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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...

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Updated: Jan 4, 2026

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

mCSM-AB2: guiding rational antibody design using graph-based signatures.

Yoochan Myung^1,2,3, Carlos H M Rodrigues^1,2,3, David B Ascher^1,2,3,4

¹Department of Biochemistry and Molecular Biology.

Bioinformatics (Oxford, England)

|October 31, 2019

Summary

We developed mCSM-AB2, a computational tool to predict how mutations affect antibody binding affinity. This method improves rational antibody engineering by accurately modeling antibody-antigen interactions.

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Area of Science:

Computational Biology
Protein Engineering
Immunology

Background:

Rational mutagenesis is crucial for antibody (Ab) development, but lacks accurate computational tools for affinity maturation.
Previous work demonstrated graph-based signatures' utility in predicting mutation effects on Ab binding affinity.

Purpose of the Study:

To present mCSM-AB2, an enhanced computational approach for modeling mutation effects on Ab-antigen binding affinity.
To improve rational antibody engineering through accurate prediction of binding affinity changes.

Main Methods:

Refined graph-based signature approach incorporating evolutionary and energetic terms.
Utilized an expanded database of over 1800 mutations with experimental binding and structural data.
Developed mCSM-AB2 as a freely accessible web server for analyzing mutations and binding interfaces.

Main Results:

mCSM-AB2 achieved high predictive accuracy, with Pearson's correlations of 0.73 (training) and 0.77 (blind test).
The method outperforms existing computational tools for rational antibody engineering.
Web server provides rapid analysis for guiding antibody affinity maturation.

Conclusions:

mCSM-AB2 offers a significant advancement in computational tools for antibody development.
The tool facilitates rational antibody affinity maturation by accurately predicting mutation impacts.
Accessible web server enables widespread use in antibody engineering research.