Predicting linear B-cell epitopes using string kernels

The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homology-reduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.     

Machine learning approaches for prediction of linear B-cell epitopes on proteins

Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set.     

Prediction of linear B-cell epitopes

The use of antigenicity scales based on physicochemical properties and the sliding window method in combination with an averaging algorithm and subsequent search for the maximum value is the classical method for B-cell epitope prediction. However, recent studies have demonstrated that the best classical methods provide a poor correlation with experimental data. We review both classical and novel algorithms and present our own implementation of the algorithms. The AAPPred software is available at http://www.bioinf.ru/aappred/.     

Prediction of linear B-cell epitopes using amino acid pair antigenicity scale

Identification of antigenic sites on proteins is of vital importance for developing synthetic peptide vaccines, immunodiagnostic tests and antibody production. Currently, most of the prediction algorithms rely on amino acid propensity scales using a sliding window approach. These methods are oversimplified and yield poor predicted results in practice. In this paper, a novel scale, called the amino acid pair (AAP) antigenicity scale, is proposed that is based on the finding that B-cell epitopes favor particular AAPs. It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity. The AAP antigenicity scale can reflect some special sequence-coupled feature in the B-cell epitopes, which is the essence why the new approach is superior to the existing ones. It is anticipated that with the continuous increase of the known epitope data, the power of the AAP antigenicity scale approach will be further enhanced.     

Stepwise prediction of conformational discontinuous B-cell epitopes using the Mapitope algorithm

Mapping the epitope of an antibody is of great interest, since it contributes much to our understanding of the mechanisms of molecular recognition and provides the basis for rational vaccine design. Here we present Mapitope, a computer algorithm for epitope mapping. The algorithm input is a set of affinity isolated peptides obtained by screening phage display peptide-libraries with the antibody of interest. The output is usually 1-3 epitope candidates on the surface of the atomic structure of the antigen. We have systematically tested the performance of Mapitope by assessing the effect of the algorithm parameters on the final prediction. Thus, we have examined the effect of the statistical threshold (ST) parameter, relating to the frequency distribution and enrichment of amino acid pairs from the isolated peptides and the D (distance) and E (exposure) parameters which relate to the physical parameters of the antigen. Two model systems were analyzed in which the antibody of interest had previously been co-crystallized with the antigen and thus the epitope is a given. The Mapitope algorithm successfully predicted the epitopes in both models. Accordingly, we formulated a stepwise paradigm for the prediction of discontinuous conformational epitopes using peptides obtained from screening phage display libraries. We applied this paradigm to successfully predict the epitope of the Trastuzumab antibody on the surface of the Her-2/neu receptor in a third model system.     

Improved method for predicting linear B-cell epitopes

Background

B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes.

Results

In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves.

Conclusion

The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at http://www.cbs.dtu.dk/services/BepiPred.     

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Background

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.

Results

In this work, based on the antigen’s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.

Conclusions

We have presented a reliable method for the identification of linear B cell epitope using antigen’s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0414-y) contains supplementary material, which is available to authorized users.     

Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins

Recently, new machine learning classifiers for the prediction of linear B-cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifiers as well as possibilities to improve the post test probability (PTP) to meet real world requirements such as high throughput epitope screening of whole proteomes. The major finding is that ROC convex hulls present an easy to use way to rank classifiers based on their prediction conservativity as well as to select candidates for ensemble classifiers when validating against the antigenicity profile of 10 HIV-1 proteins. We also show that linear models are at least equally efficient to model the available data when compared to multi-layer feed-forward neural networks.     

Mapping of linear B-cell epitopes of hepatitis B surface antigen

Eight monoclonal antibodies directed against the surface protein of hepatitis B virus (HBV) were tested using an epitope-mapping system (Pepscan) for characterizing antigenic domains. Four different amino acid sequences corresponding to linear epitopes were identified: one in pre-S1 corresponding to the sequence 29–36, two in pre-S2 corresponding to overlapping sequences 134–141 and 137–144, and one in the S region of the protein corresponding to the amino acid sequence 117–126.     

