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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: Jul 1, 2025

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody

Published on: May 16, 2020

AbDPP: Target-oriented antibody design with pretraining and prior biological structure knowledge.

Chenglei Yu¹, Xiangtian Lin², Yuxuan Cheng²

¹Department of Computer Science and Technology, Shanghai Normal University, Shanghai, China.

|March 5, 2024

Summary

This study introduces AbDPP, a deep learning method for generating novel antibody sequences. AbDPP improves antibody design efficiency and quality compared to traditional methods.

Keywords:

antibodies biological medicine computational biology deep learning machine learning

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Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Antibodies are vital protein therapeutics for treating numerous diseases.
Traditional antibody discovery methods (hybridoma, phage display) are inefficient and limit exploration of antibody sequence space.

Purpose of the Study:

To develop a novel deep learning-based method, AbDPP, for efficient and targeted antibody sequence generation.
To overcome limitations of traditional antibody discovery techniques.

Main Methods:

AbDPP integrates a pretrained antibody model with biological region information.
Incorporates an antibody property evaluation model for antigen-specific targeting and optimization.
Evaluated generation of amino acids, neutralization, binding, sequence consistency, and diversity.

Main Results:

AbDPP demonstrated superior performance in generating high-quality antibody sequences.
Outperformed existing methods in key evaluation metrics.
Showcased enhanced antibody design and screening efficiency.

Conclusions:

AbDPP offers an innovative deep learning approach for antibody generation.
Highlights the importance of integrating pretrained models and biological properties for novel antibody design.
Addresses limitations of traditional antibody discovery methods.