scholarly journals BepiTBR: T-B reciprocity enhances B cell epitope prediction

iScience ◽  
2022 ◽  
pp. 103764
Author(s):  
James Zhu ◽  
Anagha Gouru ◽  
Fangjiang Wu ◽  
Jay A. Berzofsky ◽  
Yang Xie ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
B Cell Epitope

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

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.

B Cell Epitope Prediction

2013 ◽  
pp. 63-67
Author(s):  
Yasser EL-Manzalawy ◽  
Vasant Honavar
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
B Cell Epitope

Beyond B-Cell Epitopes: Curating Positive Data on Antipeptide Paratope Binding to Support Peptide-Based Vaccine Design

2021 ◽  
Vol 28 ◽  
Author(s):  
Salvador Eugenio C. Caoili
Keyword(s):  
Biological Activity ◽  
B Cell ◽  
Immune Complex ◽  
Complex Structure ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Conformational Disorder ◽  
Binding Data ◽  
B Cell Epitope ◽  
Epitope Binding

Background: B-cell epitope prediction is a computational approach originally developed to support the design of peptide-based vaccines for inducing protective antibody-mediated immunity, as exemplified by neutralization of biological activity (e.g., pathogen infectivity). Said approach is benchmarked against experimentally obtained data on paratope-epitope binding; but such data are curated primarily on the basis of immune-complex structure, obscuring the role of antigen conformational disorder in the underlying immune recognition process. Objective: This work aimed to critically analyze the curation of epitope-paratope binding data that are relevant to B-cell epitope prediction for peptide-based vaccine design. Methods: Database records on neutralizing monoclonal antipeptide antibody immune-complex structure were retrieved from the Immune Epitope Database (IEDB) and analyzed in relation to other data from both IEDB and external sources including the Protein Data Bank (PDB) and published literature, with special attention to data on conformational disorder among paratope-bound and unbound peptidic antigens. Results: Data analysis revealed key examples of antipeptide antibodies that recognize conformationally disordered B-cell epitopes and thereby neutralize the biological activity of cognate targets (e.g., proteins and pathogens), with inconsistency noted in the mapping of some epitopes due to reliance on immune-complex structural details, which vary even among experiments utilizing the same paratope-epitope combination (e.g., with the epitope forming part of a peptide or a protein). Conclusion: The results suggest an alternative approach to curating paratope-epitope binding data based on neutralization of biological activity by polyclonal antipeptide antibodies, with reference to immunogenic peptide sequences and their conformational disorder in unbound antigen structures.

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