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Cross-reactivity

Cross-reactivity

Antibody Structure

Antibody Structure

Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...

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,...

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Related Experiment Video

Updated: Jul 18, 2025

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

Recent Progress in Antibody Epitope Prediction.

Xincheng Zeng¹, Ganggang Bai¹, Chuance Sun¹

¹Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China.

Antibodies (Basel, Switzerland)

|August 22, 2023

Summary

Epitope prediction advances vaccine and therapeutic development. Improving conformational epitope prediction accuracy is crucial for practical applications, with deep learning showing promise.

Keywords:

antibody antibody design artificial intelligence epitope

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Area of Science:

Immunology
Computational Biology
Vaccinology

Background:

Epitope prediction is vital for developing vaccines and therapeutics.
Current methods require significant accuracy improvements, particularly for conformational epitopes.
Understanding antibody-antigen recognition mechanisms is key.

Purpose of the Study:

To review antibody-antigen recognition, focusing on conformational epitope prediction.
To analyze the successes and limitations of current epitope prediction strategies.
To explore the role of deep learning and feature-based methods.

Main Methods:

Literature review of antibody-antigen recognition mechanisms.
Analysis of conformational selection in antibody-antigen binding.
Evaluation of deep learning and traditional machine learning models for epitope prediction.

Main Results:

Deep learning models show improved performance over traditional methods.
Sequence and structure features remain informative for epitope prediction.
Conformational epitope prediction presents unique challenges.

Conclusions:

Further advancements in epitope prediction are needed for clinical translation.
Integrating diverse features and advanced algorithms can enhance prediction accuracy.
Conformational epitope prediction remains a critical area for future research.