# DECODING THE INTERACTIONS BETWEEN T CELL RECEPTORS AND PEPTIDE-MHC

> **NIH NIH R01** · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · 2024 · $867,150

## Abstract

Summary
Conventional ɑβ T cell receptor (TCR) recognition of a cognate peptide-Major Histocompatibility Complex
(pMHC) is central to adaptive immune recognition of pathogens and pathologically associated self-proteins.
Despite substantial progress in structural prediction of protein-protein interactions with tools such as AlphaFold
and RoseTTAFold, de novo prediction of TCR specificity (target pMHC) from TCR sequence has not yet been
realized. Indeed, within the largest databases of curated TCR specificities, only ~105 unique TCR:pMHC
assignments have been curated, and these are focused on <100 unique pMHC epitopes. In our previous work,
we established that >200 unique receptors recognizing the same epitope are required to confidently predict
whether a previously unobserved receptor belongs to the same specificity group. This work demonstrates that
once data are sufficiently dense, local prediction of specificity becomes feasible. It follows that the current
sparse nature of the available data is the major restriction to advancing the field. Thus, our central
hypothesis is that advancing predictive models for TCR specificity requires a dramatic increase in the
magnitude and diversity of curated TCR-pMHC data, which in turn requires new approaches for
generating such useful data sets. In three Aims, we will address major limitations of the current epitope
discovery and TCR characterization pipelines. In Aim 1, we will improve methods for relating single chain
TCR sequences to specific peptides for generating large libraries of well-curated TCRα or TCRβ associations
with individual epitopes. In addition to supporting our central goal, these data will have significant independent
utility for immune profiling and diagnostics. In Aim 2, we will establish methods for assigning paired chain
TCRɑβ data from single cell experiments to epitope pools, extending our recently reported reverse epitope
discovery pipeline. Aim 3 will integrate public data and the data generated in Aims 1 and 2, with novel
structural and computational approaches to generate improved de novo specificity prediction algorithms.
These Aims will be accomplished by accessing our collection of longitudinally sampled PBMCs from >4000
humans across well-curated cohorts from diverse ancestries and infection histories.

## Key facts

- **NIH application ID:** 10807069
- **Project number:** 5R01AI136514-07
- **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:** 2024
- **Award amount:** $867,150
- **Award type:** 5
- **Project period:** 2018-06-20 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10807069

## Citation

> US National Institutes of Health, RePORTER application 10807069, DECODING THE INTERACTIONS BETWEEN T CELL RECEPTORS AND PEPTIDE-MHC (5R01AI136514-07). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10807069. Licensed CC0.

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