Conformational B-cell Epitopes Research Articles

A novel conformational B-cell epitope prediction method based on mimotope and patch analysis

A B-cell epitope is a group of residues on the surface of an antigen that stimulates humoral immune responses. Identifying B-cell epitopes is important for effective vaccine design. Predicting epitopes by experimental methods is expensive in terms of time, cost and effort; therefore, computational methods that have a low cost and high speed are widely used to predict B-cell epitopes. Recently, epitope prediction based on random peptide library screening has been viewed as a promising method. Some novel software and web-based servers have been proposed that have succeeded in some test cases. Herein, we propose a novel epitope prediction method based on amino acid pairs and patch analysis. The method first divides antigen surfaces into overlapping patches based on both radius (R) and number (N), then predict epitopes based on Amino Acid Pairs (AAPs) from mimotopes and the surface patch. The proposed method yields a mean sensitivity of 0.53, specificity of 0.77, ACC of 0.75 and F-measure of 0.45 for 39 test cases. Compared with mimotope-based methods, patch-based methods and two other prediction methods, the sensitivity of the new method offers a certain improvement. Our findings demonstrate that this proposed method was successful for patch and AAPs analysis and allowed for conformational B-cell epitope prediction.

The Relationship between B-cell Epitope and Mimotope Sequences.

B-cell epitope is a group of residues which is on the surface of an antigen. It invokes humoral responses. Locating B-cell epitope is important for effective vaccine design, and the development of diagnostic reagents. Mimotope-based B-cell epitope prediction method is a kind of conformational B-cell epitope prediction, and the core idea of the method is mapping the mimotope sequences which are obtained from a random phage display library. However, current mimotope-based B-cell epitope prediction methods cannot maintain a high degree of satisfaction in the circumstances of employing only mimotope sequences. In this study, we did a multi-perspective analysis on parameters for conformational B-cell epitopes and characteristics between epitope and mimotope on a benchmark datasets which contains 67 mimotope sets, corresponding to 40 unique complex structures. In these 67 cases, there are 25 antigen-antibody complexes and 42 protein-protein interactions. We analyzed the two parts separately. The results showed the mimotope sequences do have some epitope features, but there are also some epitope properties that mimotope sequences do not contain. In addition, the numbers of epitope segments with different lengths were obviously different between the antigen-antibody complexes and the protein-protein interactions. This study reflects how similar do mimotope sequence and genuine epitopes have; and evaluates existing mimotope-based B-cell epitope prediction methods from a novel viewpoint.

Positive-unlabeled learning for the prediction of conformational B-cell epitopes.

BackgroundThe incomplete ground truth of training data of B-cell epitopes is a demanding issue in computational epitope prediction. The challenge is that only a small fraction of the surface residues of an antigen are confirmed as antigenic residues (positive training data); the remaining residues are unlabeled. As some of these uncertain residues can possibly be grouped to form novel but currently unknown epitopes, it is misguided to unanimously classify all the unlabeled residues as negative training data following the traditional supervised learning scheme.ResultsWe propose a positive-unlabeled learning algorithm to address this problem. The key idea is to distinguish between epitope-likely residues and reliable negative residues in unlabeled data. The method has two steps: (1) identify reliable negative residues using a weighted SVM with a high recall; and (2) construct a classification model on the positive residues and the reliable negative residues. Complex-based 10-fold cross-validation was conducted to show that this method outperforms those commonly used predictors DiscoTope 2.0, ElliPro and SEPPA 2.0 in every aspect. We conducted four case studies, in which the approach was tested on antigens of West Nile virus, dihydrofolate reductase, beta-lactamase, and two Ebola antigens whose epitopes are currently unknown. All the results were assessed on a newly-established data set of antigen structures not bound by antibodies, instead of on antibody-bound antigen structures. These bound structures may contain unfair binding information such as bound-state B-factors and protrusion index which could exaggerate the epitope prediction performance. Source codes are available on request.

