Identifying Linear B-cell Epitopes Based on Incorporated Sequence Information

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.