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LYRA, a webserver for lymphocyte receptor structural modeling.

Michael Schantz Klausen1, Mads Valdemar Anderson1, Martin Closter Jespersen1

  • 1Center for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark.

Nucleic Acids Research
|May 27, 2015
PubMed
Summary
This summary is machine-generated.

LYmphocyte Receptor Automated modeling (LYRA) is a new web server that automatically builds accurate 3D structural models of B- and T-cell receptors from amino acid sequences, aiding immunity research and drug development.

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Area of Science:

  • Immunology
  • Structural Biology
  • Bioinformatics

Background:

  • Accurate structural modeling of B- and T-cell receptors is crucial for understanding immune mechanisms and developing novel therapeutics.
  • Existing methods for receptor modeling can be complex and time-consuming, limiting accessibility for researchers.

Purpose of the Study:

  • To introduce LYRA, a novel, automated web server for the structural modeling of B- and T-cell receptors.
  • To provide a user-friendly and efficient tool for generating 3D receptor models from amino acid sequences.

Main Methods:

  • LYRA utilizes an automated pipeline including sequence alignment, hypervariable loop structural class prediction, and template selection based on the canonical structure method.
  • The server allows for both fully automated modeling and manual adjustments by experienced users.

Main Results:

  • LYRA successfully generates complete 3D structural models of B- and T-cell receptors rapidly and accurately.
  • Benchmarking demonstrates an average Root Mean Square Deviation (RMSD) accuracy of 1.29 Å for B-cell receptors and 1.48 Å for T-cell receptors.
  • LYRA is the first automated server specifically designed for T-cell receptor (TCR) structure prediction.

Conclusions:

  • LYRA offers a valuable, accessible, and automated solution for B- and T-cell receptor structural modeling.
  • The server facilitates advancements in immunology research and the development of targeted therapies by providing accurate structural insights.