BCIgEPRED—a Dual-Layer Approach for Predicting Linear IgE Epitopes

Allergy is a common health problem worldwide, especially food allergy. Since B cell epitopes that are recognized by the IgE antibodies act as antigenic determinants for allergy, they play a vital role in diagnostics. Hence, knowledge of an IgE binding epitope in a protein is of particular interest for identifying allergenic proteins. Though IgE epitopes may be conformational or linear, identification of the later is useful especially in food allergens that undergo processing or digestion. Very few computational tools are available for the prediction of linear IgE epitopes. Here we report a prediction system that predicts the exact linear IgE epitope. Since our earlier study on linear B-cell epitope prediction demonstrated the effectiveness of using an exact epitope dataset (in contrast to epitope containing region datasets), the dataset in this study uses only experimentally verified exact IgE, IgG, IgM and IgA epitopes. Models for Support Vector Machine (SVM) and Random Forest (RF) were constructed adopting Dipeptide Deviation from the Expected mean (DDE) feature vector. Extensive validation procedures including five-fold cross validation and two different independent dataset tests have been performed to validate the proposed method, which achieved a balanced accuracy ranging from 74 to 78% with area under receiver operator curve greater than 0.8. Performance of the proposed method was observed to be better (accuracy difference of 16–28%) in comparison to the existing available method. The proposed method is developed as a standalone tool that could be used for predicting IgE epitopes as well as to be incorporated into any allergen prediction toolhttps://github.com/brsaran/BCIgePred.     

T cell responses to known allergen proteins are differently polarized and account for a variable fraction of total response to allergen extracts

A panel of 133 allergens derived from 28 different sources, including fungi, trees, grasses, weeds, and indoor allergens, was surveyed utilizing prediction of HLA class II-binding peptides and ELISPOT assays with PBMC from allergic donors, resulting in the identification of 257 T cell epitopes. More than 90% of the epitopes were novel, and for 14 allergen sources were the first ever identified to our knowledge. The epitopes identified in the different allergen sources summed up to a variable fraction of the total extract response. In cases of allergens in which the identified T cell epitopes accounted for a minor fraction of the extract response, fewer known protein sequences were available, suggesting that for low epitope coverage allergen sources, additional allergen proteins remain to be identified. IL-5 and IFN-γ responses were measured as prototype Th2 and Th1 responses, respectively. Whereas in some cases (e.g., orchard grass, Alternaria, cypress, and Russian thistle) IL-5 production greatly exceeded IFN-γ, in others (e.g., Aspergillus, Penicillum, and alder) the production of IFN-γ exceeded IL-5. Thus, different allergen sources are associated with variable polarization of the responding T cells. The present study represents the most comprehensive survey to date of human allergen-derived T cell epitopes. These epitopes might be used to characterize T cell phenotype/T cell plasticity as a function of seasonality, or as a result of specific immunotherapy treatment or varying disease severity (asthma or rhinitis).     

An assessment of the uses of homologous interactions

MOTIVATION: Protein-protein interactions have proved to be a valuable starting point for understanding the inner workings of the cell. Computational methodologies have been built which both predict interactions and use interaction datasets in order to predict other protein features. Such methods require gold standard positive (GSP) and negative (GSN) interaction sets. Here we examine and demonstrate the usefulness of homologous interactions in predicting good quality positive and negative interaction datasets. RESULTS: We generate GSP interaction sets as subsets from experimental data using only interaction and sequence information. We can therefore produce sets for several species (many of which at present have no identified GSPs). Comprehensive error rate testing demonstrates the power of the method. We also show how the use of our datasets significantly improves the predictive power of algorithms for interaction prediction and function prediction. Furthermore, we generate GSN interaction sets for yeast and examine the use of homology along with other protein properties such as localization, expression and function. Using a novel method to assess the accuracy of a negative interaction set, we find that the best single selector for negative interactions is a lack of co-function. However, an integrated method using all the characteristics shows significant improvement over any current method for identifying GSN interactions. The nature of homologous interactions is also examined and we demonstrate that interologs are found more commonly within species than across species. CONCLUSION: GSP sets built using our homologous verification method are demonstrably better than standard sets in terms of predictive ability. We can build such GSP sets for several species. When generating GSNs we show a combination of protein features and lack of homologous interactions gives the highest quality interaction sets. AVAILABILITY: GSP and GSN datasets for all the studied species can be downloaded from http://www.stats.ox.ac.uk/~deane/HPIV.     

Leveraging information across HLA alleles/supertypes improves epitope prediction.

