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.

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

Journal of Molecular Recognition ◽

10.1002/jmr.815 ◽

2007 ◽

Vol 20 (2) ◽

pp. 75-82 ◽

Cited By ~ 144

Author(s):

Jason A. Greenbaum ◽

Pernille Haste Andersen ◽

Martin Blythe ◽

Huynh-Hoa Bui ◽

Raul E. Cachau ◽

...

Keyword(s):

B Cell ◽

Cell Epitope ◽

Epitope Prediction ◽

Evaluation Metrics ◽

Prediction Tools ◽

B Cell Epitope

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A Review of Important Discontinuous B-Cell Epitope Prediction Tools

Journal of Clinical & Cellular Immunology ◽

10.4172/2155-9899.1000358 ◽

2015 ◽

Vol 06 (05) ◽

Cited By ~ 5

Author(s):

Michelle Mukonyora

Keyword(s):

B Cell ◽

Cell Epitope ◽

Epitope Prediction ◽

Prediction Tools ◽

B Cell Epitope

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Faculty Opinions recommendation of Fundamentals and Methods for T- and B-Cell Epitope Prediction.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732697035.793545047 ◽

2018 ◽

Author(s):

Laurence Eisenlohr ◽

Gabriela L Cosma

Keyword(s):

B Cell ◽

Cell Epitope ◽

Epitope Prediction ◽

B Cell Epitope

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Toward a Sequence-Based Physicochemical Approach to Variable-Length B-Cell Epitope Prediction for Antipeptide Paratopes Recognizing Flexibly Disordered Targets

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics - BCB '19 ◽

10.1145/3307339.3343242 ◽

2019 ◽

Author(s):

Salvador Eugenio C. Caoili

Keyword(s):

B Cell ◽

Cell Epitope ◽

Epitope Prediction ◽

Variable Length ◽

B Cell Epitope ◽

Physicochemical Approach

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B-cell epitope prediction using Extreme Learning Machine and Particle Swarm Optimization-based undersampling

2017 Artificial Intelligence and Robotics (IRANOPEN) ◽

10.1109/rios.2017.7956440 ◽

2017 ◽

Cited By ~ 1

Author(s):

Maral Arvanaghi Jadid ◽

Mahnaz Habibi

Keyword(s):

Particle Swarm Optimization ◽

B Cell ◽

Extreme Learning Machine ◽

Cell Epitope ◽

Particle Swarm ◽

Epitope Prediction ◽

Swarm Optimization ◽

B Cell Epitope ◽

Learning Machine

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Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

Pure and Applied Chemistry ◽

10.1515/pac-2020-1107 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Li Cen Lim ◽

Yee Ying Lim ◽

Yee Siew Choong

Keyword(s):

Pattern Recognition ◽

Support Vector Machine ◽

B Cell ◽

Recognition Performance ◽

Cell Epitope ◽

Epitope Prediction ◽

Support Vector ◽

B Cell Epitopes ◽

Prediction Module ◽

B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

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