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Related Concept Videos

Antibody Structure01:10

Antibody Structure

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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.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
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Antibody Structure and Classes01:25

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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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Antibody Actions01:26

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Antibodies, or immunoglobulins, are critical players in the immune system's arsenal against invading pathogens. Produced by B cells and plasma cells, their primary role is to detect and bind to specific antigens, molecules found on the surface of pathogens like bacteria or viruses. Beyond antigen recognition, antibodies perform several vital functions that contribute to immune defense.
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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: May 30, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
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Challenges and compromises: Predicting unbound antibody structures with deep learning.

Alexander Greenshields-Watson1, Odysseas Vavourakis1, Fabian C Spoendlin1

  • 1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, United Kingdom.

Current Opinion in Structural Biology
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

Understanding unbound therapeutic antibodies is crucial for drug development. New structure prediction methods and generative models show promise for improving models of unbound antibody forms, especially the challenging CDRH3 loop.

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Area of Science:

  • Biochemistry and Structural Biology
  • Computational Biology and Bioinformatics
  • Immunology and Drug Development

Background:

  • Therapeutic antibodies are typically handled in an unbound state, necessitating an understanding of their free structure for effective development.
  • Accurate modeling of unbound antibodies, particularly the CDRH3 loop, is difficult due to structural data biases favoring antibody-antigen complexes.
  • This data imbalance poses challenges for deep learning models, potentially hindering their ability to generalize to unbound antibody conformations.

Purpose of the Study:

  • To highlight the significance of unbound antibody structures in optimizing antibody development pipelines.
  • To explore the utility of advanced structure prediction tools in understanding unbound antibody conformations.
  • To investigate the potential of generative models in addressing limitations in current antibody modeling.

Main Methods:

  • Review and discussion of the importance of unbound antibody structures in the development pipeline.
  • Exploration of current-generation antibody structure prediction tools.
  • Assessment of conformational heterogeneity's influence on binding kinetics.
  • Hypothesizing the application of generative models for unbound antibody prediction.

Main Results:

  • Current structure predictors offer novel insights into unbound antibody conformations.
  • Conformational heterogeneity may significantly impact antibody binding kinetics.
  • Generative models present a potential solution to existing challenges in modeling unbound antibodies.

Conclusions:

  • Accurate modeling of unbound antibody structures is essential for advancing antibody development.
  • Further research into structure prediction and generative models is needed to improve the representation of unbound antibody forms.
  • While antibody-antigen complex prediction is vital, progress in unbound form modeling should not be overlooked.