In silico Analysis of Surface Proteins of Streptococcus pneumoniae Serotype 19F for Identification of Immunoprotective Epitopes

Pneumococcal conjugate vaccines (PCVs) were developed through chemical coupling of polysaccharide capsules of pneumococci to immunogenic carrier proteins and World Health Organization recommends inclusion of these vaccines in national immunization programs for children. However, the PCVs implementation in developing countries can be prevented by the high manufacturing costs. This issue can be overcome by construction of protein based vaccines against pneumococci. We already identified three pneumococcal surface proteins including D-alanyl-D-alanine-carboxy peptidase (DDCP), choline binding protein D (CBPD), and cell wall surface anchor family protein (CWSAP) as appropriate protein candidates for eliciting protection against S. pneumoniae serotype 19F. The protein protective antigenicity, the absence of autoimmunity induction, and the amino acid sequence conservancy in serotype 19F pneumococcal strains were used as selection criteria. However, regarding the requirement of both antibody and cellular immune responses for protection against pneumococci, analysis of protective B and T-cell epitopes of these proteins is necessary to examine their usefulness in new vaccine formulations. In the present study, therefore, we aim to identify protective epitopes of these proteins via widely used bioinformatic tools. The Bepipred program was used for identification of linear B-cell epitopes. The conformational B-cell epitopes were predicted using the CBTope program. T-cell epitopes were identified using the Immune Epitope Database tool. The immunoprotective abilities of epitopes were evaluated using VaxiJen. Our results showed that all of the three studied proteins included protective epitopes. However, the greatest number of epitopes was identified in a truncated form of CWSAP. Moreover, the most probable immunoprotective epitopes reside in this protein and these epitopes were highly conserved in CWSAPs of the most common pneumococcal serotypes in the world. Therefore, the truncated CWSAP was an appropriate candidate for development of protein based vaccines against the most common pneumococcal serotypes.

LRC: A new algorithm for prediction of conformational B-cell epitopes using statistical approach and clustering method

Identifying of B-cell epitopes from antigen is a challenging task in bioinformatics and applied in vaccine design and drug development. Recently, several methods have been presented to predict epitopes. The physicochemical or structural properties are used by these methods. In this paper, we propose a more appropriate epitope prediction method, LRC, that is based on a combination of physicochemical and structural properties. First, we construct a graph from the surface of antigen, then by using the logistic regression, we model the physicochemical and structural properties and weight each vertex of the graph. Finally, we utilize a clustering method, MCL, to cluster the graph. The effectiveness of the proposed method is benchmarked using several antibody–antigen PDB complexes. The results of LRC algorithm are compared with other methods (DiscoTope, SEPPA and Ellipro) in terms of sensitivity, specificity and other well-known measures. Results indicate that applying the LRC algorithm improves the precision of prediction epitopes in comparison with the mentioned methods. Our LRC program and supplementary material are freely available from http://bs.ipm.ir/softwares/LRC/.

Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull.

The prediction of conformational b-cell epitopes plays an important role in immunoinformatics. Several computational methods are proposed on the basis of discrimination determined by the solvent-accessible surface between epitopes and non-epitopes, but the performance of existing methods is far from satisfying. In this paper, depth functions and the k-th surface convex hull are used to analyze epitopes and exposed non-epitopes. On each layer of the protein, we compute relative solvent accessibility and four different types of depth functions, i.e., Chakravarty depth, DPX, half-sphere exposure and half space depth, to analyze the location of epitopes on different layers of the proteins. We found that conformational b-cell epitopes are rich in charged residues Asp, Glu, Lys, Arg, His; aliphatic residues Gly, Pro; non-charged residues Asn, Gln; and aromatic residue Tyr. Conformational b-cell epitopes are rich in coils. Conservation of epitopes is not significantly lower than that of exposed non-epitopes. The average depths (obtained by four methods) for epitopes are significantly lower than that of non-epitopes on the surface using the Wilcoxon rank sum test. Epitopes are more likely to be located in the outer layer of the convex hull of a protein. On the benchmark dataset, the cumulate 10th convex hull covers 84.6% of exposed residues on the protein surface area, and nearly 95% of epitope sites. These findings may be helpful in building a predictor for epitopes.

