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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...

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.

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Related Experiment Video

Updated: Dec 20, 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

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Louis Papageorgiou^1,2, Dimitris Maroulis¹, George P Chrousos^3,4

¹Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece.

Advances in Experimental Medicine and Biology

|May 30, 2020

Summary

This summary is machine-generated.

This study introduces a novel antibody V domain clustering method using antigen interaction sites and deep learning. This approach enhances antibody classification for immunology research and drug discovery, including antibody drug conjugates (ADCs).

Keywords:

Antibodies Antibody drug conjugates Antibody-antigen complexes Classification scheme Clustering Immunology

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Area of Science:

Immunology
Structural Biology
Bioinformatics

Background:

Antibody V domain clustering is crucial for immunology, disease linkage, and new medicine discovery.
Existing clustering methods lack consensus and utilize diverse data types like sequences and structures.
Antibody drug conjugates (ADCs) represent a significant area for new therapeutic development.

Purpose of the Study:

To develop a novel antibody V domain clustering method.
To leverage antigen interaction site comparisons for enhanced specificity.
To utilize deep learning and data mining for advanced classification.

Main Methods:

Comparison of antibody-antigen interaction sites.
Extraction of multidimensional information from 3D structures of antibody-antigen complexes.
Transformation of antibody V domain frameworks into a binary format using structural features.
Implementation of a three-level hybrid classification scheme.

Main Results:

A new method for antibody V domain clustering based on interaction site comparison.
Specific clustering analysis using deep learning and data mining techniques.
Identification of new antibody signatures in V domain binding activity.
Generation of multilevel information on antibodies and antibody-antigen complexes.

Conclusions:

The proposed method offers a more specific approach to antibody V domain clustering.
This technique combines sequence, structural, and interaction pattern data for comprehensive classification.
The developed clusters provide valuable insights for immunological research and therapeutic development.