Epitope-based Peptide Vaccine Research Articles

In silico discovery of antigenic proteins and epitopes of SARS-CoV-2 for the development of a vaccine or a diagnostic approach for COVID-19

In the genome of SARS-CoV-2, the 5′-terminus encodes a polyprotein, which is further cleaved into 15 non-structural proteins whereas the 3′ terminus encodes four structural proteins and eight accessory proteins. Among these 27 proteins, the present study aimed to discover likely antigenic proteins and epitopes to be used for the development of a vaccine or serodiagnostic assay using an in silico approach. For this purpose, after the full genome analysis of SARS-CoV-2 Wuhan isolate and variant proteins that are detected frequently, surface proteins including spike, envelope, and membrane proteins as well as proteins with signal peptide were determined as probable vaccine candidates whereas the remaining were considered as possible antigens to be used during the development of serodiagnostic assays. According to results obtained, among 27 proteins, 26 of them were predicted as probable antigen. In 26 proteins, spike protein was selected as the best vaccine candidate because of having a signal peptide, negative GRAVY value, one transmembrane helix, moderate aliphatic index, a big molecular weight, a long-estimated half-life, beta wrap motifs as well as having stable, soluble and non-allergic features. In addition, orf7a, orf8, and nsp-10 proteins with signal peptide were considered as potential vaccine candidates. Nucleocapsid protein and a highly antigenic GGDGKMKD epitope were identified as ideal antigens to be used in the development of serodiagnostic assays. Moreover, considering MHC-I alleles, highly antigenic KLNDLCFTNV and ITLCFTLKRK epitopes can be used to develop an epitope-based peptide vaccine.

Exploring the potentials of Acinetobacter baumanii carbapenemase genes in the design of multi-epitope subunit vaccine using immunoinformatics approach

Acinetobacter baumanii is currently being listed by the WHO as one of the priority organisms implicated in antimicrobial resistance which has mostly been ascribed to plasmid- and chromosome-encoded carbapenemases such as the OXA genes. This protein is a membrane porin of A. baumanii and the action of specific antibodies against it would exert bactericidal or bacteriostatic effect in-vitro. Thus, this study used an immunoinformatic approach to design a multi-epitope based vaccine that is targeted against A. baumanii infections using Bla-Oxa carbapenemase gene protein sequences. Methods and materials: Designed subunit vaccine was evaluated for its antigenicity, immunogenicity, allergenicity and physicochemical parameters. Results: A total of 109 CTL epitopes (9-mer) were predicted using NetCTL 1.2 server, among them only 4 epitopes with highranked binding affinity score were chosen as final CTL epitopes. Similarly, the HTL epitopes were identified using IEDB MHC-II prediction module based on the higher binding affinity with MHC- II, the mouse alleles used for the prediction were H2-1Ad, H2-1Ed, and H2-1Ab. A maximum immune response TLR-4 agonist (APPHALS) was used as an adjuvant and CTL epitopes were combined together by EAAAK linker, intra-CTL and intra-HTL epitopes joint by AAY and GPGPG linker to make a final vaccine construct of 472 amino acid residues designed using 4 CTL and 7 HTL epitopes. Conclusion: Collectively, this research provides novel candidates for epitope-based peptide vaccine design against Acinetobacter baumanii.

Epitope-based chimeric peptide vaccine design against S, M and E proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of the ongoing pandemic of coronavirus disease 2019 (COVID-19), a public health emergency of international concerns declared by the World Health Organization (WHO). An immuno-informatics approach along with comparative genomics was applied to design a multi-epitope-based peptide vaccine against SARS-CoV-2 combining the antigenic epitopes of the S, M, and E proteins. The tertiary structure was predicted, refined and validated using advanced bioinformatics tools. The candidate vaccine showed an average of ≥90.0% world population coverage for different ethnic groups. Molecular docking and dynamics simulation of the chimeric vaccine with the immune receptors (TLR3 and TLR4) predicted efficient binding. Immune simulation predicted significant primary immune response with increased IgM and secondary immune response with high levels of both IgG1 and IgG2. It also increased the proliferation of T-helper cells and cytotoxic T-cells along with the increased IFN-γ and IL-2 cytokines. The codon optimization and mRNA secondary structure prediction revealed that the chimera is suitable for high-level expression and cloning. Overall, the constructed recombinant chimeric vaccine candidate demonstrated significant potential and can be considered for clinical validation to fight against this global threat, COVID-19.

Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach

World is witnessing exponential growth of SARS-CoV2 and fatal outcomes of COVID 19 has proved its pandemic potential already by claiming more than 3 lakhs deaths globally. If not controlled, this ongoing pandemic can cause irreparable socio-economic and psychological impact worldwide. Therefore a safe and effective vaccine against COVID 19 is exigent. Recent advances in immunoinformatics approaches could potentially decline the attrition rate and accelerate the process of vaccine development in these unprecedented times. In the present study, a multivalent subunit vaccine targeting S2 subunit of the SARS-CoV2 S glycoprotein has been designed using open source, immunoinformatics tools. Designed construct comprises of epitopes capable of inducing T cell, B cell (Linear and discontinuous) and Interferon γ. physiologically, vaccine construct is predicted to be thermostable, antigenic, immunogenic, non allergen and non toxic in nature. According to population coverage analysis, designed multiepitope vaccine covers 99.26% population globally. 3D structure of vaccine construct was designed, validated and refined to obtain high quality structure. Refined structure was docked against Toll like receptors to confirm the interactions between them. Vaccine peptide sequence was reverse transcribed, codon optimized and cloned in pET vector. Our in-silico study suggests that proposed vaccine against fusion domain of virus has the potential to elicit an innate as well as humoral immune response in human and restrict the entry of virus inside the cell. Results of the study offer a framework for in-vivo analysis that may hasten the process of development of therapeutic tools against COVID 19. Communicated by Ramaswamy H. Sarma

In silico Vaccine Design against Dengue Virus Type 2 Envelope Glycoprotein

Dengue fever is caused by the mosquito-borne virus termed (DENV). However, DENV-2 has been identified as the most prevalent amongst the Indonesian pediatric urban population, in contrast with the other four serotypes. Therefore, it is important to reduce severe infection risk by adopting preventive measures, including through vaccine development. The aim of this study, therefore is to use various in silico tools in the design of epitope-based peptide vaccines (T-cell and B-cell types), based on the DENV-2 envelope glycoprotein sequences available. Therefore, in silico methods were adopted in the analysis of the retrieved protein sequences. This technique was required to determine the most immunogenic protein, and is achieved through conservancy analysis, epitope identification, molecular simulation, and allergenicity assessment. Furthermore, B4XPM1, and KAWLVHRQW were identified from positions 204-212, while the 77 to 85 peptide region was considered the most potent T-cell and B-cell epitopes. The interaction between KAWLVHRQW and HLA-C*12:03 occurs with maximum population coverage, alongside high conservancy (96.98%) and binding affinity. These results indicated a potential for the designed epitopes to demonstrate high immunity against DENV-2.

Epitope-based peptide vaccines predicted against novel coronavirus disease caused by SARS-CoV-2

The outbreak of the 2019 novel coronavirus (SARS-CoV-2) has infected millions of people with a large number of deaths across the globe. The existing therapies are limited in dealing with SARS-CoV-2 due to the sudden appearance of the virus. Therefore, vaccines and antiviral medicines are in desperate need. We took immune-informatics approaches to identify B- and T-cell epitopes for surface glycoprotein (S), membrane glycoprotein (M) and nucleocapsid protein (N) of SARS-CoV-2, followed by estimating their antigenicity and interactions with the human leukocyte antigen (HLA) alleles. Allergenicity, toxicity, physiochemical properties analysis and stability were examined to confirm the specificity and selectivity of the epitope candidates. We identified a total of five B cell epitopes in RBD of S protein, seven MHC class-I, and 18 MHC class-II binding T-cell epitopes from S, M and N protein which showed non-allergenic, non-toxic and highly antigenic features and non-mutated in 55,179 SARS-CoV-2 virus strains until June 25, 2020. The epitopes identified here can be a potentially good candidate repertoire for vaccine development.

