T cell receptors (TCRs) are key mediators of the immune response to disease, with significant therapeutic potential. For example, clinical trials have shown that adoptive transfer of T cells genetically engineered to express TCRs of defined antigen specificity can lead to tumor regression in vivo. However, the therapeutic capabilities of TCRs are limited due to their relatively weak binding affinities, and the efficacy of genetically engineered T cells is limited by the mispairing of transduced TCR chains with those endogenously produced. Efforts to improve TCR affinity via in vitro molecular evolution can impact antigen specificity, and attempts to improve chain pairing have yet to yield an effective solution. Here we propose to develop a computational modeling framework to engineer TCRs with high affinity, high antigen specificity, and biased chain pairing. Structure-guided computational design will be used to comprehensively scan interface mutations, model their impact on specificity and affinity, and tailor designs to target key antigenic determinants. This work will be accomplished through the close interaction of two laboratories with complementary expertise: protein interaction modeling and structure-based design of TCRs (PI Zhiping Weng) and experimental TCR biophysics (PI Brian Baker). Experimental assessments, including measurements of affinities and immunological assays of function and specificity, will be used to iteratively test and improve the modeling framework. The framework and associated algorithms will be publically released, facilitated via a web-accessible database serving as a repository for measurements of wild type and mutant TCR binding affinities and structures. The proposal has three specific aims:
Aim 1. Design point mutations in TCRs to improve affinity and specificity towards antigen;
Aim 2. Perform flexible redesign of TCR loops with multiple substitutions to further improve and redirect binding;
Aim 3. Bias specific pairing of engineered TCR chains via computationally designed constant regions.
The capability to design TCRs will allow the development of more effective TCR-based therapies for cancer and other diseases, as well as permit better understanding of the molecular basis of the T cell-mediated immune response. The computational framework and algorithms developed in this proposal will provide the research community with the capacity to engineer high affinity, high specificity TCRs to target disease.
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| Ayres, Cory M; Corcelli, Steven A; Baker, Brian M (2017) Peptide and Peptide-Dependent Motions in MHC Proteins: Immunological Implications and Biophysical Underpinnings. Front Immunol 8:935 |
| Singh, Nishant K; Riley, Timothy P; Baker, Sarah Catherine B et al. (2017) Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes. J Immunol 199:2203-2213 |
| Riley, Timothy P; Singh, Nishant K; Pierce, Brian G et al. (2016) Computational Reprogramming of T Cell Antigen Receptor Binding Properties. Methods Mol Biol 1414:305-18 |
| Blevins, Sydney J; Pierce, Brian G; Singh, Nishant K et al. (2016) How structural adaptability exists alongside HLA-A2 bias in the human ?? TCR repertoire. Proc Natl Acad Sci U S A 113:E1276-85 |
| Riley, Timothy P; Singh, Nishant K; Pierce, Brian G et al. (2016) Computational Modeling of T Cell Receptor Complexes. Methods Mol Biol 1414:319-40 |
| Hellman, Lance M; Yin, Liusong; Wang, Yuan et al. (2016) Differential scanning fluorimetry based assessments of the thermal and kinetic stability of peptide-MHC complexes. J Immunol Methods 432:95-101 |
| Ayres, Cory M; Scott, Daniel R; Corcelli, Steven A et al. (2016) Differential utilization of binding loop flexibility in T cell receptor ligand selection and cross-reactivity. Sci Rep 6:25070 |
| Spear, Timothy T; Riley, Timothy P; Lyons, Gretchen E et al. (2016) Hepatitis C virus-cross-reactive TCR gene-modified T cells: a model for immunotherapy against diseases with genomic instability. J Leukoc Biol 100:545-57 |
| Belden, Orrin S; Baker, Sarah Catherine; Baker, Brian M (2015) Citizens unite for computational immunology! Trends Immunol 36:385-7 |
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