Biophysical Society Thematic Meeting

Biophysics in the Understanding, Diagnosis, and Treatment of Infectious Diseases Speaker Abstracts

High Resolution, High Throughput Structural Modeling of T Cell Receptor Specificity and

Cross-Reactivity: Implications for Immunotherapy

Timothy P. Riley

, Juan L. Mendoza

, Timothy T. Spear

, Michael I. Nishimura

, K. Christopher

Garcia

Brian M. Baker

1,2

University of Notre Dame, Notre Dame, IN, USA,

Loyola University Stritch School of

Medicine, Chicago, IL, USA,

Stanford University School of Medicine, Stanford, CA, USA.

T cell receptors (TCRs) recognize antigenic peptides bound and presented by class I or class II

major histocompatibility complex proteins (peptide/MHC complexes). TCR recognition of a

peptide/MHC complex defines specificity and reactivity in cellular immune responses. While

structurally similar to antibody Fab fragments, there are key differences between TCRs and

antibodies. Notably, TCRs do not undergo affinity maturation, and unlike mature antibodies,

TCRs display a balance of specificity and cross-reactivity. Cross-reactivity is necessary given the

limited size of the TCR repertoire relative to the universe of potential antigens, yet specificity is

a fundamental feature of immunity. Many pathogens, particularly genetically unstable viruses,

take advantage of TCR specificity for immune escape. In this context, there is increasing desire

to engineer TCRs for therapeutic purposes. Design goals for engineered TCRs include efficient

recognition of key antigens as well as known escape variants. Simultaneously, engineered TCRs

must be biased against related self-antigens to avoid autoimmunity. The objective of this work is

to develop the means to perform high resolution, high throughput modeling of TCR specificity

and cross-reactivity in order to facilitate TCR targeting, identify cross-reactive antigens, and

understand and combat immune escape. Our methodology combines large-scale experimental

assessments of TCR cross-reactivity with computational modeling, structural biology, and

biophysical analyses. Our results demonstrate the potential of this approach and highlight

possible uses for the immunotherapy of genetically unstable viruses such as HCV and HIV, as

well as other conditions with genomic instability.