Paper Info
Title
Bepipred-2.0: Improving Sequence-Based B-Cell Epitope Prediction Using Conformational Epitopes
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 nonexpert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
Year
DOI
Venue
2017
10.1093/nar/gkx346
NUCLEIC ACIDS RESEARCH
Field
DocType
Volume
Epitope,Linear epitope,Antigen,Biology,B cell,Computational biology,Genetics,Antibody
Journal
45
Issue
ISSN
Citations 
W1
0305-1048
2
PageRank 
References 
Authors
0.37
8
4
Name
Order
Citations
PageRank
Martin Closter Jespersen1202.32
Bjoern Peters261044.67
M. Nielsen38710.67
Paolo Marcatili413510.61