Epitope Prediction Tools Research Articles

Multi-epitope based peptide vaccine design using three structural proteins (S, e, and m) of sars-cov-2: An in silico approach

Introduction: The ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has jeopardized our health system and leaving everyone in disarray. Despite the diligent cumulative effort of academia, there is hardly any light in the end tunnel so far in developing efficient and sustainable treatment options to tackle this public health threat. Therefore, designing a suitable vaccine to overcome this hurdle calls for immediate attention. The current study aimed to design a multi-epitope based vaccine using immunoinformatics tools. Materials and Methods: We approached the structural proteins: S, E, and M proteins of SARS-CoV-2 since they facilitate the infection of the virus into a host cell. By using different bioinformatics tools and servers, the multiple B-cell and T-cell epitopes were predicted potential for the required vaccine design. The phylogenetic analysis provides in-depth knowledge on ancestral molecular changes and the molecular evolutionary relationship of S, E, and M proteins. Results: Based on the antigenicity and surface accessibility of the spike (S), envelope (E), and membrane (M) proteins, eight epitopes were selected by various B cell and T cell epitope prediction tools. Molecular docking was executed to interpret the binding interactions of these epitopes from where three potential epitopes WTAGAAAYY, YVYSRVKNL, and GTITVEELK were finalized with their noticeable higher binding affinity scores-9.1,-7.4, and-7.0 kcal/mol, respectively. It is noteworthy to mention that the targeted epitopes are believed to cover 91.09% of the population coverage worldwide. Conclusions: In sum, we identified the three most potential epitopes at length, which might be turned to our purpose of designing the peptide-based vaccine against SARS-CoV-2. © 2021 The Author(s).

Computational Design of a Potential Therapeutic Peptide Against Spike Protein of SARS-CoV-2

SARS-CoV-2 entrance to the host cells is started by the interaction between receptor binding domain (RBD) of the spike (S) protein on the virus with the angiotensin-converting enzyme 2 (ACE2) receptor which is very important in the onset of viral infection. Interference with this interaction can be a promising way to prevent Covid-19 infection. In this study, a novel potential therapeutic peptide was designed in silico based on the key interacting amino acids of ACE2 against SARS-CoV-2 S protein. In our computational analysis, a peptide consisting of residues 19–48 of ACE2 was chosen as the wild-type peptide. Based on this peptide, six mutant peptides (Mu_P1-6) were designed and then assessed in term of interaction with S protein. The result of protein-peptide docking by HADDOCK web server and then immunological analysis by SVMTriP epitope prediction tool leads to choose Mu_P3 as the best mutant. Molecular dynamics simulations of wild-type peptide-S protein complex and Mu_P3-S protein complex, showed Mu_P3 has better interaction with S protein than wild type peptide (interaction energies [Formula: see text] vs. [Formula: see text] (kJ/mol)) which can be a potential therapeutic peptide for Covid-19 pandemic.

The Serpin-like Loop Insertion of Ovalbumin Increases the Stability and Decreases the OVA 323-339 Epitope Processing Efficiency.

Chicken ovalbumin (cOVA) has been studied for decades primarily due to the robust genetic and molecular resources that are available for experimental investigations. cOVA is a member of the serpin superfamily of proteins that function as protease inhibitors, although cOVA does not exhibit this activity. As a serpin, cOVA possesses a protease-sensitive reactive center loop that lies adjacent to the OVA 323–339 CD4+ T-cell epitope. We took advantage of the previously described single-substitution variant, OVA R339T, which can undergo the dramatic structural transition observed in serpins, to study how changes in loop size and protein stability influence the processing and presentation of the OVA 323–339 epitope. We observed that the OVA R339T loop insertion increases the stability and protease resistance, resulting in the reduced presentation of the OVA 323–339 epitope in vitro. These findings have implications for the design of more effective vaccines for the treatment of infectious diseases and cancer as well as the development of more robust CD4+ T-cell epitope prediction tools.

Comparison of Immunological Profiles of SARS-CoV-2 Variants in the COVID-19 Pandemic Trends: An Immunoinformatics Approach.

