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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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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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Improving Loop Modeling of the Antibody Complementarity-Determining Region 3 Using Knowledge-Based Restraints.

Jessica A Finn1, Julia Koehler Leman2, Jordan R Willis3

  • 1Department of Pathology, Microbiology and Immunology, Vanderbilt University, Nashville, Tennessee, United States of America.

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Summary
This summary is machine-generated.

Antibody modeling of the heavy chain CDR3 (complementary determining region 3) loop is improved using knowledge-based restraints. These restraints guide computational models toward experimentally observed conformations, enhancing accuracy in predicting antibody structures.

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Area of Science:

  • Structural biology
  • Immunology
  • Computational chemistry

Background:

  • Complementary determining region (CDR) loops are critical for antibody diversity and function.
  • Canonical structures are defined for most CDR loops, but the heavy chain CDR3 (HCDR3) loop remains challenging to classify.
  • The HCDR3 loop has distinct "torso" and "head" domains, with "bulged" and "non-bulged" torso structures identified.

Purpose of the Study:

  • To improve computational modeling of the antibody HCDR3 loop.
  • To develop and apply knowledge-based structural restraints for HCDR3 loop modeling.

Main Methods:

  • Utilized Rosetta loop modeling on 28 benchmark bulged HCDR3 loops.
  • Developed knowledge-based structural restraints from Protein Data Bank (PDB) antibody crystal structures.
  • Applied restraints to limit sampling of residue dihedral angles (φ and ψ) in the HCDR3 torso domain.

Main Results:

  • Rosetta loop modeling accuracy for bulged HCDR3 loops was significantly improved.
  • Knowledge-based restraints restricted Rosetta's sampling space to experimentally observed conformations.
  • Improved score-based differentiation of native-like HCDR3 models was achieved.

Conclusions:

  • Knowledge-based structural restraints enhance the accuracy of antibody HCDR3 loop modeling.
  • Restraints effectively guide computational models toward biologically relevant conformations.
  • This approach offers a significant improvement in predicting antibody HCDR3 structures.