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Related Concept Videos

B Cell Activation and Differentiation01:24

B Cell Activation and Differentiation

The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
When naive B cells encounter a specific antigen that can bind to the B cell receptor (BCR) on their surface, they undergo sensitization to respond to the antigen's presence. Sensitization begins with...

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Related Experiment Video

Updated: May 15, 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

B-cell epitope prediction through a graph model.

Liang Zhao1, Limsoon Wong, Lanyuan Lu

  • 1Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore.

BMC Bioinformatics
|January 4, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for predicting B-cell epitopes, improving accuracy for both planar and protrusive epitopes. The approach successfully identifies multiple epitopes on antigens, advancing vaccine design and drug development.

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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

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Last Updated: May 15, 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

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
  • Computational Biology
  • Structural Biology

Background:

  • B-cell epitope prediction is crucial for understanding antibody-antigen interactions, vaccine design, and drug development.
  • Existing methods struggle with planar epitopes and often fail to identify multiple distinct epitopes on a single antigen.

Purpose of the Study:

  • To develop an improved method for B-cell epitope prediction that addresses limitations of current approaches.
  • To enhance the accuracy of predicting both protrusive and planar B-cell epitopes.
  • To enable the identification of multiple, non-overlapping epitopes on antigen surfaces.

Main Methods:

  • Utilized a Markov Clustering algorithm to segment antigen surface graphs into subgraphs.
  • Developed a classifier to distinguish between epitope and non-epitope subgraphs.
  • Applied the classifier to predict epitopes on a dataset of 92 antigen-antibody PDB complexes.

Main Results:

  • The proposed method significantly outperforms state-of-the-art epitope prediction techniques, achieving a 24.7% higher averaged f-score.
  • Successfully identified small, non-planar epitopes missed by other models.
  • Demonstrated the capability to detect multiple epitopes on antigens when present.

Conclusions:

  • Graphical models combined with clustering and supervised learning can effectively distinguish various epitope conformations.
  • The subgraph approach simplifies the identification of multiple epitopes.
  • Identified residue combinations offer potential for new hypotheses in future research.