The current dynamics of the COVID-19 pandemic have become a serious concern with the emergence of a series of mutant variants of the SARS-CoV-2 virus. Unlike the previous strain, it is reported that the descendants are associated with increased risk of transmission yet causing less impact in terms of hospital admission, the severity of illness, or mortality. Moreover, the vaccine efficacy is also not believed to vary among the population depending on the variants of the virus and ethnicity. It has been determined that the mutations recorded in the spike gene and protein of the newly evolved viruses are specificallyresponsible for this transformation in the behavior of the virus and its disease condition. Hence, this study aimed to compare the immunogenic profiles of the spike protein from the latest variants of the SARS-CoV-2 virus concerning the probability of COVID-19 severity. Genome sequences of the latest SARS-CoV-2 variants were obtained from GISAID and NCBI repositories. The translated protein sequences were run against T-cell and B-cell epitope prediction tools. Subsequently, antigenicity, immunogenicity, allergenicity, toxicity, and conservancy of the identified epitopes were ascertained using various prediction servers. Only the non-allergic and non-toxic potential epitopes were matched for population relevance by using the Human Leucocyte Antigen population registry in IEDB. Finally, the selected epitopes were validated by docking and simulation studies. The evaluated immunological parameters would concurrently reveal the severity of COVID-19, determining the infection rate of the pathogen. Our immunoinformatics approach disclosed that spike protein of the five variants was capable of forming potential T and B-cell epitopes with varying immune responses. Although the Wuhan strain showed a high number of epitope/HLA combinations, relatively less antigenicity and higher immunogenicity results in poor neutralizing capacity, which could be associated with increased disease severity. Our data demonstrate that increased viral antigenicity with moderate to high immunogenicity, and several potential epitope/HLA combinations in England strain, the USA, India, and South Africa variants, could possess a high neutralizing ability. Therefore, our findings reinforce that the newly circulating variants of SARS-CoV-2 might be associated with more infectiousness and less severe disease condition despite their greater viremia, as reported in the recent COVID-19 cases, whichconsequently determine their increased epidemiological fitness.

DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information

BackgroundEpitope prediction is a useful approach in cancer immunology and immunotherapy. Many computational methods, including machine learning and network analysis, have been developed quickly for such purposes. However, regarding clinical applications, the existing tools are insufficient because few of the predicted binding molecules are immunogenic. Hence, to develop more potent and effective vaccines, it is important to understand binding and immunogenic potential. Here, we observed that the interactive association constituted by human leukocyte antigen (HLA)-peptide pairs can be regarded as a network in which each HLA and peptide is taken as a node. We speculated whether this network could detect the essential interactive propensities embedded in HLA-peptide pairs. Thus, we developed a network-based deep learning method called DeepNetBim by harnessing binding and immunogenic information to predict HLA-peptide interactions.ResultsQuantitative class I HLA-peptide binding data and qualitative immunogenic data (including data generated from T cell activation assays, major histocompatibility complex (MHC) binding assays and MHC ligand elution assays) were retrieved from the Immune Epitope Database database. The weighted HLA-peptide binding network and immunogenic network were integrated into a network-based deep learning algorithm constituted by a convolutional neural network and an attention mechanism. The results showed that the integration of network centrality metrics increased the power of both binding and immunogenicity predictions, while the new model significantly outperformed those that did not include network features and those with shuffled networks. Applied on benchmark and independent datasets, DeepNetBim achieved an AUC score of 93.74% in HLA-peptide binding prediction, outperforming 11 state-of-the-art relevant models. Furthermore, the performance enhancement of the combined model, which filtered out negative immunogenic predictions, was confirmed on neoantigen identification by an increase in both positive predictive value (PPV) and the proportion of neoantigen recognition.ConclusionsWe developed a network-based deep learning method called DeepNetBim as a pan-specific epitope prediction tool. It extracted the attributes of the network as new features from HLA-peptide binding and immunogenic models. We observed that not only did DeepNetBim binding model outperform other updated methods but the combination of our two models showed better performance. This indicates further applications in clinical practice.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules.

