Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Research article

EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information

Thammakorn Saethang1*, Osamu Hirose2, Ingorn Kimkong3, Vu Anh Tran1, Xuan Tho Dang1, Lan Anh T Nguyen1, Tu Kien T Le1, Mamoru Kubo2, Yoichi Yamada2 and Kenji Satou2

Author Affiliations

1 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan

2 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan

3 Department of Microbiology, Faculty of Science, Kasetsart University, Bangkok, Thailand

For all author emails, please log on.

BMC Bioinformatics 2012, 13:313  doi:10.1186/1471-2105-13-313

Published: 24 November 2012

Abstract

Background

Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms.

Results

We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo+ and EpicCapo+REF. Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo+ and EpicCapo+REF outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo+REF was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments.

Conclusions

Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo+REF is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip webcite. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip webcite.