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Antibody Structure and Classes

Antibody Structure and Classes

Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.

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...

Affinity and Avidity

Affinity and Avidity

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Related Experiment Video

Updated: Jun 5, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

Large scale paired antibody language models.

Henry Kenlay¹, Frédéric A Dreyer¹, Aleksandr Kovaltsuk¹

¹Exscientia, Oxford Science Park, Oxford, United Kingdom.

Plos Computational Biology

|December 6, 2024

Summary

This summary is machine-generated.

New language models, IgBert and IgT5, significantly improve antibody design for therapeutics by analyzing vast antibody sequence data. These models offer enhanced performance in engineering better antibody-based drugs.

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Area of Science:

Immunoinformatics
Computational Biology
Biotechnology

Background:

Antibodies are crucial biotherapeutics due to their high specificity and affinity for antigens.
Vast amounts of antibody sequence data are available, but their complexity hinders therapeutic design.
Existing methods struggle to effectively utilize large-scale antibody sequence datasets.

Purpose of the Study:

To develop advanced antibody-specific language models for improved therapeutic design.
To create models capable of processing both paired and unpaired antibody variable region sequences.
To enhance the application of machine learning in antibody engineering.

Main Methods:

Trained IgBert and IgT5 models on over two billion unpaired and two million paired antibody sequences from the Observed Antibody Space dataset.
Utilized large-scale datasets and high-performance computing for model development.
Evaluated model performance on diverse antibody design and regression tasks.

Main Results:

IgBert and IgT5 demonstrated superior performance compared to existing antibody and protein language models.
The models effectively handle both paired and unpaired antibody variable region sequences.
Achieved state-of-the-art results on various antibody engineering tasks.

Conclusions:

IgBert and IgT5 represent a significant advancement in antibody-specific language modeling.
These models facilitate enhanced antibody design for therapeutic development.
Machine learning and large datasets are key to unlocking the potential of antibody engineering.