We present a model for predicting HLA class I restricted CTL epitopes. In contrast to almost all other work in this area, we train a single model on epitopes from all HLA alleles and supertypes, yet retain the ability to make epitope predictions for specific HLA alleles. We are therefore able to leverage data across all HLA alleles and/or their supertypes, automatically learning what information should be shared and also how to combine allele-specific, supertype-specific, and global information in a principled way. We show that this leveraging can improve prediction of epitopes having HLA alleles with known supertypes, and dramatically increases our ability to predict epitopes having alleles which do not fall into any of the known supertypes. Our model, which is based on logistic regression, is simple to implement and understand, is solved by finding a single global maximum, and is more accurate (to our knowledge) than any other model.     

Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

Discovery of discontinuous B-cell epitopes is a major challenge in vaccine design. Previous epitope prediction methods have mostly been based on protein sequences and are not very effective. Here, we present DiscoTope, a novel method for discontinuous epitope prediction that uses protein three-dimensional structural data. The method is based on amino acid statistics, spatial information, and surface accessibility in a compiled data set of discontinuous epitopes determined by X-ray crystallography of antibody/antigen protein complexes. DiscoTope is the first method to focus explicitly on discontinuous epitopes. We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15.5% of residues located in discontinuous epitopes with a specificity of 95%. At this level of specificity, the conventional Parker hydrophilicity scale for predicting linear B-cell epitopes identifies only 11.0% of residues located in discontinuous epitopes. Predictions by the DiscoTope method can guide experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification.     

Brachypodium distachyon as a model for defining the allergen potential of non-prolamin proteins

Epitope databases and the protein sequences of published plant genomes are suitable to identify some of the proteins causing food allergies and sensitivities. Brachypodium distachyon, a diploid wild grass with a sequenced genome and low prolamin content, is the closest relative of the allergen cereals, such as wheat or barley. Using the Brachypodium genome sequence, a workflow has been developed to identify potentially harmful proteins which may cause either celiac disease or wheat allergy-related symptoms. Seed tissue-specific expression of the potential allergens has been determined, and intact epitopes following an in silico digestion with several endopeptidases have been identified. Molecular function of allergen proteins has been evaluated using Gene Ontology terms. Biologically overrepresented proteins and potentially allergen protein families have been identified.     

Prediction of protein stability changes for single-site mutations using support vector machines

Accurate prediction of protein stability changes resulting from single amino acid mutations is important for understanding protein structures and designing new proteins. We use support vector machines to predict protein stability changes for single amino acid mutations leveraging both sequence and structural information. We evaluate our approach using cross-validation methods on a large dataset of single amino acid mutations. When only the sign of the stability changes is considered, the predictive method achieves 84% accuracy-a significant improvement over previously published results. Moreover, the experimental results show that the prediction accuracy obtained using sequence alone is close to the accuracy obtained using tertiary structure information. Because our method can accurately predict protein stability changes using primary sequence information only, it is applicable to many situations where the tertiary structure is unknown, overcoming a major limitation of previous methods which require tertiary information. The web server for predictions of protein stability changes upon mutations (MUpro), software, and datasets are available at http://www.igb.uci.edu/servers/servers.html.     

Analysis of food allergen structures and development of foods for allergic patients

Food allergy is one of the important health problems, and countermeasures are socially required. We have been undertaking studies on wheat allergens and their epitopes, and have developed a method for producing hypoallergenic wheat flour by enzymatic modification. The hypoallergenic products are now provided to patients. More noteworthy, by taking hypoallergenic cupcakes over a long period, more than half of patients are hyposensitized and become able to eat normal wheat products. This suggests that the hypoallergenic wheat flour can act as anti-allergenic via allergen-specific immunotolerance. This series of studies was followed by expansive research on food allergy: analysis of epitopes of bovine serum albumin (the major beef allergen), isolation and identification of inhibitory peptides for allergen absorption at the intestine, evaluation of hesperetin as an inhibitor of degranulation of mast cells, and the development of PCR detection methods for verifying allergen labeling and for identifying hidden allergic ingredients in processed foods.     

