Cell Epitope Prediction Research Articles

Prediction of epitope-based peptides for the utility of vaccine development from fusion and glycoprotein of nipah virus using in silico approach.

This study aims to design epitope-based peptides for the utility of vaccine development by targeting glycoprotein G and envelope protein F of Nipah virus (NiV) that, respectively, facilitate attachment and fusion of NiV with host cells. Using various databases and tools, immune parameters of conserved sequence(s) from G and F proteins of different isolates of NiV were tested to predict probable epitope(s). Binding analyses of the peptides with MHC class-I and class-II molecules, epitope conservancy, population coverage, and linear B cell epitope prediction were analyzed. Predicted peptides interacted with seven or more MHC alleles and illustrated population coverage of more than 99% and 95%, for G and F proteins, respectively. The predicted class-I nonamers, SLIDTSSTI and EWISIVPNF, superimposed on the putative decameric B cell epitopes, were also identified as core sequences of the most probable class-II 15-mer peptides GPKVSLIDTSSTITI and EWISIVPNFILVRNT. These peptides were further validated for their binding to specific HLA alleles using in silico docking technique. Our in silico analysis suggested that the predicted epitopes, either GPKVSLIDTSSTITI or EWISIVPNFILVRNT, could be a better choice as universal vaccine component against NiV irrespective of different isolates which may elicit both humoral and cell-mediated immunity.

Identification of Potential Vaccine Candidates Against Streptococcus pneumoniae by Reverse Vaccinology Approach

In the past few decades, genome-based approaches have contributed significantly to vaccine development. Our aim was to identify the most conserved and immunogenic antigens of Streptococcus pneumoniae, which can be potential vaccine candidates in the future. BLASTn was done to identify the most conserved antigens. PSORTb 3.0.2 was run to predict the subcellular localization of the proteins. B cell epitope prediction was done for the immunogenicity testing. Finally, BLASTp was done for verifying the extent of similarity to human proteome to exclude the possibility of autoimmunity. Proteins failing to comply with the set parameters were filtered at each step. Based on the above criteria, out of the initial 22 pneumococcal proteins selected for screening, pavB and pullulanase were the most promising candidate proteins.

Computational and experimental validation of B and T-cell epitopes of the in vivo immune response to a novel malarial antigen.

Vaccine development efforts will be guided by algorithms that predict immunogenic epitopes. Such prediction methods rely on classification-based algorithms that are trained against curated data sets of known B and T cell epitopes. It is unclear whether this empirical approach can be applied prospectively to predict epitopes associated with protective immunity for novel antigens. We present a comprehensive comparison of in silico B and T cell epitope predictions with in vivo validation using an previously uncharacterized malaria antigen, CelTOS. CelTOS has no known conserved structural elements with any known proteins, and thus is not represented in any epitope databases used to train prediction algorithms. This analysis represents a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses emphasizes the need to improve their predictive capabilities for use as tools in vaccine design.

Computational identification of Brassica napus pollen specific protein Bnm1 as an allergen

Bnm1 is a pollen specific protein from Brassica napus(oilseed rape) and it is specifically expressed in the bi-cellular and tri-cellular stages of pollen development. Since the incidence of pollinosis due to oilseed rape (Brassica napus) is increasing day by day, keeping pace with its high cultivation rate, the search for its allergens is a demand of time to develop effective immune therapy. In the present study, different computational tools were adopted to predict the po tential of Bnm1 as a candidate allergen. Physico chemical properties of Bnm1 showed its molecular weight (~20kD) and theoretical pI (5.27) along with other properties to be fallen between the ranges essential for a protein to be an allergen. Keeping in min d the capability of allergen to induce both humoral and cell mediated immune response, we checked both the potential B cell and T cell epitope candidates of Bnm1 using different immune -informatics tools housed at IEDB analysis resource. For B cell epitopeprediction, potential antigenic sites on the protein surface were predicted by both propensity scale and machine learning method followed by their mapping on Bnm1 3D structure predicted from homology modeling. In case of T cell epitope prediction, interact ion of the core sequence with seven abundant MHC -II alleles (DRB1*0101, DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1301 and DRB1*1501) within an IC50 range (IC50<25) was the basis. We observed multiple epitope interactions with DRB1*0101 allele, impl ying that this interactions might evoke a strong TH2 response ensuring an increased production of IgE response. Based on our prediction we hereby claim that the Bnm1 is a potential allergen having capability to induce both humoral and cell mediated allergi c responses. However, in vitro analysis was out of our scope and therefore it should have to be performed to validate the potency of Bnm1 as an allergen.

