Ying-Tsang Lo1, Tao-Chuan Shih2, Tun-Wen Pai3,4, Li-Ping Ho5, Jen-Leih Wu6,7, Hsin-Yiu Chou8. 1. Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. 2. Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan. 3. Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. twp@csie.ntut.edu.tw. 4. Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan. twp@csie.ntut.edu.tw. 5. Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan. 6. Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan. 7. Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan. 8. Department of Aquaculture, College of Life Science, National Taiwan Ocean University, Keelung, Taiwan. hychou@mail.ntou.edu.tw.
Abstract
BACKGROUND: A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. RESULTS: We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. CONCLUSIONS: The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.
BACKGROUND: A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. RESULTS: We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. CONCLUSIONS: The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.
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