented on other pathogens.

IN SILICO DESIGN OF B-CELL EPITOPE BASED PEPTIDE VACCINE FOR VARICELLA ZOSTER VIRUS

More than 90% of the world's population has the varicella zoster virus (VZV) or better known as herpes zoster (snakepox/shingles) when they are exposed to chickenpox as children. The majority of children infected with chickenpox will recover. However, this virus can "sleep" for decades in the body and one-third of people with this virus can suddenly become active again in the form of herpes zoster or shingles. Herpes zoster or snake pox is a disease characterized by the appearance of a rash and water-filled nodules accompanied by pain on one side of the body. Herpes zoster can cause a painful rash as well as a wide spectrum of complications such as ischemic stroke which occurs when the blood supply to the brain is restricted by narrowed arteries or blocked by clots. Therefore, vaccines are needed as a preventive measure. Epitope-based peptide vaccines have advantages in terms of both selectivity and safety. The use of computational methods is a cost-effective way to develop vaccines. In this study the aim was to look at the ZVZ glycoprotein E protein sequence conservation areas using the in-silico method, to see the potential for the most immunogenic epitope of the protein sequence. Varicella zoster virus as a vaccine candidate through in-silico use. Some of the software and websites used are MEGA-X, IEDB, VaxiJen 2.0, BLASTp NCBI. Of the 7 sequences that have been collected, 3 candidate epitopes have antigenic properties and have also passed the similarity test so that they have the potential to develop peptide vaccines including: KGDLNPKPQGQ, PPATTKPKE, PAIQHICLKHTTCFQDVVVDVDCA

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine.

A vaccine is defined as a biologic preparation that trains the immune system, boosts immunity, and protects against a deadly microbial infection. They have been used for centuries to combat a variety of contagious illnesses by means of subsiding the disease burden as well as eradicating the disease. Since infectious disease pandemics are a recurring global threat, vaccination has emerged as one of the most promising tools to save millions of lives and reduce infection rates. The World Health Organization reports thatimmunization protects three million individuals annually. Currently, multi-epitope-based peptide vaccines are a unique concept in vaccine formulation. Epitope-based peptide vaccines utilize small fragments of proteins or peptides (parts of the pathogen), called epitopes, that trigger an adequate immune response against a particular pathogen. However, conventional vaccine designing and development techniques are too cumbersome, expensive, and time-consuming. With the recent advancement in bioinformatics, immunoinformatics, and vaccinomicsdiscipline, vaccine science has entered a new era accompanying a modern, impressive, and more realistic paradigm in designing and developing next-generation strong immunogens. In silico designing and developing a safe and novel vaccine construct involves knowledge of reverse vaccinology, various vaccine databases, and high throughput techniques. The computational tools and techniques directly associated with vaccine research are extremely effective, economical, precise, robust, and safe for human use. Many vaccine candidates have entered clinical trials instantly and are available prior to schedule. In light of this, the present article provides researchers with up-to-date information on various approaches, protocols, and databases regarding the computational designing and development of potent multi-epitope-based peptide vaccines that can assist researchers in tailoring vaccines more rapidly and cost-effectively.

In silico EsxG EsxH rational epitope selection: Candidate epitopes for vaccine design against pulmonary tuberculosis.

Rational design of new vaccines against pulmonary tuberculosis is imperative. Early secreted antigens (Esx) G and H are involved in metal uptake, drug resistance, and immune response evasion. These characteristics make it an ideal target for rational vaccine development. The aim of this study is to show the rational design of epitope-based peptide vaccines by using bioinformatics and structural vaccinology tools. A total of 4.15 μs of Molecular Dynamics simulations were carried out to describe the behavior in solution of heterodimer, single epitopes, and epitopes loaded into MHC-II complexes. In order to predict T and B cell epitopes for antigenic activation, bioinformatic tools were used. Hence, we propose three epitopes with the potential to design pulmonary tuberculosis vaccines. The possible use of the proposed epitopes includes subunit vaccines, as a booster in BCG vaccination to improve its immune response, as well as the generation of antibodies that interfere with the Mycobacterium tuberculosis homeostasis, affecting its survival.