A paradigm shift: Cancer therapy with peptide-based B-cell epitopes and peptide immunotherapeutics targeting multiple solid tumor types: Emerging concepts and validation of combination immunotherapy

There is a recognizable and urgent need to speed the development and application of novel, more efficacious anti-cancer vaccine therapies that inhibit tumor progression and prevent acquisition of tumor resistance. We have created and established a portfolio of validated peptide epitopes against multiple receptor tyrosine kinases and we have identified the most biologically effective combinations of EGFR (HER-1), HER-2, HER-3, VEGF and IGF-1R peptide vaccines/mimics to selectively inhibit multiple receptors and signaling pathways. The strategy is based on the use of chimeric conformational B-cell epitope peptides incorporating “promiscuous” T-cell epitopes that afford the possibility of generating an enduring immune response, eliciting protein-reactive high-affinity anti-peptide antibodies as potential vaccines and peptide mimics that act as antagonists to receptor signaling that drive cancer metastasis. In this review we will summarize our ongoing studies based on the development of combinatorial immunotherapeutic strategies that act synergistically to enhance immune-mediated tumor killing aimed at addressing mechanisms of tumor resistance for several tumor types.

Computational prediction and analysis of envelop glycoprotein epitopes of DENV-2 and DENV-3 Pakistani isolates: a first step towards Dengue vaccine development.

Dengue fever of tropics is a mosquito transmitted devastating disease caused by dengue virus (DENV). There is no effective vaccine available, so far, against any of its four serotypes (DENV-1, DENV-2, DENV-3, and DENV-4). There is a need for the development of preventive and therapeutic vaccines against DENV to decrease the prevalence of dengue fever, especially in Pakistan. In this research, linear and conformational B-cell epitopes of envelope glycoprotein of DENV-2 and DENV-3 (the most prevalent serotypes in Pakistan) were predicted. We used Kolaskar and Tongaonkar method for linear epitope prediction, Emini’s method for surface accessibility prediction and Karplus and Schulz’s algorithm for flexibility determination. To propose three dimensional epitopes, the E proteins for both serotypes were homology modeled by using Phyre2 V 2.0 server, and ElliPro was used for the prediction of surface epitopes on their globular structure. Total 21 and 19 linear epitopes were predicted for DENV-2 and DENV-3 Pakistani isolates respectively. Whereas, 5 and 4 discontinuous epitopes were proposed for DENV-2 and DENV-3 Pakistani isolates respectively. Moreover, the values of surface accessibility, flexibility and solvent-accessibility can be helpful in analyzing vaccines against DENV-2 and DENV-3. In conclusion, the proposed continuous and discontinuous antigenic peptides can be valuable candidates for diagnostic and therapeutics of DENV.

Molecular modeling and in-silico engineering of Cardamom mosaic virus coat protein for the presentation of immunogenic epitopes of Leptospira LipL32

Expression of Cardamom mosaic virus (CdMV) coat protein (CP) in E. coli forms virus-like particles. In this study, the structure of CdMV CP was predicted and used as a platform to display epitopes of the most abundant surface-associated protein, LipL32 of Leptospira at C, N, and both the termini of CdMV CP. In silico, we have mapped sequential and conformational B-cell epitopes from the crystal structure of LipL32 of Leptospira interrogans serovar Copenhageni str. Fiocruz L1-130 using IEDB Elipro, ABCpred, BCPRED, and VaxiJen servers. Our results show that the epitopes displayed at the N-terminus of CdMV CP are promising vaccine candidates as compared to those displayed at the C-terminus or at both the termini. LipL32 epitopes, EP2, EP3, EP4, and EP6 are found to be promising B-cell epitopes for vaccine development. Based on the type of amino acids, length, surface accessibility, and docking energy with CdMV CP model, the order of antigenicity of the LipL32 epitopes was found to be EP4 > EP3 > EP2 > EP6.

In silico analysis for identifying potential vaccine candidates against Staphylococcus aureus.

PurposeStaphylococcus aureus is one of the most important causes of nosocomial and community-acquired infections. The increasing incidence of multiple antibiotic-resistant S. aureus strains and the emergence of vancomycin resistant S. aureus strains have placed renewed interest on alternative means of prevention and control of infection. S. aureus produces a variety of virulence factors, so a multi-subunit vaccine will be more successful for preventing S. aureus infections than a mono-subunit vaccine.Materials and MethodsWe selected three important virulence factors of S. aureus, clumping factor A (ClfA), iron-regulated surface determinant (IsdB), and gamma hemolysin (Hlg) that are potential candidates for vaccine development. We designed synthetic genes encoding the clfA, isdB, and hlg and used bioinformatics tools to predict structure of the synthetic construct and its stabilities. VaxiJen analysis of the protein showed a high antigenicity. Linear and conformational B-cell epitopes were identified.ResultsThe proteins encoded by these genes were useful as vaccine candidates against S. aureus infections.ConclusionIn silico tools are highly suited to study, design, and evaluate vaccine strategies.

