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Related Concept Videos

Affinity and Avidity01:41

Affinity and Avidity

Overview
Hybridoma Technology01:31

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, polyethylene glycol...
Antibody Structure01:10

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: May 15, 2026

Characterization of Glycoproteins with the Immunoglobulin Fold by X-Ray Crystallography and Biophysical Techniques
08:58

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Published on: July 5, 2018

Antibody Affinity Maturation by Computational Design.

Esam Tolba Abualrous1, Wade Miller2, Daniel Andrew Cannon3

  • 1Schrödinger GmbH, Life Sciences Software, Mannheim, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|May 13, 2026
PubMed
Summary

This chapter details computational algorithms for enhancing antibody affinity, even without structural data. It guides users through antibody structure prediction, complex modeling, and affinity maturation for improved therapeutic antibodies.

Keywords:
Affinity maturationAntibody developabilityAntibody engineeringComputational protein designIn silico mutagenesisProtein structure predictionProtein–protein docking

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Last Updated: May 15, 2026

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Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library
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Area of Science:

  • Computational biology
  • Immunology
  • Protein engineering

Background:

  • Antibody engineering is crucial for developing targeted therapeutics.
  • Obtaining structural data for antibodies and their targets can be a limitation.
  • Computational approaches offer solutions for antibody design when experimental data is scarce.

Purpose of the Study:

  • To provide an overview of computational algorithms for improving antibody affinity.
  • To guide readers on using computational tools when structural information is unavailable.
  • To address limitations in identifying affinity-matured antibody variants with desired properties.

Main Methods:

  • Antibody structure prediction using computational algorithms.
  • Prediction of antibody-antigen complexes.
  • Computational assessment of antibody affinity, stability, and developability.

Main Results:

  • Demonstration of a bottom-up approach for antibody design without prior structural data.
  • Workflows for leveraging computational tools to overcome data limitations.
  • Identification of strategies for affinity maturation of antibodies.

Conclusions:

  • Computational algorithms are powerful tools for antibody affinity improvement.
  • A systematic computational approach can guide the design of superior antibody variants.
  • Best practices and guidance notes for computational antibody design are provided.