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Study on immunogenicity and antigenicity of a novel brucella multiepitope recombined protein

Currently, brucellosis is a reemerged zoonotic infectious disease with an increased incidence in recent years. A simple, rapid and sensitive method for diagnosing brucellosis can help to reduce medical burden and economic loss. Previously, a multiple epitope recombinant protein was constructed based on linear B-cell epitope prediction tools. In this study, the recombinant protein was used as an antigen to study the immune response produced by immunized mice, and goat serum was used to verify its diagnostic accuracy. The production of antibodies was successfully induced in the vaccinated mice. Flow cytometric analysis revealed that the percentage of CD4+, CD8+ and the CD4+/CD8+ ratios were increased by T cell subsets in mouse splenocytes, indicating that the recombinant protein induced a strong immune response had strong immunoreactivity. Using indirect ELISA, the recombinant protein correctly diagnosed positive and negative brucellosis samples. Compared with the whole bacterial antigen, the recombinant protein had a weaker sensitivity but a stronger specificity. Animal experiments showed that the recombinant protein had good antigenicity, and indirect ELISA indicates that it can be used as an antigen to diagnose brucellosis. Therefore, the recombinant protein is a potential candidate antigen for brucellosis vaccine development and serological diagnosis.

Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools

The outbreak of SARS-CoV-2 (2019-nCoV) virus has highlighted the need for fast and efficacious vaccine development. Stimulation of a proper immune response that leads to protection is highly dependent on presentation of epitopes to circulating T-cells via the HLA complex. SARS-CoV-2 is a large RNA virus and testing of all of its overlapping peptides in vitro to deconvolute an immune response is not feasible. Therefore HLA-binding prediction tools are often used to narrow down the number of peptides to test. We tested NetMHC suite tools' predictions by using an in vitro peptide-MHC stability assay. We assessed 777 peptides that were predicted to be good binders across 11 MHC alleles in a complex-stability assay and tested a selection of 19 epitope-HLA-binding prediction tools against the assay. In this investigation of potential SARS-CoV-2 epitopes we found that current prediction tools vary in performance when assessing binding stability, and they are highly dependent on the MHC allele in question. Designing a COVID-19 vaccine where only a few epitope targets are included is therefore a very challenging task. Here, we present 174 SARS-CoV-2 epitopes with high prediction binding scores, validated to bind stably to 11 HLA alleles. Our findings may contribute to the design of an efficacious vaccine against COVID-19.

The Anti-Proliferative Effect of a Newly-Produced Anti-PSCA-Peptide Antibody by Multiple Bioinformatics Tools, on Prostate Cancer Cells.

Prostate Stem Cell Antigen (PSCA) is a small cell surface protein, overexpressed in 90% of prostate cancers. Determination of epitopes that elicit an appropriate response to the antibody generation is vital for diagnostic and immunotherapeutic purposes for prostate cancer treatment. Presently, bioinformatics B-cell prediction tools can predict the location of epitopes, which is uncomplicated, faster, and more cost-effective than experimental methods. We aimed to predict a novel linear peptide for Prostate Stem Cell Antigen (PSCA) protein in order to generate anti-PSCA-peptide (p) antibody and to investigate its effect on prostate cancer cells. In the current study, a novel linear peptide for PSCA was predicted using in silico methods that utilize a set of linear B-cell epitope prediction tools. Polyclonal antibody (anti-PSCA-p antibody "Patent No. 99318") against PSCA peptide was generated. The antibody reactivity was determined by the Enzyme-Linked Immunosorbent Assay (ELISA) and its specificity by immunocytochemistry (ICC), immunohistochemistry (IHC), and Western Blotting (WB) assays. The effect of the anti-PSCA-p antibody on PSCA-expressing prostate cancer cell line was assessed by Methylthiazolyldiphenyl- Tetrazolium bromide (MTT) assay. New peptide-fragment of PSCA sequence as "N-CVDDSQDYYVGKKN-C" (PSCA-p) was selected and synthesized. The anti-PSCA-p antibody against the PSCA-p showed immunoreactivity with PSCA-p specifically bound to PC-3 cells. Also, the anti-PSCA-p antibody strongly stained the prostate cancer tissues as compared to Benign Prostatic Hyperplasia (BPH) and normal tissues (P < 0.001). As the degree of malignancy increased, the staining intensity was also elevated in prostate cancer tissue (P < 0.001). Interestingly, the anti-PSCA-p antibody showed anti-proliferative effects on PC-3 cells (31%) with no growth inhibition effect on PSCA-negative cells. In this study, we developed a new peptide sequence (PSCA-p) of PSCA. The PSCA-p targeting by anti-PSCA-p antibody inhibited the proliferation of prostate cancer cells, suggesting the potential of PSCA-p immunotherapy for future prostate cancer studies.