Conformational B-cell epitope prediction method based on antigen preprocessing and mimotopes analysis.

Identification of epitopes which invokes strong humoral responses is an essential issue in the field of immunology. Various computational methods that have been developed based on the antigen structures and the mimotopes these years narrow the search for experimental validation. These methods can be divided into two categories: antigen structure-based methods and mimotope-based methods. Though new methods of the two kinds have been proposed in these years, they cannot maintain a high degree of satisfaction in various circumstances. In this paper, we proposed a new conformational B-cell epitope prediction method based on antigen preprocessing and mimotopes analysis. The method classifies the antigen surface residues into “epitopes” and “nonepitopes” by six epitope propensity scales, removing the “nonepitopes” and using the preprocessed antigen for epitope prediction based on mimotope sequences. The proposed method gives out the mean F score of 0.42 on the testing dataset. When compared with other publicly available servers by using the testing dataset, the new method yields better performance. The results demonstrate the proposed method is competent for the conformational B-cell epitope prediction.

B‐cell epitopes of antigenic proteins in Leishmania infantum: an in silico analysis

Serodiagnosis of visceral leishmaniasis is often hindered by cross-reactions to other parasitic diseases. Identifying specific B-cell epitopes in proteins is therefore important for immunodiagnostics, as well as for disease control by vaccines. This study aimed to identify linear and conformational B-cell epitopes and to evaluate the secondary structure of antigen proteins in Leishmania infantum using in silico analysis. Linear epitopes were predicted using the Immune Epitope Database and Analysis Resource (IEDB), BepiPred and BcePred programs. The conformational B-cell epitopes were identified using the CBTOPE server. The combination of the predictions using IEDB, BepiPred and BcePred generated 148 linear epitopes from the calpain-like cysteine peptidase (CP), thiol-dependent reductase 1 (TDR1) and HSP70 proteins. In total, 164 conformational epitopes were predicted, mostly located in the linear epitope region. The predicted epitopes are located in α helix and random coil regions in the thiol-dependent reductase 1 and HSP70 proteins. New linear and conformational B-cell epitopes of L.infantum proteins were identified in silico, and the prediction using various programs ensures greater accuracy of the results, as suggested by confirmation of previously identified HSP70 epitopes.

Tertiary structure-based prediction of conformational B-cell epitopes through B factors.

Motivation: B-cell epitope is a small area on the surface of an antigen that binds to an antibody. Accurately locating epitopes is of critical importance for vaccine development. Compared with wet-lab methods, computational methods have strong potential for efficient and large-scale epitope prediction for antigen candidates at much lower cost. However, it is still not clear which features are good determinants for accurate epitope prediction, leading to the unsatisfactory performance of existing prediction methods.Method and results: We propose a much more accurate B-cell epitope prediction method. Our method uses a new feature B factor (obtained from X-ray crystallography), combined with other basic physicochemical, statistical, evolutionary and structural features of each residue. These basic features are extended by a sequence window and a structure window. All these features are then learned by a two-stage random forest model to identify clusters of antigenic residues and to remove isolated outliers. Tested on a dataset of 55 epitopes from 45 tertiary structures, we prove that our method significantly outperforms all three existing structure-based epitope predictors. Following comprehensive analysis, it is found that features such as B factor, relative accessible surface area and protrusion index play an important role in characterizing B-cell epitopes. Our detailed case studies on an HIV antigen and an influenza antigen confirm that our second stage learning is effective for clustering true antigenic residues and for eliminating self-made prediction errors introduced by the first-stage learning.Availability and implementation: Source codes are available on request.Contact: jinyan.li@uts.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

An ensemble method for prediction of conformational B-cell epitopes from antigen sequences