An insight into the epitope-based peptide vaccine design strategy and studies against COVID-19.

SARS-CoV-2 is a new member of the coronavirus family and caused the pandemic of coronavirus disease 2019 (COVID-19) in 2020. It is crucial to design and produce an effective vaccine for the prevention of rapid transmission and possible deaths wcaused by the disease. Although intensive work and research are being carried out all over the world to develop a vaccine, an effective and approved formulation that can prevent the infection and limit the outbreak has not been announced yet. Among all types of vaccines, epitope-based peptide vaccines outshine with their low-cost production, easy modification in the structure, and safety. In this review, vaccine studies against COVID-19 have been summarized and detailed information about the epitope-based peptide vaccines against COVID-19 has been provided. We have not only compared the peptide vaccine with other types of vaccines but also presented comprehensive literature information about development steps for an effective and protective formulation to give an insight into on-going peptide vaccine studies against SARS-CoV-2.

T cell epitope designing for dengue peptide vaccine using docking and molecular simulation studies

ABSTRACT Around the world, emergence and re-emergence of Dengue Virus (DENV) are one of the serious public health concerns. The infection of DENV to human population is mainly caused by the bite of an Aedes mosquito. In an estimate, it is found that annually around 100–125 million new dengue incidences are reported from approximately 120 endemic countries. The lone licensed dengue vaccine was not effective to prevent the disease globally. Many dengue vaccines are under clinical trial but there are concerns about the trial reports on safety and efficacy of vaccine. In these circumstances, an epitope-based peptide vaccine is expected to be safe and efficacious against dengue. In this study, the computational prediction of T cell epitope-based peptide vaccine for dengue virus strain BR/97-111 Envelop protein is performed. Total 28 CTL epitopes were predicted using NetCTL 1.2 server. Out of these, five were found to possess antigenicity through VaxiJen 2.0. Further, toxicity prediction using ToxinPred and their conservancy prediction resulted in three epitopes, i.e. TSEIQLTDY, IGIGILLTW and IAVGMVTLY epitope peptide. All these were physically docked and studied for molecular dynamics simulation. In-depth analysis of these results suggests that the predicted three epitopes could be used as a potent vaccine candidate against global dengue disease challenges, although an experimental validation is required for final confirmation.

Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach.

Background A new endemic disease has spread across Wuhan City, China, in December 2019. Within few weeks, the World Health Organization (WHO) announced a novel coronavirus designated as coronavirus disease 2019 (COVID-19). In late January 2020, WHO declared the outbreak of a “public-health emergency of international concern” due to the rapid and increasing spread of the disease worldwide. Currently, there is no vaccine or approved treatment for this emerging infection; thus, the objective of this study is to design a multiepitope peptide vaccine against COVID-19 using an immunoinformatics approach. Method Several techniques facilitating the combination of the immunoinformatics approach and comparative genomic approach were used in order to determine the potential peptides for designing the T-cell epitope-based peptide vaccine using the envelope protein of 2019-nCoV as a target. Results Extensive mutations, insertion, and deletion were discovered with comparative sequencing in the COVID-19 strain. Additionally, ten peptides binding to MHC class I and MHC class II were found to be promising candidates for vaccine design with adequate world population coverage of 88.5% and 99.99%, respectively. Conclusion The T-cell epitope-based peptide vaccine was designed for COVID-19 using the envelope protein as an immunogenic target. Nevertheless, the proposed vaccine rapidly needs to be validated clinically in order to ensure its safety and immunogenic profile to help stop this epidemic before it leads to devastating global outbreaks.

