# Decoding the interactions between T cell receptors and peptide-MHC

> **NIH NIH R01** · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · 2021 · $681,906

## Abstract

PROJECT SUMMARY
T cell receptor (TCR) recognition of a cognate peptide-major histocompatibility complex (pMHC) is central to
adaptive immune recognition. Certain features of this interaction are well-understood, including many of the rules
governing peptide binding to MHC. However, our ability to model the ternary TCR:pMHC complex remains limited
for three primary reasons: (1) Data availability; (2) Binding; and (3) Cross-reactivity. In elucidating the rules by
which the TCR:pMHC interface operates, these efforts stand to address fundamental questions at the heart of
adaptive immune recognition, with important theoretical and practical implications that include the potential for
the forward design of novel receptors with selected specificities, the “decoding” of the recent influx of TCR
sequencing data for specific antigenic targets, and an understanding of the cross-reactive potential of the
repertoire. Previously, we developed novel approaches that provided training data for the construction of
algorithms that predict various aspects of TCR specificity (1), including an algorithm we call TCRdist - a simple
and effective distance measure to compare TCR sequences. TCRdist can be used to cluster antigen-specific
TCR sequences and can be incorporated into a distance-based classifier capable of correctly assigning
previously unobserved TCRs to characterized repertoires with robust sensitivity and specificity. Taken together,
the results of these experiments and the general success of the TCRdist algorithm provide compelling evidence
for the central premises of this proposal: Given a sufficient number of experimentally verified epitope-
specific TCR sequences, the epitope specificity of a TCR can be predicted from its sequence;
furthermore, the generation of epitope-specific TCR sequence data, in combination with structurally
informed computational analysis, provides a roadmap for building a predictive model of the TCR:pMHC
interaction. While we have made significant progress in this line of inquiry, the largest remaining hurdle is the
apparent broad cross-reactivity within the repertoire. In order to fully elucidate the complex network of
interactions among TCRs and pMHCs, the questions we must address then are: what do diverse TCRs that see
the same pMHC have in common? And what do diverse pMHCs that are seen by the same TCRs have in
common? The ultimate consequence of these studies, beyond their immediate biological applications, will be to
assist in the development of the next generation of analytical tools for the modeling of TCR:pMHC interaction,
leading to the ultimate goal of a true “decoder” for this essential interface.

## Key facts

- **NIH application ID:** 10158266
- **Project number:** 5R01AI136514-04
- **Recipient organization:** ST. JUDE CHILDREN'S RESEARCH HOSPITAL
- **Principal Investigator:** Paul G. Thomas
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $681,906
- **Award type:** 5
- **Project period:** 2018-06-20 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10158266

## Citation

> US National Institutes of Health, RePORTER application 10158266, Decoding the interactions between T cell receptors and peptide-MHC (5R01AI136514-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10158266. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*