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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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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

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression.

Yao Lian¹, Meng Ge², Xian-Ming Pan³

¹The Key Laboratory of Bioinformatics, Ministry of Education, School of Life Sciences, Tsinghua University, Beijing, 100084, China. lianyao1112@gmail.com.

BMC Bioinformatics

|December 20, 2014

Summary

This summary is machine-generated.

A new method accurately predicts linear B-cell epitopes using only antigen sequence data. This computational approach aids in developing vaccines and diagnostics by identifying key immunological targets.

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

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Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Area of Science:

Immunoinformatics
Computational Biology
Molecular Immunology

Background:

B-cell epitopes are crucial for vaccine development, antibody production, and disease diagnostics.
Accurate prediction of linear B-cell epitopes remains a significant challenge in immunology.

Purpose of the Study:

To develop a novel computational model for predicting linear B-cell epitopes.
To leverage antigen primary sequence information for epitope prediction.

Main Methods:

Development of a prediction model using Multiple Linear Regression (MLR).
Evaluation using a 10-fold cross-validation on a large, non-redundant dataset.
Performance enhancement through 300 experiments on sub-datasets to mitigate noise.

Main Results:

Achieved 81.8% sensitivity and 64.1% precision.
Obtained an Area Under the Curve (AUC) of 0.728.
Demonstrated a reliable method for identifying linear B-cell epitopes from primary sequence data.

Conclusions:

Presented a dependable method for linear B-cell epitope identification.
Developed the EPMLR web server for accessible B-cell epitope prediction.
The EPMLR web server is available at: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/