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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...

B Cell Activation and Differentiation

B Cell Activation and Differentiation

The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
When naive B cells encounter a specific antigen that can bind to the B cell receptor (BCR) on their surface, they undergo sensitization to respond to the antigen's presence. Sensitization begins with...

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,...

Humoral Immune Responses

Humoral Immune Responses

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Diversity of Antigen Receptors

Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...

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Related Experiment Video

Updated: Jan 13, 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

Multidimensional maturation of antibody variable domains with machine-learning assistance.

Tomoyuki Ito¹, Sakiya Kawada¹, Hikaru Nakazawa¹

¹Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan.

|January 7, 2026

Summary

Machine learning combined with molecular evolution enhances antibody VHH domains for improved affinity and expression. This approach optimizes antibody properties, overcoming limitations of traditional mutagenesis.

Keywords:

Deep sequencing SARS-CoV-2 VHH machine learning phage display

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Last Updated: Jan 13, 2026

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Characterization of Thymus-dependent and Thymus-independent Immunoglobulin Isotype Responses in Mice Using Enzyme-linked Immunosorbent Assay

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Flow Cytometric Characterization of Murine B Cell Development

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Published on: January 22, 2021

Area of Science:

Biotechnology
Immunology
Computational Biology

Background:

Traditional antibody development using mutagenesis can negatively impact biophysical properties.
Camelid heavy-chain antibody variable domains (VHHs) are crucial for therapeutic antibody development.

Purpose of the Study:

To simultaneously enhance antibody affinity and expression levels of VHH domains.
To apply machine learning-assisted molecular evolution for multidimensional antibody optimization.

Main Methods:

Phage display and deep sequencing were used for affinity maturation of an anti-SARS-CoV-2 VHH.
Machine learning models were trained on experimentally measured expression levels and affinities of 117 variants.
High-ranking variants were predicted and validated computationally.

Main Results:

Selected VHH variants demonstrated 50-70 fold increased affinity in the picomolar range.
Engineered VHHs exhibited 4-5 fold higher expression levels compared to wild-type.
One variant displayed a 9.5°C improvement in thermal stability.

Conclusions:

Machine learning-assisted molecular evolution is effective for multidimensional antibody property optimization.
This strategy successfully improved both affinity and expression levels of VHH domains.
The developed VHH variants show promise for therapeutic applications.