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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 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...

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Related Experiment Video

Updated: Jun 14, 2025

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

Pretrainable geometric graph neural network for antibody affinity maturation.

Huiyu Cai^1,2,3, Zuobai Zhang^2,3, Mingkai Wang^4,5

¹BioGeometry, Beijing, China.

Nature Communications

|September 6, 2024

Summary

This study introduces GearBind, a novel geometric deep learning model for enhancing antibody binding affinity. GearBind successfully improved antibody-antigen interactions in silico, demonstrating its potential for antibody therapeutics development.

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Rapid Antibody Glycoengineering in Chinese Hamster Ovary Cells

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Published on: June 2, 2022

Area of Science:

Computational biology
Structural biology
Machine learning

Background:

Antibody therapeutics development requires optimizing antibody-antigen binding affinity.
Current computational methods for affinity maturation have limitations.

Purpose of the Study:

To present GearBind, a pretrainable geometric graph neural network for in silico antibody affinity maturation.
To evaluate GearBind's performance against state-of-the-art approaches.

Main Methods:

Utilized multi-relational graph construction and multi-level geometric message passing.
Employed contrastive pretraining on large-scale unlabeled protein structural data.
Developed an ensemble model based on GearBind for enhancing antibody binding.

Main Results:

GearBind outperformed existing methods on benchmark datasets (SKEMPI) and an independent test set.
The ensemble model successfully enhanced binding affinity for two distinct antibodies.
Achieved up to 17-fold decrease in ELISA EC50 and 6.1-fold decrease in KD values for designed mutants.

Conclusions:

Geometric deep learning and effective pretraining are powerful tools for modeling macromolecule interactions.
GearBind shows significant promise for accelerating antibody therapeutics design and development.