BEST: improved prediction of B-cell epitopes from antigen sequences

Accurate identification of immunogenic regions in a given antigen chain is a difficult and actively pursued problem. Although accurate predictors for T-cell epitopes are already in place, the prediction of the B-cell epitopes requires further research. We overview the available approaches for the prediction of B-cell epitopes and propose a novel and accurate sequence-based solution. Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new architecture based on averaging selected scores generated from sliding 20-mers by a Support Vector Machine (SVM). The SVM predictor utilizes a comprehensive and custom designed set of inputs generated by combining information derived from the chain, sequence conservation, similarity to known (training) epitopes, and predicted secondary structure and relative solvent accessibility. Empirical evaluation on benchmark datasets demonstrates that BEST outperforms several modern sequence-based B-cell epitope predictors including ABCPred, method by Chen et al. (2007), BCPred, COBEpro, BayesB, and CBTOPE, when considering the predictions from antigen chains and from the chain fragments. Our method obtains a cross-validated area under the receiver operating characteristic curve (AUC) for the fragment-based prediction at 0.81 and 0.85, depending on the dataset. The AUCs of BEST on the benchmark sets of full antigen chains equal 0.57 and 0.6, which is significantly and slightly better than the next best method we tested. We also present case studies to contrast the propensity profiles generated by BEST and several other methods.     

Analysis and prediction of protective continuous B-cell epitopes on pathogen proteins

Background

The application of peptide based diagnostics and therapeutics mimicking part of protein antigen is experiencing renewed interest. So far selection and design rationale for such peptides is usually driven by T-cell epitope prediction, available experimental and modelled 3D structure, B-cell epitope predictions such as hydrophilicity plots or experience. If no structure is available the rational selection of peptides for the production of functionally altering or neutralizing antibodies is practically impossible. Specifically if many alternative antigens are available the reduction of required synthesized peptides until one successful candidate is found is of central technical interest. We have investigated the integration of B-cell epitope prediction with the variability of antigen and the conservation of patterns for post-translational modification (PTM) prediction to improve over state of the art in the field. In particular the application of machine-learning methods shows promising results.

Results

We find that protein regions leading to the production of functionally altering antibodies are often characterized by a distinct increase in the cumulative sum of three presented parameters. Furthermore the concept to maximize antigenicity, minimize variability and minimize the likelihood of post-translational modification for the identification of relevant sites leads to biologically interesting observations. Primarily, for about 50% of antigen the approach works well with individual area under the ROC curve (AROC) values of at least 0.65. On the other hand a significant portion reveals equivalently low AROC values of < = 0.35 indicating an overall non-Gaussian distribution. While about a third of 57 antigens are seemingly intangible by our approach our results suggest the existence of at least two distinct classes of bioinformatically detectable epitopes which should be predicted separately. As a side effect of our study we present a hand curated dataset for the validation of protectivity classification. Based on this dataset machine-learning methods further improve predictive power to a class separation in an equilibrated dataset of up to 83%.

Conclusion

We present a computational method to automatically select and rank peptides for the stimulation of potentially protective or otherwise functionally altering antibodies. It can be shown that integration of variability, post-translational modification pattern conservation and B-cell antigenicity improve rational selection over random guessing. Probably more important, we find that for about 50% of antigen the approach works substantially better than for the overall dataset of 57 proteins. Essentially as a side effect our method optimizes for presumably best applicable peptides as they tend to be likely unmodified and as invariable as possible which is answering needs in diagnosis and treatment of pathogen infection. In addition we show the potential for further improvement by the application of machine-learning methods, in particular Random Forests.     