A monoclonal antibody specific to the non-epitope region of hepatitis B virus preS1 contributes to more effective HBV detection

ObjectivesThe hepatitis B virus (HBV) preS1 protein is divided into an epitope region and a non-epitope region based on the respective antigenicities of these regions. Most of the antibodies that are currently used to detect the large surface protein of HBV (HBV LHB) are specific to the epitope region of preS1, which may contribute to the false negative results of HBV LHB detection assays. Here, we established a mouse monoclonal antibody (mAb) that could improve the efficiency of HBV LHB detection. Design and methodsThe HBV preS1 protein was expressed in E. coli strain BL21 and used to screen hybridoma clones. HBV preS1-specific mAb was produced by immunizing mice with a chemically synthesized peptide antigen derived from the non-epitope region of HBV preS1. The mAb was characterized by ELISA, Western blot, and immunocytochemistry and was subsequently used in serum sample tests. ResultsBased on in silico B cell epitope predictions, the HBV preS1 aa 91–117 peptide was synthesized as an antigen. Recombinant HBV preS1 was expressed in E. coli and identified by SDS-PAGE. The mAb D8 (IgG2b) recognized the recombinant preS1 protein in both ELISA and Western blot assays and also recognized the preS1 protein expressed in plasmid-transfected HepG2.2.15 cells by immunocytochemistry. Furthermore, the D8 mAb, which is specific for the non-epitope region of preS1, contributed to the improved sensitivity and specificity of HBV detection. ConclusionsWe established an mAb that is specific to the non-epitope region of HBV preS1 and improved the detection of HBV LHB in an ELISA assay. This mAb could help increase the accuracy of the clinical measurement of preS1.

HPV-16 E6 and E7 protein T cell epitopes prediction analysis based on distributions of HLA-A loci across populations: An in silico approach

Human papillomavirus type 16 (HPV-16) is the most prevalent virus in human cervical cancers, as it is present in more than half of all cases. Many studies have found continued expression of E6 and E7 proteins in the majority of cervical cancer cases, but not in normal tissues. These results indicated that the E6 and E7 proteins could be ideal candidate therapeutic vaccines against HPV-16 infection and cervical cancer. Using the Immune Epitope Database Analysis Resource, cytotoxic T lymphocyte (CTL) epitopes of the HPV-16 E6 and E7 proteins were predicted according to worldwide frequency distributions of HLA-A alleles (HLA-A*01:01, -A*02:01, -A*02:06, -A*03:01, -A*11:01, -A*24:02, -A*26:01, -A*31:01 and -A*33:03). Our results predicted a total of 81 epitopes of HPV-16 E6 (n=59) and E7 (n=22). Epitope cluster analysis showed that among the 20 clusters of HPV-16 E6, cluster 3 contained the most epitopes (10 epitopes), which was represented by HLA-A*31:01 and -A*33:03. Of the 10 clusters of HPV-16 E7, cluster 3 contained the most epitopes (5 epitopes), which was represented by HLA-A*01:01 and -A*26:01. Our results indicated that the combination of epitopes FAFRDLCIVYR52-62 of E6 (HLA-A*02:06, HLA-A*31:01, and HLA-A*33:03), PYAVCDKCLKF66-76 of E6 (HLA-A*11:01 and HLA-A*24:02), HGDTPTLHEY2-11 of E7 (HLA-A*01:01 and HLA-A*26:01), and YMLDLQPETT11-20 of E7 (HLA-A*02:01) could vaccinate >50% of all individuals worldwide. Our results propose CTL epitopes or combinations of them predicted in current study for candidate therapeutic vaccines to effectively control HPV-16 infection and development of cervical cancer.

Sequence Analysis and B Cell Epitope Prediction of Duck Hepatitis A Virus 1 VP1 Gene

Molecular characterization and phylogenetic analysis of the DHAV-1 VP1 gene, structure and biological function of the VP1 protein were analyzed by using the programs of the DNAstar software ,MEGA software and other bioinformatics software on the line. The nucleotide sequence homologies among and the amino acids homologies of the VP1 gene of DHAV H stain with other 13 DHAV-1 strains in the GenBank were 92.54%-99.86% and 93.75%-99.57%.The putative secondary structure of the VP1 protein contained 10.08% H(alpha helix), 38.24% E(beta-sheet), 51.68%L(loop/coil), Protein can be classified as mixed. The results showed that the B cell epitopes of the VP1 protein were located at the C-terminal 131-136, and 209-218 regions according to Kyte-Doolitte method, Emini method,Karlus-Schulz method, and Jameson-Wolf method implemented in Protean program of DNAStar 7.The VP1 protein in the surface of the virus most exposed, it is to decide the major components of the effects of the virus of Picornaviridae. The VP1 protein of DHAV is a good biomaterial for vaccine research, and genetic and phylogenetic analyses of the VP1 and the B cell epitope prediction can help us to develop multi-epitope vaccine against DHAV variant viruses infections.

