The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction
Open accessAbstract
Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.
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Cites
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Cited by
Computational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219
“To achieve such goals, epitopes can be identified by various experimental methods [163] or be predicted by computational protocols [164].”
BackgroundKazi, A., Chuah, C., Majeed, A. B. A., Leow, C. H., Lim, B. H., & Leow, C. Y. (2018) · Pathogens and Global Health · doi:10.1080/20477724.2018.1446773 · Cited by 122
Integrating machine learning to advance epitope mapping
Grewal, S., Hegde, N., & Yanow, S. K. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1463931 · Cited by 35
Garzon, T., Ortega-Tirado, D., Lopez-Romero, G., Alday, E., Robles-Zepeda, R. E., Garibay-Escobar, A., & Velazquez, C. (2021) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2021.769446 · Cited by 14
ProteinGLUE multi-task benchmark suite for self-supervised protein modeling
Capel, H., Weiler, R., Dijkstra, M., Vleugels, R., Bloem, P., & Feenstra, K. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19608-4 · Cited by 11
Metadata
- Authors
- Potocnakova, L., Bhide, M., & Pulzova, L. B.
- Venue
- Journal of Immunology Research
- Publisher
- Wiley
- Published
- 2016
- Volume
- 2016
- Pages
- 1-11
- ISSN
- 2314-7156 (ISSN-L)
- 2314-8861
- OpenAlex
- W2562638954
- Cited by
- 367
- References
- 90
- FWCI
- 9.03
- Type
- journal article
- Language
- en
- Open access
- gold
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