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Related Concept Videos

Antibody Structure01:10

Antibody Structure

60.2K
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

Antibody Structure and Classes

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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

Antibody Actions

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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.
Neutralization
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Antigens Involved in Adaptive Immunity01:26

Antigens Involved in Adaptive Immunity

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An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
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Complete antigens possess both immunogenicity and...
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Immunological Memory01:23

Immunological Memory

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Immunological memory, a pivotal pillar of the adaptive immune system, is responsible for the body's ability to remember and respond more swiftly and effectively to previously encountered pathogens. This remarkable feature is what makes vaccines so effective in preventing diseases.
What is Immunological Memory?
Immunological memory is an integral function of the immune system that allows it to recognize and react more rapidly and effectively to pathogens previously encountered. This feature...
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Humoral Immune Responses01:36

Humoral Immune Responses

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Building Representation Learning Models for Antibody Comprehension.

Justin Barton1, Aretas Gaspariunas1, Jacob D Galson1

  • 1Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom.

Cold Spring Harbor Perspectives in Biology
|November 27, 2023
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Summary
This summary is machine-generated.

Machine learning, particularly deep representation learning, accelerates antibody therapeutic development by predicting protein properties from sequences. This approach enhances antibody structure prediction and optimization, improving drug discovery efficiency.

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Immunology

Background:

  • Antibodies are crucial therapeutic proteins, but their development is time-consuming and expensive.
  • Predicting antibody function and biophysical properties from amino acid sequences alone is experimentally challenging.

Purpose of the Study:

  • To review advances in machine learning for antibody sequence representation.
  • To highlight how these methods improve antibody structure prediction and engineering.
  • To discuss challenges and opportunities in applying machine learning to antibody discovery.

Main Methods:

  • Review of deep representation learning techniques applied to antibody sequences.
  • Analysis of machine learning's impact on antibody structure prediction.
  • Discussion of machine learning's role in antibody optimization and engineering.

Main Results:

  • Deep representation learning enhances the prediction of antibody properties from sequences.
  • Machine learning accelerates antibody structure prediction and facilitates optimization.
  • Progress in machine learning can significantly improve the efficiency of antibody drug discovery.

Conclusions:

  • Machine learning, especially deep representation learning, is a powerful tool to accelerate antibody therapeutic development.
  • Addressing data limitations is key to unlocking the full potential of machine learning in this field.
  • Advancements promise more efficient development of novel antibody therapeutics, increasing patient access to medicines.