Revealing the Structural Determinants of TCR Cross-Recognition via Extended Positional Scanning

Project Summary
T cell receptors (TCRs) are of increasing therapeutic interest due to the role of T cell mediated immune
responses in conditions such as viral infection, cancer, cardiomyopathy, autoimmunity, and graft rejection. A
T cell response begins when TCRs associate with peptide antigens presented by major histocompatibility
complex (MHC) proteins on antigen-presenting cells. The formation of the TCR-peptide-MHC (TCR-pMHC)
complex triggers an intracellular cascade that results in T cell activation and, for cytotoxic T cells, target cell
killing. While a T cell response can be highly specific, TCRs cross-recognize multiple peptides. This
feature, though biologically necessary, may cause off-target effects in therapeutic applications, as evidenced
by tragic outcomes in clinical trials of T cell therapy. A challenge in developing safer T cell or TCR-based
therapies thus lies in accurately predicting the cross-reactivity profile of a TCR - that is, the range and types
of peptides to which it can and cannot respond. Current prediction methods are limited by a lack of high
quality training data covering ranges of peptides, instead typically focusing on a single "cognate" peptide for
each TCR, limiting the ability of prediction algorithms to generalize beyond what is already known. Various
library-based or genetic screens have been developed, but these do not allow assessment of discrete
peptides and prohibit control of relevant biologic variables. Others have tried positional scanning libraries
(PSL), or X-scans, to probe the positional sensitivity of TCR recognition. While traditional PSLs overcome
the limitations of other screens, they cannot probe the range of diversity needed to characterize a TCR’s
cross-reactivity profile. I hypothesize that by systematically increasing the diversity of peptide libraries
and integrating this data with advanced structural modeling and machine learning techniques, I can
develop a more complete knowledge-base of the structural and chemical determinants of TCR
cross-recognition. To test this hypothesis, I will develop an extended positional scanning library (ePSL)
approach to generate more diverse peptide datasets. I will then leverage state-of-the-art protein language
models and structure prediction tools to reveal the determinants of TCR specificity and cross-recognition. I
will integrate our experimental and computational approaches to create robust and generalizable
predictive models for TCR recognition of diverse peptides, which will be tested and refined on unknown
TCRs. My approach combines sophisticated AI approaches with structural and molecular immunology,
aiming to capture the intricate physicochemical features driving specificity and cross-reactivity. This
research addresses a fundamental gap in the current understanding of T cell biology. By improving
our knowledge of what drives TCR cross-reactivity and building more accurate predictive models,
this work will further fuel efforts to develop safer therapeutics for cancer and other diseases.

Grant Number: 5F32AI191525-02
NIH Institute/Center: NIH
Principal Investigator: Chad Brambley