Epitopes are immunogenic regions in antigen protein. Prediction of B-cell epitopes is critical for immunological applications. B-cell epitopes are categorized into linear and conformational. The majority of B-cell epitopes are conformational. Several machine learning methods have been proposed to identify conformational B-cell epitopes. However, the quality of these methods is not ideal. One question is whether or not the prediction of conformational B-cell epitopes can be improved by using ensemble methods. In this paper, we propose an ensemble method, which combined 12 support vector machine-based predictors, to predict the conformational B-cell epitopes, using an unbound dataset. AdaBoost and resampling methods are used to deal with an imbalanced labeled dataset. The proposed method achieves AUC of 0.642–0.672 on training dataset with 5-fold cross validation and AUC of 0.579–0.604 on test dataset. We also find some interesting results with the bound and unbound datasets. Epitopes are more accessible than non-epitopes, in bound and unbound datasets. Epitopes are also preferred in beta-turn, in bound and unbound datasets. The flexibility and polarity of epitopes are higher than non-epitopes. In a bound dataset, Asn (N), Glu (E), Gly (G), Lys (K), Ser (S), and Thr (T) are preferred in epitope regions, while Ala (A), Leu (L) and Val (V) are preferred in non-epitope regions. In the unbound dataset, Glu (E) and Lys (K) are preferred in epitope sites, while Leu (L) and Val (V) are preferred in non-epitiopes sites.

Cation–π, amino–π, π–π, and H‐bond interactions stabilize antigen–antibody interfaces

The identification of immunogenic regions on the surface of antigens, which are able to stimulate an immune response, is a major challenge for the design of new vaccines. Computational immunology aims at predicting such regions--in particular B-cell epitopes--but is far from being reliably applicable on a large scale. To gain understanding into the factors that contribute to the antigen-antibody affinity and specificity, we perform a detailed analysis of the amino acid composition and secondary structure of antigen and antibody surfaces, and of the interactions that stabilize the complexes, in comparison with the composition and interactions observed in other heterodimeric protein interfaces. We make a distinction between linear and conformational B-cell epitopes, according to whether they consist of successive residues along the polypeptide chain or not. The antigen-antibody interfaces were shown to differ from other protein-protein interfaces by their smaller size, their secondary structure with less helices and more loops, and the interactions that stabilize them: more H-bond, cation-π, amino-π, and π-π interactions, and less hydrophobic packing; linear and conformational epitopes can clearly be distinguished. Often, chains of successive interactions, called cation/amino-π and π-π chains, are formed. The amino acid composition differs significantly between the interfaces: antigen-antibody interfaces are less aliphatic and more charged, polar and aromatic than other heterodimeric protein interfaces. Moreover, paratopes and epitopes-albeit to a lesser extent-have amino acid compositions that are distinct from general protein surfaces. This specificity holds promise for improving B-cell epitope prediction.

Limited genetic diversity and purifying selection in Iranian Plasmodium falciparum Generative Cell Specific 1 (PfGCS1), a potential target for transmission-blocking vaccine

Among vaccines, those that have an impact on transmission are in priority for malaria elimination and eradication. One of the new identified transmission-blocking vaccine (TBV) candidate antigens is Generative Cell Specific 1 (GCS1) located on the male gametocytes of Plasmodium species. Since the antigenic diversity could hamper vaccine development, it is essential to determine the gene diversity of gcs1 in global malaria-endemic areas in order to develop efficient TBVs. Therefore, in this study, nucleotide diversity and selection in the Plasmodium falciparum GCS1 (PfGCS1) antigen were analyzed in 36 Iranian clinical isolates by using PCR sequencing in order to provide useful information on this TBV candidate antigen. For this purpose, successful sequence analysis was carried out in 36 isolates. The results showed three single-nucleotide polymorphisms including one synonymous (G1475A) and two non-synonymous (A697G and G1479A) mutations leading to 3 distinct haplotypes with different frequencies: GCS1-A (N184/D445, 16.7%), GCS1-B (S184/D445, 63.9%), and GCS1-C (N184/N445, 19.4%). The overall nucleotide diversity (π) for all 36 sequences of Iranian pfgcs1 was 0.00066±0.00012, and the dN–dS value (−0.00028) was negative, suggesting the possible action of purifying selection in this gene. Epitope mapping prediction of PfGCS1 antigen showed that most of the potential linear and conformational B-cell epitopes are located in conserved regions. However, N184S and D445N mutations were also involved in linear and conformational B-cell epitopes, respectively that should be considered in vaccine design. In conclusion, the present study showed a very low genetic diversity of pfgcs1 gene among Iranian isolates. Considering PfGCS1 as a conserved TBV candidate, our data provides valuable information to develop a PfGCS1-based TBV.

