title: 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/).
authors.affiliation: Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India.
authors.forename: Harinder
authors.initials: H
authors.lastname: Singh
authors.forename: Hifzur Rahman
authors.initials: HR
authors.lastname: Ansari
authors.forename: Gajendra P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019985:Benchmarking; D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D018604:Epitope Mapping; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry* / Q000276:immunology
chemical_list: D018985:Epitopes, B-Lymphocyte