Construction of Epitope-Based Peptide Vaccine Against SARS-CoV-2: Immunoinformatics Study

Recently, a novel coronavirus (SARS-CoV-2) appeared which is conscientious for the current outbreak in China and rapidly spread worldwide. Unluckily, there is no approved vaccine found against SARS-CoV-2. Therefore, there is an urgent need for designing a suitable peptide vaccine constituent against the SARS-CoV-2. In this study, we characterized the spike glycoprotein of SARS-CoV-2 to obtain immunogenic epitopes. In addition, we used 58 SARS-CoV-2 isolates were retrieved from the Global Initiative on Sharing All Influenza Data (GISAID) and National Center for Biotechnology Information (NCBI), then aligned to obtain the conserved region of SARS-CoV-2 spike glycoprotein. The interaction between the conserved region with ACE2 receptor, a SARS-CoV-2 receptor on the host cell, has been evaluated through molecular docking approach. The B-cell epitope was identified using the immune epitope database (IEDB) web server. Interestingly, we recommend Pep_4 ADHQPQTFVNTELH as a epitope-based peptide vaccine candidate to deal with the SARS-CoV-2 outbreak. Pep_4 has a high level of immunogenicity and does not trigger autoimmune mechanisms. Pep_4 is capable of forming BCR/Fab molecular complexes with the lowest binding energy for activation of transduction signal the direct B-cell immune response. However, further study is suggested for confirmation (in vitro and in vivo).

Prediction of Epitope-Based Peptide Vaccine Against the Chikungunya Virus by Immuno-informatics Approach.

Chikungunya is an arthropod-borne viral disease characterized by abrupt onset of fever frequently accompanied by joint pain, which has been identified in over 60 countries in Africa, the Americas, Asia, and Europe. Regardless of the availability of molecular knowledge of this virus, no definite vaccine or other remedial agents have been developed yet. In the present study, a combination of B-cell and T-cell epitope predictions, followed by molecular docking simulation approach has been carried out to design a potential epitope-based peptide vaccine, which can trigger a critical immune response against the viral infections. A total of 52 sequences of E1 glycoprotein from the previously reported isolates of Chikungunya outbreaks were retrieved and examined through in silico methods to identify a potential B-cell and T-cell epitope. From the two separate epitope prediction servers, five potential B-cell epitopes were selected, among them "NTQLSEAHVEKS" was found highly conserved across strains and manifests high antigenicity with surface accessibility, flexibility, and hydrophilicity. Similarly, two highly conserved, non-allergenic, non-cytotoxic putative T-cell epitopes having maximum population coverage were screened to bind with the HLA-C 12*03 molecule. Molecular docking simulation revealed potential T-cell based epitope "KTEFASAYR" as a vaccine candidate for this virus. A combination of these B-cell and T-cell epitope-based vaccine can open up a new skyline with broader therapeutic application against Chikungunya virus with further experimental and clinical investigation.

Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): Immunoinformatics approach.

Recently, a novel coronavirus (SARS‐COV‐2) emerged which is responsible for the recent outbreak in Wuhan, China. Genetically, it is closely related to SARS‐CoV and MERS‐CoV. The situation is getting worse and worse, therefore, there is an urgent need for designing a suitable peptide vaccine component against the SARS‐COV‐2. Here, we characterized spike glycoprotein to obtain immunogenic epitopes. Next, we chose 13 Major Histocompatibility Complex‐(MHC) I and 3 MHC‐II epitopes, having antigenic properties. These epitopes are usually linked to specific linkers to build vaccine components and molecularly dock on toll‐like receptor‐5 to get binding affinity. Therefore, to provide a fast immunogenic profile of these epitopes, we performed immunoinformatics analysis so that the rapid development of the vaccine might bring this disastrous situation to the end earlier.

