Predicting flexible length linear B-cell epitopes

Abstract

Identifying B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes. The first approach utilizes four sequence kernels for determining a similarity score between any arbitrary pair of variable length sequences. The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector. Based on our empirical comparisons, we propose FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel. Our results demonstrate that FBCPred significantly outperforms all other classifiers evaluated in this study. An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.

Fig. 1

Length distribution of unique linear B-cell epitopes in Bcipep database.

Fig. 2

ROC curves for different methods on original dataset of unique flexible length linear B-cell epitopes. The ROC curve of $K_{(4,0.5)}^{\mathit{sub}}$ based classifier almost dominates all other ROC curves.

Fig. 3

ROC curves for different methods on homology-reduced dataset of flexible length linear B-cell epitopes. The ROC curve of $K_{(4,0.5)}^{\mathit{sub}}$ based classifier almost dominates all other ROC curves.