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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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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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Diversity of Antigen Receptors01:28

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Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
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Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application
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Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design.

Xuezhi Xie1,2, Pedro A Valiente1, Jin Sub Lee1,3

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

Journal of Chemical Information and Modeling
|August 27, 2024
PubMed
Summary
This summary is machine-generated.

Antibody-SGM, a novel joint structure-sequence diffusion model, generates full-atom antibodies by integrating sequence and structure. This protein design approach optimizes antibody function and sequence, advancing protein engineering.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Artificial intelligence in drug discovery

Background:

  • Traditional antibody design relies on random mutagenesis and energy assessments.
  • Recent diffusion models excel at generation but often neglect full structural or sequence details.
  • Existing models require additional steps to predict missing structural or sequence components.

Purpose of the Study:

  • To introduce Antibody-SGM, a joint structure-sequence diffusion model for comprehensive antibody design.
  • To address limitations of current models by integrating sequence-specific attributes and functional properties.
  • To generate native-like, full-atom antibody heavy chains with valid sequence-structure pairs.

Main Methods:

  • Development of Antibody-SGM, a joint structure-sequence diffusion model.
  • Refinement of generation process to ensure valid sequence-structure pairings.
  • Integration of sequence-specific attributes and functional properties into the generative process.
  • Application of active inpainting for simultaneous sequence and structure optimization.

Main Results:

  • Successful generation of full-atom, native-like antibody heavy chains.
  • Demonstrated versatility in applications: full-atom antibody design, antigen-specific CDR design, and heavy chain optimization.
  • Validation of generated antibodies using AlphaFold3.
  • Identification of critical antibody sequences and structural features.
  • Optimization of protein function through active inpainting learning.

Conclusions:

  • Antibody-SGM represents a significant advancement in protein design, offering a versatile and powerful tool.
  • The model successfully integrates sequence and structure for enhanced antibody generation and optimization.
  • This approach holds promise for revolutionizing protein engineering and antibody design.