Prediction of Th Cell Epitopes Based on ISC-SVR Method

Exogenous antigen proteins are absorbed by antigen presenting cells (APC) and translated into lysosome in which they are degraded into short peptides with different length. A fragment called class II-associated invariant chain peptide (CLIP) lodges in the peptide-binding groove after the Ii chain is progressively degraded by lysosomal proteases. With the help of HLA-DM molecule, CLIP is removed from the binding groove of MHC class II molecule, which makes the degraded peptides enter into the empty binding groove to form steady peptide-MHC class II complexes. The peptide-MHC class II complexes are ultimately trafficked to the cell surface to be recognized by CD4 + T cell surface receptor (TCR). This process can activate Th cells to secrete cell factors that promote CTL cells to kill target cells specifically or assist B cells to make antibody against bacteria. Activation of Th cells has important supplementary effect on the cellular immunity and humoral immunity function. In this paper, the support vector regression method combined with iterative self-consistent strategy (ISC-SVR) were used to build the models to predict affinities of exogenous antigen peptides binding to MHC class II mole- cules. In order to improve the predictive performance, the predictive models were based on 13mer extended core binding sequence rather than traditional 9mer core binding sequence. We computed the affinity of MHC class II molecule binding to peptide with four methods, which were the mean method, the max method, the combine method and the eave method respec- tively. The predictive performance of the ISC-SVR model is validated on data sets of 17 MHC class II alleles. The results show that the model has certain rationality and accuracy in T cell epitopes prediction. Compared to other predictive models with the same data set, the predictive performance of our model is more satisfying. For each MHC class II allele, the ISC-SVR model got the best AUC value which was used to measure the predictive performance. Furthermore, for HLA DRB1*0101 molecule, we obtained the specificities of MHC class II molecule binding to peptides which were consistent with previous experimental results. Keywords T cell; epitope; exogenous antigen; MHC class II molecule; support vector regression

Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking

The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0.

In silico prediction of Ara h 2 T cell epitopes in peanut‐allergic children

Despite the frequency and severity of peanut allergy, the only approved treatment is strict avoidance. Different types of immunotherapy with crude peanut extract are not universally effective and have been associated with relatively high adverse reaction rates. We sought to determine whether in silico predictive algorithms were useful in identifying candidate peptides for an Ara h 2 peptide-based vaccine using peanut-allergic patients' peripheral blood mononuclear cells (PBMCs) in vitro. A human leucocyte antigen (HLA) distribution analysis was also performed. Major histocompatibility complex (MHC)-class II-binding peptides were predicted using NetMHCIIpan-2.0 and NetMHCII-2.2 algorithms. PBMCs from 80 peanut-allergic patients were stimulated with overlapping 20-mer Ara h 2 peptides. Cell supernatant cytokine profiles were evaluated by multiplex assays. HLA-DRB1* and HLA-DQB1* typing were performed. Four regions of overlapping sequences induced PBMC proliferation and predominant Th2 cytokine production. HLA genotyping showed 30 different DRB1* allele specificities and eight DQ serological specificities. The in silico analysis revealed similar relevant regions and predicted identical or similar core 9-mer epitopes to those identified in vitro. If relevant peptides, as determined by either in vitro or in silico analysis (15 peptides and 9 core epitopes respectively), were used in a peptide-based vaccine, they would cover virtually all subjects in the cohort studied. Four dominant regions in Ara h 2 have been identified, containing sequences that could serve as potential candidates for peptide-based immunotherapy. MHC-class II-based T cell epitope prediction algorithms for HLA-DR and -DQ loci accurately predicted Ara h 2 T cell epitopes in peanut-allergic subjects, suggesting their potential utility in a peptide-based vaccine design for food allergy.

