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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.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
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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.
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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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DLAB: deep learning methods for structure-based virtual screening of antibodies.

Constantin Schneider1, Andrew Buchanan2, Bruck Taddese3

  • 1Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.

Bioinformatics (Oxford, England)
|September 21, 2021
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Summary
This summary is machine-generated.

A new deep learning framework, DLAB, enables structure-based virtual screening of antibody therapeutics. This computational approach predicts antibody-antigen binding, accelerating drug discovery for novel targets.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Antibodies are crucial pharmaceuticals, with over 80 approved for various diseases.
  • Current antibody drug discovery relies heavily on costly and time-consuming high-throughput screening.
  • Predicting antibody-antigen interactions is vital for developing new antibody therapeutics.

Purpose of the Study:

  • Introduce a structure-based deep learning framework for antibodies (DLAB).
  • Enable virtual screening of potential antibody drug candidates against antigen targets.
  • Predict antibody-antigen binding, even for antigens lacking known binders.

Main Methods:

  • Developed a deep learning framework (DLAB) for structure-based antibody analysis.
  • Applied DLAB for virtual screening of antibody-antigen interactions.
  • Utilized DLAB for improving antibody-antigen docking and pose ranking.

Main Results:

  • DLAB enhances antibody-antigen docking accuracy and pose ranking.
  • The framework successfully identifies binding antibodies against specific antigens.
  • Demonstrated DLAB's capability in predicting binding for novel antibody-antigen pairs.

Conclusions:

  • Deep learning methods show significant promise for structure-based virtual screening of antibodies.
  • DLAB offers a powerful computational tool to accelerate antibody drug discovery.
  • The DLAB framework can aid in identifying novel antibody therapeutics more efficiently.