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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.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Characterization of Glycoproteins with the Immunoglobulin Fold by X-Ray Crystallography and Biophysical Techniques
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Structure-based cross-docking analysis of antibody-antigen interactions.

Krishna Praneeth Kilambi1,2, Jeffrey J Gray3

  • 1Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA.

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Computational structure-based cross-docking accurately identifies native antibody-antigen pairs using bound structures. This method shows promise for distinguishing binders from non-binders in antibody research.

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Area of Science:

  • Immunology
  • Structural Biology
  • Computational Chemistry

Background:

  • Antibody-antigen interactions are crucial for immune responses.
  • Understanding the biophysical basis of antibody-antigen binding is essential for specificity and affinity.
  • Computational methods can predict these interactions.

Purpose of the Study:

  • To evaluate a structure-based cross-docking approach for identifying native antibody-antigen complexes.
  • To assess the accuracy of distinguishing cognate (native) from non-cognate (non-native) antibody-antigen pairs.
  • To explore the impact of using bound, unbound, and homology-modeled structures on prediction accuracy.

Main Methods:

  • A dataset of 17 antibody-antigen complexes was used.
  • Computational cross-docking with Rosetta was performed.
  • The Rosetta interface score was employed as a classifier to rank potential binding pairs.

Main Results:

  • The method correctly identified the cognate antibody-antigen pair as the top model in 80% of cases when using bound monomer structures.
  • Accuracy decreased to 35% with unbound structures and 12% with homology-modeled backbones.
  • Increasing model diversity through local docking improved accuracy for homology models but not for bound/unbound models.

Conclusions:

  • Structure-based cross-docking, particularly with bound structures, shows potential for identifying native antibody-antigen interactions.
  • The method's accuracy is sensitive to the quality of input structures (bound vs. unbound vs. homology models).
  • Further development is needed to enhance the method's applicability for high-throughput antibody discovery workflows.