Identification of epitopes in Leptospira borgpetersenii leucine-rich repeat proteins

Leptospirosis is considered to be a re-emerging disease that has impacts on public health both globally and in and Thailand. The leptospires outbreak in Thailand during 1999 was largely due to the etiologic agent Leptospira borgpetersenii serogroup Sejroe. This had a related immunity profile in cows from Thailand, which serovars Tarassovi and Sejroe were prevalent. Development of a vaccine protecting against leptospiral infection in livestock has been considered. One family of proteins being targeted as candidates for vaccine development are leucine-rich repeats (LRRs), which have diverse functions such as bacterial host-pathogen interactions, membrane anchoring, invasion and stimulating host defense mechanisms. Identifying leptospiral LRR proteins containing immunogenic epitopes is important for Leptospirosis vaccine development. In this study, we searched LRR genes from L. borgpetersenii serovar Hardjo-bovis strain L550 and LB179 genomic databases in an attempt to find appropriate LRR proteins for vaccine candidates covering the common genospecies detected in Thailand. The in silico analysis, LRR protein secondary and tertiary structures by 3D modeling, and T cell epitope prediction & analysis were performed. In conclusion, we have found seven pairs of conserved LRR genes in L. borpetersenii serovars Hardjo-bovis strains JB197 and L550. Only the LBJ_2271 gene was predicted to be LRR motif subfamily membrane protein with an N-terminal signal sequence, 2 transmembrane domains and an N-glycosylated site. The LRR consensus sequence LXXLXLXXNXL was classified in a typical LRR subfamily. The LBJ_2271 gene sequence has highly homology to genes in other pathogenic Leptospira interrogans serovars; LA_1324, LIC_12401, JX26069, JX26070, JX26071 and JX06072. The LBJ_2271 protein was predicted to containin 14 T cell epitopes, 8 of which are predicted T cell epitopes for Major-Histo-Compatability (MHC) alleles HLA-A∗0101, A∗0202, A∗0203, A∗1101, A∗3101, A∗6802, and HLA-DRB∗0401 and DRB∗0701, with IC50 values of 0.90–32.28nM, residing outside of the transmembrane domains. At least 5 promising predicted T cell epitopes for alleles HLA-A0202, -A0203, -A1101, -DRB0401 and -DRB0701 had their IC50 lower – equal to IC50 values for the same allele epitopes of known antigenic proteins LigA, LipL32, OMPL1 and LipL36. This study has successfully identified LBJ_2271 as a protein candidate for further study of antigenic immune stimulation for vaccine development.

Linear B cell epitope prediction for epitope vaccine design against meningococcal disease and their computational validations through physicochemical properties

Neisseria meningitidis serogroup B is predominantly known for its leading role in bacterial meningitis and septicemia worldwide. Although, polysaccharide conjugate vaccines have been developed and used successfully against many of the serogroups of N. meningitidis, such strategy has proved ineffective against group B meningococci. Here, we proposed to develop peptide epitope-based vaccine candidates from outer membrane (OM) protein contained in the outer membrane vesicles (OMV) based on our in silico analysis. In OMV, a total of 236 proteins were identified, only 15 (6.4 %) of which were predicted to be located in the outer membrane. For the preparation of specific monoclonal antibodies against pathogenic bacterial protein, identification and selection of B cell epitopes that act as a vaccine target are required. We selected 13 outer membrane proteins from OMV proteins while taking into consideration the removal of cross-reactivity. Epitopia web server was used for the prediction of B cell epitopes. Epitopes are distinguished from non-epitopes by properties such as amino acid preference on the basis of amino acid composition, secondary structure composition, and evolutionary conservation. Predicted results were subject to verification with experimental data and we performed string-based search through IEDB. Our finding shows that epitopes have general preference for charged and polar amino acids; epitopes are enriched with loop as a secondary structure element that renders them flexible and also exposes another view of antibody–antigen interaction.

Linear and Conformational B Cell Epitope Prediction of the HER 2 ECD-Subdomain III by in silico Methods

