SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico...

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Bibliographic Details
Published in:BMC genomics 2010-12, Vol.11 Suppl 4 (S4), p.S21-S21, Article S21
Main Authors: Wee, Lawrence J K, Simarmata, Diane, Kam, Yiu-Wing, Ng, Lisa F P, Tong, Joo Chuan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Antigens - chemistry
Antigens - immunology
Bayes Theorem
Benchmarking
Computer Simulation
Data mining
Decision trees
Epitopes, B-Lymphocyte - immunology
Genomics
Internet
Methods
Peptides - chemistry
Peptides - immunology
Predictive Value of Tests
Proceedings
Protein folding
Studies
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Summary:The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply. We analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes. We developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html.
ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-11-S4-S21