Identifying Linear B-cell Epitopes Based on Incorporated Sequence Information

Title:Identifying Linear B-cell Epitopes Based on Incorporated Sequence Information

Volume: 15 Issue: 3

Author(s): Yixia Shi*

Affiliation:

• Department of Mathematics and Statistics, Lingnan Normal University, Zhanjiang City,China

Keywords: B-cell, PSAAP, AAC, prediction, SVM, feature extraction.

Abstract: Background: An epitope is a specific portion of a macromolecular antigen that can determine antigen specificity, and has great significance in studying adaptive immune responses. It can be a linear fragment in the antigen structure (also called a linear B-cell epitope) or an area of conformational structure in space (also known as a conformational B-cell epitope). However, the methods of empirical testing used to identify epitopes are costly and time consuming.

Objective: The objective of this study is to provide an efficient predictor for distinguishing linear B-cell epitopes.

Method: In this study, we present a predictor model based on the incorporation of information on the position- specific amino acid propensity, composition of amino acids, composition of pairs of amino acids and position-specific pair of amino acids propensity. And F-Score was used to select valid features.

Results: In jackknife cross-validation, our model achieved an overall sensitivity of 92.59%, specificity of 95.47%, accuracy of 94.36% and Matthews correlation coefficient of 0.8729 on a non-redundant dataset.

Conclusion: The results confirm the constructed model is superior to other existing methods.

Cite this article as:

Shi Yixia *, Identifying Linear B-cell Epitopes Based on Incorporated Sequence Information, Current Proteomics 2018; 15 (3) . https://dx.doi.org/10.2174/1570164615666180328145806