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Hybridoma Technology

Hybridoma Technology

Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation, polyethylene glycol...

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Updated: Jun 20, 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

Epitopia: a web-server for predicting B-cell epitopes.

Nimrod D Rubinstein¹, Itay Mayrose, Eric Martz

¹Department of Cell Research and Immunology, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel. rubi@post.tau.ac.il

BMC Bioinformatics

|September 16, 2009

Summary

This summary is machine-generated.

Epitopia is a new web server that predicts immunogenic regions in proteins. This computational tool uses a machine-learning algorithm for higher accuracy in B-cell epitope detection.

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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:

Detecting B-cell epitopes is crucial for immunological applications.
Experimental epitope scanning is often impractical.
A computational tool for predicting probable epitope regions is needed.

Purpose of the Study:

To develop a user-friendly web-based tool for predicting immunogenic regions.
To predict epitopes from both protein 3D structures and linear sequences.

Main Methods:

Implementation of a machine-learning algorithm trained to identify antigenic features.
Development of a user-friendly graphical interface for result visualization.

Main Results:

The Epitopia server predicts immunogenic regions in proteins.
Epitopia demonstrated higher predictive power compared to other existing tools.
The tool offers a user-friendly interface for displaying predictions.

Conclusions:

Epitopia is an accurate and functional web server for B-cell epitope prediction.
It serves as a valuable tool for both protein structures and sequences.
The server is accessible online with detailed explanations and examples.