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Hybridoma Technology

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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.
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Updated: Jan 19, 2026

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

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Published on: January 17, 2015

In Silico Methods in Antibody Design.

Jun Zhao^1,2,3, Ruth Nussinov^4,5, Wen-Jin Wu⁶

¹Division of Biotechnology Review and Research I, Office of Biotechnology Products, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA. jun.zhao@nih.gov.

Antibodies (Basel, Switzerland)

|September 24, 2019

Summary

Computational methods accelerate antibody design for enhanced therapeutics. In silico approaches predict structures, engineer functions, and improve antibody properties for better drug development.

Keywords:

affinity maturation allosteric effect antibody design antibody stability antibody-antigen recognition epitope prediction immunogenicity vaccine design

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Area of Science:

Biochemistry
Computational Biology
Immunology

Background:

Antibody therapeutics are crucial for treating diseases, offering high efficiency and low toxicity.
Advancements in high-throughput sequencing and structural biology provide data for computational antibody design.
In silico methods are increasingly vital for predicting and engineering antibody structures and functions.

Purpose of the Study:

To review recent progress in in silico antibody design.
To highlight computational approaches for antibody structure modeling, antigen complex prediction, and stability evaluation.
To discuss the role of molecular dynamics in understanding allosteric effects in antibody function.

Main Methods:

In silico antibody structure modeling
Antibody-antigen complex prediction algorithms
Molecular dynamics simulations for stability and allosteric effect analysis

Main Results:

Computational approaches have successfully improved antibody affinities and physicochemical properties in experimental studies.
In silico design aids in predicting antibody structures and engineering antibody functions.
Molecular dynamics simulations reveal allosteric effects in antibody-antigen and antibody-effector recognition.

Conclusions:

In silico design is a powerful tool for advancing antibody therapeutics.
Computational methods enable the engineering of antibodies with enhanced properties and functionalities.
Understanding allosteric effects through molecular dynamics is key to optimizing antibody-based drugs.