Human epidermal growth factor receptor 2 (HER2) is a member of the epidermal growth factor receptor family of receptor tyrosine kinases that play important roles in all processes of cell development. Their overexpression is related to many cancers, including examples in the breast, ovaries and stomach. Anticancer therapies targeting the HER2 receptor have shown promise, and monoclonal antibodies against subdomains II and IV of the HER2 extra-cellular domain (ECD), Pertuzumab and Herceptin, are currently used in treatments for some types of breast cancers. Since anti HER2 antibodies targeting distinct epitopes have different biological effects on cancer cells; in this research linear and conformational B cell epitopes of HER2 ECD, subdomain III, were identified by bioinformatics analyses using a combination of linear B cell epitope prediction web servers such as ABCpred, BCPREDs, Bepired, Bcepred and Elliprro. Then, Discotope, CBtope and SUPERFICIAL software tools were employed for conformational B cell epitope prediction. In contrast to previously reported epitopes of HER2 ECD we predicted conformational B cell epitopes P1C: 378-393 (PESFDGDPASNTAPLQ) and P2C: 500-510 (PEDECVGEGLA) by the integrated strategy and and P4: PESFDGD-X-TAPLQ; P5: PESFDGDP X TAPLQ; P6: ESFDGDP X NTAPLQP; P7: PESFDGDP-X-NTAPLQ; P8: ESFDG-XX-TAPLQPEQL and P9: ESFDGDP- X-NTAPLQP by SUPERFICIAL software. These epitopes could be further used as peptide antigens to actively immune mice for development of new monoclonal antibodies and peptide cancer vaccines that target different epitopes or structural domains of HER2 ECD.

Construction, Expression, Purification and Biotin Labeling of a Single Recombinant Multi-Epitope Antigen for Double-Antigen Sandwich ELISA to Detect Hepatitis C Virus Antibody

Based on B cell epitope predictions, a recombinant antigen with multiple epitopes from four Hepatitis C Virus fragments (C, NS3, NS4 and NS5) were engineered. The recombinant gene was then highly expressed in E. coli. The non-modified and C-terminal-modified recombinant proteins were used for coating and biotin labeling, respectively, to establish the double-antigen sandwich ELISA. Ten positive reference samples confirmed by the CHIRON RIBA HCV 3.0 SIA kit were detected positive, Forty one plasma samples were positive among samples from 441 volunteers, which indicated that the recombinant antigen could readily react well with plasma HCV antibody. As critical reagents of double-antigen sandwich ELISA, the recombinant multi-epitope antigen and the C-terminal-modified and biotin-conjugated antigen show good antigenicity. In this study, we provide a simple approach to produce multiple epitopes within one recombinant protein in order to avoid the costly expression of less-effective pools of multiple proteins, which is the conventional strategy of diagnostic antigen production for HCV antibody detection.

Predicting B cell epitope residues with network topology based amino acid indices.

We evaluate the performance of six amino acid indices in B cell epitope residue prediction using the classical sliding window method on five data sets. Four of the indices: i.e. relative connectivity, clustering coefficient, closeness and betweenness are newly derived from the topological parameters of residue networks. The other two are Parker's hydrophilicity and Levitt's index, known as the best indices so far for B cell epitope prediction. On four of the data sets, the performance of all the indices was comparable and poor in general. When applied to one well-annotated data set, the performances improved and the 4 network based indices showed better performance than that of Parker's hydrophilicity and Levitt's index. When using the relative connectivity index on this data set, the prediction accuracy, sensitivity and specificity reached 73.6%, 73.0% and 75.0% respectively, with an area under the curve about 0.796. Thus, we suggested that this index is a good choice for B cell epitope prediction. It also indicates that the low performance of B cell epitope prediction is not only due to the methods and amino acid indices used, but also the data set as well. Interestingly, on the well-annotated data set, the performance of B cell epitope residue prediction is very similar to that of protein surface residue prediction, especially at the 10 and 20 A2 cutoffs. It is suggested that the performance in surface residue prediction might form a theoretical upper limit for the performance of B cell epitope residue prediction methods.

Identification of a novel HLA-A2-restricted cytotoxic T lymphocyte epitope from cancer-testis antigen PLAC1 in breast cancer

Identification of cytotoxic T lymphocyte (CTL) epitopes from tumor antigens is essential for the development of peptide vaccines against tumor immunotherapy. Among all the tumor antigens, the caner-testis (CT) antigens are the most widely studied and promising targets. PLAC1 (placenta-specific 1, CT92) was considered as a novel member of caner-testis antigen, which expressed in a wide range of human malignancies, most frequently in breast cancer. In this study, three native peptides and their analogues derived from PLAC1 were predicted by T cell epitope prediction programs including SYFPEITHI, BIMAS and NetCTL 1.2. Binding affinity and stability assays in T2 cells showed that two native peptides, p28 and p31, and their analogues (p28-1Y9 V, p31-1Y2L) had more potent binding activity towards HLA-A*0201 molecule. In ELISPOT assay, the CTLs induced by these four peptides could release IFN-γ. The CTLs induced by these four peptides from the peripheral blood mononuclear cells (PBMCs) of HLA-A*02+ healthy donor could lyse MCF-7 breast cancer cells (HLA-A*0201+, PLAC1+) in vitro. When immunized in HLA-A2.1/Kb transgenic mice, the peptide p28 could induce the most potent peptide-specific CTLs among these peptides. Therefore, our results indicated that the peptide p28 (VLCSIDWFM) could serve as a novel candidate epitope for the development of peptide vaccines against PLAC1-positive breast cancer.

