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Improved method for linear B-cell epitope prediction using antigen's primary sequence.

Singh H, Ansari HR, Raghava GP - PLoS ONE (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India.

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/).

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Two-sample logo showing dominance of surface accessible residues in B-cell epitopes.Yellow and black color residues indicate to surface accessible and non-accessible residues respectively.
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pone-0062216-g002: Two-sample logo showing dominance of surface accessible residues in B-cell epitopes.Yellow and black color residues indicate to surface accessible and non-accessible residues respectively.

Mentions: We analyzed B-cell epitopes to understand their charters tics. First, length wise distribution of B-cell epitopes was computed. As shown in Figure 1, most of the epitopes are in the range of 5–22 amino acid length. In order to understand the preference of residues in B-cell epitopes, we generated two-sample logo plot [22] using 20 mer epitope (upper panel) and non-epitope (lower panel). As shown in Figure 2, there is indeed elevated occurrence of surface accessible and flexible residue in the epitope region as compared to the non-epitope region. In addition, we observed high propensity of Proline and Glycine residue in the epitope region, which might be responsible for the creation of bends or flexibility in the epitope region.


Improved method for linear B-cell epitope prediction using antigen's primary sequence.

Singh H, Ansari HR, Raghava GP - PLoS ONE (2013)

Two-sample logo showing dominance of surface accessible residues in B-cell epitopes.Yellow and black color residues indicate to surface accessible and non-accessible residues respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3646881&req=5

pone-0062216-g002: Two-sample logo showing dominance of surface accessible residues in B-cell epitopes.Yellow and black color residues indicate to surface accessible and non-accessible residues respectively.
Mentions: We analyzed B-cell epitopes to understand their charters tics. First, length wise distribution of B-cell epitopes was computed. As shown in Figure 1, most of the epitopes are in the range of 5–22 amino acid length. In order to understand the preference of residues in B-cell epitopes, we generated two-sample logo plot [22] using 20 mer epitope (upper panel) and non-epitope (lower panel). As shown in Figure 2, there is indeed elevated occurrence of surface accessible and flexible residue in the epitope region as compared to the non-epitope region. In addition, we observed high propensity of Proline and Glycine residue in the epitope region, which might be responsible for the creation of bends or flexibility in the epitope region.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India.

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/).

Show MeSH