Improved method for predicting linear B-cell epitopes

By J. Larsen, Ole Lund, Morten Nielsen. Immunome Research, 2006

1,296 citations recorded by the source corpus.

BackgroundB-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes.ResultsIn order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves.ConclusionThe best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at http://www.cbs.dtu.dk/services/BepiPred.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0dab053c9ff1b41aead7d3b14cd219e322f8ee5b

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Citations in the corpus

IEDB-AR: immune epitope database—analysis resource in 2019 (2019, methodology): “The IEDB-AR hosts linear B cell epitope prediction tools, such as BepiPred (11), various amino acid physicochemical property based scales (http://tools.”
High-throughput prediction of protein antigenicity using protein microarray data (2010, background): “The goal of this article is to develop and test a predictor of protein antigenicity that can be used on a high-throughput scale on existing or new proteomes to identify key antigenic proteins that may have protective qualities and may be used in vaccine design applications.”
Immune epitope database analysis resource (IEDB-AR) (2008, methodology): “BepiPred combines a PSSM with a propensity scale method to predict linear epitopes (23).”
Predicting linear B-cell epitopes using string kernels (2008, neutral): “The complete ABCPred and BepiPred predictions are provided in the Supplementary Materials.”
An overview of bioinformatics tools for epitope prediction: Implications on vaccine development (2015, result): “On the other hand, the evaluation of AUC values for B-cell epitopes algorithms (BepiPred and AAP12) revealed inferior performances than those observed for previous algorithms [91,92].”
The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, methodology): “BepiPred is a prediction method that is based on a combination of HMM and Parker’s hydrophilicity and Levitt’s secondary structure scales.”
SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity (2012, methodology): “…such as IEDB [9], Bcipep [10], and FIMM [11], there are also some methods using machine learning approaches, such as Hidden Markov Model (HMM) [12], Artificial Neural Network (ANN) [13], and Support Vector Machine (SVM) [14,15], to locate linear epitopes, such as PREDITOP [8,16], PEOPLE [17],…”