Metrics details. Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing NGS methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction.

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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. The selected epitopes showed The docking simulation ensured the binding interaction with high affinity.

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. Oropouche virus OROV is the most common Orthobunyavirus of the Bunyaviridae family, an important causative agent of Oropouche fever in human widespread in South America, especially in Brazil. This fever is clinically characterized as an acute febrile urban arboviral disease [ 1 , 2 ]. Usually, the patients recover within two to three weeks without any apparent sequel or death [ 3 ].

In some patients, OROV can cause viral meningitis with skin rash, neck stiffness, and photophobia [ 4 ]. Lastly, it causes inflammation and severe manifestations of encephalitis [ 5 ]. In spite of its clinical relevance to public health, very little is known about the pathogenesis of OROV in humans and in other vertebrate animals. The second strain of OROV was isolated and collected from the pool of Coquillettidia venezuelensis mosquitoes in Trinidad in [ 1 , 7 , 8 ].

The virus is deliberated as a public health threat in tropical and subtropical areas of Central and South America, with thirty outbreaks and over half a million , infected people as of The virus is a single-stranded negative-sense and three-segmented RNA virus.

Initially, the partial genome was sequenced for the Brazilian prototype OROV strain BeAn, and the complete and corrected genome sequences of the three segments, especially the M segment, were published in [ 10 , 11 ]. The M segment comprises a single ORF which encodes a large polyprotein that is cleaved during or after the translation and produces a nonstructural protein NSm , and two structural surface glycoproteins Gn and Gc [ 2 ].

These glycoproteins Gn and Gc help in the interaction between the virus particle and the cell receptor. According to Tilston-Lunel et al. The transmission of OROV is maintained in nature by two distinct cycles such as sylvatic and urban [ 12 ]. Bradypus tridactylus sloth , some wild birds, and nonhuman primates NHPs control the sylvatic cycle, whereas the urban cycle is maintained only by humans [ 3 , 8 ]. Diagnosis of OROV infection is performed by using various types of classic methods such as virus isolation and cell culture and molecular techniques such as serologic assays CF, HI, and NT tests and in-house enzyme-linked immunosorbent assay and real-time polymerase chain reaction RT-PCR in acute samples for genome detection [ 14 — 16 ].

So, immunity development in NHPs against this OROV should be taken into consideration as a vital warning sign of ongoing virus circulation. Since effective and specific commercial rapid diagnostic tests are unavailable, there are great concerns for its possible future outbreaks similar to Zika in South American countries, especially in Brazil. Furthermore, there is no specific approved drug or vaccine against OROV available in the market.

In this situation, adaptive immunity development against OROV could play an effective role in the complete elimination of the virus. Adaptive immunity works based on the recognition of specific epitopes by T-cells after reinfection. Consequently, humoral immune response is activated in humans. In the present study, we have analyzed the complete proteome of the OROV for the identification of the most antigenic protein and its highest immunogenic and antigenic T-cell epitopes along with B-cell epitopes for the development of the epitope-based peptide vaccine using immunoinformatic approaches.

We have also predicted the 3D structure of the most antigenic polyprotein as well as inhibitor-binding sites for the docking simulation study using various bioinformatics tools. Ultimately, this study is aimed at assisting the future laboratory efforts in developing effective vaccination for the prevention of OROV infection. The flow chart representing the overall procedures of epitope-based vaccine design and ligand-binding pocket prediction for OROV polyprotein and is illustrated in Figure 1.

So, a total available 21 polymerase, 6 glycoprotein, 39 nucleocapsids, 20 polyproteins, and 46 nucleoprotein sequences were primarily selected for antigenicity prediction. To uncover the highest antigenic protein, the FASTA formatted amino acid sequences of total structural proteins were submitted to the VaxiJen v2.

This approach led to the selction of the highly antigenic protein for further analysis. The T-cell epitopes are typically peptide fragments which are immunodominant and can elicit specific immune responses, important for epitope-based peptide vaccine design. The NetCTL 1. MHC class I binding, proteasomal C terminal cleavage, and TAP transport efficiency are predicted using artificial neural networks and the weight matrix, respectively [ 20 , 21 ]. We identified epitopes for all the 12 supertypes.

In this study, the threshold value for epitope identification was set at 1. The weight on C terminal cleavage and TAP transport efficiency were used as default parameters. Initially, epitopes were selected based on the highest combined score, but the final selection completed after the antigenicity and immunogenicity prediction of the epitopes through the VaxiJen v2. The amino acid length of peptide 9. So, the lower IC50 value indicates higher affinity. IEDB is a resourceful server, and this server was also used for the prediction of processing score, proteasomal cleavage, TAP score, and the MHC-I-binding score of the selected epitopes and their respective alleles using the SMM [ 24 , 25 ].

The conservancy indicates the specific portion of a protein sequence that restrains the epitope and shows availability with a specific level of identity. More immunogenic peptides are superior to the less immunogenic peptides and considered as effective T-cell epitopes. So, the epitope with high immunogenicity was selected for further analysis. Allergenicity was anticipated through AllerTOP v. AllergenFP 1. On the other hand, AllerTOP v. The comparison among different allergen prediction servers designates that AllerTOP v.

This method was developed based on the machine learning technique and quantitative matrix using different properties of peptides.

