BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes
By Martin Closter Jespersen, Bjoern Peters, Morten Nielsen, Paolo Marcatili. Nucleic Acids Res., 2017
1,496 citations recorded by the source corpus.
Abstract 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.
Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/dbdb289b3a98deaec34132572afc5b9cb47e8eb0
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In Molecular Biology
- Bjoern Peters
- Alessandro Sette
- Morten Nielsen
- Massimo Andreatta
- Sinu Paul
- Marinka Zitnik
- John Sidney
- Paolo Marcatili
In Radiology, Nuclear Medicine and Imaging
- Bjoern Peters
- Alessandro Sette
- Sinu Paul
- Massimo Andreatta
- Morten Nielsen
- John Sidney
- Paolo Marcatili
- Marta Gomez-Perosanz
Citations in the corpus
- IEDB-AR: immune epitope database—analysis resource in 2019 (2019, methodology): “Since the last update of IEDB-AR, the recommended B cell epitope prediction methods, BepiPred (14) and DiscoTope (15), were updated to their 2.”
- A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2 (2020, methodology): “…Vita et al., 2019 https://www.iedb.org BebiPred 2.0 Jespersen et al., 2017 http://tools.iedb.org/bcell/ Discotope 2.0 Kringelum et al., 2012 http://tools.iedb.org/bcell/ NetMHCpan EL 4.0 Jurtz et al., 2017 http://tools.iedb.org/mhci/ Tepitool Paul et al., 2016 http://tools.iedb.org/tepitool/”
- Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “It is from Jespersen et al. (2017), curates a dataset from IEDB (Vita et al. 2019), containing 3159 antigens.”
- Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “BepiPred is based on random forests trained on B-cell epitopes obtained from 3D-structures of antigen-antibody complexes [110].”