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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.

Affinity and Avidity

Affinity and Avidity

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,...

Antibody Actions

Antibody Actions

Antibodies, or immunoglobulins, are critical players in the immune system's arsenal against invading pathogens. Produced by B cells and plasma cells, their primary role is to detect and bind to specific antigens, molecules found on the surface of pathogens like bacteria or viruses. Beyond antigen recognition, antibodies perform several vital functions that contribute to immune defense.
Neutralization
Antibodies can bind to pathogens, preventing them from infecting host cells. This process...

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Related Experiment Video

Updated: Jun 22, 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 design using deep learning: from sequence and structure design to affinity maturation.

Sara Joubbi^1,2, Alessio Micheli¹, Paolo Milazzo¹

¹Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy.

Briefings in Bioinformatics

|July 3, 2024

Summary

This summary is machine-generated.

Deep learning accelerates antibody discovery by integrating computational and experimental methods. This approach streamlines the development of therapeutic antibodies against complex targets.

Keywords:

antibody antibody design antibody optimization deep learning nanobody

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Related Experiment Videos

Last Updated: Jun 22, 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

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Published on: January 2, 2017

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

Area of Science:

Computational Biology
Biotechnology
Drug Discovery

Background:

Deep learning excels in computer vision and natural language processing, offering powerful applications in biology.
Traditional drug development using deep learning focused on small molecules.
Recent advancements integrate deep learning into biological molecule discovery, especially antibodies.

Purpose of the Study:

To survey advancements in deep learning for antibody design and optimization.
To highlight computational techniques streamlining antibody development.
To cover protein design, folding, docking, and affinity maturation for antibodies.

Main Methods:

In silico and in vitro methods integration for antibody development.
Computational power for lead candidate generation and scaling.
Analysis of protein design and optimization techniques.

Main Results:

Deep learning significantly enhances antibody discovery and development processes.
Computational approaches expedite the identification of antibody candidates.
Novel techniques streamline antibody engineering for complex antigens.

Conclusions:

Deep learning is a transformative tool in modern antibody discovery.
Integrating computational and experimental methods accelerates therapeutic antibody development.
This survey provides insights into cutting-edge antibody design and optimization strategies.