Estimation and extraction of B-cell linear epitopes predicted by mathematical morphology approaches

B-cell epitope prediction facilitates the design and synthesis of short peptides for various immunological applications. Several algorithms have been developed to predict B-cell linear epitopes (LEs) from primary sequences of antigens, providing important information for immunobiological experiments and antibody design. This paper describes two robust methods, LE prediction with/without local peak extraction (LEP-LP and LEP-NLP), based on antigenicity scale and mathematical morphology for the prediction of B-cell LEs. Previous studies revealed that LEs could occur in regions with low-to-moderate but not globally high antigenicity scales. Hence, we developed a method adopting mathematical morphology to extract local peaks from a linear combination of the propensity scales of physico-chemical characteristics at each antigen residue. Comparison among LEP-LP/LEP-NLP, BepiPred and BEPITOPE revealed that our algorithms performed better in retrieving epitopes with low-to-moderate antigenicity and achieved comparable performance according to receiver operation characteristics (ROC) curve analysis. Of the identified LEs, over 30% were unable to be predicted by BepiPred and BEPITOPE employing an average threshold of antigenicity index or default settings. Our LEP-LP method provides a bioinformatics approach for predicting B-cell LEs with low- to-moderate antigenicity. The web-based server was established at http://biotools.cs.ntou.edu.tw/lepd_antigenicity. php for free use.     

Immunoinformatics prediction of linear epitopes from Taenia solium TSOL18

Cysticercosis is a public health problem in several developing countries. The oncosphere protein TSOL18 is the most immunogenic and protective antigen ever reported against porcine cysticercosis, although no specific epitope has been identified to account for these properties. Recent evidence suggests that protection might be associated with conformational epitopes. Linear epitopes from TSOL18 were computationally predicted and evaluated for immunogenicity and protection against porcine cysticercosis. A synthetic peptide was designed based on predicted linear B cell and T cell epitopes that are exposed on the surface of the theoretically modeled structure of TSOL18. Three surface epitopes from TSOL18 were predicted as immunogenic. A peptide comprising a linear arrangement of these epitopes was chemically synthesized. The capacity of the synthetic peptide to protect pigs against an oral challenge with Taenia solium proglottids was tested in a vaccine trial. The synthetic peptide was able to produce IgG antibodies in pigs and was associated to a reduction of the number of cysts, although was not able to provide complete protection, defined as the complete absence of cysts in necropsy. This study demonstrated that B cell and T cell predicted epitopes from TSOL18 were not able to completely protect pigs against an oral challenge with Taenia solium proglottids. Therefore, other linear epitopes or eventually conformational epitopes may be responsible for the protection conferred by TSOL18.     

Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning

Motivation

The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences.

Methods

In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences.

Results

Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction.

Availability

The web server and datasets are freely available at http://bcell.whu.edu.cn.     

B-cell epitopes of scleroderma-specific autoantigens

Conclusions It can be concluded that the precise localization of the epitopes on autoantigens associated with scleroderma has not been determined yet, and further subcloning experiments will be required to map the epitopes more precisely. However, the fact that the antigenicities of the C-terminal ends of topo I as well as of CENP-B are highly affected by the length of the fusion segments suggest that most, if not all, antigenic determinants on these parts of the autoantigens are conformational epitopes. Studies based upon molecular modelling of antibodies reacting with antigens suggest that over 90% of B-cell epitopes are conformational [50]. This implies that the most successful approach to allocate B-cell epitopes on autoantigens in the near future may be the use of techniques for mapping conformational epitopes. Such techniques are currently being developed [reviewed in 51]. Until now, the limited data available indicate that the B-cell epitopes on the scleroderma-associated autoantigens are distributed over the entire proteins. The C-terminal parts of the antigens seem to be good candidates for harboring the major autoimmune epitopes, but more experimental data will be necessary to confirm this suggestion.     