Biochemical,Biophysical and IgE-Epitope Characterization of the Wheat Food Allergen,Tri a 37

Wheat is an important staple food and potent allergen source. Recently, we isolated a cDNA coding for wheat alpha-purothionin which is recognized by wheat food allergic patients at risk for severe wheat-induced allergy. The purpose of the present study was the biochemical, biophysical and IgE epitope characterization of recombinant alpha-purothionin. Synthetic genes coding for alpha-purothionin were expressed in a prokaryotic system using Escherichia coli and in a eukaryotic expression system based on baculovirus-infected Sf9-insect cells. Recombinant proteins were purified and characterized by SDS-PAGE, mass spectrometry, circular dichroism, chemical cross-linking and size exclusion chromatography. Five overlapping peptid were synthesized for epitope mapping. Alpha-purothionin-specific rabbit antibodies were raised to perform IgE-inhibition experiments and to study the resistance to digestion. The IgE reactivity of the proteins and peptides from ten wheat food allergic patients was studied in non-denaturing RAST-based binding assays. Alpha-purothionin was expressed in the prokaryotic (EcTri a 37) and in the eukaryotic system (BvTri a 37) as a soluble and monomeric protein. However, circular dichroism analysis revealed that EcTri a 37 was unfolded whereas BvTri a 37 was a folded protein. Both proteins showed comparable IgE-reactivity and the epitope mapping revealed the presence of sequential IgE epitopes in the N-terminal basic thionin domain (peptide1:KSCCRSTLGRNCYNLCRARGAQKLCAGVCR) and in the C-terminal acidic extension domain (peptide3:KGFPKLALESNSDEPDTIEYCNLGCRSSVC, peptide4:CNLGCRSSVCDYMVNAAADDEEMKLYVEN). Natural Tri a 37 was digested under gastric conditions but resistant to duodenal digestion. Immunization with EcTri a 37 induced IgG antibodies which recognized similar epitopes as IgE antibodies from allergic patients and inhibited allergic patients'' IgE binding. Reactivity to Tri a 37 does not require a folded protein and the presence of sequential IgE epitopes indicates that sensitization to alpha-purothionin occurs via the gut. Both allergens can be used for in-vitro diagnosis of wheat food allergy. The induction of blocking IgG antibodies suggests the usefulness for immunotherapy.     

Improved prediction of allergenicity by combination of multiple sequence motifs

The identification and validation of protein allergens have become more important nowadays as more and more transgenic proteins are introduced into our food chains. Current allergen prediction algorithms focus on the identification of single motif or single allergen peptide for allergen detection. However, an analysis of the 575 allergen dataset shows that most allergens contain multiple motifs. Here, we present a novel algorithm that detects allergen by making use of combinations of motifs. Sensitivity of 0.772 and specificity of 0.904 were achieved by the proposed algorithm to predict allergen. The specificity of the proposed approach is found to be significantly higher than traditional single motif approaches. The high specificity of the proposed algorithm is useful in filtering out false positives, especially when laboratory resources are limited.     

AllergenPro: an integrated database for allergenicity analysis and prediction

The National Agricultural Biotechnology Information Center (NABIC) reconstructed an AllergenPro database for allergenic proteins analysis and allergenicity prediction. The AllergenPro is an integrated web-based system providing information about allergen in foods, microorganisms, animals and plants. The allergen database has the three main features namely, (1) allergen list with epitopes, (2) searching of allergen using keyword, and (3) methods for allergenicity prediction. This updated AllergenPro outputs the search based allergen information through a user-friendly web interface, and users can run tools for allergenicity prediction using three different methods namely, (1) FAO/WHO, (2) motif-based and (3) epitope-based methods.

Availability

The database is available for free at http://nabic.rda.go.kr/allergen/     

Exploiting multi-layered information to iteratively predict protein functions

BackgroundSimilarity based computational methods are a useful tool for predicting protein functions from protein–protein interaction (PPI) datasets. Although various similarity-based prediction algorithms have been proposed, unsatisfactory prediction results have occurred on many occasions. The purpose of this type of algorithm is to predict functions of an unannotated protein from the functions of those proteins that are similar to the unannotated protein. Therefore, the prediction quality largely depends on how to select a set of proper proteins (i.e., a prediction domain) from which the functions of an unannotated protein are predicted, and how to measure the similarity between proteins. Another issue with existing algorithms is they only believe the function prediction is a one-off procedure, ignoring the fact that interactions amongst proteins are mutual and dynamic in terms of similarity when predicting functions. How to resolve these major issues to increase prediction quality remains a challenge in computational biology.ResultsIn this paper, we propose an innovative approach to predict protein functions of unannotated proteins iteratively from a PPI dataset. The iterative approach takes into account the mutual and dynamic features of protein interactions when predicting functions, and addresses the issues of protein similarity measurement and prediction domain selection by introducing into the prediction algorithm a new semantic protein similarity and a method of selecting the multi-layer prediction domain. The new protein similarity is based on the multi-layered information carried by protein functions. The evaluations conducted on real protein interaction datasets demonstrated that the proposed iterative function prediction method outperformed other similar or non-iterative methods, and provided better prediction results.ConclusionsThe new protein similarity derived from multi-layered information of protein functions more reasonably reflects the intrinsic relationships among proteins, and significant improvement to the prediction quality can occur through incorporation of mutual and dynamic features of protein interactions into the prediction algorithm.     