EpiDope: a deep neural network for linear B-cell epitope prediction.

By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly reduced using in silico prediction. Here, we present EpiDope, a python tool which uses a deep neural network to detect linear B-cell epitope regions on individual protein sequences. With an area under the curve between 0.67 ± 0.07 in the receiver operating characteristic curve, EpiDope exceeds all other currently used linear B-cell epitope prediction tools. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach. EpiDope is available on GitHub (http://github.com/mcollatz/EpiDope). Supplementary data are available at Bioinformatics online.

Computational Screening of Potential Mhc Class I Restricted Cytotoxic T lymphocytes-based Common multi-epitopes of Major Arboviral Diseases

Aim: Arboviral diseases are a health hazard and Flavivirus and Alphavirus infections are the most common in humans. This study focuses on immunoinformatic approaches to identify potential MHC class I restricted epitopes common for some selected arboviral diseases. Materials &amp; methods: T-cell epitope prediction tool of Immune Epitope Database was employed to identify putative epitopes from the polyproteins of the selected viruses. Further, population coverage, conservancy, antigenic properties and docking analyses were performed to identify potential common epitopes for the selected viruses. Results: Eight common epitopes were screened for the selected viruses based on their population coverage, conservancy, antigenic properties and binding affinity. Conclusion: Considering the in silico potency, identified epitopes may further be subjected for candidate vaccine development against these arboviruses.

A behind-the-scenes tour of the IEDB curation process: an optimized process empirically integrating automation and human curation efforts.

The Immune Epitope Database and Analysis Resource (IEDB) provides the scientific community with open access to epitope data, as well as epitope prediction and analysis tools. The IEDB houses the most extensive collection of experimentally validated B-cell and T-cell epitope data, sourced primarily from published literature by expert curation. The data procurement requires systematic identification, categorization, curation and quality-checking processes. Here, we provide insights into these processes, with particular focus on the dividends they have paid in terms of attaining project milestones, as well as how objective analyses of our processes have identified opportunities for process optimization. These experiences are shared as a case study of the benefits of process implementation and review in biomedical big data, as well as to encourage idea-sharing among players in this ever-growing space.

Design and Construction of a Fusion Peptide Containing B1, B2, B4, and EPC1 Epitopes for Diagnosis of Human Cystic Echinococcosis

Cystic echinococcosis (CE), larval stage of Echinococcus granulosus, immunodiagnostics is still a challenge due to asymptomatic nature of CE during the early phase of infection and imperfection of diagnostic antigens. In silico design and assessments of hydatid cyst antigens provide preeminent information for novel and favorable diagnostic methods. This study was performed at the Tehran University of Medical Sciences, Tehran, Iran in 2018. The sequences of B2, EPC1, B1 and B4 antigens were collected and analyzed for sequence conservancy by protein BLAST search and CLUSTALW multiple sequence alignment. The secondary and 3D structures were predicted using ab initio and threading methods. The antigens were analyzed for their B cell epitopic content using linear and conformational B cell epitope prediction tools. The final diagnostic antigen was designed by fusing the selected epitopic determinants form each antigen. Given the conservancy results and B cell epitope predictions, the whole B2 antigen along with amino acids spanning 1-50, 1-30, and 30-81 regions of EPC1, B1 and B4 antigens were selected to design the final antigen. High surface accessibility (75%), protein stability, low free energy and high number of amino acids involved in B cell epitopes were desirable properties for the final antigen to interact with antibodies against CE. In silico design of such antigens is useful for better diagnosis of CE, decrease the cost and the time required for antigen design, while avoiding the ethical aspects of in vivo studies.

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

CD4+ Th immunogenicity of the Ascaris spp. secreted products

Ascaris spp. is a major health problem of humans and animals alike, and understanding the immunogenicity of its antigens is required for developing urgently needed vaccines. The parasite-secreted products represent the most relevant, yet complex (>250 proteins) antigens of Ascaris spp. as defining the pathogen-host interplay. We applied an in vitro antigen processing system coupled to quantitative proteomics to identify potential CD4+ Th cell epitopes in Ascaris-secreted products. This approach considerably restricts the theoretical list of epitopes using conventional CD4+ Th cell epitope prediction tools. We demonstrate the specificity and utility of our approach on two sets of candidate lists, allowing us identifying hits excluded by either one or both computational methods. More importantly, one of the candidates identified experimentally, clearly demonstrates the presence of pathogen-reactive T cells in healthy human individuals against these antigens. Thus, our work pipeline identifies the first human T cell epitope against Ascaris spp. and represents an easily adaptable platform for characterization of complex antigens, in particular for those pathogens that are not easily amenable for in vivo experimental validation.

