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Antibody Structure01:10

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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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SAbPred: a structure-based antibody prediction server.

James Dunbar1, Konrad Krawczyk2, Jinwoo Leem2

  • 1Department of Statistics, Oxford Protein Informatics Group, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, 82377, Penzberg, Germany.

Nucleic Acids Research
|May 1, 2016
PubMed
Summary
This summary is machine-generated.

SAbPred is a novel antibody informatics server that predicts antibody structures and properties. This tool aids in understanding immune responses and designing therapeutic antibodies.

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Area of Science:

  • Immunoinformatics
  • Structural Biology
  • Computational Biology

Background:

  • Antibody informatics tools are crucial for understanding immune responses and developing therapeutic antibodies.
  • Accurate prediction of antibody structures and properties is essential for antibody engineering.

Purpose of the Study:

  • To introduce SAbPred, a comprehensive server for antibody structure and property prediction.
  • To provide a unified platform for various antibody informatics tasks.

Main Methods:

  • SAbPred integrates multiple applications for sequence analysis, homology modeling, and property prediction.
  • The server generates antibody variable fragment homology models with accuracy estimations.
  • It annotates models with sequence, structural properties, and potential developability issues.

Main Results:

  • SAbPred offers functionalities including sequence numbering, alignment, and homology model generation.
  • The server predicts paratope residues and epitope patches on protein antigens.
  • It provides annotations for model accuracy and potential developability concerns.

Conclusions:

  • SAbPred serves as a valuable resource for antibody research and development.
  • The platform facilitates a deeper understanding of antibody structure-function relationships.
  • It supports the design and engineering of novel therapeutic antibodies.