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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.
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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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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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Fully automated antibody structure prediction using BIOVIA tools: Validation study.

Helen Kemmish1, Marc Fasnacht1, Lisa Yan1

  • 1Dassault Systèmes Biovia Corp., San Diego, California, United States of America.

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|May 26, 2017
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Summary
This summary is machine-generated.

This study validated an automated antibody structure prediction tool using a larger dataset. The Top5 framework method slightly improved model quality, offering new best-practice guidelines for antibody modeling.

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Area of Science:

  • Computational biology
  • Structural biology
  • Immunology

Background:

  • Antibody structure prediction is crucial for therapeutic development.
  • Previous validation of automated tools used limited datasets.
  • Understanding parameter influence is key to optimizing prediction accuracy.

Purpose of the Study:

  • To validate an automated antibody structure prediction tool using an expanded dataset.
  • To investigate the impact of various parameter settings on antibody model quality.
  • To refine best practices for computational antibody structure modeling.

Main Methods:

  • Validation of a fully automated antibody structure prediction tool within the BIOVIA protein modeling suite.
  • Utilized a dataset of 157 unique antibody Fv domains.
  • Explored parameter variations including framework construction methods, numbering schemes (Chothia, IMGT, Honegger, Kabat), and loop template compatibility.

Main Results:

  • The recently introduced Top5 framework modeling method demonstrated a small but significant improvement in antibody model quality.
  • Variations in other tested parameters (numbering schemes, loop template filtering) showed no significant impact on model quality.
  • Analysis identified limitations of the current computational model for user evaluation.

Conclusions:

  • The Top5 framework method offers a slight enhancement for automated antibody structure prediction.
  • Established improved guidelines for utilizing the protocol for antibody structure building.
  • Highlighted areas for future development in computational antibody modeling.