Apparent Lack of BRAFV600E Derived HLA Class I Presented Neoantigens Hampers Neoplastic Cell Targeting by CD8+ T Cells in Langerhans Cell Histiocytosis.

Langerhans Cell Histiocytosis (LCH) is a neoplastic disorder of hematopoietic origin characterized by inflammatory lesions containing clonal histiocytes (LCH-cells) intermixed with various immune cells, including T cells. In 50–60% of LCH-patients, the somatic BRAFV600E driver mutation, which is common in many cancers, is detected in these LCH-cells in an otherwise quiet genomic landscape. Non-synonymous mutations like BRAFV600E can be a source of neoantigens capable of eliciting effective antitumor CD8+ T cell responses. This requires neopeptides to be stably presented by Human Leukocyte Antigen (HLA) class I molecules and sufficient numbers of CD8+ T cells at tumor sites. Here, we demonstrate substantial heterogeneity in CD8+ T cell density in n = 101 LCH-lesions, with BRAFV600E mutated lesions displaying significantly lower CD8+ T cell:CD1a+ LCH-cell ratios (p = 0.01) than BRAF wildtype lesions. Because LCH-lesional CD8+ T cell density had no significant impact on event-free survival, we investigated whether the intracellularly expressed BRAFV600E protein is degraded into neopeptides that are naturally processed and presented by cell surface HLA class I molecules. Epitope prediction tools revealed a single HLA class I binding BRAFV600E derived neopeptide (KIGDFGLATEK), which indeed displayed strong to intermediate binding capacity to HLA-A*03:01 and HLA-A*11:01 in an in vitro peptide-HLA binding assay. Mass spectrometry-based targeted peptidomics was used to investigate the presence of this neopeptide in HLA class I presented peptides isolated from several BRAFV600E expressing cell lines with various HLA genotypes. While the HLA-A*02:01 binding BRAF wildtype peptide KIGDFGLATV was traced in peptides isolated from all five cell lines expressing this HLA subtype, KIGDFGLATEK was not detected in the HLA class I peptidomes of two distinct BRAFV600E transduced cell lines with confirmed expression of HLA-A*03:01 or HLA-A*11:01. These data indicate that the in silico predicted HLA class I binding and proteasome-generated neopeptides derived from the BRAFV600E protein are not presented by HLA class I molecules. Given that the BRAFV600E mutation is highly prevalent in chemotherapy refractory LCH-patients who may qualify for immunotherapy, this study therefore questions the efficacy of immune checkpoint inhibitor therapy in LCH.

In Silico Design and Evaluation of Acinetobacter baumannii Outer Membrane Protein a Antigenic Peptides As Vaccine Candidate in Immunized Mice.

Acinetobacter baumannii is a Gram-negative bacterium that has recently been identified as a leading nosocomial pathogen. Infections by this pathogen result in significant mortality due to antibiotic resistance. An effective vaccine would help alleviate the burden of disease incurred by this pathogen; however, there are currently no licensed vaccines offering protection against Acinetobacter baumannii infection. In this study, considering the fact that outer membrane protein A is one of the most promising vaccine candidates, we predicted T cell and B cell epitopes on this protein using sequence-based epitope prediction tools and determined whether or not mice immunized with these peptides induce an immune response. We selected consensus epitopes including five peptides in different tools with the highest score. 48 female C5BL/6 SPF injected subcutaneously with the peptides (peptide1 to peptide 5 separately) in 100 μL of the solution and sham groups received adjuvant and PBS alone on the same schedule: on day 0 (primary dose) and two booster doses were administered on days 14 and 28. At the end of time, animals euthanized by Isoflurane, and collected sera for assessment of specific antibodies against each peptide by ELISA (Enzyme-linked immunosorbent assay). Immunization of mice showed one of the novel synthetic peptides (peptide 1 (24-50 amino acids)) elicited immune responses. We conclude to combine theoretical methods of epitope prediction and evaluating the potential of immunogenicity for developing vaccines is important.