Delineating blueprint of an epitope-based peptide vaccine against the multiple serovars of dengue virus: A hierarchical reverse vaccinology approach

Abstract Dengue is one of the most common life-threatening neglected tropical diseases of the world, therapeutics for whose treatment and prevention still remains undeveloped. Despite having been licensed, the existing vaccine (Dengvaxia) is rather inefficacious due to the presence of multiple serovars of the Dengue Virus (DENV). A dengue vaccine potent enough to work against all the serovars is very crucial, the development of which is time-intensive. Here, we have used a comprehensive hierarchical reverse vaccinology approach to design an epitope-based vaccine targeted against multiple serovars of the DENV. Conservancy and population coverage analysis of the predicted epitopes revealed robust immune response against multiple serovars of the DENV and various ethnicities of the human population. The final vaccine construct was designed by adding B and T-cell epitopes, Universal pan-HLA DR or PADRE (AKFVAAWTLKAAA) sequence, and an adjuvant (β-defensin) at the N-terminal with suitable linkers. Physiochemical properties and secondary structure profiling of the vaccine protein secured its hydrophilic, thermostable and other structural nature. Molecular docking analysis indicates the deep binding of the proposed vaccine in the binding groove of the human immune TLR4 receptor present on the dendritic cell. Additionally, disulfide engineering was performed to extend its stability. Furthermore, molecular dynamics simulation of the modeled vaccine-TLR4 complex showed minimum deformability. Finally, in silico cloning approach of the vaccine construct within an expression vector (pET28a+) was carried out to check translational potency and microbial expression. This proposed vaccine may provide a novel immunotherapeutic agent for dengue outbreak prevention and management.

Prediction of Potential Epitopes for Peptide Vaccine Formulation Against Teschovirus A Using Immunoinformatics.

Teschovirus A belongs to the family Picornaviridae and is a causal agent of the disease Teschovirus encephalomyelitis and other infections that remain asymptomatic. The present study was performed to design epitope-based peptide vaccine against Teschovirus A by identifying the potential T cell and B-cell epitopes from capsid proteins (VP1, VP3 and VP2) of the virus using reverse vaccinology and immunoinformatics approaches. In the current study, hexapeptide T-cell and octapeptide B-cell epitopes were analyzed for immunogenicity, antigenicity and hydrophilicity scores of each epitope. Each potential epitope was further characterized using ExPASy-ProtParam and Antimicrobial Peptide Database (APD3) tools for determining various physical and chemical parameters of the epitope. One linear hexapeptide T-cell epitope, i.e., RPVNDE (epitope position 77–82) and one linear octapeptide B-cell epitope, i.e., AYSRSHPQ (236–243) were identified from the viral capsid protein as they possess the capability to raise effective immunogenic reaction in the host organism against the virus. Pharmaceutical industries could harness the results of this investigation to develop epitope-based peptide vaccines by loading the identified epitopes in combination with targeting signal peptides of T-cells and B-cells and then inserting the combination into virus like particle (vlp) or constructing subunit vaccines for further trial.

In Silico Identification of Epitope-Based Peptide Vaccine for Nipah Virus

Nipah virus was first appeared in Malaysian 1998, which further found be outburst in neighbor countries i.e. Bangladesh, Singapore, and India. Currently there is no effective drug or vaccine available only supportive care and prevention are way to manage it. Epitope based vaccine could be the best way to cure Nipah virus infection. In this study, the proteome of Nipah virus was retrieve from UniProt database and were subjected to check for allergenicity using Allergen FP v.1.0 tool. NetMHCII 2.3 server screened epitopes from non-allergen proteins and Vaxijen tool was used to identify the most antigenic epitopes binds with MHC class II molecules. Two potent T cell epitopes LLFVFGPNL and KYKIKSNPL were found, which binds with HLA-DRB1*01:01 and HLA-DRB1*07:01 MHC class II alleles. PepstrMod and Swiss-Model server were used to build 3D structure model of epitopes and alleles, respectively. Further identified epitopes were docked with HLA alleles using AutoDock vina tool to confirm binding ability. The epitopes LLFVFGPNL and KYKIKSNPL had binding affinity of − 8.1 kcal/mol and − 5.7 kcal/mol with HLA-DRB1*01:01 and HLA-DRB1*07:01 alleles, respectively. Solidity of predicted epitope—allele docked complex were evaluating by molecular dynamics simulation.HLA distribution analysis was performed for predicted epitopes using the population coverage tool of Immune Epitope Database (IEDB). These predicted epitopes cover maximum number of populations in India as well as worldwide. Therefore, these epitopes could be potent vaccine candidates to counter Nipah virus by testing in wet lab.

