The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes
Open accessAbstract
Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
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Cited by
Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R. H., Peters, B., & Sette, A. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.03.002 · Cited by 940
Self-citationViral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity
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COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning
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Metadata
- Authors
- Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-05-02
- Volume
- 45
- Issue
- W1
- Pages
- W24-W29
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2611314002
- Cited by
- 1,746
- References
- 25
- FWCI
- 32.22
- Type
- journal article
- Language
- en
- Open access
- gold
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