A Conformational Epitope Prediction System Based on Sequence and Structural Characteristics

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• 关键词：Conformational epitope ; Antigen ; antibody complex ; Sequence antigenicity ; Residue surface rate ; Neighboring spatial distance clustering
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9799
• 期：1
• 页码：472-483
• 全文大小：507 KB
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• 作者单位：Wan-Li Chang (18)
  Ying-Tsang Lo (18)
  Tun-Wen Pai (18)
  
  18. Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, Republic of China
  
• 丛书名：Trends in Applied Knowledge-Based Systems and Data Science
• ISBN：978-3-319-42007-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9799

文摘

An epitope is composed of several amino acids located on structural surface of an antigen. These gathered amino acids can be specifically recognized by antibodies, B cells, or T cells through immune responses. Precise recognition of epitopes plays an important role in immunoinformatics for vaccine design applications. Conformational epitope (CE) is the major type of epitopes in a vertebrate organism, but neither regular combinatorial patterns nor fixed geometric features are known for a CE. In this paper, a novel CE prediction system was established based on physico-chemical propensities of sequence contents, spatial geometrical conformations, and surface rates of amino acids. In addition, a support vector machine technique was also applied to train appearing frequencies of combined neighboring surface residues of known CEs, and it was applied to classify the best predicted CE candidates. In order to evaluate prediction performance of the proposed system, an integrated dataset was constructed by removing redundant protein structures from current literature reports, and three testing datasets from three different systems were collected for validation and comparison. The results have shown that our proposed system improves in both specificity and accuracy measurements. The performance of average sensitivity achieves 36 %, average specificity 92 %, average accuracy 89 %, and average positive predictive value 25 %.