In silico analysis for development of epitopes-based peptide vaccine against Alkhurma hemorrhagic fever virus

Alkhurma hemorrhagic fever virus (ALKV) causes a fatal clinical disease in human beings of different tropical and sub-tropical regions. Recently, the ALKV epidemics have raised a great public health concern with the room for improvement in the essential therapeutic interventions. Despite increased realistic clinical cases of ALKV infection, the efficient vaccine or immunotherapy is not yet available to-date. Therefore, the current study aimed to analyze the envelope glycoprotein of ALKV for the development of B-cells and T-cells epitope-based peptide vaccine using the computational in silico method. Utilizing various immunoinformatics approaches, a total of 5 B-cells and 25 T-cells (MHC-I = 17, MHC-II = 8) epitope-based peptides were predicted in the current study. All predicted peptides had highest antigenicity and immunogenicity scores along with high binding affinity to human leukocyte antigen (HLA) class II alleles. Among 25T-cell epitopes, three peptides were found alike to have affinity to bind both MHC-I and MHC-II alleles. These outcomes suggested that these predicted epitopes could potentially be used in the development of an efficient vaccine against ALKV, which may enable to elicit both humoral and cell-mediated immunity. Although, these predicted peptides could be useful in designing a candidate vaccine for the prevention of ALKV; however, it’s in vitro and in vivo assessments are prerequisite.Communicated by Ramaswamy H. Sarma

In Silico screening of T-cell and B-cell Epitopes of Rotavirus VP7 and VP4 proteins for Effective Vaccine Design

Rotavirus is one of the deadliest causative agents of childhood diarrhea which causes half a million child death across the globe, mostly in developing countries. However, effective vaccine strategies against rotavirus are yet to be established to prevent these unwanted premature deaths. In this regard, in silico vaccine design for rotavirus could be a promising alternative for developing countries due to its efficiency in shortening valuable time and cost. The present study described an epitope-based peptide vaccine design against rotavirus, using a combination of T-cell and B-cell epitope predictions and molecular docking approach. To perform this, sequences of rotavirus VP7 and VP4 proteins were retrieved from the NCBI database and subjected to different bioinformatics tools to predict most immunogenic T-cell and B-cell epitopes. From the identified epitopes, the sequence VMSKRSRSL of VP7 and TQFTDFVSL of VP4 was identified as the most potential epitopes based on their antigenicity, conservancy and interaction with major histocompatability complex I (MHC-I) alleles. Moreover, the peptide VMSKRSRSL interacted with human leukocyte antigen, HLA-B*08:01 and TQFTDFVSL interacted with HLA-A*02:06 with considerable binding energy and affinity score. Combined population coverage for our identified epitopes was found 70.53% and 45.64% for world population and South Asian population respectively. All these results suggest that, the epitopes identified in this study could be a very good vaccine candidate for the strains of rotavirus circulating in Bangladesh. However, as this study is completely dependent on computational prediction algorithms, further in vivo screening is required to come up in a precise conclusion about these epitopes for effective rotavirus vaccination.
 Bangladesh J Microbiol, Volume 35 Number 1 June 2018, pp 45-55

In Silico Approach To Design a B-cell Epitope Based Vaccine Target Against Yellow Fever Virus