Selection of cryptic B-cell epitopes using informational analysis of protein sequences

Sub-unit vaccines are synthetic or recombinant peptides representing T- or B-cell epitopes of major protein antigens from a particular pathogen. Epitope selection requires the synthesis of peptides that overlap the protein sequences and screening for the most effective ones. In this study a new method of immunogenic peptide selection based on the analysis of information structure of protein sequences is suggested. The analysis of known B-cell epitope location in the information structure of Aspergillus fumigatus proteins Asp f 2 and Asp f 3 has shown that epitopes are scattered along the sequences of proteins for the exception of sites with Increased Degree Information Coordination (IDIC). Based on these results peptides from different allergens such as Asp f 2, Der p 1, and Fel d 1 were selected and produced in a recombinant form in the context of yeast virus-like particles (VLPs). Immunization of mice with VLPs containing peptides form allergens has induced the production of IgG able to recognize full-length antigens. This result suggests that the analysis of information structure of proteins can be used for the selection of peptides possessing cryptic B-cell epitope activity.     

Enhancement of the antibody response to flavivirus B-cell epitopes by using homologous or heterologous T-cell epitopes.

We have been investigating the T-helper (Th)-cell response to the flavivirus envelope (E) glycoprotein. In our studies with Murray Valley encephalitis (MVE) virus, we previously identified synthetic peptides capable of priming Th lymphocytes for an in vitro antivirus proliferative response (J. H. Mathews, J. E. Allan, J. T. Roehrig, J. R. Brubaker, and A. R. Hunt, J. Virol. 65:5141-5148, 1991). We have now characterized in vivo Th-cell priming activity of one of these peptides (MVE 17, amino acids 356 to 376) and an analogous peptide derived from the E-glycoprotein sequence of the dengue (DEN) 2, Jamaica strain (DEN 17, amino acids 352 to 368). This DEN peptide also primed the Th-cell compartment in BALB/c mice, as measured by in vitro proliferation and interleukin production. The failure of some MVE and DEN virus synthetic peptides to elicit an antibody response in BALB/c mice could be overcome if a Th-cell epitope-containing peptide was included in the immunization mixture. A more detailed analysis of the structural interactions between Th-cell and B-cell epitope donor peptides revealed that the peptides must be linked to observe the enhanced antibody response. Blockage or deletion of the free cysteine residue on either peptide abrogated the antibody response. The most efficient T-B-cell epitope interaction occurred when the peptides were colinearly synthesized. These Th-cell-stimulating peptides were also functional with the heterologous B-cell epitope-containing peptides. The Th-cell epitope on DEN 17 was more potent than the Th-cell epitope on MVE 17.     

Identification of immunodominant linear B-cell epitopes within the major outer membrane protein of Chlamydia trachomatis

Chlamydia trachomatis is one of the most prevalent sexually transmitted pathogens. Chlamydial major outer membrane protein (MOMP) can induce strong cellular and humoral immune responses in murine models and has been regarded as a potential vaccine candidate. In this report, the amino acid sequence of MOMP was analyzed using computer-assisted techniques to scan B-cell epitopes, and three possible linear B-cell epitopes peptides (VLKTDVNKE, TKDASIDYHE, TRLIDERAAH) with high predicted antigenicity and high conservation were investigated. The DNA coding region for each potential epitope was cloned into pET32a(+) and expressed as Trx-His-tag fusion proteins in Escherichia coli. The fusion proteins were purified by Ni-NTA agarose beads and followed by SDS-PAGE and western blot analysis. We immunized mice with these three fusion proteins. The sera containing anti-epitope antibodies from the immunized mice could recognize C. trachomatis serovars D and E in ELISA. Antisera of these fusion proteins displayed an inhibitory effect on invasion of serovar E by in vitro neutralization assays. In addition, serum samples from convalescent C. trachomatis-infected patients were reactive with the epitope fusion proteins by western blot assay. Our results showed that the epitope sequences selected by bioinformatic analysis are highly conserved C. trachomatis MOMP B-cell epitopes, and could be good candidates for the development of subunit vaccines, which can be used in clinical diagnosis.