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Background

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.

Results

AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.

Conclusion

AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.     

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.     

BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity

Effective identification of major histocompatibility complex (MHC) molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class Ⅱ MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler, ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC (http:∥biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request). Using a standard measure of AUC (Area Under the receiver operating characteristic Curves), we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class Ⅱ MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods.     

Molecular and immunological characterization of tri a 36, a low molecular weight glutenin, as a novel major wheat food allergen

Wheat is an essential element in our nutrition but one of the most important food allergen sources. Wheat allergic patients often suffer from severe gastrointestinal and systemic allergic reactions after wheat ingestion. In this study, we report the molecular and immunological characterization of a new major wheat food allergen, Tri a 36. The cDNA coding for a C-terminal fragment of Tri a 36 was isolated by screening a wheat seed cDNA expression library with serum IgE from wheat food-allergic patients. Tri a 36 is a 369-aa protein with a hydrophobic 25-aa N-terminal leader peptide. According to sequence comparison it belongs to the low m.w. glutenin subunits, which can be found in a variety of cereals. The mature allergen contains an N-terminal domain, a repetitive domain that is rich in glutamine and proline residues, and three C-terminal domains with eight cysteine residues contributing to intra- and intermolecular disulfide bonds. Recombinant Tri a 36 was expressed in Escherichia coli and purified as soluble protein. It reacted with IgE Abs of ～80% of wheat food-allergic patients, showed IgE cross-reactivity with related allergens in rye, barley, oat, spelt, and rice, and induced specific and dose-dependent basophil activation. Even after extensive in vitro gastric and duodenal digestion, Tri a 36 released distinct IgE-reactive fragments and was highly resistant against boiling. Thus, recombinant Tri a 36 is a major wheat food allergen that can be used for the molecular diagnosis of, and for the development of specific immunotherapy strategies against, wheat food allergy.     

扁豆过敏原LenC3蛋白B细胞抗原表位预测及同源建模

目的：通过应用生物信息学软件模拟、分析预测扁豆过敏原Len c 3蛋白的结构、性质及B 细胞抗原表位, 为探索基于扁豆过敏原Len c 3 抗原表位的改造提供依据。方法：从Uniprot 蛋白质数据库中得到扁豆过敏原Len c 3的氨基酸序列,通过生物信息学软件Swiss-Model及Swiss-PdbViewer 4.0 模拟和分析扁豆过敏原Len c 3蛋白的结构,使用DNAStar软件对其B细胞抗原表位进行模拟、分析预测。结果：扁豆过敏原Len c 3蛋白为疏水性蛋白,在氨基酸残基第7-26 位有一跨膜区,Ramachandran 图评估扁豆过敏原Len c 3蛋白的三维结构显示其空间构象稳定,Len c 3蛋白潜在的B细胞抗原表位为35-36,48-50,66-71,87-90。结论：本研究通过对扁豆过敏原Len c 3蛋白进行生物信息学分析获得了该过敏原的结构、性质及潜在的B 细胞抗原表位,为进一步了解和掌握扁豆过敏原Len c 3蛋白的结构功能以及抗原性改造、单克隆抗体制备、表位疫苗设计等提供重要的线索。     

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.     

运用PDB中的同源信息提高NetTurnP的蛋白质β转角预测精度

β转角作为一种蛋白质二级结构类型在蛋白质折叠、蛋白质稳定性、分子识别等方面具有重要作用．现有的β转角预测方法,没有将PDB等结构数据库中先前存在的同源序列的结构信息映射到待预测的蛋白质序列上．PDB存储的结构已超过70 000,因此对一条新确定的序列,有较大可能性从PDB中找到其同源序列．本文融合PDB中提取的同源结构信息(对每一待测序列,仅使用先于该序列存储于PDB中的同源信息)与NetTurnP预测,提出了一种新的β转角预测方法BTMapping,在经典的BT426数据集和本文构建的数据集EVA937上,以马修斯相关系数表示的预测精度分别为0.56、0.52,而仅使用NetTurnP的为0.50、0.46,以Q_total表示的预测精度分别为81.4%、80.4%,而仅使用NetTurnP的为78.2%、77.3%．结果证实同源结构信息结合先进的β转角预测器如NetTurnP有助于改进β转角识别．BTMapping程序及相关数据集可从http://www.bio530.weebly.com获得．