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Computational Design of Ligand Binding Proteins

Volume 1414 of the series Methods in Molecular Biology pp 305-318

Date:

Computational Reprogramming of T Cell Antigen Receptor Binding Properties

  • Timothy P. RileyAffiliated withDepartment of Chemistry and Biochemistry, University of Notre DameHarper Cancer Research Institute, University of Notre Dame
  • , Nishant K. SinghAffiliated withDepartment of Chemistry and Biochemistry, University of Notre DameHarper Cancer Research Institute, University of Notre Dame
  • , Brian G. PierceAffiliated withInstitute for Bioscience and Biotechnology Research, University of Maryland
  • , Brian M. BakerAffiliated withDepartment of Chemistry and Biochemistry, University of Notre DameHarper Cancer Research Institute, University of Notre Dame Email author 
  • , Zhiping WengAffiliated withProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School

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Abstract

T-cell receptor (TCR) binding to peptide/MHC is key to antigen-specific cellular immunity, and there has been considerable interest in modulating TCR affinity and specificity for the development of therapeutics and imaging reagents. While in vitro engineering efforts using molecular evolution have yielded remarkable improvements in TCR affinity, such approaches do not offer structural control and can adversely affect receptor specificity, particularly if the attraction towards the MHC is enhanced independently of the peptide. Here we describe an approach to computational design that begins with structural information and offers the potential for more controlled manipulation of binding properties. Our design process models point mutations in selected regions of the TCR and ranks the resulting change in binding energy. Consideration is given to designing optimized scoring functions tuned to particular TCR-peptide/MHC interfaces. Validation of highly ranked predictions can be used to refine the modeling methodology and scoring functions, improving the design process. Our approach results in a strong correlation between predicted and measured changes in binding energy, as well as good agreement between modeled and experimental structures.

Key words

T cell receptor Structure-guided design Rosetta Binding