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B Cell Activation and Differentiation

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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Updated: Dec 9, 2025

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

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

Published on: March 24, 2017

EpiDope: a deep neural network for linear B-cell epitope prediction.

Maximilian Collatz¹, Florian Mock¹, Emanuel Barth^1,2

¹RNA Bioinformatics /High Throughput Analysis, Faculty of Mathematics and Computer Science.

Bioinformatics (Oxford, England)

|September 11, 2020

Summary

Identifying B-cell epitopes is crucial for diagnostics and therapy. A new deep learning tool, EpiDope, accurately predicts linear B-cell epitopes from protein sequences, significantly reducing lab work and costs.

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Area of Science:

Immunoinformatics
Computational Biology
Bioinformatics

Background:

B-cell antibodies neutralize pathogens by binding to epitopes on antigenic proteins.
Accurate identification of B-cell epitopes is vital for developing serodiagnostic assays and optimizing medical therapies.
Conventional epitope identification methods are labor-intensive, time-consuming, and costly.

Purpose of the Study:

To develop an efficient in silico method for predicting linear B-cell epitopes.
To reduce the time, cost, and labor associated with experimental epitope detection.
To present EpiDope, a novel deep neural network-based tool for B-cell epitope prediction.

Main Methods:

Development of EpiDope, a Python tool utilizing a deep neural network.
Application of the deep neural network to predict linear B-cell epitope regions on protein sequences.
Performance evaluation using receiver operating characteristic (ROC) curve analysis.

Main Results:

EpiDope achieved an area under the curve (AUC) of 0.67 ± 0.07 in ROC analysis.
EpiDope outperformed existing linear B-cell epitope prediction tools.
The tool reliably predicts linear B-cell epitopes, minimizing the need for extensive laboratory experiments.

Conclusions:

EpiDope offers a significant advancement in B-cell epitope prediction.
The software contributes to reducing laboratory experiments and associated costs.
EpiDope is readily available for use in research and development.