An Immunoinformatics Approach in Design of Synthetic Peptide Vaccine Against Influenza Virus.

Peptide-based vaccines are an appealing strategy which involves usage of short synthetic peptides to engineer a highly targeted immune response. These short synthetic peptides contain potential T- and B-cell epitopes. Experimental approaches in identifying these epitopes are time-consuming and expensive; hence immunoinformatics approach came into picture. Immuninformatics approach involves epitope prediction tools, molecular docking, and population coverage analysis in design of desired immunogenic peptides. In order to overcome the antigenic variation of viruses, conserved regions are targeted to find the potential epitopes. The present chapter demonstrates the use of immunoinformatics approach to select potential peptide containing multiple T- (CD8+ and CD4+) and B-cell epitopes from Avian H3N2 M1 Protein. Further, molecular docking (to analyseHLA-peptide interaction) and population coverage analysis have been used to verifythe potential of peptide to be presented by polymorphic HLA molecules. In silico approach of epitope prediction has proven to be successful methodology in screening the putative epitopes among numerous possible vaccine targets in a given protein.

Sequence Homology and Epitope Prediction of 37 kDa Outer Membrane Protein H(ompH) Gene of Pasteurella Multocida Type B Isolate from Nusa Tenggara Timur (NTT)

Haemorrhagic Septicaemia (HS), in Indonesia known as Ngorok disease is a fatal acute septicemia disease in cattle and buffalo caused by Pasteurella multocida. Outer membrane Protein H (OmpH) is one of the major proteins in P. multocida envelope has been purified and characterized as porin immunodominant. The aim of this study is to find out the homology of nucleotide sequence of 37 kDa OmpH gene of P. multocida of local isolate from NTT with vaccine strain (Katha) and 5 referen isolates and predict the epitope of 37 kDa OmpH gene. Detection of 37 kDa OmpH gene was done using PCR. Homology analysis of nucleotide and amino acid sequences was performed on the BLAST program at NCBI. Epitope prediction of 37 kDa OmpH genes P. multocida from NTT and vaccine strains was performed using the online programme B Cell Epitope Prediction Tools. The results showed that 37 kDa OmpH gene of P. multocida from NTT has 99% homology to vaccine strain (Katha) and isolates from China, Iran, India, Mesir, and 89% homology to isolate from USA and 37 kDa OmpH gene of P. multocida local isolate from NTT were likely to be immunogen candidates based on B cell epitope that had 11 epitopes. These findings indicate that P. multocida local isolate from NTT has a chance as immunogen candidate, which can be developed as a vaccine candidate and diagnostic kit for HS.

Introducing of an integrated artificial neural network and Chou's pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens

This study was aimed to introduce a novel algorithm for determining linear B- and T-cell epitopes from Crimean-Congo haemorrhagic fever virus (CCHFV) antigens. To this end, 387 approved B- and T-cell epitopes, as well as 331 non-epitope peptides from different serotypes of the virus were collected from IEDB database for generating of the train datasets. After that, the physicochemical properties of the epitopes were expressed as the numeric vectors using Chou's pseudo amino acid composition method. The vectors then were used for training of four machine learning algorithms including artificial neural network (ANN), k-nearest neighbors (kNN), support vector machine (SVM) and Random forest (RF). The results confirmed that ANN was the most accurate algorithm for discriminating between the epitopes and non-epitopes with the accuracy of 0.90. Furthermore, for evaluating the performance of the ANN algorithm, an epitope prediction challenge was performed to a random peptide library from envelopment polyprotein of CCHFV. Moreover, the efficiency of the predicted epitopes in term of antigenicity and affinity to MHC-II were compared to the predicted epitope by standard epitope prediction tools based on their VaxiJen 2.0 score and molecular docking outputs. Finally, the ability of the screened epitopes to stimulation of humoral and cellular responses was evaluated by an in silico immune simulation process thought C-Immsim 10.1 server. The results confirmed that this method has more accuracy for epitope-mapping than the standard tools and could considered as an effective algorithm to develop a serotype independent one-click automated epitope based vaccine design tool.