Immune system simulation online

The recognition of antigenic peptides is a major event of an immune response. In current mesoscopic-scale simulators of the immune system, this crucial step has been modeled in a very approximated way. We have equipped an agent-based model of the immune system with immuno-informatics methods to allow the simulation of the cardinal events of the antigenic recognition, going from single peptides to whole proteomes. The recognition process accounts for B cell-epitopes prediction through Parker-scale affinity estimation, class I and II HLA peptide prediction and binding through position-specific scoring matrices based on information from known HLA epitopes prediction tools, and TCR binding to HLA-peptide complex calculated as the averaged sum of a residue-residue contact potential. These steps are executed for all lymphocytes agents encountering the antigen in a wide-reaching Monte Carlo simulation. http://www.cbs.dtu.dk/services/C-ImmSim-10.1/

Identification of epitopes in Indian human papilloma virus 16 E6: A bioinformatics approach

HPV-16 is reported as the cause of cervical and other related carcinomas. The early expressed protein E6 in cancer cells is found to be the target for immune therapeutic methods. The sequence of HPV-16 E6 (Accession No: ABK32509) from NCBI databank has been taken for this study. Hydrophilicity, flexibility, accessibility, turns, exposed surface, polarity and antigenic propensity scales were used for the B cell epitope prediction. MHC Class I and Class II alleles for the accession were predicted by the MHCPred 2.0 Program. The epitope sequences were also found out. Computer-based prediction program results show, A0203 and DRB0101 lower IC50 than other alleles. The best peptide binding affinity was 21HLCTELQTT30 of A0203 allele. In DRB0101 allele the peptide found was 39YCKQQLLRR48. Different structural features of the protein have also been predicted including glycosylation, kinase C phosphorylation, casein kinase II phosphorylation and N-myristylation sites. These computational prediction programs show four glycosylation, five kinase C phosphorylation, two casein kinase II phosphorylation, zero N-myristylation sites and seven disulphide sites. Development and approval of new vaccines are the keys for control of cancer. Epitopes and other structural features of protein prediction could be the best source of information and can help in molecular and medical studies of viral infection and development of HPV associated cancer drugs.

Epitope specificity of myeloperoxidase antibodies: identification of candidate human immunodominant epitopes

Anti-neutrophil cytoplasmic autoantibodies (ANCA) are a common feature of systemic vasculitides and have been classified as autoimmune conditions based, in part, on these autoantibodies. ANCA are subdivided further based on their primary target: cytoplasm (c-ANCA) or perinuclear region (p-ANCA). p-ANCAs commonly target myeloperoxidase (MPO), an enzyme with microbicidal and degradative activity. MPO antibodies are non-specific for any single disease and found in a variety of vasculitides, most commonly microscopic polyangiitis. Despite their prevalence, their role in human disease pathogenesis remains undefined. We sought to characterize the sequential antigenic determinants of MPO in vasculitis patients with p-ANCA. Of 68 patients with significant levels of p-ANCA, 12 have significant levels of MPO antibodies and were selected for fine specificity epitope mapping. Sequential antigenic targets, including those containing amino acids (aa) 213-222 (WTPGVKRNGF) and aa 511-522 (RLDNRYQPMEPN), were commonly targeted with a prevalence ranging from 33% to 58%. Subsequent analysis of autoantibody binding to the RLDNRYQPMEPN peptide was assessed using a confirmatory enzyme-linked immunosorbent assay format, with six patients displaying significant binding using this method. Antibodies against this epitope, along with four others (aa 393-402, aa 437-446, aa 479-488 and aa 717-726), were reactive to the heavy chain structure of the MPO protein. One epitope, GSASPMELLS (aa 91-100), was within the pro-peptide structure of MPO. B cell epitope prediction algorithms identified all or part of the seven epitopes defined. These results provide major common human anti-MPO immunodominant antigenic targets which can be used to examine further the potential pathogenic mechanisms for these autoantibodies.

Advances of bioinformatics tools applied in virus epitopes prediction

In recent years, the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein, a general overview of different tools currently available, including T cell and B cell epitopes prediction tools, is presented. And the principles of different prediction algorithms are reviewed briefly. Finally, several examples are present to illustrate the application of the prediction tools.