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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...

Affinity and Avidity

Affinity and Avidity

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.

Cross-reactivity

Cross-reactivity

Humoral Immune Responses

Humoral Immune Responses

Diversity of Antigen Receptors

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: Jun 3, 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

Learning the language of antibody hypervariability.

Rohit Singh¹, Chiho Im¹, Yu Qiu²

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America

|January 10, 2025

Summary

This summary is machine-generated.

Antibody Mutagenesis-Augmented Processing (AbMAP) enhances protein language models for antibody design. This framework improves prediction of antibody binding and enables faster discovery of therapeutic antibodies.

Keywords:

antibody modeling protein language models transfer learning

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Last Updated: Jun 3, 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 Escape Variants of Neutralizing Influenza Virus Monoclonal Antibodies

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Published on: August 29, 2017

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

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Published on: March 24, 2017

Area of Science:

Computational Biology
Immunology
Protein Engineering

Background:

General-purpose protein language models (PLMs) struggle with antibody hypervariable regions.
Antibody modeling requires specialized approaches beyond standard evolutionary conservation principles.

Purpose of the Study:

To develop a transfer learning framework, Antibody Mutagenesis-Augmented Processing (AbMAP), for improved antibody modeling.
To enhance the performance of foundational PLMs for antibody sequence and structure analysis.

Main Methods:

Fine-tuning foundational PLMs using antibody structure and binding specificity data.
Developing learned feature representations for predicting antibody properties like mutational effects and paratope identification.

Main Results:

AbMAP accurately predicts mutational effects on antigen binding and paratope identification.
Experimental validation showed an 82% hit-rate and up to 22-fold binding affinity increase for SARS-CoV-2 antibodies.
Analysis of immune repertoires revealed convergent structural and functional coverage despite sequence diversity.

Conclusions:

AbMAP accelerates antibody design, modeling, and therapeutic discovery.
The transfer learning approach is adaptable to new PLM advancements.
AbMAP deepens the understanding of humoral immunity and B-cell receptor repertoire characteristics.