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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM103773-01
Application #
8415346
Study Section
Special Emphasis Panel (ZRG1-BCMB-B (02))
Program Officer
Swain, Amy L
Project Start
2013-04-01
Project End
2017-03-31
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
1
Fiscal Year
2013
Total Cost
$313,375
Indirect Cost
$76,508
Name
University of Massachusetts Medical School Worcester
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code
01655
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Blevins, Sydney J; Pierce, Brian G; Singh, Nishant K et al. (2016) How structural adaptability exists alongside HLA-A2 bias in the human αβ TCR repertoire. Proc Natl Acad Sci U S A 113:E1276-85
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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
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Belden, Orrin S; Baker, Sarah Catherine; Baker, Brian M (2015) Citizens unite for computational immunology! Trends Immunol 36:385-7
Pierce, Brian G; Vreven, Thom; Weng, Zhiping (2014) Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes. BMC Bioinformatics 15:319
Smith, Sheena N; Wang, Yuhang; Baylon, Javier L et al. (2014) Changing the peptide specificity of a human T-cell receptor by directed evolution. Nat Commun 5:5223
Duan, Fei; Duitama, Jorge; Al Seesi, Sahar et al. (2014) Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J Exp Med 211:2231-48

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