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Updated: May 11, 2026

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

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

Harinder Singh1, Hifzur Rahman Ansari, Gajendra P S Raghava

  • 1Bioinformatics Center, Institute of Microbial Technology, Chandigarh, India.

Plos One
|May 14, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an improved method for predicting linear B-cell epitopes, crucial for peptide-based vaccines. The new approach utilizes experimentally validated non B-cell epitopes, enhancing prediction accuracy for vaccine design.

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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
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Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

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Published on: March 24, 2017

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Area of Science:

  • Immunoinformatics
  • Vaccine Design
  • Computational Biology

Background:

  • Designing peptide-based vaccines requires identifying B-cell epitopes that elicit immune responses.
  • Existing methods for predicting linear B-cell epitopes have limitations in accuracy.
  • Previous prediction models often used random peptides as negative datasets, which is suboptimal.

Purpose of the Study:

  • To develop an improved computational method for predicting linear B-cell epitopes.
  • To enhance the accuracy of B-cell epitope prediction by incorporating experimentally validated non-epitopes.
  • To provide a user-friendly web server for B-cell epitope prediction and design.

Main Methods:

  • Creation of Lbtope_Variable and Lbtope_Fixed length datasets using experimentally validated B-cell epitopes and non-epitopes from the Immune Epitope Database.
  • Development of a third dataset, Lbtope_Confirm, comprising epitopes and non-epitopes validated in at least two studies.
  • Application of machine learning techniques (Support Vector Machine, K-Nearest Neighbor) with features like binary profile, dipeptide composition, and amino acid pair profile.

Main Results:

  • Achieved prediction accuracies ranging from approximately 54% to 86% on the developed datasets.
  • Demonstrated the effectiveness of using experimentally validated non B-cell epitopes in prediction models.
  • Developed and launched the LBtope web server (http://crdd.osdd.net/raghava/lbtope/) for public use.

Conclusions:

  • The developed method, utilizing experimentally validated non-epitopes, significantly improves the prediction of linear B-cell epitopes.
  • The LBtope web server offers a valuable tool for researchers in B-cell epitope prediction and vaccine design.
  • This work advances the field of immunoinformatics by providing a more accurate and reliable approach to epitope identification.