Prediction of conformational B-cell epitopes.

Conformational B-cell epitopes play an important role in the epitope-based vaccine design. The increase of available data promotes the development of computational methods. Compared with the wet experiments, the computational methods are faster and more economic. In the past few years, a number of computational methods (especially the machine learning-based methods) have been developed to predict the conformational B-cell epitopes. In this chapter, we introduce important data resources and computational methods, which are publicly available. Moreover, we introduce our ensemble learning-based method that can predict the conformational epitopes from sequences. These promising methods may assist immunologists in identifying potential vaccine candidates.

Epitopes Identification for Vaccine Design and Structural Aspects of Dengue Virus 3 Envelope Protein

Dengue is one of the most imperative emerging vector-borne viral diseases. A foremost hitch in designing vaccine for the dengue virus has been the high antigenic variability in the envelope protein of different virus strains. To foster operational vaccines it is essential to target multiple antigenic components of the virus, thus focusing the immune system to protect the host from the virus. Consequently, it is essential to study the structural and functional features of this DENV3 envelope protein in the stoppage of the disease. The purpose of this study was in silico structural characterization of DENV3 envelope protein and to predict their antigenic determinants. This endeavor represents the first structural and epitopes prediction study of DENV3 envelope protein of Indian origin. Computational analyses were performed and a homology model of DENV3 envelope protein was generated. The quality of the model was evaluated by PROCHECK, VERRIFY-3D, PROSA and Errat. Results indicate that 89.88% overall quality of predicted model with a -4.67 Z-score. The model structure was finally submitted in Protein Model Database. The results of MetaPocket server predicted the binding sites, which are good and helpful for docking purpose. The analysis revealed trustworthy conformational B-cell and CTL epitopes that can promote the desired immune response against dengue virus. This information may also help in designing vaccine against dengue in deficiency of experimentally resolved structures.

Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering.

B-cell epitopes are regions of the antigen surface which can be recognized by certain antibodies and elicit the immune response. Identification of epitopes for a given antigen chain finds vital applications in vaccine and drug research. Experimental prediction of B-cell epitopes is time-consuming and resource intensive, which may benefit from the computational approaches to identify B-cell epitopes. In this paper, a novel cost-sensitive ensemble algorithm is proposed for predicting the antigenic determinant residues and then a spatial clustering algorithm is adopted to identify the potential epitopes. Firstly, we explore various discriminative features from primary sequences. Secondly, cost-sensitive ensemble scheme is introduced to deal with imbalanced learning problem. Thirdly, we adopt spatial algorithm to tell which residues may potentially form the epitopes. Based on the strategies mentioned above, a new predictor, called CBEP (conformational B-cell epitopes prediction), is proposed in this study. CBEP achieves good prediction performance with the mean AUC scores (AUCs) of 0.721 and 0.703 on two benchmark datasets (bound and unbound) using the leave-one-out cross-validation (LOOCV). When compared with previous prediction tools, CBEP produces higher sensitivity and comparable specificity values. A web server named CBEP which implements the proposed method is available for academic use.

Computational prediction of B cell epitopes from antigen sequences.

Computational identification of B-cell epitopes from antigen chains is a difficult and actively pursued research topic. Efforts towards the development of method for the prediction of linear epitopes span over the last three decades, while only recently several predictors of conformational epitopes were released. We review a comprehensive set of 13 recent approaches that predict linear and 4 methods that predict conformational B-cell epitopes from the antigen sequences. We introduce several databases of B-cell epitopes, since the availability of the corresponding data is at the heart of the development and validation of computational predictors. We also offer practical insights concerning the use and availability of these B-cell epitope predictors, and motivate and discuss feature research in this area.