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This article is part of the supplement: Selected Articles on Computational Vaccinology

Open Access Research

Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics

Ying-Tsang Lo1, Tun-Wen Pai12*, Wei-Kuo Wu1 and Hao-Teng Chang34*

Author Affiliations

1 Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, R.O.C

2 Center of Excellence for Marine Bioenvironment and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan, R.O.C

3 Graduate Institute of Molecular Systems Biomedicine, China Medical University, Taichung, Taiwan, R.O.C

4 China Medical University Hospital, Taichung, Taiwan, R.O.C

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BMC Bioinformatics 2013, 14(Suppl 4):S3  doi:10.1186/1471-2105-14-S4-S3

Published: 8 March 2013



A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow.


We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen.


Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at webcite.