http://scholars.ntou.edu.tw/handle/123456789/17389
標題: | Conformational epitope matching and prediction based on protein surface spiral features | 作者: | Lo, Ying-Tsang Shih, Tao-Chuan Pai, Tun-Wen Ho, Li-Ping Wu, Jen-Leih Chou, Hsin-Yiu |
關鍵字: | B-CELL EPITOPES;ANTIGENIC EPITOPES;SPATIAL EPITOPE;SERVER;CONSENSUS;DATABASE;VACCINE;VIRUS;SEPPA | 公開日期: | 31-五月-2021 | 出版社: | BMC | 卷: | 22 | 期: | SUPPL 2 | 來源出版物: | BMC GENOMICS | 摘要: | 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. |
URI: | http://scholars.ntou.edu.tw/handle/123456789/17389 | ISSN: | 1471-2164 | DOI: | 10.1186/s12864-020-07303-5 |
顯示於: | 水產養殖學系 03 GOOD HEALTH AND WELL-BEING 資訊工程學系 |
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