An introduction to main B-cell epitope prediction methods and software based on phase display library

Author(s):  
Yang Lu ◽  
Yongpeng Xiao ◽  
Qiao Li ◽  
Man Liu
2019 ◽  
Author(s):  
Kosmas A. Galanis ◽  
Katerina C. Nastou ◽  
Nikos C. Papandreou ◽  
Georgios N. Petichakis ◽  
Vassiliki A. Iconomidou

ABSTRACTLinear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and significantly cheaper vaccine design process. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology, of some the most widely used linear B-cell epitope predictors: BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope and LBEEP. Additionally, we attempt to remedy performance issues of the individual methods by developing a consensus classifier, that combines the separate predictions of these methods into a single output. The performance of these methods was evaluated using a large unbiased data set. All methods performed worse than documented in the original manuscripts, since all predictors performed marginally better than random classification against the test data set. While the method comparison was performed with some necessary caveats, we hope that this update in performance can aid researchers towards the choice of a predictor, whilst conducting their research. The necessary files for the execution of the consensus method that we developed can be found at http://thalis.biol.uoa.gr/BCEconsensus/.KEY POINTSReview of the performance and methodology of currently available BCE predictorsDesign and development of consensus predictorComparison of consensus with state-of-the-art BCE prediction methodsConsensus method available at http://thalis.biol.uoa.gr/BCEconsensus/Kosmas A. Galanis has a BSc in Biology and has performed his undergrad thesis in Bioinformatics. He is interested in the development of computational methods for protein function prediction.Katerina C. Nastou is a Biologist with a PhD in Bioinformatics. Her research focuses on the study of biological networks, the computational prediction of protein function and biological database development.Nikos C. Papandreou has a PhD in Biophysics and works as Special Laboratory Teaching Staff in “Bioinformatics-Biophysics” at the Department of Biology, National & Kapodistrian University of Athens.Georgios N. Petichakis is a Computer Scientist with an MSc in Bioinformatics. His research focuses on the development of computational methods for the functional annotation of proteomes.Vassiliki A. Iconomidou is an Assistant Professor of Molecular Biophysics and the group leader of the Biophysics and Bioinformatics Lab at the Department of Biology, National and Kapodistrian University of Athens.


2010 ◽  
Vol 6 (Suppl 2) ◽  
pp. S2 ◽  
Author(s):  
Yasser EL-Manzalawy ◽  
Vasant Honavar

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong

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.


Author(s):  
Yasser EL-Manzalawy ◽  
Vasant Honavar

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