To know the interaction between binding alleles and predicted epitope, the molecular docking simulation study was performed using AutoDockTools [ 33 ] and AutoDock Vina software [ 34 ]. However, the predicted crystal structure was in a complex form with protein and an epitope. So, the Discovery Studio version For the identification of binding energy at the binding groove of HLA-B with an epitope, the space box center was set at 4. Finally, the docking simulation was performed by using AutoDock Vina.

Another docking study was performed as a control for the critical evaluation and scientific acceptance of our docking study. The docking method and parameters were set just like the previously mentioned parameters so that we can easily compare between sample and control. In the control section, the docking simulation was completed between influenza NP epitope from the strain and the HLA-B protein molecule.

This docking was completed at a 0. B-cell epitope identification is the vital step for epitope-based peptide vaccine design. So, the B-cell epitopes were identified from the highest antigenic protein through the online BepiPred LBtope is another powerful tool for linear B-cell epitope prediction which has been developed based on the experimentally validated non B-cell epitopes obtained from the IEDB database [ 37 ].

The conformational B-cell epitope is the sequence of amino acid or subunits which comprise an antigen and can come in direct interaction with a receptor of the immune system. However, ElliPro is the most comprehensive method because it can predict both the linear and conformational epitopes based on a protein 3D structure and showed the score as a PI protrusion index value [ 38 ].

The parameters for conformational epitope prediction were set at 0. ExPASy ProtParam [ 39 ] was used for the prediction of amino acid composition, molecular weight, extinction coefficient, isoelectric point pI , instability index, aliphatic index, and grand average hydropathicity GRAVY value. The secondary structural properties such as alpha helix, extended strands, and random coils were predicted through the PSIPRED server [ 40 ].

This energy score was applied to all protein atoms, using the steepest descent and conjugate gradient method to reduce the bad contacts between protein atoms and structural water molecules.

The hydrophobicity properties of the protein were calculated through the Discovery Studio version The active site or ligand-binding site analyses make a clear perception of the molecular docking simulation study. This server identifies ligand-binding sites by computing the interactions between a chemical probe and a protein structure. The query for Oropouche virus structural and nonstructural proteins resulted in a total of hits in the ViPR database.

All the structural proteins were evaluated by the VaxiJen v2. The protein itself is the OROV polyprotein containing amino acid residues, and this polyprotein was used for further analyses.

In this study, we unraveled epitopes in total, which achieved the selected threshold value 1. It has been repeated twice due to the loss of antigenicity property perceived in some T-cell epitopes. This repetition results facilitated to get a good rate of T-cell epitope prediction.

Herein, among primarily selected T-cell epitopes, 59 epitopes were under the threshold value 0. So, the results revealed that all epitopes with the highest combined score could not be the best epitopes. Among epitopes, we selected 37 epitopes which have positive antigenicity and immunogenicity scores, so that these highly antigenic epitopes can interact to the MHC alleles with high affinity and can create an effective immune response Table S2.

Epitope conservancy among the different strains of the selected protein can provide immunization effectively. So, the higher conservancy of an epitope ensures the better target for improved vaccine design. The selected epitopes, conservancy score, and their position are shown in Table 1.

The immunogenicity score of the epitope can be a good criterion for the selection of the best epitope. The higher score designates a greater probability of eliciting an effective immune response. Before being presented to the T-cells on the plasma membrane of the cell, the protein is degraded into small peptides in the proteasome by the cytosolic proteases and MHC-1 forms a complex with the peptides as well. However, the higher the total score of the epitopes with the HLA alleles ensures the presentation to the T-cell and a successful immune response critically depends on it.

The epitopes with their respective alleles and total scores are summarized in Table 1 and Table S2. Effective immune response not only depends on the successful recognition of epitopes by HLA molecules with significant affinity but also depends on the antigenicity and immunogenicity score. So, the epitopes that were recognized by the considerable number of HLA alleles and contained the highest immunogenicity, antigenicity value, and nontoxic to human were considered as the potential epitope to induce a strong immune response.

So, we selected two MHC HLA allele distribution differs among diverse geographic regions and ethnic groups around the world. Therefore, population coverage must be taken into consideration during the design of an effective vaccine. In this study, for the population coverage, identified MHC-I-binding alleles of 18 epitopes were considered.


IMGT®, the international ImMunoGeneTics information system®

Immunoinformatics pp Cite as. With advancements in sequencing technologies, vast amount of experimental data has accumulated. Due to rapid progress in the development of bioinformatics tools and the accumulation of data, immunoinformatics or computational immunology emerged as a special branch of bioinformatics which utilizes bioinformatics approaches for understanding and interpreting immunological data. One extensively studied aspect of applied immunology involves using available databases and tools for prediction of B- and T-cell epitopes. B and T cells comprise two arms of adaptive immunity. This chapter first reviews the methodology we used for computational identification of B- and T-cell epitopes against enterotoxigenic Escherichia coli ETEC. Then we discuss other databases of epitopes and analysis tools for T-cell and B-cell epitope prediction and vaccine design.


Immunoinformatics: an integrated scenario

At the intersection of experimental and computational sciences, the second edition of Immunoinformatics provides biological insights as well as a simpler way to implement approaches and algorithms in the immunoinformatics research domain. After an introductory section, this extensive volume moves on to cover topics such as databases, tools for prediction, systems biology approaches, as well as a variety of immunoinformatics applications. As part of the highly successful Methods in Molecular Biology series, chapters include the type of detailed information and implementation advice to ensure successful results. Skip to main content Skip to table of contents.


Immunoinformatics and Epitope Prediction

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. The selected epitopes showed

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