Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence

Abstract

One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell’s response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from ∼54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes (http://crdd.osdd.net/raghava/lbtope/).

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

“…Concatenated Epitope/Non-epitope Virtual ProteinsEpitopes and non-epitopes of the Lbtope_Fixed_non_redun-dant and Lbtope_ Confirm datasets (24) were grouped by sequence length. Concatenated polyproteins of the sequences of each group were constructed by randomly combining all sequences.…”

“…We hypothesized that removing short epitope/nonepitope sequences from public datasets would allow correct performance ranking of B-cell epitope prediction scales. To test this hypothesis, we used the "Lbtope_Variable_non_redun-dant" dataset with 8,011 B-cell epitopes and 10,868 nonepitopes, retrieved by Singh et al (24) from experimentally validated epitopes as well as non-epitopes from the Immune Epitope Database (IEDB). Importantly, 5-10-aa non-epitopes include ϳ50% and 5-16-aa non-epitopes include ϳ80% of all non-epitopes deposited in the parent IEDB (24).…”

“…To test this hypothesis, we used the "Lbtope_Variable_non_redun-dant" dataset with 8,011 B-cell epitopes and 10,868 nonepitopes, retrieved by Singh et al (24) from experimentally validated epitopes as well as non-epitopes from the Immune Epitope Database (IEDB). Importantly, 5-10-aa non-epitopes include ϳ50% and 5-16-aa non-epitopes include ϳ80% of all non-epitopes deposited in the parent IEDB (24). Among the 6 -11-, 12-15-, 16 -20-, and 21-30-aa sequences, the Lbtope_ Variable_non_redundant dataset contains 2.12ϫ, 1.49ϫ, 0.93ϫ, and 0.54ϫ numbers of non-epitopes compared with epitopes.…”

“…With the availability of B-cell epitope databases, antigenicity scales (17,18) and machine learning approaches (19 -24) have been attempted, and improved prediction accuracy has been reported. Nevertheless, due to epitope redundancy (20), the predictive power may have been overestimated because these algorithms performed poorly on independent data (24). Therefore, B-cell epitope prediction algorithms must be evaluated on independent datasets that had not been used to train/develop the algorithms/scales.…”

In silico analysis of Shiga toxins (Stxs) to identify new potential vaccine targets for Shiga toxin-producing Escherichia coli

“…Although a large majority of B-cell epitopes are discontinuous, epitope prediction methods have primarily focused on identifying linear B-cell epitopes that have crucial role in protection (ELManzalawy et al 2008b). Linear B-cell epitope has vast application in the area of antibody production, immunodiagnostics, epitope-based vaccine design and selective immunization of therapeutic proteins (Singh et al 2013).…”