Yellow fever virus is a prototype member of the Flaviviridae family causing high fever and jaundice. Though YF 17D vaccine is administered to yellow fever patients, however it can produce adverse effects in immunocompromised, older people and young infants. The aim of this study is to design an epitope-based peptide vaccine by targeting envelope (E) protein of Yellow Fever Virus. Thirty sequences of E protein of Yellow Fever Virus strains were retrieved from NCBI database. E protein was found to be mostly conserved among all the sequences with little variability and also was identified as a probable antigen. Different epitope prediction tools predicted 4 common epitopes, 3 of which were found to be antigenic. A peptide VKNPTDTGin E protein was predicted to have surface accessibility which overlaps with the VKNPTDTGHGT epitope.So, the whole VKNPTDTGHGT epitope was taken for further analysis. The VKNPTDTGHGT epitope showed 96.67% conservancy and also possesses flexibility, hydrophilicity and non-toxicity. Therefore, VKNPTDTGHGT can be regarded as a potential vaccine candidate against Yellow fever virus with further in vitro and in vivo validation.
 Bangladesh J Microbiol, Volume 35 Number 1 June 2018, pp 27-35

Designing of an epitope-based peptide vaccine against walking pneumonia: an immunoinformatics approach

Mycoplasma pneumoniae is a substantial respiratory pathogen that develops not only pneumonia but also other respiratory diseases, which mimic viral respiratory syndromes. Nevertheless, vaccine development for this pathogen delays behind as immunity correlated with protection is now predominantly unknown. In the present study, an immunoinformatics pipeline is utilized for epitope-based peptide vaccine design, which can trigger a critical immune response against M. pneumoniae. A total of 105T-cell epitopes from 12 membrane associated proteins and 7T-cell epitopes from 5 cytadherence proteins of M. pneumoniae were obtained and validated. Thus, 18 peptides with 9-mer core sequence were identified as best T-cell epitopes by considering the number of residues with > 75% in favored region. Further, the crucial screening studies predicted three peptides with good binding affinity towards HLA molecules as best T-cell and B-cell epitopes. Based on this result, visualization, and dynamic simulation for the three epitopes (WIHGLILLF, VILLFLLLF, and LLAWMLVLF) were assessed. The predicted epitopes needs to be further validated for their adept use as vaccine. Collectively, the study opens up a new horizon with extensive therapeutic application against M. pneumoniae and its associated diseases.

Immunoinformatics Approach for Epitope-Based Peptide Vaccine Design and Active Site Prediction against Polyprotein of Emerging Oropouche Virus.

Oropouche virus (OROV) is an emerging pathogen which causes Oropouche fever and meningitis in humans. Several outbreaks of OROV in South America, especially in Brazil, have changed its status as an emerging disease, but no vaccine or specific drug target is available yet. Our approach was to identify the epitope-based vaccine candidates as well as the ligand-binding pockets through the use of immunoinformatics. In this report, we identified both T-cell and B-cell epitopes of the most antigenic OROV polyprotein with the potential to induce both humoral and cell-mediated immunity. Eighteen highly antigenic and immunogenic CD8+ T-cell epitopes were identified, including three 100% conserved epitopes (TSSWGCEEY, CSMCGLIHY, and LAIDTGCLY) as the potential vaccine candidates. The selected epitopes showed 95.77% coverage for the mixed Brazilian population. The docking simulation ensured the binding interaction with high affinity. A total of five highly conserved and nontoxic linear B-cell epitopes “NQKIDLSQL,” “HPLSTSQIGDRC,” “SHCNLEFTAITADKIMSL,” “PEKIPAKEGWLTFSKEHTSSW,” and “HHYKPTKNLPHVVPRYH” were selected as potential vaccine candidates. The predicted eight conformational B-cell epitopes represent the accessibility for the entered virus. In the posttherapeutic strategy, ten ligand-binding pockets were identified for effective inhibitor design against emerging OROV infection. Collectively, this research provides novel candidates for epitope-based peptide vaccine design against OROV.