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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,...

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Updated: Jul 6, 2025

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Published on: January 17, 2015

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning.

Edward B Irvine¹, Sai T Reddy¹

¹Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.

Journal of Immunology (Baltimore, Md. : 1950)

|January 2, 2024

Summary

This summary is machine-generated.

Antibodies (Abs) are versatile therapeutics. Advanced engineering and machine learning accelerate the design of synthetic antibodies for drug development and infectious disease treatment.

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Last Updated: Jul 6, 2025

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Published on: January 17, 2015

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Published on: January 2, 2017

Construction of Synthetic Phage Displayed Fab Library with Tailored Diversity

Construction of Synthetic Phage Displayed Fab Library with Tailored Diversity

Published on: May 1, 2018

Area of Science:

Biotechnology
Protein Engineering
Immunology

Background:

Antibodies (Abs) are crucial therapeutic molecules with high target binding affinity and favorable biophysical properties.
Synthetic antibody discovery and engineering have been revolutionized by protein display and directed evolution systems.
High-throughput screening, deep sequencing, and machine learning are enhancing in vitro antibody optimization.

Approach:

This review discusses experimental and computational tools for synthetic antibody engineering and optimization.
It explores therapeutic challenges in developing antibodies for infectious diseases.
The review highlights prospects of machine learning-guided protein engineering for designing antibodies resistant to viral escape.

Key Points:

Protein display and directed evolution enable large-scale screening of antibody clones.
Integration of high-throughput screening, deep sequencing, and machine learning accelerates antibody design.
Machine learning offers potential for engineering antibodies with resistance to viral escape.

Conclusions:

Synthetic antibody engineering is rapidly advancing through integrated experimental and computational approaches.
Machine learning holds promise for overcoming therapeutic challenges, particularly in infectious diseases.
Future antibody design can be guided by AI to create robust therapeutics against evolving pathogens.