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

• Published in: BMC genomics Vol. 11; no. Suppl 4; p. S21
• Main Authors: Wee, Lawrence JK, Simarmata, Diane, Kam, Yiu-Wing, Ng, Lisa FP, Tong, Joo Chuan
• Format: Journal Article
• Language: English
• Published: London BioMed Central 02.12.2010; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Animal Genetics and Genomics; Antigens - chemistry; Antigens - immunology; Bayes Theorem; Benchmarking; Biomedical and Life Sciences; Computer Simulation; Data mining; Decision trees; Epitopes, B-Lymphocyte - immunology; Genomics; Internet; Life Sciences; Methods; Microarrays; Microbial Genetics and Genomics; Peptides - chemistry; Peptides - immunology; Plant Genetics and Genomics; Prediction models; Predictive Value of Tests; Proceedings; Protein folding; Proteomics; Studies; Support Vector Machine Training; Support Vector Machine Classifier; Support Vector Machine; Amino Acid Pair; Support Vector Machine Prediction
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-11-S4-S21

Backgound 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. Results 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 A ROC 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. Conclusion 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 .