B-cell Epitope Prediction Tools Research Articles

Comparison of Anti-Trop2 Extracellular Domain Antibodies Generated Against Peptide and Protein Immunogens for Targeting Trop2-Positive Tumour Cells.

Trophoblast antigen 2 (Trop2) is a transmembrane glycoprotein upregulated in multiple solid tumours. Trop2-based passive immunotherapies are in clinical trials, while Trop2 targeting CAR-T cell-based therapies are also reported. Information about its T- and B-cell epitopes is needed for it to be pursued as an active immunotherapeutic target. This study focused on identification of immunodominant epitopes in the Trop2 extracellular domain (ECD) that can mount an efficient anti-Trop2 antibody response. In silico analysis using various B-cell epitope prediction tools was carried out to identify linear and conformational B-cell epitopes in the ECD of Trop2. Three linear peptide immunogens were shortlisted and synthesized. Along with linear peptides, truncated Trop2 ECD that possesses combination of linear and conformational epitopes was also selected. Recombinant protein immunogen was produced in 293-F suspension culture system and affinity purified. Antisera against different immunogens were characterized by ELISA and Western blotting. Two anti-peptide antisera detected recombinant and ectopically expressed Trop2 protein; however, they were unable to recognize the endogenous Trop2 protein expressed by cancer cells. Antibodies against truncated Trop2 ECD could bind to the endogenous Trop2 expressed on the surface of cancer cells. In addition to their high avidity, these polyclonal anti-sera against truncated Trop2 protein also mediated antibody-dependent cell-mediated cytotoxicity (ADCC). In summary, our comparative analysis demonstrated the utility of truncated Trop2 ECD as a promising candidate to be pursued as an active immunotherapeutic molecule against Trop2-positive cancer cells.

Analisis in-silico Peptida Berasaskan Epitop Daripada Protein-Protein Imunogen Burkholderia pseudomallei

Melioidosis is an infectious disease caused by the gram-negative bacterium Burkholderia pseudomallei. This disease is associated with high human mortality rate, especially in tropical and subtropical regions. Rapid diagnosis is crucial in ensuring proper management and treatment of the disease with effective antibiotics. Currently, melioidosis diagnosis, bacterial culture is time consuming, difficult, and not sensitive, while IHA is hindered by high antibody background in healthy population and ELISA lacks standardization and is hindered by antigen inconsistency. To improve the current diagnosis method for melioidosis, this research aims to identify, screen, and develop epitope-spanning peptides to be used as standardized B. pseudomallei antigen in serodiagnosis of melioidosis. Peptides are favoured as they are more stable, and they offer effective and rapid antibody detection. Using in silico analysis, a pool of 154 B. pseudomallei proteins previously reported as immunogenic were screened and ranked based on their antigenicity, subcellular localization, stability, adhesive properties, and ability to interact with class I and class II major histocompatibility complex (MHC). The selected candidate from the analysis, BPSS0908 and BPSL2152 were then taken for further analysis to identify linear B-cell epitopes using several sequence-based B-cell epitope prediction tools. Consensus sequences that are confidently predicted by more than two prediction tools and are longer than 15 amino acids were then selected as linear B-cell epitopes. Through ELISA analysis, low sensitivity was demonstrated by the epitope-spanning peptides as compared to B. pseudomallei crude lysate. However, high specificity of 100% was observed for all tested peptides. The low sensitivity demonstrated by the peptides in ELISA could be due to lack of 3-dimensional structure which is needed for a stable antigen-antibody binding.

BepiPred-3.0: Improved B-cell epitope prediction using protein language models.

B-cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred-3.0, a sequence-based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance was further improved, thus achieving unprecedented predictive power. Our tool can predict epitopes across hundreds of sequences in minutes. It is freely available as a web server and a standalone package at https://services.healthtech.dtu.dk/service.php?BepiPred-3.0 with a user-friendly interface to navigate the results.

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.

The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, preventing the system from collapsing and millions of people dying every year. However, to develop these vaccines and effectively monitor these diseases, it is necessary to use accurate diagnostic methods capable of identifying highly immunogenic regions within a given pathogenic protein. Existing experimental methods are expensive, time-consuming, and require arduous laboratory work, as they require the screening of a large number of potential candidate epitopes, making the methods extremely laborious, especially for application to larger microorganisms. In the last decades, researchers have developed in silico prediction methods, based on machine learning, to identify these markers, to drastically reduce the list of potential candidate epitopes for experimental tests, and, consequently, to reduce the laborious task associated with their mapping. Despite these efforts, the tools and methods still have low accuracy, slow diagnosis, and offline training. Thus, we develop a method to predict B-cell linear epitopes which are based on a Fuzzy-ARTMAP neural network architecture, called BepFAMN (B Epitope Prediction Fuzzy ARTMAP Artificial Neural Network). This was trained using a linear averaging scheme on 15 properties that include an amino acid ratio scale and a set of 14 physicochemical scales. The database used was obtained from the IEDB website, from which the amino acid sequences with the annotations of their positive and negative epitopes were taken. To train and validate the knowledge models, five-fold cross-validation and competition techniques were used. The BepiPred-2.0 database, an independent database, was used for the tests. In our experiment, the validation dataset reached sensitivity = 91.50%, specificity = 91.49%, accuracy = 91.49%, MCC = 0.83, and an area under the curve (AUC) ROC of approximately 0.9289. The result in the testing dataset achieves a significant improvement, with sensitivity = 81.87%, specificity = 74.75%, accuracy = 78.27%, MCC = 0.56, and AOC = 0.7831. These achieved values demonstrate that BepFAMN outperforms all other linear B-cell epitope prediction tools currently used. In addition, the architecture provides mechanisms for online training, which allow the user to find a new B-cell linear epitope, and to improve the model without need to re-train itself with the whole dataset. This fact contributes to a considerable reduction in the number of potential linear epitopes to be experimentally validated, reducing laboratory time and accelerating the development of diagnostic tests, vaccines, and immunotherapeutic approaches.

