Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India.
PLoS One. 2013 May 7;8(5):e62216. doi: 10.1371/journal.pone.0062216. Print 2013.
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/).
设计基于肽的疫苗的主要挑战之一是鉴定抗原中能够刺激 B 细胞反应的抗原性区域,也称为 B 细胞表位。过去,已经开发了几种用于预测构象和线性(或连续)B 细胞表位的方法。然而,现有的预测线性 B 细胞表位的方法远非完美。在这项研究中,我们试图开发一种改进的预测线性 B 细胞表位的方法。我们从免疫表位数据库中检索了经过实验验证的 B 细胞表位和非 B 细胞表位,并生成了两种类型的数据集,称为 Lbtope_Variable 和 Lbtope_Fixed length 数据集。Lbtope_Variable 数据集包含 14876 个 B 细胞表位和 23321 个可变长度的非表位,而 Lbtope_Fixed length 数据集包含 12063 个 B 细胞表位和 20589 个固定长度的非表位。我们还在从数据集中去除高度相同的肽后,对上述数据集上的模型性能进行了评估。此外,我们还衍生了第三个数据集 Lbtope_Confirm,其中包含 1042 个表位和 1795 个非表位,每个表位或非表位在至少两项研究中都经过了实验验证。我们使用不同的机器学习技术(如支持向量机和 K-最近邻)开发了许多模型来区分表位和非表位。我们使用多种特征(如二进制谱、二肽组成、AAP(氨基酸对)谱)获得了约 54%至 86%的准确性。在这项研究中,我们首次使用经过实验验证的非 B 细胞表位来开发预测线性 B 细胞表位的方法。在以前的研究中,随机肽被用作非 B 细胞表位。为了向科学界提供服务,我们开发了一个用于预测和设计 B 细胞表位的网络服务器 LBtope(http://crdd.osdd.net/raghava/lbtope/)。