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This article is part of the supplement: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics

Open Access Proceedings

Prediction of B-cell epitopes using evolutionary information and propensity scales

Scott Yi-Heng Lin1, Cheng-Wei Cheng2 and Emily Chia-Yu Su3*

Author affiliations

1 School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

2 Institute of Information Sciences, Academia Sinica, Taipei, Taiwan

3 Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan

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Citation and License

BMC Bioinformatics 2013, 14(Suppl 2):S10  doi:10.1186/1471-2105-14-S2-S10

Published: 21 January 2013

Abstract

Background

Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists.

Methods

We propose a method, BEEPro (

    B
-cell
    e
pitope prediction by
    e
volutionary information and
    pro
pensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results.

Results

In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner et al. for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066.

Conclusions

Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus, we have shown that an appropriate combination of evolutionary information and propensity scales with a support vector machine model can significantly enhance the prediction performance of both linear and conformational B-cell epitopes.