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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

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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Updated: Jul 19, 2025

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Published on: June 16, 2023

Simplifying complex antibody engineering using machine learning.

Emily K Makowski¹, Hsin-Ting Chen², Peter M Tessier³

¹Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA.

|August 17, 2023

Summary

This summary is machine-generated.

Machine learning accelerates antibody engineering by predicting antibody variants with desired properties. This approach reduces experimental effort for developing stable and high-affinity monoclonal antibodies.

Keywords:

CDR IgG affinity antigen complementarity-determining region deep learning directed evolution mAb protein design stability variable region

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Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Machine learning (ML) is revolutionizing antibody engineering.
ML enables efficient generation of drug-like monoclonal antibodies.
Predictive models reduce experimental workload.

Purpose of the Study:

Review recent advances in ML for antibody engineering.
Discuss the impact of ML on antibody discovery and development.
Identify challenges and opportunities for ML methods.

Main Methods:

Unsupervised ML algorithms for predicting intrinsic antibody properties (e.g., stability).
Supervised ML algorithms trained on deep sequencing data for extrinsic properties (e.g., affinity).
Analysis of large protein sequence and antibody library datasets.

Main Results:

ML predicts antibody variants with native-like intrinsic properties, reducing experimentation.
ML predicts variants with desired extrinsic properties without additional screening.
Significant advancements in efficiency and success rates for antibody engineering.

Conclusions:

ML is a paradigm-changing tool in antibody engineering.
ML enhances the prediction of functional antibody candidates.
Future opportunities lie in refining ML models and addressing challenges.