Deep Mutational Scans as a Guide to Engineering High Affinity T Cell Receptor Interactions with Peptide-bound Major Histocompatibility Complex

• Published in: The Journal of biological chemistry 2016-11, Vol.291 (47), p.24566-24578
• Main Authors: Harris, Daniel T., Wang, Ningyan, Riley, Timothy P., Anderson, Scott D., Singh, Nishant K., Procko, Erik, Baker, Brian M., Kranz, David M.
• Format: Article
• Language: English
• Subjects: cancer targets; computational analysis; Computer Simulation; HLA-A2 Antigen - chemistry; HLA-A2 Antigen - genetics; HLA-A2 Antigen - immunology; Humans; Immunology; immunotherapy; major histocompatibility complex (MHC); Models, Molecular; Mutagenesis; Peptides - chemistry; Peptides - genetics; Peptides - immunology; protein engineering; protein-protein interaction; Receptors, Antigen, T-Cell - chemistry; Receptors, Antigen, T-Cell - genetics; Receptors, Antigen, T-Cell - immunology; T cell receptor (TCR)
• ISSN: 0021-9258; 1083-351X
• DOI: 10.1074/jbc.M116.748681

Proteins are often engineered to have higher affinity for their ligands to achieve therapeutic benefit. For example, many studies have used phage or yeast display libraries of mutants within complementarity-determining regions to affinity mature antibodies and T cell receptors (TCRs). However, these approaches do not allow rapid assessment or evolution across the entire interface. By combining directed evolution with deep sequencing, it is now possible to generate sequence fitness landscapes that survey the impact of every amino acid substitution across the entire protein-protein interface. Here we used the results of deep mutational scans of a TCR-peptide-MHC interaction to guide mutational strategies. The approach yielded stable TCRs with affinity increases of >200-fold. The substitutions with the greatest enrichments based on the deep sequencing were validated to have higher affinity and could be combined to yield additional improvements. We also conducted in silico binding analyses for every substitution to compare them with the fitness landscape. Computational modeling did not effectively predict the impacts of mutations distal to the interface and did not account for yeast display results that depended on combinations of affinity and protein stability. However, computation accurately predicted affinity changes for mutations within or near the interface, highlighting the complementary strengths of computational modeling and yeast surface display coupled with deep mutational scanning for engineering high affinity TCRs.