Mapping Sequential IgE-Binding Epitopes on Major and Minor Egg Allergens

Introduction: Molecular studies of hen’s egg allergens help define allergic phenotypes, with IgE to sequential (linear) epitopes on the ovomucoid (OVM) protein associated with a persistent disease. Epitope profiles of other egg allergens are largely unknown. The objective of this study was to construct an epitope library spanning across 7 allergens and further evaluate sequential epitope-specific (ses-)IgE and ses-IgG₄ among baked-egg reactive or tolerant children. Methods: A Bead-Based Epitope Assay was used to identify informative IgE epitopes from 15-mer overlapping peptides covering the entire OVM and ovalbumin (OVA) proteins in 38 egg allergic children. An amalgamation of 12 B-cell epitope prediction tools was developed using experimentally identified epitopes. This ensemble was used to predict epitopes from ovotransferrin, lysozyme, serum albumin, vitellogenin-II fragment, and vitellogenin-1 precursor. Ses-IgE and ses-IgG₄ repertoires of 135 egg allergic children (82 reactive to baked-egg, the remaining 52 tolerant), 46 atopic controls, and 11 healthy subjects were compared. Results: 183 peptides from OVM and OVA were screened and used to create an aggregate algorithm, improving predictions of 12 individual tools. A final library of 65 sequential epitopes from 7 proteins was constructed. Egg allergic children had higher ses-IgE and lower ses-IgG₄ to predominantly OVM epitopes than both atopic and healthy controls. Baked-egg reactive children had similar ses-IgG₄ but greater ses-IgE than tolerant group. A combination of OVA-sIgE with ses-IgEs to OVM-023 and OVA-028 was the best predictor of reactive phenotype. Conclusion: We have created a comprehensive epitope library and showed that ses-IgE is a potential biomarker of baked-egg reactivity.

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei.

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

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

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

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

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

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

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

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

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

Computational Ensemble Approach for Immune System Study: Conformational B-cell Epitope Prediction

Various tools have been developed to predict B-cell epitopes. We proposed a multistrategy approach by integrating two ensemble learning techniques, namely bagging and meta-decision tree, with a threshold-based cost-sensitive method. By exploiting the synergy among multiple retrainable inductive learners, it directly learns a tree-like classification architecture from the data, and is not limited by a prespecified structure. In addition, we introduced a new three-dimensional sphere-based structural feature to improve the window-based linear features for increased residue description. We performed independent and cross-validation tests, and compared with previous ensemble meta-learners and state-of-the-art B-cell epitope prediction tools using bound-state and unboundstate antigens. The results demonstrated the superior performance of the bagging meta-decision tree approach compared with single epitope predictors, and showed performance comparable to previous meta-learners. The new approach—requiring no predictions from other B-cell epitope tools—is more flexible and applicable than are previous meta-learners relying on specific pretrained B-cell epitope prediction tools.

In Silico Prediction of Linear B-Cell Epitopes on Proteins.

Antibody-protein interactions play a critical role in the humoral immune response. B-cells secrete antibodies, which bind antigens (e.g., cell surface proteins of pathogens). The specific parts of antigens that are recognized by antibodies are called B-cell epitopes. These epitopes can be linear, corresponding to a contiguous amino acid sequence fragment of an antigen, or conformational, in which residues critical for recognition may not be contiguous in the primary sequence, but are in close proximity within the folded protein 3D structure.Identification of B-cell epitopes in target antigens is one of the key steps in epitope-driven subunit vaccine design, immunodiagnostic tests, and antibody production. In silico bioinformatics techniques offer a promising and cost-effective approach for identifying potential B-cell epitopes in a target vaccine candidate. In this chapter, we show how to utilize online B-cell epitope prediction tools to identify linear B-cell epitopes from the primary amino acid sequence of proteins.