Identification of B and T Cell Epitopes to Design an Epitope-Based Peptide Vaccine against the Cell Surface Binding Protein of Monkeypox Virus: An Immunoinformatics Study.

Although the monkeypox virus-associated illness was previously confined to Africa, recently, it has started to spread across the globe and become a significant threat to human lives. Hence, this study was designed to identify the B and T cell epitopes and develop an epitope-based peptide vaccine against this virus's cell surface binding protein through an in silico approach to combat monkeypox-associated diseases. The analysis revealed that the cell surface binding protein of the monkeypox virus contains 30 B cell and 19 T cell epitopes within the given parameter. Among the T cell epitopes, epitope "ILFLMSQRY" was found to be one of the most potential peptide vaccine candidates. The docking analysis revealed an excellent binding affinity of this epitope with the human receptor HLA-B∗15:01 with a very low binding energy (-7.5 kcal/mol). The outcome of this research will aid the development of a T cell epitope-based peptide vaccine, and the discovered B and T cell epitopes will facilitate the creation of other epitope and multi-epitope-based vaccines in the future. This research will also serve as a basis for further in vitro and in vivo analysis to develop a vaccine that is effective against the monkeypox virus.

Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine.

Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein-epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein-epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution.

Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2.

Dengue fever (DF) continues to be one of the tropical and subtropical health concerns. Its prevalence tends to increase in some places in these regions. This disease is caused by the dengue virus (DENV), which is transmitted through the mosquitoes Aedes aegypti and A. albopictus. The treatment of DF to date is only supportive and there is no definitive vaccine to prevent this disease. The non-structural DENV protein, RNA-dependent RNA Polymerase (RdRp), is involved in viral replication. The RdRp-derived peptides can be used in the construction of a universal dengue vaccine. These peptides can be utilized as epitopes to induce immunity. This study was an in silico evaluation of the affinity of the potential epitope for the universal dengue vaccine to dendritic cells and the bonds between the epitope and the dendritic cell receptor. The peptide sequence MGKREKKLGEFGKAKG generated from dengue virus subtype 2 (DENV-2) RdRp was antigenic, did not produce allergies, was non-toxic, and had no homology with the human genome. The potential epitope-based vaccine MGKREKKLGEFGKAKG binds stably to dendritic cell receptors with a binding free energy of −474,4 kcal/mol. This epitope is anticipated to induce an immunological response and has the potential to serve as a universal dengue virus vaccine candidate.

Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates

Zika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen’s selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew’s correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.

Design of an Epitope-Based Peptide Vaccine Against Dengue Virus Isolate from Eastern Uttar Pradesh, India.

Dengue outbreaks are a serious public health concern that occurs on a regular basis in various locations of India. According to the Government of India's National Center for Vector-Borne Disease Control, a total of 1,23,106 dengue cases were identified in India as of October 2021. The currently available dengue vaccine was found to be ineffective against all serotypes of the virus. Dengue virus serotype 2 was reported to be the sole predominant serotype in Eastern Uttar Pradesh, India. An epitope-based peptide vaccine is believed to be safe and effective against all serotypes of the dengue virus. In this work, an epitope-based peptide vaccine based on envelope protein against the dengue virus was developed using the reverse vaccinology method. T-cell epitopes present in the envelope protein were screened using different immunoinformatic tools. Epitopes predicted by all servers were chosen and additionally picked out on the grounds of their antigenic reactivity, immunogenicity, toxicity, and allergenicity assessment. Three potent T cell epitopes as IVQPENLEY, ILIGVVITW, and DTAWDFGSL were screened, which binds with HLA-B*35:01, HLA-B*58:01, HLA-A*26:01 alleles, respectively. To build a 3D structure model of epitopes and alleles, the PepstrMod and Swiss-Model servers were used. Predicted epitopes and HLA alleles were docked using the HPEPDOCK server to confirm binding ability. These anticipated epitopes were found to cover the greatest number of populations in India and around the world. These identified epitopes have a high potential for eliciting an immune response in the development of a vaccine against the dengue virus, while further experimental validation is required for final confirmation.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10989-022-10402-4.