Evaluation of spatial epitope computational tools based on experimentally-confirmed dataset for protein antigens

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• 作者：XiaoLian Xu (1) (2)
  Jing Sun (2) (3)
  Qi Liu (3)
  XiaoJing Wang (2)
  TianLei Xu (2) (3)
  RuiXin Zhu (3)
  Di Wu (2) (3)
  ZhiWei Cao (1) (2) (3)
  
• 关键词：discontinuous epitope ; conformational epitope ; independent dataset ; epitope prediction ; protein antigen
• 刊名：Chinese Science Bulletin
• 出版年：2010
• 出版时间：July 2010
• 年：2010
• 卷：55
• 期：20
• 页码：2169-2174
• 全文大小：516KB
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• 作者单位：XiaoLian Xu (1) (2)
  Jing Sun (2) (3)
  Qi Liu (3)
  XiaoJing Wang (2)
  TianLei Xu (2) (3)
  RuiXin Zhu (3)
  Di Wu (2) (3)
  ZhiWei Cao (1) (2) (3)
  
  1. State Key Laboratory of Bioreactor Engineering, East China University of Science & Technology, Shanghai, 200237, China
  2. Shanghai Center for Bioinformation Technology, Shanghai, 200235, China
  3. School of Life Science and Technology, Tongji University, Shanghai, 200292, China
  
• ISSN：1861-9541

文摘

Antibody molecules interact with antigen proteins through the epitope area, where the epitope residues are found to be discontinuous or spatial or conformational rather than linear on the protein surface. There are various computational algorithms to predict the spatial epitopes, and each of them have an outstanding performance based on their individual testing dataset. In this work, an independent dataset was created through collection of the epitope residual sites which have been confirmed by experiments. Based on this dataset, 6 popular web-servers developed for B-cell structural epitope prediction, including SEPPA, CEP, DiscoTope, ElliPro, PEPOP and BEpro, were evaluated and compared according to sensitivity, the positive predictive value, the successful pick-up rate and the area under the curve of the receiver operator characteristic (AUC). The results showed that the general performance of spatial epitope prediction tools did obtain substantial advancement, and SEPPA gave the best performance among the 6 tools. However, the current prediction accuracy was still far from satisfaction. Moreover, our comparison elucidated that the performance of the web-servers was significantly affected by their training datasets and the algorithms adopted. In this sense, the results of our research may improve the design of B-cell epitope prediction tools and provide additional clues when the users utilize these tools in their related research.