A Review of Important Discontinuous B-Cell Epitope Prediction Tools

The identification of B-cell epitopes is imperative for the rational design of vaccines, diagnostics and immunotherapeutics. Several bioinformatics resources are freely available for the prediction of B-cell epitopes, however despite advances in recent years, they still possess limited predictive capabilities. The aim of this review is to highlight and describe the algorithms of the most widely used free B-cell epitope prediction resources. The reasons behind the limited predictive powers of these algorithms are also discussed.

A meta-learning approach for B-cell conformational epitope prediction.

BackgroundOne of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.ResultsWe developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor.ConclusionsComputational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0378-y) contains supplementary material, which is available to authorized users.

Databases for B-cell epitopes.

Identification and characterization of B-cell epitopes in target antigens is one of the key steps in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. For localizing epitopes by experimental methods is time consuming and cost expensive, researchers have been developing in silico or computational models for the prediction of B-cell epitopes, enabling immunologists and clinicians to identify the most promising epitopes for characterization in the laboratory. A sufficient number of available B-cell epitopes are indispensable for establishing the prediction models. To our knowledge, some popular databases associated with the B-cell epitopes are proposed and widely used in the immunoinformatics. In this chapter, we present an overview of the important databases and introduce how to compile datasets for the development of B-cell epitope prediction tools.

The identification and characterization of epitopes in the 30–34kDa Trypanosoma cruzi proteins recognized by antibodies in the serum samples of chagasic patients

Trypanosoma cruzi proteins with molecular weight between 30 and 34 kDa have shown high reactivity in western blot assays with serum samples from chagasic individuals. However, in-depth analysis of the constituents of these protein fractions has not been performed. This is the first report of an immunoaffinity proteomic approach to identify the immunodominant 30-34 kDa proteins of T. cruzi that could eventually be used for the diagnosis of Chagas disease. We used two different sample preparation protocols for protein digestion coupled to mass spectrometry to identify proteins in the protein fraction. The immunodominant proteins and their respective epitopes were then identified by co-immunoprecipitation and excision-epitope mapping/mass spectrometry, using human sera followed by the prediction and three-dimensional structural modeling of reactive epitopes. The use of different sample preparation methods allowed the identification of a relatively high number of proteins, some of which were only identified after one or multiple sample preparation and digestion protocols. Seven immunodominant proteins were identified by co-immunoprecipitation with purified IgGs from chagasic serum samples. Moreover, six reactive peptide epitopes were detected in four of these proteins by excision-epitope mapping/mass spectrometry. Three-dimensional structural models were obtained for the immunoreactive peptides, which correlated well with the linear B-cell epitope prediction tools.

Evaluation of spatial epitope computational tools based on experimentally-confirmed dataset for protein antigens

Antibody molecules interact with antigen proteins through the epitope area, where the epitope residues are found to be discontinuous or spatial or conformational rather than linear on the protein surface. There are various computational algorithms to predict the spatial epitopes, and each of them have an outstanding performance based on their individual testing dataset. In this work, an independent dataset was created through collection of the epitope residual sites which have been confirmed by experiments. Based on this dataset, 6 popular web-servers developed for B-cell structural epitope prediction, including SEPPA, CEP, DiscoTope, ElliPro, PEPOP and BEpro, were evaluated and compared according to sensitivity, the positive predictive value, the successful pick-up rate and the area under the curve of the receiver operator characteristic (AUC). The results showed that the general performance of spatial epitope prediction tools did obtain substantial advancement, and SEPPA gave the best performance among the 6 tools. However, the current prediction accuracy was still far from satisfaction. Moreover, our comparison elucidated that the performance of the web-servers was significantly affected by their training datasets and the algorithms adopted. In this sense, the results of our research may improve the design of B-cell epitope prediction tools and provide additional clues when the users utilize these tools in their related research.

Immune epitope database analysis resource (IEDB-AR)

We present a new release of the immune epitope database analysis resource (IEDB-AR, http://tools.immuneepitope.org), a repository of web-based tools for the prediction and analysis of immune epitopes. New functionalities have been added to most of the previously implemented tools, and a total of eight new tools were added, including two B-cell epitope prediction tools, four T-cell epitope prediction tools and two analysis tools.

Methods and protocols for the assessment of protein allergenicity and cross-reactivity

Allergy is a prevalent health problem in developed countries. With advances in genomic and proteomic technologies, there is a rapid increase in allergy-related data, including allergen sequences, allergic cross-reactivity, molecular structures, clinical measurements, and atmospheric concentrations. The more and more complex allergy data is fueling the need for advanced ways in information management and analysis. Computational methods and resources are increasingly the driving force in allergy research. For example, allergen-specific databases are important data sources for allergen characterization. T-cell and B-cell epitope prediction tools focus on identifying immunogenic regions on allergenic proteins. Allergenicity and cross-reactivity prediction tools are increasingly being applied to assess the potential allergenicity of proteins. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in bioinformatics relevant to the study of allergens.

Towards a consensus on datasets and evaluation metrics for developing B‐cell epitope prediction tools

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.