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Related Concept Videos

Hybridoma Technology01:31

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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Antibody Structure01:10

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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.
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Related Experiment Video

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Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries
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Computational and artificial intelligence-based methods for antibody development.

Jisun Kim1, Matthew McFee2, Qiao Fang2

  • 1Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada.

Trends in Pharmacological Sciences
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) are revolutionizing therapeutic antibody development. These computational tools enhance antibody design, overcoming limitations of traditional empirical methods for greater specificity and affinity.

Keywords:
antibody developmentartificial intelligencecomputational engineeringdeep learning

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Area of Science:

  • Biotherapeutics and Drug Development
  • Computational Biology and Bioinformatics
  • Artificial Intelligence in Medicine

Background:

  • Therapeutic antibodies represent the largest class of biotherapeutics due to their high target specificity and binding affinity.
  • Traditional antibody development is empirical, robust, but faces significant limitations and challenges.
  • Recent advancements in computational and artificial intelligence (AI) technologies offer solutions to these limitations.

Purpose of the Study:

  • To provide an overview of AI methods applicable to antibody development.
  • To highlight the integration of AI and machine learning (ML) into antibody development pipelines.
  • To discuss computational tools for antibody property prediction, structure analysis, and design.

Main Methods:

  • Review of AI-driven databases for antibody research.
  • Exploration of computational predictors for antibody properties and structural analysis.
  • Focus on machine learning (ML) models for computational antibody design, including complementarity-determining region (CDR) loop design.

Main Results:

  • AI and ML methods are increasingly integrated into antibody development pipelines.
  • Computational tools can predict antibody properties and structures with improved accuracy.
  • AI facilitates the design of novel antibodies, particularly optimizing CDR loops for enhanced binding.

Conclusions:

  • AI and computational approaches are overcoming limitations in traditional antibody development.
  • These technologies enhance the efficiency and effectiveness of creating therapeutic antibodies.
  • The integration of AI promises to accelerate the discovery and design of next-generation biotherapeutics.