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Updated: Jun 28, 2026

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

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

Published on: March 25, 2014

A machine-learning approach for predicting B-cell epitopes.

Nimrod D Rubinstein¹, Itay Mayrose, Tal Pupko

¹Department of Cell Research and Immunology, Tel Aviv University, Tel Aviv 69978, Israel.

Molecular Immunology

|October 25, 2008

Summary

This summary is machine-generated.

We developed a computational method to predict immunogenic regions (epitopes) on antigens. This machine learning approach uses protein structure or sequence data, outperforming existing tools for faster epitope detection in immunology research.

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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

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

Published on: March 24, 2017

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Antibodies recognize specific antigen regions called epitopes, crucial for immunodetection and immunotherapy.
Experimental epitope detection is labor-intensive and resource-heavy.
Computational prediction of immunogenic regions can streamline experimental workflows.

Purpose of the Study:

To develop a computational method for predicting immunogenic regions (epitopes).
To enable prediction from protein 3D structure or sequence data.
To provide a faster, more efficient alternative to experimental epitope mapping.

Main Methods:

Developed a machine learning algorithm.
Trained the algorithm on a large dataset of validated epitopes from antigen structures and sequences.
Enabled prediction using either protein 3D structure or amino acid sequence.

Main Results:

The developed method accurately predicts immunogenic regions.
The method demonstrated superior performance compared to existing epitope prediction tools.
Successfully predicted epitopes from both structural and sequence data.

Conclusions:

The new computational method offers an efficient and accurate approach for immunogenic region prediction.
This tool can significantly aid in guiding experimental epitope detection and characterization.
The method advances the application of bioinformatics in immunology and drug development.